scriptoriumadmin/litellm
1
0
Fork 0
mirror of https://github.com/BerriAI/litellm.git synced 2026-10-06 02:48:13 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions 103

fix(tracing): preserve spend identity and gateway correlation (#44421)

Browse source 
* fix(tracing): preserve spend identity and gateway correlation

* test(tracing): refresh real SDK spend captures

* fix(tracing): resolve complete gateway attempt costs across SDKs

* docs: add trace cost screenshot for PR 44421

* update fixtures

* wip

* docs: remove trace cost screenshot from PR evidence
This commit is contained in:
yujonglee 2026-10-03 16:00:49 -07:00 • committed by GitHub
parent 01b4ffe16b
commit c7e60f03de
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
116 changed files with 21728 additions and 7457 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

1
litellm-rust/Cargo.lock generated
View file

@ -4454,6 +4454,7 @@ name = "litellm-traces"
version = "0.1.0"
dependencies = [
"askama",
"base64 0.22.1",
"criterion",
"indexmap 2.14.0",
"litellm-llms-types",

6
litellm-rust/crates/traces-clickhouse/migrations/0015_spend_gateway_call_id.sql Normal file
View file

@ -0,0 +1,6 @@
ALTER TABLE {database}.spend_logs
ADD COLUMN IF NOT EXISTS litellm_call_id String DEFAULT '' AFTER response_id,
ADD INDEX IF NOT EXISTS idx_litellm_call_id litellm_call_id
TYPE bloom_filter(0.001) GRANULARITY 1,
ADD INDEX IF NOT EXISTS idx_request_id request_id
TYPE bloom_filter(0.001) GRANULARITY 1

5
litellm-rust/crates/traces-clickhouse/query/spend_batch.sql
View file

@ -1,5 +1,5 @@
SELECT * FROM (
SELECT request_id, response_id, upstream_response_id, trace_id, span_id, team_id, api_key, user, spend,
SELECT request_id, litellm_call_id, response_id, upstream_response_id, trace_id, span_id, team_id, api_key, user, spend,
toUnixTimestamp64Milli(start_time) AS start_ms
FROM (
SELECT *,
@ -17,7 +17,8 @@ FROM (
)
WHERE response_id IN {response_ids:Array(String)}
OR upstream_response_id IN {response_ids:Array(String)}
OR request_id IN {request_ids:Array(String)}
OR litellm_call_id IN {request_ids:Array(String)}
OR (litellm_call_id = '' AND request_id IN {request_ids:Array(String)})
OR (trace_id != '' AND trace_id IN {trace_ids:Array(String)})
ORDER BY start_time DESC
)

5
litellm-rust/crates/traces-clickhouse/query/spend_by_response_ids.sql
View file

@ -1,4 +1,4 @@
SELECT request_id, response_id, upstream_response_id, trace_id, span_id, team_id, api_key, user, spend,
SELECT request_id, litellm_call_id, response_id, upstream_response_id, trace_id, span_id, team_id, api_key, user, spend,
toUnixTimestamp64Milli(start_time) AS start_ms
FROM (
SELECT *,
@ -16,6 +16,7 @@ FROM (
)
WHERE response_id IN {response_ids:Array(String)}
OR upstream_response_id IN {response_ids:Array(String)}
OR request_id IN {request_ids:Array(String)}
OR litellm_call_id IN {request_ids:Array(String)}
OR (litellm_call_id = '' AND request_id IN {request_ids:Array(String)})
OR (trace_id != '' AND trace_id IN {trace_ids:Array(String)})
ORDER BY start_time DESC

7
litellm-rust/crates/traces-clickhouse/src/query/named.rs
View file

@ -204,6 +204,7 @@ impl From<contracts::SpendByResponseIdsParams> for SpendByResponseIdsParams {
#[serde(remote = "contracts::SpendByResponseIdsRow")]
struct SpendByResponseIdsRowEncoding {
pub request_id: String,
pub litellm_call_id: String,
pub response_id: String,
pub upstream_response_id: String,
pub trace_id: String,
@ -355,7 +356,7 @@ mod tests {
quoted,
);
round_trip::<SpendByResponseIdsRow>(
json!({"request_id": "request", "response_id": "response", "upstream_response_id": "upstream", "trace_id": "trace", "span_id": "span", "team_id": "team", "api_key": "key", "user": "user", "spend": 0.125, "start_ms": -1}),
json!({"request_id": "request", "litellm_call_id": "gateway", "response_id": "response", "upstream_response_id": "upstream", "trace_id": "trace", "span_id": "span", "team_id": "team", "api_key": "key", "user": "user", "spend": 0.125, "start_ms": -1}),
quoted,
);
}
@ -386,7 +387,7 @@ mod tests {
#[case] expected: Option<f64>,
) {
let row: SpendByResponseIdsRow = serde_json::from_value(json!({
"request_id": "request", "response_id": "response", "upstream_response_id": "",
"request_id": "request", "litellm_call_id": "gateway", "response_id": "response", "upstream_response_id": "",
"trace_id": "trace", "span_id": "span", "team_id": "team", "api_key": "key",
"user": "user", "spend": cost, "start_ms": 0
}))
@ -399,7 +400,7 @@ mod tests {
#[case::boolean(json!(true))]
fn spend_rows_reject_invalid_cost(#[case] cost: serde_json::Value) {
let row = serde_json::from_value::<SpendByResponseIdsRow>(json!({
"request_id": "request", "response_id": "response", "upstream_response_id": "",
"request_id": "request", "litellm_call_id": "gateway", "response_id": "response", "upstream_response_id": "",
"trace_id": "trace", "span_id": "span", "team_id": "team", "api_key": "key",
"user": "user", "spend": cost, "start_ms": 0
}));

4
litellm-rust/crates/traces-clickhouse/src/span_row.rs
View file

@ -210,8 +210,8 @@ fn request_id(evidence: &CallEvidence) -> &str {
.into_iter()
.flatten()
.find_map(|key| match key {
CallKey::LiteLlmRequest(id) | CallKey::ProviderResponse(id) => Some(id.as_str()),
CallKey::Transport => None,
CallKey::ProviderResponse(id) => Some(id.as_str()),
CallKey::LiteLlmRequest(_) | CallKey::Transport => None,
})
.unwrap_or_default()
}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/README.md vendored
View file

@ -25,3 +25,5 @@ The LlamaIndex captures contain provider IDs inside `output.value.raw.id`. Regre
Curated SQL lives in `tests/queries/*.sql`. Each query has a matching `.expected.json` containing ordered result rows for `admin`, `team`, `key`, and `other_team` readers. Update the exports and expected results together. Add a named case in `tests/queries.rs` for each new query. Assertions compare only result data, excluding server statistics and execution timing
Typed query tests execute the production SQL through `litellm_storage_clickhouse::fetch` using contracts from `litellm-traces`. The fixture projection is test setup, so this suite covers the Rust decoder, normalization, inserts, schema, readers, and queries. Python ingress transformations, including payload truncation and exception-event fallback, remain covered by the Python tests
`crates/traces/tests/captures.rs` resolves every capture against its spend rows without ClickHouse and checks the unrelated-transport and redundant-response-ID invariants

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/claude_agent_sdk_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/claude_agent_sdk_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/crewai_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

6
litellm-rust/crates/traces-clickhouse/tests/fixtures/crewai_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/deepagents_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/deepagents_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/google_adk_billed_failure_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1CtycizaTWvf3rjCEenOd7gXLRl","response_id":"chatcmpl-EV1CtycizaTWvf3rjCEenOd7gXLRl","litellm_call_id":"5034129d-560a-4a8b-85da-4f0f3628e70b","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.37e-05,"prompt_tokens":32,"completion_tokens":41,"total_tokens":73,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"8110744290a0840bc6029401723da262","trace_id":"8110744290a0840bc6029401723da262","span_id":"43984ccbf7e93ffd","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"google_adk_billed_failure\",\"trace_id\":\"8110744290a0840bc6029401723da262\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"You are an agent. Your internal name is \\\"research_agent\\\".\"}, {\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1CtycizaTWvf3rjCEenOd7gXLRl\", \"created\": 1791061515, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions, decisions, and tool calls.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 41, \"prompt_tokens\": 32, \"total_tokens\": 73, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 18, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061515632,"end_time":1791061516529,"completion_start_time":1791061516529}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/google_adk_retry_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1,2 @@
{"request_id":"chatcmpl-EV1DqRSm66p9hLCDdlrh9loQ2slG2","response_id":"chatcmpl-EV1DqRSm66p9hLCDdlrh9loQ2slG2","litellm_call_id":"4d1e094d-24e7-49f6-8d33-656a7d7712ae","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":3.7199999999999996e-05,"prompt_tokens":32,"completion_tokens":68,"total_tokens":100,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"cdd888fb2417b10d57e2b2cee4dd428c","trace_id":"cdd888fb2417b10d57e2b2cee4dd428c","span_id":"977a62497f2dda9d","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"google_adk_retry\",\"trace_id\":\"cdd888fb2417b10d57e2b2cee4dd428c\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"You are an agent. Your internal name is \\\"research_agent\\\".\"}, {\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1DqRSm66p9hLCDdlrh9loQ2slG2\", \"created\": 1791061574, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions, tool calls, and outcomes.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 68, \"prompt_tokens\": 32, \"total_tokens\": 100, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 45, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061574518,"end_time":1791061576007,"completion_start_time":1791061576007}
{"request_id":"chatcmpl-EV1Dsf8Gh5U6VZykhfciNU1lmsTgS","response_id":"chatcmpl-EV1Dsf8Gh5U6VZykhfciNU1lmsTgS","litellm_call_id":"38acfc26-d8f3-4adc-9528-0196c28b8640","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.67e-05,"prompt_tokens":32,"completion_tokens":47,"total_tokens":79,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"cdd888fb2417b10d57e2b2cee4dd428c","trace_id":"cdd888fb2417b10d57e2b2cee4dd428c","span_id":"2856caaa423beba1","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"google_adk_retry\",\"trace_id\":\"cdd888fb2417b10d57e2b2cee4dd428c\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"You are an agent. Your internal name is \\\"research_agent\\\".\"}, {\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1Dsf8Gh5U6VZykhfciNU1lmsTgS\", \"created\": 1791061576, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces show the steps an agent takes to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 47, \"prompt_tokens\": 32, \"total_tokens\": 79, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 25, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061576510,"end_time":1791061577760,"completion_start_time":1791061577760}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/google_adk_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/google_adk_stream_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1C3uQ69CnjfRcZy2MDlreaXkRVd","response_id":"chatcmpl-EV1C3uQ69CnjfRcZy2MDlreaXkRVd","litellm_call_id":"73d0b177-86e5-4e11-9704-bff2667a0a75","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":9.489999999999999e-05,"prompt_tokens":29,"completion_tokens":184,"total_tokens":213,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"59fb70ffc358dfe30a5e0d4615e4ca7f","trace_id":"59fb70ffc358dfe30a5e0d4615e4ca7f","span_id":"fc3bcda50363d82c","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"google_adk_stream\",\"trace_id\":\"59fb70ffc358dfe30a5e0d4615e4ca7f\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"You are an agent. Your internal name is \\\"research_agent\\\".\"}, {\"role\": \"user\", \"content\": \"What is an agent trace?\"}]","response":"{\"id\": \"chatcmpl-EV1C3uQ69CnjfRcZy2MDlreaXkRVd\", \"created\": 1791061464, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"An **agent trace** is a record of an AI agent\\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\\n\\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\\u2019t have to include the model\\u2019s private reasoning; it can record only observable steps.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 184, \"prompt_tokens\": 29, \"total_tokens\": 213, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 84, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 9.489999999999999e-05}, \"service_tier\": \"default\"}","start_time":1791061463025,"end_time":1791061465613,"completion_start_time":1791061464712}

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/google_adk_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/langchain_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/langchain_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/langgraph_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

4
litellm-rust/crates/traces-clickhouse/tests/fixtures/langgraph_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/llamaindex_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

6
litellm-rust/crates/traces-clickhouse/tests/fixtures/llamaindex_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/mastra_simple_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV19kkSsaD6PMG5TGol2OVhYek5te","response_id":"chatcmpl-EV19kkSsaD6PMG5TGol2OVhYek5te","litellm_call_id":"afc4951d-7141-4476-b4d5-529b294e56b7","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":7.329999999999999e-05,"prompt_tokens":23,"completion_tokens":142,"total_tokens":165,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"6573bbd66c74e1ceb09fd173fe4cdc45","trace_id":"6573bbd66c74e1ceb09fd173fe4cdc45","span_id":"453659faa8fb4313","request_tags":["User-Agent: ai-sdk-openai-compatible","User-Agent: ai-sdk-openai-compatible/3.0.62 ai-sdk-provider-utils/5.0.53 node.js/25"],"metadata":"{\"fixture_capture\":{\"name\":\"mastra_simple\",\"trace_id\":\"6573bbd66c74e1ceb09fd173fe4cdc45\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"Answer the question concisely.\"}, {\"role\": \"user\", \"content\": \"What is an agent trace?\"}]","response":"{\"id\": \"chatcmpl-EV19kkSsaD6PMG5TGol2OVhYek5te\", \"created\": 1791061320, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"An **agent trace** is a record of an AI agent\\u2019s steps during a task\\u2014such as the inputs it received, actions or tool calls it made, results it got back, and its final response. Traces help people debug, evaluate, and audit agent behavior.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 142, \"prompt_tokens\": 23, \"total_tokens\": 165, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 78, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061319992,"end_time":1791061322400,"completion_start_time":1791061322400}

5
litellm-rust/crates/traces-clickhouse/tests/fixtures/mastra_swarm_spend_logs.jsonl vendored Normal file
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/openai_agents_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/openai_agents_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/opentelemetry_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

4
litellm-rust/crates/traces-clickhouse/tests/fixtures/opentelemetry_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_billed_failure_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1DWkeu7A7n33K6iCaSxCDK1G71w","response_id":"chatcmpl-EV1DWkeu7A7n33K6iCaSxCDK1G71w","litellm_call_id":"27fa6242-6416-4749-9c1f-bc3ab3f83f35","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.45e-05,"prompt_tokens":15,"completion_tokens":46,"total_tokens":61,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"478c93b0465e5e11cc19e599738eadeb","trace_id":"478c93b0465e5e11cc19e599738eadeb","span_id":"877363da53522020","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_billed_failure\",\"trace_id\":\"478c93b0465e5e11cc19e599738eadeb\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1DWkeu7A7n33K6iCaSxCDK1G71w\", \"created\": 1791061554, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces show the steps an AI agent takes to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 46, \"prompt_tokens\": 15, \"total_tokens\": 61, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 23, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061554409,"end_time":1791061555799,"completion_start_time":1791061555799}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_retry_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1,2 @@
{"request_id":"chatcmpl-EV1EcoG4Hwa0tTYV7gLus0fwAJ7wY","response_id":"chatcmpl-EV1EcoG4Hwa0tTYV7gLus0fwAJ7wY","litellm_call_id":"bbff9fcc-930d-47fe-b0eb-0ab1eba0eff0","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.8e-05,"prompt_tokens":15,"completion_tokens":53,"total_tokens":68,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"213790ddf7f01594d52d54818775b98d","trace_id":"213790ddf7f01594d52d54818775b98d","span_id":"10cd8cbd5f72b220","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_retry\",\"trace_id\":\"213790ddf7f01594d52d54818775b98d\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1EcoG4Hwa0tTYV7gLus0fwAJ7wY\", \"created\": 1791061622, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces reveal the steps an agent takes to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 53, \"prompt_tokens\": 15, \"total_tokens\": 68, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 31, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061622557,"end_time":1791061623931,"completion_start_time":1791061623931}
{"request_id":"chatcmpl-EV1Ee6ZsIx2AYZLIIcwgTLCt8rYAp","response_id":"chatcmpl-EV1Ee6ZsIx2AYZLIIcwgTLCt8rYAp","litellm_call_id":"b1b0392c-9022-4369-88ce-7d8bdc57c90c","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.55e-05,"prompt_tokens":15,"completion_tokens":48,"total_tokens":63,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"213790ddf7f01594d52d54818775b98d","trace_id":"213790ddf7f01594d52d54818775b98d","span_id":"c80b675f9912b72c","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_retry\",\"trace_id\":\"213790ddf7f01594d52d54818775b98d\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1Ee6ZsIx2AYZLIIcwgTLCt8rYAp\", \"created\": 1791061624, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions and decisions over time.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 48, \"prompt_tokens\": 15, \"total_tokens\": 63, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 27, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061624398,"end_time":1791061625775,"completion_start_time":1791061625775}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_stream_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1CVbDkWDEsratBuf9bhvEG3Av51","response_id":"chatcmpl-EV1CVbDkWDEsratBuf9bhvEG3Av51","litellm_call_id":"94605f85-b0da-4596-8538-9072fce7119e","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":0.0001117,"prompt_tokens":12,"completion_tokens":221,"total_tokens":233,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"f3f8852c4f360b03ce2a36e6d3ed6f51","trace_id":"f3f8852c4f360b03ce2a36e6d3ed6f51","span_id":"a383ecf3f8beb7a6","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_stream\",\"trace_id\":\"f3f8852c4f360b03ce2a36e6d3ed6f51\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]","response":"{\"id\": \"chatcmpl-EV1CVbDkWDEsratBuf9bhvEG3Av51\", \"created\": 1791061493, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\\u2019s activity while completing a task. It may include:\\n\\n- The task or input it received\\n- Actions it took, such as tool calls\\n- Results or observations it received\\n- Changes in state or decisions\\n- The final response or outcome\\n\\nFor example: *User asks for the weather \\u2192 agent calls a weather API \\u2192 API returns the forecast \\u2192 agent summarizes it.*\\n\\nA trace helps people debug or evaluate an agent. It doesn\\u2019t necessarily include the agent\\u2019s private reasoning; often it records only observable steps and tool interactions.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 221, \"prompt_tokens\": 12, \"total_tokens\": 233, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 93, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 0.0001117}, \"service_tier\": \"default\"}","start_time":1791061491444,"end_time":1791061494417,"completion_start_time":1791061493102}

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

5
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_swarm_stream_spend_logs.jsonl vendored Normal file
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/pydantic_ai_token_limit_swarm_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1,2 @@
{"request_id":"resp_bGl0ZWxsbTpjdXN0b21fbGxtX3Byb3ZpZGVyOm9wZW5haTttb2RlbF9pZDplNjhmYmQxY2UyNmFhN2E4OWIwNTljOWE2ZGYxMjAwN2U2MjRjNmEyMTA2Y2QzZTZhZjQ5MmFiN2EyNTlmMTE3O3Jlc3BvbnNlX2lkOnJlc3BfMDE5OTdjMDdlZDQ3NzkzNTAwNmFjMTVhMjVmNzc4ODdkMGFjNjQzZDM1M2E2Zjc1MmI=","response_id":"resp_bGl0ZWxsbTpjdXN0b21fbGxtX3Byb3ZpZGVyOm9wZW5haTttb2RlbF9pZDplNjhmYmQxY2UyNmFhN2E4OWIwNTljOWE2ZGYxMjAwN2U2MjRjNmEyMTA2Y2QzZTZhZjQ5MmFiN2EyNTlmMTE3O3Jlc3BvbnNlX2lkOnJlc3BfMDE5OTdjMDdlZDQ3NzkzNTAwNmFjMTVhMjVmNzc4ODdkMGFjNjQzZDM1M2E2Zjc1MmI=","litellm_call_id":"274a6199-ce75-4cbd-a1c8-a09f18b85de3","call_type":"responses","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1/responses","spend":2.18e-05,"prompt_tokens":73,"completion_tokens":29,"total_tokens":102,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"58534afa-54a4-48d3-be19-a4882953d3c6","trace_id":"","span_id":"","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_token_limit_swarm\",\"trace_id\":\"33551f56e73710cee91fb7beb1be6cc3\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"Call search first, then write with the facts, and return the written answer.\"}, {\"role\": \"user\", \"content\": \"What is an agent trace?\"}]","response":"{\"id\": \"resp_bGl0ZWxsbTpjdXN0b21fbGxtX3Byb3ZpZGVyOm9wZW5haTttb2RlbF9pZDplNjhmYmQxY2UyNmFhN2E4OWIwNTljOWE2ZGYxMjAwN2U2MjRjNmEyMTA2Y2QzZTZhZjQ5MmFiN2EyNTlmMTE3O3Jlc3BvbnNlX2lkOnJlc3BfMDE5OTdjMDdlZDQ3NzkzNTAwNmFjMTVhMjVmNzc4ODdkMGFjNjQzZDM1M2E2Zjc1MmI=\", \"created_at\": 1791056421, \"error\": null, \"incomplete_details\": null, \"instructions\": \"Call search first, then write with the facts, and return the written answer.\", \"metadata\": {}, \"model\": \"gpt-6-luna\", \"object\": \"response\", \"output\": [{\"arguments\": \"{\\\"query\\\":\\\"definition of agent trace in AI agents execution trace tool calls observations steps\\\"}\", \"call_id\": \"call_hILz5R2pcOQ6GLcp907fCVeQ\", \"name\": \"search\", \"type\": \"function_call\", \"id\": \"fc_01997c07ed477935006ac15a267d9887d0adb7c782b1832764\", \"namespace\": null, \"status\": \"completed\"}], \"parallel_tool_calls\": true, \"temperature\": 1.0, \"tool_choice\": \"auto\", \"tools\": [{\"name\": \"search\", \"parameters\": {\"additionalProperties\": false, \"properties\": {\"query\": {\"type\": \"string\"}}, \"required\": [\"query\"], \"type\": \"object\"}, \"strict\": true, \"type\": \"function\", \"defer_loading\": null, \"description\": null, \"output_schema\": null}, {\"name\": \"write\", \"parameters\": {\"additionalProperties\": false, \"properties\": {\"facts\": {\"type\": \"string\"}}, \"required\": [\"facts\"], \"type\": \"object\"}, \"strict\": true, \"type\": \"function\", \"defer_loading\": null, \"description\": null, \"output_schema\": null}], \"top_p\": 0.98, \"max_output_tokens\": 256, \"previous_response_id\": null, \"reasoning\": {\"context\": \"all_turns\", \"effort\": \"medium\", \"mode\": \"standard\", \"summary\": null}, \"status\": \"completed\", \"text\": {\"format\": {\"type\": \"text\"}, \"verbosity\": \"medium\"}, \"truncation\": \"disabled\", \"usage\": {\"completion_tokens\": 29, \"prompt_tokens\": 73, \"total_tokens\": 102, \"completion_tokens_details\": {\"accepted_prediction_tokens\": null, \"audio_tokens\": null, \"reasoning_tokens\": 0, \"rejected_prediction_tokens\": null, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": null, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"user\": null, \"store\": true, \"access_programs\": {\"cyber\": \"daybreak_blue\"}, \"background\": false, \"billing\": {\"payer\": \"developer\"}, \"completed_at\": 1791056423, \"frequency_penalty\": 0.0, \"max_tool_calls\": null, \"moderation\": null, \"presence_penalty\": 0.0, \"prompt_cache_key\": null, \"prompt_cache_retention\": \"24h\", \"safety_identifier\": null, \"service_tier\": \"default\", \"tool_usage\": {\"image_gen\": {\"input_tokens\": 0, \"input_tokens_details\": {\"image_tokens\": 0, \"text_tokens\": 0}, \"output_tokens\": 0, \"output_tokens_details\": {\"image_tokens\": 0, \"text_tokens\": 0}, \"total_tokens\": 0}, \"web_search\": {\"num_requests\": 0}}, \"top_logprobs\": 0}","start_time":1791056421823,"end_time":1791056425435,"completion_start_time":1791056425435}
{"request_id":"chatcmpl-EUzsnFpH3W2H1tbWvpBypOpEPupRA","response_id":"chatcmpl-EUzsnFpH3W2H1tbWvpBypOpEPupRA","litellm_call_id":"790f284d-7a98-4550-99ab-7374965c8365","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":0.00013099999999999999,"prompt_tokens":30,"completion_tokens":256,"total_tokens":286,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"71f6c352-9a97-47c2-89ed-9149c035b978","trace_id":"","span_id":"","request_tags":["User-Agent: pydantic-ai","User-Agent: pydantic-ai/2.53.0"],"metadata":"{\"fixture_capture\":{\"name\":\"pydantic_ai_token_limit_swarm\",\"trace_id\":\"33551f56e73710cee91fb7beb1be6cc3\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"system\", \"content\": \"Find key facts about the topic.\"}, {\"role\": \"user\", \"content\": \"definition of agent trace in AI agents execution trace tool calls observations steps\"}]","response":"{\"id\": \"chatcmpl-EUzsnFpH3W2H1tbWvpBypOpEPupRA\", \"created\": 1791056425, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"length\", \"index\": 0, \"message\": {\"content\": \"\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 256, \"prompt_tokens\": 30, \"total_tokens\": 286, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 256, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791056425493,"end_time":1791056428619,"completion_start_time":1791056428619}

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/strands_billed_failure_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1Bc8dQIuPElIn5vaTQdxXaUzAiX","response_id":"chatcmpl-EV1Bc8dQIuPElIn5vaTQdxXaUzAiX","litellm_call_id":"c958e5e9-a1d6-411c-b070-8f5aab06de79","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":1.92e-05,"prompt_tokens":112,"completion_tokens":16,"total_tokens":128,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"951c8f6dbe7350974f563de3f282b3ac","trace_id":"951c8f6dbe7350974f563de3f282b3ac","span_id":"b6c34c8c5588fdf5","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"strands_billed_failure\",\"trace_id\":\"951c8f6dbe7350974f563de3f282b3ac\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": [{\"text\": \"Reply with one short sentence about agent traces.\", \"type\": \"text\"}]}]","response":"{\"id\": \"chatcmpl-EV1Bc8dQIuPElIn5vaTQdxXaUzAiX\", \"created\": 1791061436, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces show the steps an agent takes to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 16, \"prompt_tokens\": 112, \"total_tokens\": 128, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 0, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 1.92e-05}, \"service_tier\": \"default\"}","start_time":1791061436369,"end_time":1791061437050,"completion_start_time":1791061436941}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/strands_retry_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1,2 @@
{"request_id":"chatcmpl-EV1BGShr9D1AfRCWu7DGwLNROCcTT","response_id":"chatcmpl-EV1BGShr9D1AfRCWu7DGwLNROCcTT","litellm_call_id":"cc203841-e4f8-42da-bd36-cdbe2bd9a4c0","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":1.9699999999999998e-05,"prompt_tokens":112,"completion_tokens":17,"total_tokens":129,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"d1eb10a194f225edbe397b5d11775b00","trace_id":"d1eb10a194f225edbe397b5d11775b00","span_id":"8364872d770021af","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"strands_retry\",\"trace_id\":\"d1eb10a194f225edbe397b5d11775b00\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": [{\"text\": \"Reply with one short sentence about agent traces.\", \"type\": \"text\"}]}]","response":"{\"id\": \"chatcmpl-EV1BGShr9D1AfRCWu7DGwLNROCcTT\", \"created\": 1791061414, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions, decisions, and tool interactions.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 17, \"prompt_tokens\": 112, \"total_tokens\": 129, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 0, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 1.9699999999999998e-05}, \"service_tier\": \"default\"}","start_time":1791061413879,"end_time":1791061414741,"completion_start_time":1791061414584}
{"request_id":"chatcmpl-EV1BHJSXrImjpnUFx4dnRWjUmC9jV","response_id":"chatcmpl-EV1BHJSXrImjpnUFx4dnRWjUmC9jV","litellm_call_id":"400e4cf8-4aa5-4c4a-b998-b6f9aa175b56","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":1.92e-05,"prompt_tokens":112,"completion_tokens":16,"total_tokens":128,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"d1eb10a194f225edbe397b5d11775b00","trace_id":"d1eb10a194f225edbe397b5d11775b00","span_id":"99aad174e55e03b0","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 2.54.0"],"metadata":"{\"fixture_capture\":{\"name\":\"strands_retry\",\"trace_id\":\"d1eb10a194f225edbe397b5d11775b00\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": [{\"text\": \"Reply with one short sentence about agent traces.\", \"type\": \"text\"}]}]","response":"{\"id\": \"chatcmpl-EV1BHJSXrImjpnUFx4dnRWjUmC9jV\", \"created\": 1791061416, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces reveal the steps an AI takes to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 16, \"prompt_tokens\": 112, \"total_tokens\": 128, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 0, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 1.92e-05}, \"service_tier\": \"default\"}","start_time":1791061415156,"end_time":1791061416292,"completion_start_time":1791061416186}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/strands_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/strands_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_billed_failure_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1Be4YkRbpmqEp1VHkrj1vEGeerd","response_id":"chatcmpl-EV1Be4YkRbpmqEp1VHkrj1vEGeerd","litellm_call_id":"5d4283ed-39e1-4530-9200-24585098215f","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.1499999999999997e-05,"prompt_tokens":15,"completion_tokens":40,"total_tokens":55,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"da0164cd74393d84076fef5253c18441","trace_id":"da0164cd74393d84076fef5253c18441","span_id":"5a53a9fc62fda14c","request_tags":["User-Agent: ai-sdk-openai-compatible","User-Agent: ai-sdk-openai-compatible/3.0.62 ai-sdk-provider-utils/5.0.53 node.js/25"],"metadata":"{\"fixture_capture\":{\"name\":\"vercel_ai_sdk_billed_failure\",\"trace_id\":\"da0164cd74393d84076fef5253c18441\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1Be4YkRbpmqEp1VHkrj1vEGeerd\", \"created\": 1791061439, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions, decisions, and tool calls.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 40, \"prompt_tokens\": 15, \"total_tokens\": 55, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 17, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 2.1499999999999997e-05}, \"service_tier\": \"default\"}","start_time":1791061438594,"end_time":1791061439640,"completion_start_time":1791061439421}

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_py_simple_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV19oQg42x1rA1HfR9Biuy5ImkI7s","response_id":"chatcmpl-EV19oQg42x1rA1HfR9Biuy5ImkI7s","litellm_call_id":"2e7ef64f-35cd-432d-a684-14214cd9dd2d","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":9.82e-05,"prompt_tokens":12,"completion_tokens":194,"total_tokens":206,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"5b5f03a176b0b4fb74290922de689a21","trace_id":"5b5f03a176b0b4fb74290922de689a21","span_id":"6f8e118ee0c01d1a","request_tags":["User-Agent: AsyncOpenAI","User-Agent: AsyncOpenAI/Python 3.24.0"],"metadata":"{\"fixture_capture\":{\"name\":\"vercel_ai_sdk_py_simple\",\"trace_id\":\"5b5f03a176b0b4fb74290922de689a21\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]","response":"{\"id\": \"chatcmpl-EV19oQg42x1rA1HfR9Biuy5ImkI7s\", \"created\": 1791061325, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did while completing a task. It may include the user\\u2019s request, the agent\\u2019s actions and tool calls, the results it received, and its final response.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather tool.\\n3. Tool returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces are useful for debugging, evaluating, and auditing an agent. The exact contents vary by system; a trace usually records observable steps and results, not necessarily the model\\u2019s private internal reasoning.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 194, \"prompt_tokens\": 12, \"total_tokens\": 206, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 65, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 9.82e-05}, \"service_tier\": \"default\"}","start_time":1791061324025,"end_time":1791061327041,"completion_start_time":1791061325560}

5
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_py_swarm_spend_logs.jsonl vendored Normal file
View file

File diff suppressed because one or more lines are too long

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_retry_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1,2 @@
{"request_id":"chatcmpl-EV1BG2mxTwq38bkzLa9xWiE472RFi","response_id":"chatcmpl-EV1BG2mxTwq38bkzLa9xWiE472RFi","litellm_call_id":"cd72f919-eb8e-4854-8c74-1a2d22fbe271","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":3.35e-05,"prompt_tokens":15,"completion_tokens":64,"total_tokens":79,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"18b74e8029c4c62d6ee2acbfaccd05d7","trace_id":"18b74e8029c4c62d6ee2acbfaccd05d7","span_id":"c2bdb1aefaca871b","request_tags":["User-Agent: ai","User-Agent: ai/7.0.127 ai-sdk-provider-utils/5.0.53 node.js/25"],"metadata":"{\"fixture_capture\":{\"name\":\"vercel_ai_sdk_retry\",\"trace_id\":\"18b74e8029c4c62d6ee2acbfaccd05d7\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1BG2mxTwq38bkzLa9xWiE472RFi\", \"created\": 1791061414, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s steps, decisions, and tool calls.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 64, \"prompt_tokens\": 15, \"total_tokens\": 79, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 41, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061414000,"end_time":1791061415440,"completion_start_time":1791061415440}
{"request_id":"chatcmpl-EV1BJQ4BvQRI5X8f1m4LZ7uTX4sKt","response_id":"chatcmpl-EV1BJQ4BvQRI5X8f1m4LZ7uTX4sKt","litellm_call_id":"13406f69-913c-4ed9-87fd-03785496f128","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":2.2499999999999998e-05,"prompt_tokens":15,"completion_tokens":42,"total_tokens":57,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"18b74e8029c4c62d6ee2acbfaccd05d7","trace_id":"18b74e8029c4c62d6ee2acbfaccd05d7","span_id":"75cf929b3549bf74","request_tags":["User-Agent: ai","User-Agent: ai/7.0.127 ai-sdk-provider-utils/5.0.53 node.js/25"],"metadata":"{\"fixture_capture\":{\"name\":\"vercel_ai_sdk_retry\",\"trace_id\":\"18b74e8029c4c62d6ee2acbfaccd05d7\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1BJQ4BvQRI5X8f1m4LZ7uTX4sKt\", \"created\": 1791061417, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces record an agent\\u2019s actions, decisions, and tool calls over time.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null, \"provider_specific_fields\": {\"refusal\": null}, \"annotations\": []}, \"provider_specific_fields\": {}}], \"usage\": {\"completion_tokens\": 42, \"prompt_tokens\": 15, \"total_tokens\": 57, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 17, \"rejected_prediction_tokens\": 0}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}}, \"service_tier\": \"default\"}","start_time":1791061417451,"end_time":1791061418708,"completion_start_time":1791061418708}

2
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_simple_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

1
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_stream_spend_logs.jsonl vendored Normal file
View file

@ -0,0 +1 @@
{"request_id":"chatcmpl-EV1AyMCFfzO4g0UVvSESWJ3Q5rFZK","response_id":"chatcmpl-EV1AyMCFfzO4g0UVvSESWJ3Q5rFZK","litellm_call_id":"d269912a-2203-4e1f-8fd8-640a09f9cb40","call_type":"acompletion","api_key":"fixture-key","key_alias":"","team_id":"fixture-team","team_alias":"","organization_id":"","user":"fixture-user","end_user":"","model":"openai/gpt-6-luna","model_group":"openai/gpt-6-luna","model_id":"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117","custom_llm_provider":"openai","api_base":"https://api.openai.com/v1","spend":9.5e-06,"prompt_tokens":15,"completion_tokens":16,"total_tokens":31,"cache_read_tokens":0,"cache_write_tokens":0,"status":"success","error_str":"","cache_hit":false,"session_id":"2acad168d77811f8fa89100e5e1fc0e7","trace_id":"2acad168d77811f8fa89100e5e1fc0e7","span_id":"a08a06551dfef63e","request_tags":["User-Agent: ai-sdk-openai-compatible","User-Agent: ai-sdk-openai-compatible/3.0.62 ai-sdk-provider-utils/5.0.53 node.js/25"],"metadata":"{\"fixture_capture\":{\"name\":\"vercel_ai_sdk_stream\",\"trace_id\":\"2acad168d77811f8fa89100e5e1fc0e7\",\"spend_linked\":true,\"spend_complete\":true}}","messages":"[{\"role\": \"user\", \"content\": \"Reply with one short sentence about agent traces.\"}]","response":"{\"id\": \"chatcmpl-EV1AyMCFfzO4g0UVvSESWJ3Q5rFZK\", \"created\": 1791061396, \"model\": \"openai/gpt-6-luna\", \"object\": \"chat.completion\", \"system_fingerprint\": null, \"choices\": [{\"finish_reason\": \"stop\", \"index\": 0, \"message\": {\"content\": \"Agent traces show the steps an agent took to complete a task.\", \"role\": \"assistant\", \"tool_calls\": null, \"function_call\": null}}], \"usage\": {\"completion_tokens\": 16, \"prompt_tokens\": 15, \"total_tokens\": 31, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 0, \"rejected_prediction_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cached_tokens\": 0, \"text_tokens\": null, \"image_tokens\": null, \"video_tokens\": null, \"cache_write_tokens\": 0, \"cache_creation_tokens\": 0}, \"cost\": 9.5e-06}, \"service_tier\": \"default\"}","start_time":1791061396267,"end_time":1791061397062,"completion_start_time":1791061396960}

10
litellm-rust/crates/traces-clickhouse/tests/fixtures/vercel_ai_sdk_swarm_spend_logs.jsonl vendored
View file

File diff suppressed because one or more lines are too long

39
litellm-rust/crates/traces-clickhouse/tests/migrations.rs
View file

@ -2132,7 +2132,7 @@ async fn nullable_spend_upgrade_preserves_existing_costs_and_unknown_new_costs(
let writer = Connection::writer(&database.url)?;
let timestamp = (time::OffsetDateTime::now_utc().unix_timestamp_nanos() / 1_000_000) as i64;
let statements = schema_statements("trace_test", 7)?;
for statement in &statements[..statements.len() - 1] {
for statement in &statements[..14] {
execute_write(&database, statement).await?;
}
let legacy = serde_json::from_value(serde_json::json!({
@ -2174,3 +2174,40 @@ async fn nullable_spend_upgrade_preserves_existing_costs_and_unknown_new_costs(
);
Ok(())
}
#[rstest]
#[tokio::test]
async fn gateway_id_upgrade_preserves_legacy_rows_and_accepts_new_ids(
#[future(awt)] database: TestResult<ClickHouseDatabase>,
) -> TestResult {
let database = database?;
let writer = Connection::writer(&database.url)?;
let timestamp = (time::OffsetDateTime::now_utc().unix_timestamp_nanos() / 1_000_000) as i64;
let statements = schema_statements("trace_test", 7)?;
for statement in &statements[..15] {
execute_write(&database, statement).await?;
}
let legacy = serde_json::from_value(serde_json::json!({
"request_id": "legacy", "response_id": "response", "spend": 0.25,
"start_time": timestamp, "end_time": timestamp + 100
}))?;
insert_rows(&database, "spend_logs", vec![legacy]).await?;
ensure_schema(&database.client, &writer, "trace_test", 7).await?;
ensure_schema(&database.client, &writer, "trace_test", 7).await?;
let current = serde_json::from_value(serde_json::json!({
"request_id": "current", "response_id": "response", "litellm_call_id": "gateway",
"spend": null, "start_time": timestamp, "end_time": timestamp + 100
}))?;
insert_rows(&database, "spend_logs", vec![current]).await?;
let result = read_json(&database,
"SELECT request_id, litellm_call_id, spend FROM trace_test.spend_logs FINAL ORDER BY request_id"
).await?;
assert_eq!(
result["data"],
serde_json::json!([
{"request_id": "current", "litellm_call_id": "gateway", "spend": null},
{"request_id": "legacy", "litellm_call_id": "", "spend": 0.25},
])
);
Ok(())
}

117
litellm-rust/crates/traces-clickhouse/tests/reads.rs
View file

@ -47,6 +47,7 @@ async fn list_costs_match_each_run_when_response_ids_are_reused(
("UserId".into(), json!(user_id)),
("Duration".into(), json!(1_000_000)),
("LiteLLMRequestId".into(), json!("reused-response")),
("CallEvidence".into(), json!("complete")),
])
})
.collect(),
@ -152,6 +153,14 @@ async fn large_runs_remain_complete_under_default_reader_limits(
("TeamId".into(), json!("team-a")),
("ApiKeyHash".into(), json!("key-a")),
("Duration".into(), json!(1000)),
(
"CallEvidence".into(),
json!(if costed && step > 0 {
"complete"
} else {
"unknown"
}),
),
(
"LiteLLMRequestId".into(),
json!(if costed && step > 0 {
@ -593,3 +602,111 @@ async fn an_oversized_span_keeps_the_run_list_available_with_partial_totals(
));
Ok(())
}
#[rstest]
#[tokio::test]
async fn gateway_ids_resolve_through_detail_and_batch_reads_with_legacy_fallback(
#[future(awt)] migrated_database: TestResult<SeededDatabase>,
) -> TestResult {
let fixture = migrated_database?;
let client = &fixture.database.client;
let writer = Connection::writer(&fixture.database.url)?;
let start_ms = 1_790_000_000_000_i64;
let cases = [
(
"gateway",
"provider-request",
"gateway",
"team-a",
"key-a",
Some(0.25),
),
("legacy", "legacy", "", "team-a", "key-a", Some(0.25)),
("conflict", "conflict", "different", "team-a", "key-a", None),
(
"foreign-team",
"request",
"foreign-team",
"team-b",
"key-a",
None,
),
(
"foreign-key",
"request",
"foreign-key",
"team-a",
"key-b",
None,
),
];
insert_rows(
client,
&writer,
DATABASE,
InsertTable::OtelTraces,
cases
.iter()
.map(|(id, _, _, _, _, _)| {
BTreeMap::from([
("Timestamp".into(), json!(start_ms * 1_000_000)),
("Duration".into(), json!(1_000_000)),
("TraceId".into(), json!(id)),
("SpanId".into(), json!("call")),
("ObservationType".into(), json!("llm")),
("TeamId".into(), json!("team-a")),
("ApiKeyHash".into(), json!("key-a")),
("CallKeys".into(), json!([format!("litellm_request:{id}")])),
("CallEvidence".into(), json!("complete")),
])
})
.collect(),
)
.await?;
insert_rows(
client,
&writer,
DATABASE,
InsertTable::SpendLogs,
cases
.iter()
.map(|(_, request, call_id, team, key, _)| {
BTreeMap::from([
("request_id".into(), json!(request)),
("response_id".into(), json!("provider-response")),
("litellm_call_id".into(), json!(call_id)),
("team_id".into(), json!(team)),
("api_key".into(), json!(key)),
("start_time".into(), json!(start_ms)),
("end_time".into(), json!(start_ms + 1)),
("spend".into(), json!(0.25)),
])
})
.collect(),
)
.await?;
let reader = fixture
.readers
.connection(client, &QueryScope::All, "fixture-secret")
.await?;
let access = ReadAccessParams {
all_teams: false,
user_id: String::new(),
team_ids: vec!["team-a".into()],
};
let page = list_traces(client, &reader, &access, 0, 2_000_000_000_000, None, 50).await?;
assert_eq!(page.data.len(), cases.len());
for (id, _, _, _, _, expected) in cases {
let summary = page
.data
.iter()
.find(|summary| summary.trace_id == id)
.ok_or("missing run")?;
let detail = get_trace(client, &reader, &access, id, &summary.trace_ref)
.await?
.ok_or("missing trace")?;
assert_eq!(detail.summary.spend, expected, "{id}");
assert_eq!(summary.spend, expected, "{id}");
}
Ok(())
}

22
litellm-rust/crates/traces-clickhouse/tests/span_rows.rs
View file

@ -214,3 +214,25 @@ fn absent_identity_fields_are_empty_only_in_storage(tenant: Tenant) {
assert_eq!(row["CallKeys"], json!([]));
assert_eq!(row["CallEvidence"], "unknown");
}
#[rstest]
fn compatibility_id_keeps_provider_semantics_with_gateway_keys(tenant: Tenant) {
let body = export(vec![(
vec![],
vec![span(
&"02".repeat(8),
vec![
attribute("gen_ai.response.id", "response"),
attribute("litellm.call_id", "gateway"),
],
json!({}),
)],
)]);
let stored = rows(&body, &tenant, MAX_VALUE_BYTES);
assert_eq!(stored[0]["LiteLLMRequestId"], "response");
assert_eq!(stored[0]["CallEvidence"], "partial");
assert_eq!(
stored[0]["CallKeys"],
json!(["litellm_request:gateway", "provider_response:response"])
);
}

1
litellm-rust/crates/traces/Cargo.toml
View file

@ -23,6 +23,7 @@ thiserror.workspace = true
time.workspace = true
[dev-dependencies]
base64.workspace = true
criterion.workspace = true
rstest.workspace = true

15
litellm-rust/crates/traces/src/normalize/format/genai.rs
View file

@ -2,8 +2,8 @@ use super::{Extraction, Format, Payload, SpanFacts};
use crate::{
Error,
normalize::{
ObservationType, RoleEvidence, SpanContext, attr, messages, present, select_attribute,
usage_tokens,
CallEvidence, CallKey, ObservationType, RoleEvidence, SpanContext, attr, messages, present,
select_attribute, usage_tokens,
},
};
@ -107,10 +107,17 @@ impl Format for GenAi {
let (input_tokens, output_tokens) = usage_tokens(attributes)?;
let input = payload(context, &INPUT_KEYS);
let output = payload(context, &OUTPUT_KEYS);
let role = Operation::from_context(context).map(Operation::role);
let calls = match (role, present(attributes, &["gen_ai.response.id"])) {
(Some(ObservationType::Llm), Some(id)) => {
CallEvidence::complete(CallKey::ProviderResponse(id))
}
_ => CallEvidence::Unknown,
};
Ok(Extraction {
facts: SpanFacts {
role: Operation::from_context(context)
.map(|operation| RoleEvidence::Declared(operation.role())),
role: role.map(RoleEvidence::Declared),
calls,
model: present(
attributes,
&["gen_ai.request.model", "gen_ai.response.model"],

51
litellm-rust/crates/traces/src/normalize/format/langsmith.rs
View file

@ -179,25 +179,52 @@ impl Format for LangSmith {
let observation_type = ObservationType::try_from(attr(attributes, "langsmith.span.kind"))
.unwrap_or(ObservationType::Chain);
let io = span_io(observation_type, attributes);
let legacy_input = !attr(attributes, "gen_ai.prompt").is_empty();
let legacy_output = !attr(attributes, "gen_ai.completion").is_empty();
Ok(Extraction {
facts: SpanFacts {
role: Some(RoleEvidence::Declared(observation_type)),
input: if attr(attributes, "gen_ai.prompt").is_empty() {
String::new()
} else {
input: if legacy_input {
io.input
},
output: if attr(attributes, "gen_ai.completion").is_empty() {
String::new()
} else {
io.output
base.facts.input
},
calls: io.calls,
..SpanFacts::default()
}
.or(base.facts),
output: if legacy_output {
io.output
} else {
base.facts.output
},
calls: match io.calls {
CallEvidence::Unknown => base.facts.calls,
calls => calls,
},
..base.facts
},
display_name: None,
consumed_attributes: base.consumed_attributes,
consumed_attributes: base
.consumed_attributes
.into_iter()
.filter(|source| {
!(legacy_input
&& matches!(
*source,
"gen_ai.input.messages"
| "gen_ai.tool.call.arguments"
| "gen_ai.retrieval.query.text"
| "gen_ai.prompt"
))
&& !(legacy_output
&& matches!(
*source,
"gen_ai.output.messages"
| "gen_ai.tool.call.result"
| "gen_ai.retrieval.documents"
| "gen_ai.completion"
))
})
.chain(legacy_input.then_some("gen_ai.prompt"))
.chain(legacy_output.then_some("gen_ai.completion"))
.collect(),
})
}
}

10
litellm-rust/crates/traces/src/normalize/instrumentation/http_client.rs
View file

@ -13,6 +13,16 @@ const SCOPES: [&str; 7] = [
pub(super) fn matches(context: &SpanContext<'_>) -> bool {
SCOPES.contains(&context.scope)
|| (context.scope == "litellm.gateway.client"
&& context.name == "gateway.request"
&& context
.attributes
.get("litellm.gateway.attempt")
.is_some_and(|value| value == "true")
&& context
.attributes
.get("http.request.method")
.is_some_and(|value| value == "POST"))
}
pub(super) fn adjust(facts: SpanFacts) -> SpanFacts {

24
litellm-rust/crates/traces/src/normalize/instrumentation/mod.rs
View file

@ -147,10 +147,8 @@ impl Instrumentation {
facts,
display_name,
consumed_attributes,
} = self.adjust(
context,
prepared.map_facts(|facts| with_response_id(context, facts)),
);
} = self.adjust(context, prepared);
let facts = with_call_ids(context, facts);
let role = match (facts.role, metadata.ls_agent_type) {
(
None
@ -216,15 +214,15 @@ impl Instrumentation {
}
}
/// `gen_ai.response.id` names one provider response, whichever convention recorded it.
fn with_response_id(context: &SpanContext<'_>, facts: SpanFacts) -> SpanFacts {
match present(context.attributes, &["gen_ai.response.id"]) {
Some(id) => SpanFacts {
calls: facts.calls.with(CallKey::ProviderResponse(id)),
..facts
},
None => facts,
}
fn with_call_ids(context: &SpanContext<'_>, facts: SpanFacts) -> SpanFacts {
let calls = [
present(context.attributes, &["gen_ai.response.id"]).map(CallKey::ProviderResponse),
present(context.attributes, &["litellm.call_id"]).map(CallKey::LiteLlmRequest),
]
.into_iter()
.flatten()
.fold(facts.calls, CallEvidence::with);
SpanFacts { calls, ..facts }
}
fn recorded_agent_name(

6
litellm-rust/crates/traces/src/normalize/mod.rs
View file

@ -47,7 +47,7 @@ pub enum ObservationType {
#[derive(Clone, Debug, Deserialize, Eq, Ord, PartialEq, PartialOrd)]
#[serde(try_from = "String")]
pub enum CallKey {
/// LiteLLM's own id for the request (`spend_logs.request_id`).
/// LiteLLM's gateway call id, with a fallback to legacy spend request ids.
LiteLlmRequest(String),
/// The provider response id returned to the caller (`spend_logs.response_id`).
ProviderResponse(String),
@ -126,7 +126,7 @@ impl CallEvidence {
pub(crate) fn from_row(row: &crate::query::named::TraceSpansRow) -> Self {
let kind = row
.call_evidence
.unwrap_or(if row.litellm_request_id.is_empty() {
.unwrap_or(if Self::row_keys(row).is_empty() {
CallEvidenceKind::Unknown
} else {
CallEvidenceKind::Complete
@ -146,7 +146,7 @@ impl CallEvidence {
/// convention found, but says nothing about completeness.
fn with(self, key: CallKey) -> Self {
match self {
Self::Unknown => Self::complete(key),
Self::Unknown => Self::Partial(BTreeSet::from([key])),
Self::Partial(keys) => Self::Partial(keys.into_iter().chain([key]).collect()),
Self::Complete(keys) => Self::Complete(keys.into_iter().chain([key]).collect()),
}

9
litellm-rust/crates/traces/src/query/named.rs
View file

@ -64,7 +64,7 @@ pub struct TraceSpansParams {
pub trace_ref: String,
}
#[derive(Debug, Deserialize, Serialize)]
#[derive(Clone, Debug, Deserialize, Serialize)]
pub struct TraceSpansRow {
#[serde(default)]
pub trace_id: String,
@ -172,6 +172,7 @@ pub struct SpendByResponseIdsParams {
#[derive(Debug, Deserialize, Serialize)]
pub struct SpendByResponseIdsRow {
pub request_id: String,
pub litellm_call_id: String,
pub response_id: String,
pub upstream_response_id: String,
pub trace_id: String,
@ -183,6 +184,12 @@ pub struct SpendByResponseIdsRow {
pub start_ms: i64,
}
impl SpendByResponseIdsRow {
pub(crate) fn identity(&self) -> (&str, i64, &str) {
(&self.team_id, self.start_ms, &self.request_id)
}
}
#[derive(Debug, Deserialize, Serialize)]
pub struct TraceIdentityParams {
#[serde(flatten)]

7
litellm-rust/crates/traces/src/resolve/graph.rs
View file

@ -46,6 +46,13 @@ impl<'a> Graph<'a> {
parent.is_empty() || !self.by_id.contains_key(parent.as_str())
}
pub(super) fn children(&self, index: usize) -> Vec<usize> {
self.children
.get(self.id(index))
.map(|children| children.to_vec())
.unwrap_or_default()
}
pub(super) fn ancestors(&self, index: usize) -> Vec<usize> {
let mut seen = HashSet::from([self.id(index)]);
let mut found = Vec::new();

48
litellm-rust/crates/traces/src/resolve/resolution.rs
View file

@ -102,14 +102,8 @@ impl<'a> Resolution<'a> {
.map(|source| self.requests(source))
.collect();
let transports: Vec<_> = self
.graph
.descendants(call)
.transports(call)
.into_iter()
.filter(|descendant| {
self.row(*descendant)
.call_keys
.contains(&CallKey::Transport)
})
.map(|transport| self.requests(transport))
.collect();
let transport_requests: Option<Vec<Requests<'a>>> = (!transports.is_empty())
@ -133,13 +127,51 @@ impl<'a> Resolution<'a> {
Some(
selected
.into_iter()
.map(|request| (request.request_id.as_str(), request))
.map(|request| (request.identity(), request))
.collect::<IndexMap<_, _>>()
.into_values()
.collect(),
)
}
/// The request attempts a model call made: its transport descendants, or, for bridges that
/// emit the request beside the call instead of under it, transport siblings inside the call's
/// time window when the call is the only model call under that parent.
fn transports(&self, call: usize) -> Vec<usize> {
let is_transport = |index: &usize| self.row(*index).call_keys.contains(&CallKey::Transport);
let nested: Vec<usize> = self
.graph
.descendants(call)
.into_iter()
.filter(is_transport)
.collect();
let Some(parent) = self.graph.parent(call).filter(|_| nested.is_empty()) else {
return nested;
};
let siblings = self.graph.children(parent);
let lone_call = siblings
.iter()
.filter(|sibling| self.kind(**sibling) == ObservationType::Llm)
.count()
== 1;
if !lone_call {
return nested;
}
let call_row = self.row(call);
let call_start_ns = i128::from(call_row.start_ns);
let call_end_ns = call_start_ns + i128::from(call_row.duration_ns);
siblings
.into_iter()
.filter(is_transport)
.filter(|sibling| {
let transport = self.row(*sibling);
let transport_start_ns = i128::from(transport.start_ns);
let transport_end_ns = transport_start_ns + i128::from(transport.duration_ns);
transport_start_ns >= call_start_ns && transport_end_ns <= call_end_ns
})
.collect()
}
pub(super) fn unique_tools(&self) -> Vec<usize> {
let mut by_call: IndexMap<&str, usize> = IndexMap::new();
for index in

41
litellm-rust/crates/traces/src/resolve/spend.rs
View file

@ -107,14 +107,14 @@ impl<'a> KeyMatch<'a> {
Self::Missing => false,
Self::Unique(request) => selected
.iter()
.any(|row| row.request_id == request.request_id),
.any(|row| row.identity() == request.identity()),
Self::Ambiguous(requests) => {
requests
.iter()
.filter(|request| {
selected
.iter()
.any(|row| row.request_id == request.request_id)
.any(|row| row.identity() == request.identity())
})
.count()
== 1
@ -136,7 +136,7 @@ impl<'a> SpendEvidence<'a> {
let requests: Requests<'a> = matches
.iter()
.filter_map(KeyMatch::unique)
.map(|request| (request.request_id.as_str(), request))
.map(|request| (request.identity(), request))
.collect::<IndexMap<_, _>>()
.into_values()
.collect();
@ -164,12 +164,16 @@ fn matches<'a>(
spend_rows: &'a [SpendRow],
key: &CallKey,
row: &TraceSpansRow,
) -> IndexMap<&'a str, &'a SpendRow> {
) -> IndexMap<(&'a str, i64, &'a str), &'a SpendRow> {
let matches = |spend: &SpendRow| match key {
CallKey::ProviderResponse(id) => {
!id.is_empty() && (spend.response_id == *id || spend.upstream_response_id == *id)
}
CallKey::LiteLlmRequest(id) => !id.is_empty() && spend.request_id == *id,
CallKey::LiteLlmRequest(id) => {
!id.is_empty()
&& (spend.litellm_call_id == *id
|| (spend.litellm_call_id.is_empty() && spend.request_id == *id))
}
CallKey::Transport => {
!row.trace_id.is_empty()
&& !row.span_id.is_empty()
@ -180,7 +184,7 @@ fn matches<'a>(
spend_rows
.iter()
.filter(|spend| ownership.owns(spend) && matches(spend))
.map(|spend| (spend.request_id.as_str(), spend))
.map(|spend| (spend.identity(), spend))
.collect()
}
@ -190,18 +194,35 @@ pub(super) fn requests<'a>(
spend_rows: &'a [SpendRow],
) -> SpendEvidence<'a> {
let evidence = CallEvidence::from_row(row);
let matches = evidence
let keyed: Vec<(&CallKey, Requests<'a>)> = evidence
.key_set()
.into_iter()
.flatten()
.map(|key| {
KeyMatch::new(
(
key,
matches(ownership, spend_rows, key, row)
.into_values()
.collect(),
)
})
.collect();
let anchored: Vec<&SpendRow> = keyed
.iter()
.filter(|(key, _)| !matches!(key, CallKey::LiteLlmRequest(_)))
.flat_map(|(_, requests)| requests.iter().copied())
.collect();
let legacy_rows = !anchored.is_empty()
&& anchored
.iter()
.all(|request| request.litellm_call_id.is_empty());
let matches = keyed
.into_iter()
.filter(|(key, requests)| {
!(legacy_rows && requests.is_empty() && matches!(key, CallKey::LiteLlmRequest(_)))
})
.map(|(_, requests)| KeyMatch::new(requests))
.collect();
match evidence.kind() {
CallEvidenceKind::Complete => SpendEvidence::Complete(matches),
CallEvidenceKind::Partial => SpendEvidence::Partial(matches),
@ -226,9 +247,9 @@ pub(super) fn total(calls: &[Option<Requests<'_>>]) -> Option<f64> {
.map(|requests| requests.as_ref())
.collect::<Option<Vec<_>>>()
.map(|calls| calls.into_iter().flatten().copied().collect());
let unique: IndexMap<&str, &SpendRow> = requests?
let unique: IndexMap<(&str, i64, &str), &SpendRow> = requests?
.into_iter()
.map(|request| (request.request_id.as_str(), request))
.map(|request| (request.identity(), request))
.collect();
request_cost(&unique.into_values().collect::<Vec<_>>())
}

445
litellm-rust/crates/traces/tests/captures.rs Normal file
View file

@ -0,0 +1,445 @@
use std::collections::{BTreeMap, BTreeSet};
use std::path::{Path, PathBuf};
use base64::{Engine as _, engine::general_purpose::STANDARD};
use litellm_traces::{
CallEvidence, CallEvidenceKind, CallKey, DecodedSpan, ObservationType, SpanStatus, decode_otlp,
query::named::{SpendByResponseIdsRow, TraceSpansRow},
resolve_trace,
};
use rstest::rstest;
use serde::Deserialize;
use serde_json::{Value, json};
struct CaptureData {
otlp: Vec<u8>,
}
fn capture_name(spend_log_path: &Path) -> &str {
spend_log_path
.file_stem()
.and_then(|stem| stem.to_str())
.and_then(|stem| stem.strip_suffix("_spend_logs"))
.unwrap_or_else(|| {
panic!(
"spend log filename must end with _spend_logs: {}",
spend_log_path.display()
)
})
}
fn manifest_path(path: &Path) -> PathBuf {
if path.is_absolute() {
path.to_path_buf()
} else {
Path::new(env!("CARGO_MANIFEST_DIR")).join(path)
}
}
fn capture_data(spend_log_path: &Path) -> CaptureData {
let name = capture_name(spend_log_path);
let otlp_path = Path::new(env!("CARGO_MANIFEST_DIR"))
.join("tests/fixtures")
.join(format!("{name}.json"));
let otlp = std::fs::read(&otlp_path).unwrap_or_else(|error| {
panic!(
"missing OTLP export for spend capture {name} at {}: {error}",
otlp_path.display()
)
});
CaptureData { otlp }
}
#[derive(Deserialize)]
struct CapturedSpend {
request_id: String,
#[serde(default)]
litellm_call_id: String,
response_id: String,
trace_id: String,
span_id: String,
team_id: String,
api_key: String,
user: String,
spend: Option<f64>,
start_time: i64,
metadata: String,
}
#[derive(Deserialize)]
struct SpendMetadata {
fixture_capture: FixtureCapture,
}
#[derive(Deserialize)]
struct FixtureCapture {
name: String,
trace_id: String,
spend_linked: bool,
#[serde(default = "true_value")]
spend_complete: bool,
}
fn true_value() -> bool {
true
}
fn upstream_response_id(response_id: &str) -> String {
let Some(encoded) = response_id.strip_prefix("resp_") else {
return String::new();
};
STANDARD
.decode(encoded)
.ok()
.and_then(|bytes| String::from_utf8(bytes).ok())
.and_then(|decoded| {
let (_, response_id) = decoded.split_once("response_id:")?;
Some(response_id.split(';').next()?.to_owned())
})
.unwrap_or_default()
}
fn captured_spend_rows(spend_logs: &str) -> (FixtureCapture, Vec<SpendByResponseIdsRow>) {
let records: Vec<CapturedSpend> = spend_logs
.lines()
.filter(|line| !line.trim().is_empty())
.map(|line| serde_json::from_str(line).expect("valid spend fixture row"))
.collect();
let metadata: SpendMetadata =
serde_json::from_str(&records.first().expect("spend fixture rows").metadata)
.expect("valid spend fixture metadata");
let spends = records
.into_iter()
.map(|record| {
let upstream_response_id = upstream_response_id(&record.response_id);
SpendByResponseIdsRow {
request_id: record.request_id,
litellm_call_id: record.litellm_call_id,
response_id: record.response_id,
upstream_response_id,
trace_id: record.trace_id,
span_id: record.span_id,
team_id: record.team_id,
api_key: record.api_key,
user: record.user,
spend: record.spend,
start_ms: record.start_time,
}
})
.collect();
(metadata.fixture_capture, spends)
}
fn status_message(span: &DecodedSpan) -> &str {
if !span.status_message.is_empty() {
return &span.status_message;
}
span.events
.iter()
.find(|event| event.name == "exception")
.and_then(|event| {
event
.attributes
.get("exception.message")
.filter(|message| !message.is_empty())
.or_else(|| event.attributes.get("exception.type"))
})
.map(String::as_str)
.unwrap_or_default()
}
fn trace_span(span: DecodedSpan) -> TraceSpansRow {
let service = span
.resource_attributes
.get("service.name")
.cloned()
.unwrap_or_default();
let message = status_message(&span);
let error_truncated = message.chars().count() > 128;
let status_message = message.chars().take(128).collect();
let normalized = span.normalized;
let call_keys = normalized
.calls
.key_set()
.into_iter()
.flatten()
.cloned()
.collect();
let litellm_request_id = normalized
.calls
.key_set()
.into_iter()
.flatten()
.find_map(|key| match key {
CallKey::ProviderResponse(id) => Some(id.clone()),
CallKey::LiteLlmRequest(_) | CallKey::Transport => None,
})
.unwrap_or_default();
TraceSpansRow {
trace_id: span.trace_id,
span_id: span.span_id,
parent_span_id: span.parent_span_id,
name: span.name,
kind: normalized.observation_type,
wrapper_candidate: normalized.wrapper_candidate,
agent: normalized.agent_name.unwrap_or_default(),
framework: normalized
.framework
.map(|framework| framework.to_string())
.unwrap_or_default(),
status: match span.status_code.as_str() {
"STATUS_CODE_OK" => SpanStatus::Ok,
"STATUS_CODE_ERROR" => SpanStatus::Error,
_ => SpanStatus::Unset,
},
status_message,
error_truncated,
start_ns: i64::try_from(span.start_ns).expect("valid trace start timestamp"),
duration_ns: span.end_ns - span.start_ns,
service,
input_preview: normalized.input_preview,
model: normalized.model.unwrap_or_default(),
input_tokens: normalized.input_tokens,
output_tokens: normalized.output_tokens,
litellm_request_id,
call_keys,
call_evidence: Some(normalized.calls.kind()),
tool_call_id: normalized.tool_call_id.unwrap_or_default(),
team_id: "fixture-team".into(),
api_key_hash: "fixture-key".into(),
user_id: "fixture-user".into(),
}
}
fn trace_rows(otlp: &[u8]) -> Vec<TraceSpansRow> {
let mut seen = BTreeSet::new();
decode_otlp(otlp, Some("application/json"))
.expect("valid OTLP fixture")
.into_iter()
.filter_map(|span| seen.insert(span.span_id.clone()).then(|| trace_span(span)))
.collect()
}
fn fixture(
spend_log_path: &Path,
) -> (
CaptureData,
FixtureCapture,
Vec<TraceSpansRow>,
Vec<SpendByResponseIdsRow>,
) {
let spend_log_path = manifest_path(spend_log_path);
let name = capture_name(&spend_log_path);
let spend_log_contents = std::fs::read_to_string(&spend_log_path).unwrap_or_else(|error| {
panic!(
"unable to read spend log fixture {}: {error}",
spend_log_path.display()
)
});
let (capture, spends) = captured_spend_rows(&spend_log_contents);
assert_eq!(capture.name, name);
let data = capture_data(&spend_log_path);
let rows = trace_rows(&data.otlp);
(data, capture, rows, spends)
}
fn assert_spend_close(actual: Option<f64>, expected: Option<f64>, capture: &str) {
match (actual, expected) {
(Some(actual), Some(expected)) => assert!(
(actual - expected).abs() <= 1e-12,
"{capture}: expected {expected}, got {actual}"
),
_ => assert_eq!(actual, expected, "{capture}"),
}
}
fn agent_spends(trace: &litellm_traces::Trace) -> BTreeMap<String, Option<f64>> {
trace
.agents
.iter()
.map(|agent| (agent.name.clone(), agent.spend))
.collect()
}
fn unrelated_transport(call: &TraceSpansRow) -> TraceSpansRow {
let start_ns =
i64::try_from(i128::from(call.start_ns) + i128::from(call.duration_ns) + 1_000_000)
.expect("valid unrelated transport timestamp");
TraceSpansRow {
trace_id: call.trace_id.clone(),
span_id: format!("unrelated-transport-{}", call.span_id),
parent_span_id: call.parent_span_id.clone(),
name: "unrelated-http".into(),
kind: ObservationType::Framework,
wrapper_candidate: false,
agent: String::new(),
framework: String::new(),
status: SpanStatus::Ok,
status_message: String::new(),
error_truncated: false,
start_ns,
duration_ns: 1_000_000,
service: call.service.clone(),
input_preview: String::new(),
model: String::new(),
input_tokens: 0,
output_tokens: 0,
litellm_request_id: String::new(),
call_keys: vec![CallKey::Transport],
call_evidence: Some(CallEvidenceKind::Complete),
tool_call_id: String::new(),
team_id: call.team_id.clone(),
api_key_hash: call.api_key_hash.clone(),
user_id: call.user_id.clone(),
}
}
fn append_response_id(document: &mut Value, trace_id: &str, span_id: &str, response_id: &str) {
let resources = document["resourceSpans"]
.as_array_mut()
.expect("OTLP resource spans");
for resource in resources {
let scopes = resource["scopeSpans"]
.as_array_mut()
.expect("OTLP scope spans");
for scope in scopes {
let spans = scope["spans"].as_array_mut().expect("OTLP spans");
for span in spans {
let matches = span.get("traceId").and_then(Value::as_str) == Some(trace_id)
&& span.get("spanId").and_then(Value::as_str) == Some(span_id);
if !matches {
continue;
}
let attributes = span
.as_object_mut()
.expect("OTLP span object")
.entry("attributes")
.or_insert_with(|| json!([]))
.as_array_mut()
.expect("OTLP span attributes");
attributes.push(json!({
"key": "gen_ai.response.id",
"value": { "stringValue": response_id },
}));
return;
}
}
}
panic!("missing OTLP span {trace_id}/{span_id}");
}
#[rstest]
fn captured_trace_cost_matches_spend_logs(
#[files("../traces-clickhouse/tests/fixtures/*_spend_logs.jsonl")]
#[exclude("deeplite_swarm")]
spend_logs: PathBuf,
) {
let name = capture_name(&spend_logs);
let (_, capture, rows, spends) = fixture(&spend_logs);
let trace = resolve_trace(&capture.trace_id, "", &rows, &spends).expect("captured trace");
let expected = if capture.spend_linked && capture.spend_complete {
Some(spends.iter().map(|row| row.spend.unwrap_or(0.0)).sum())
} else {
None
};
assert_spend_close(trace.summary.spend, expected, name);
}
#[rstest]
fn unrelated_sibling_transport_leaves_cost_unchanged(
#[files("../traces-clickhouse/tests/fixtures/*_spend_logs.jsonl")]
#[exclude("deeplite_swarm")]
spend_logs: PathBuf,
) {
let name = capture_name(&spend_logs);
let (_, capture, rows, spends) = fixture(&spend_logs);
let baseline = resolve_trace(&capture.trace_id, "", &rows, &spends).expect("captured trace");
let baseline_spend = baseline.summary.spend;
let baseline_agent_spends = agent_spends(&baseline);
let calls: Vec<_> = rows
.iter()
.filter(|row| {
row.kind == ObservationType::Llm && !row.call_keys.contains(&CallKey::Transport)
})
.cloned()
.collect();
assert!(!calls.is_empty(), "{name} has no model call rows");
for call in calls {
let augmented_rows = rows
.iter()
.cloned()
.chain([unrelated_transport(&call)])
.collect::<Vec<_>>();
let augmented =
resolve_trace(&capture.trace_id, "", &augmented_rows, &spends).expect("captured trace");
assert_eq!(
augmented.summary.spend, baseline_spend,
"{name}, model span {}",
call.span_id
);
assert_eq!(
agent_spends(&augmented),
baseline_agent_spends,
"{name}, model span {}",
call.span_id
);
}
}
#[rstest]
fn redundant_genai_response_id_keeps_call_evidence(
#[files("../traces-clickhouse/tests/fixtures/*_spend_logs.jsonl")]
#[exclude("deeplite_swarm")]
spend_logs: PathBuf,
) {
let (data, capture, _, _) = fixture(&spend_logs);
let name = capture.name;
let decoded = decode_otlp(&data.otlp, Some("application/json")).expect("valid OTLP fixture");
let targets: Vec<_> = decoded
.iter()
.filter_map(|span| {
let CallEvidence::Complete(keys) = &span.normalized.calls else {
return None;
};
if span.attributes.contains_key("gen_ai.response.id") {
return None;
}
let response_ids: Vec<_> = keys
.iter()
.filter_map(|key| match key {
CallKey::ProviderResponse(id) => Some(id.clone()),
CallKey::LiteLlmRequest(_) | CallKey::Transport => None,
})
.collect();
(!response_ids.is_empty()).then(|| {
(
span.trace_id.clone(),
span.span_id.clone(),
span.normalized.calls.clone(),
response_ids,
)
})
})
.collect();
for (trace_id, span_id, expected, response_ids) in targets {
for response_id in response_ids {
let mut document: Value =
serde_json::from_slice(&data.otlp).expect("valid OTLP JSON fixture");
append_response_id(&mut document, &trace_id, &span_id, &response_id);
let modified = serde_json::to_vec(&document).expect("serializable OTLP JSON");
let spans = decode_otlp(&modified, Some("application/json"))
.expect("OTLP with redundant response ID");
let actual = spans
.iter()
.find(|span| span.trace_id == trace_id && span.span_id == span_id)
.expect("modified span")
.normalized
.calls
.clone();
assert_eq!(
actual, expected,
"{name}, span {span_id}, response id {response_id}"
);
}
}
}

3608
litellm-rust/crates/traces/tests/fixtures/claude_agent_sdk_detailed_export.json vendored
View file

File diff suppressed because it is too large Load diff

2076
litellm-rust/crates/traces/tests/fixtures/claude_agent_sdk_export.json vendored
View file

File diff suppressed because it is too large Load diff

108
litellm-rust/crates/traces/tests/fixtures/claude_agent_sdk_simple.json vendored
View file

@ -9,12 +9,6 @@
"stringValue": "research_agent"
}
},
{
"key": "service.name",
"value": {
"stringValue": "claude-agent-sdk-simple-linked"
}
},
{
"key": "host.arch",
"value": {
@ -24,13 +18,19 @@
{
"key": "os.type",
"value": {
"stringValue": "linux"
"stringValue": "darwin"
}
},
{
"key": "os.version",
"value": {
"stringValue": "7.0.11-orbstack-00360-gc9bc4d96ac70"
"stringValue": "25.6.0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "claude-code"
}
},
{
@ -49,13 +49,13 @@
},
"spans": [
{
"traceId": "68d4ab5c1bb4cdff9f7fa72ce5e360d4",
"spanId": "2202f91fa2679814",
"parentSpanId": "f0281548ccd4d661",
"traceId": "207bf859f653150fd4346add5cb11ad3",
"spanId": "1825d4fb63cffcec",
"parentSpanId": "8a420dd58d6ff237",
"name": "claude_code.llm_request",
"kind": 1,
"startTimeUnixNano": "1791013731536000000",
"endTimeUnixNano": "1791013738679125375",
"startTimeUnixNano": "1791061376589000000",
"endTimeUnixNano": "1791061384361645125",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -66,19 +66,19 @@
{
"key": "user.id",
"value": {
"stringValue": "4493a11fb6084c04be89c081b455b16d3b2df792ebf4dc2014e12d078d4732e8"
"stringValue": "999dc5afd40b65ac74b33bdc01c83510d935a57dfc4b1c232b51504bb8caed2e"
}
},
{
"key": "session.id",
"value": {
"stringValue": "7b36c5c7-8eb5-45ad-8ffc-2966f64389b7"
"stringValue": "0e5bb47c-6b08-4a1b-bde4-9ab142589572"
}
},
{
"key": "terminal.type",
"value": {
"stringValue": "non-interactive"
"stringValue": "ghostty"
}
},
{
@ -168,7 +168,7 @@
{
"key": "system_reminders_count",
"value": {
"intValue": "1"
"intValue": "2"
}
},
{
@ -180,25 +180,25 @@
{
"key": "system_reminders",
"value": {
"stringValue": "# Environment\nYou have been invoked in the following environment: \n - Primary working directory: /fixtures/claude-agent-sdk\n - Is a git repository: true\n - Platform: linux\n - Shell: unknown\n - OS Version: Linux 7.0.11-orbstack-00360-gc9bc4d96ac70\n\nYou are powered by the model openai/gpt-6-luna.\n\n<total_tokens>15000000 tokens left</total_tokens>\n\nToday's date is 2026-10-03."
"stringValue": "Codebase and user instructions are shown below. Be sure to adhere to these instructions. IMPORTANT: These instructions OVERRIDE any default behavior and you MUST follow them exactly as written.\n\nContents of /home/user/.claude/projects/-home-user-dev-litellm-lens-example/memory/MEMORY.md (user's auto-memory, persists across conversations):\n\n- [LiteLLM spend correlation contract](litellm-spend-correlation-contract.md) \u2014 source PR #44421, offline validation with mock gateway + recorder\n- [SDK wiring limits](litellm-lens-example-sdk-wiring-limits.md) \u2014 which SDKs cannot take the shared gateway transport and why\n\n---\n\n# Environment\nYou have been invoked in the following environment: \n - Primary working directory: /home/user/dev/litellm-lens-example/claude-agent-sdk\n - Is a git repository: true\n - Platform: darwin\n - Shell: zsh\n - OS Version: Darwin 25.6.0\n\nYou are powered by the model openai/gpt-6-luna.\n\n<total_tokens>15000000 tokens left</total_tokens>\n\nToday's date is 2026-10-03."
}
},
{
"key": "duration_ms",
"value": {
"intValue": "7143"
"intValue": "7772"
}
},
{
"key": "input_tokens",
"value": {
"intValue": "172"
"intValue": "323"
}
},
{
"key": "output_tokens",
"value": {
"intValue": "665"
"intValue": "671"
}
},
{
@ -234,25 +234,25 @@
{
"key": "request_id",
"value": {
"stringValue": "msg_7117e61b-cb2a-4e2f-8155-9b8bab62a4b9"
"stringValue": "msg_b65eb4b6-da48-4eb3-a564-0f65202f4797"
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "msg_7117e61b-cb2a-4e2f-8155-9b8bab62a4b9"
"stringValue": "msg_b65eb4b6-da48-4eb3-a564-0f65202f4797"
}
},
{
"key": "ttft_ms",
"value": {
"intValue": "540"
"intValue": "769"
}
},
{
"key": "first_content_ms",
"value": {
"intValue": "5584"
"intValue": "6113"
}
},
{
@ -264,7 +264,7 @@
{
"key": "response.model_output",
"value": {
"stringValue": "An **agent trace** is the chronological record of an agent run: the input it received, the model’s intermediate messages, any tools it called and their results, and how the run ended.\n\nIt shows **how** the agent reached its final answer—not just the answer itself—and is useful for debugging and monitoring. In the Claude Agent SDK, you can follow a run through its streamed messages and events. Traces may contain prompts or other sensitive data, so handle them accordingly."
"stringValue": "An **agent trace** is the connected record of one agent task from start to finish\u2014not just a single LLM request.\n\nFor example, a task might produce a trace containing:\n1. The agent\u2019s model call\n2. A weather-tool call and its result\n3. A follow-up model call that writes the answer\n\nThose steps are linked as spans in a timeline, often with timestamps, status, token usage, and cost. Traces help you understand what the agent did, where it failed or spent time, and which calls contributed to the cost.\n\nA trace may contain prompts and tool inputs or outputs, depending on the instrumentation, but it **doesn\u2019t mean the agent\u2019s hidden chain-of-thought is being recorded**."
}
},
{
@ -288,7 +288,7 @@
],
"events": [
{
"timeUnixNano": "1791013731538265232",
"timeUnixNano": "1791061376591988250",
"name": "gen_ai.request.attempt",
"attributes": [
{
@ -304,13 +304,13 @@
"flags": 257
},
{
"traceId": "68d4ab5c1bb4cdff9f7fa72ce5e360d4",
"spanId": "f0281548ccd4d661",
"parentSpanId": "d8aa87f2b774735d",
"traceId": "207bf859f653150fd4346add5cb11ad3",
"spanId": "8a420dd58d6ff237",
"parentSpanId": "c2c6b0971076f31c",
"name": "claude_code.interaction",
"kind": 1,
"startTimeUnixNano": "1791013731512000000",
"endTimeUnixNano": "1791013738692801566",
"startTimeUnixNano": "1791061376541000000",
"endTimeUnixNano": "1791061384366705375",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -321,19 +321,19 @@
{
"key": "user.id",
"value": {
"stringValue": "4493a11fb6084c04be89c081b455b16d3b2df792ebf4dc2014e12d078d4732e8"
"stringValue": "999dc5afd40b65ac74b33bdc01c83510d935a57dfc4b1c232b51504bb8caed2e"
}
},
{
"key": "session.id",
"value": {
"stringValue": "7b36c5c7-8eb5-45ad-8ffc-2966f64389b7"
"stringValue": "0e5bb47c-6b08-4a1b-bde4-9ab142589572"
}
},
{
"key": "terminal.type",
"value": {
"stringValue": "non-interactive"
"stringValue": "ghostty"
}
},
{
@ -381,7 +381,7 @@
{
"key": "interaction.duration_ms",
"value": {
"intValue": "7181"
"intValue": "7826"
}
}
],
@ -416,7 +416,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "ff3ae854-67ab-4a61-98b3-8840846062f8"
"stringValue": "5ac3598e-aed9-4cda-ac0e-1ab2f82a2673"
}
},
{
@ -425,17 +425,17 @@
"stringValue": "research_agent"
}
},
{
"key": "service.name",
"value": {
"stringValue": "claude-agent-sdk-simple-linked"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
@ -447,12 +447,12 @@
},
"spans": [
{
"traceId": "68d4ab5c1bb4cdff9f7fa72ce5e360d4",
"spanId": "d8aa87f2b774735d",
"traceId": "207bf859f653150fd4346add5cb11ad3",
"spanId": "c2c6b0971076f31c",
"name": "ClaudeAgentSDK.query",
"kind": 1,
"startTimeUnixNano": "1791013731360778595",
"endTimeUnixNano": "1791013738788489656",
"startTimeUnixNano": "1791061376098056000",
"endTimeUnixNano": "1791061384413648000",
"attributes": [
{
"key": "llm.system",
@ -481,7 +481,7 @@
{
"key": "llm.output_messages.0.message.content.0",
"value": {
"stringValue": "An **agent trace** is the chronological record of an agent run: the input it received, the model’s intermediate messages, any tools it called and their results, and how the run ended.\n\nIt shows **how** the agent reached its final answer—not just the answer itself—and is useful for debugging and monitoring. In the Claude Agent SDK, you can follow a run through its streamed messages and events. Traces may contain prompts or other sensitive data, so handle them accordingly."
"stringValue": "An **agent trace** is the connected record of one agent task from start to finish\u2014not just a single LLM request.\n\nFor example, a task might produce a trace containing:\n1. The agent\u2019s model call\n2. A weather-tool call and its result\n3. A follow-up model call that writes the answer\n\nThose steps are linked as spans in a timeline, often with timestamps, status, token usage, and cost. Traces help you understand what the agent did, where it failed or spent time, and which calls contributed to the cost.\n\nA trace may contain prompts and tool inputs or outputs, depending on the instrumentation, but it **doesn\u2019t mean the agent\u2019s hidden chain-of-thought is being recorded**."
}
},
{
@ -499,7 +499,7 @@
{
"key": "output.value",
"value": {
"stringValue": "An **agent trace** is the chronological record of an agent run: the input it received, the model’s intermediate messages, any tools it called and their results, and how the run ended.\n\nIt shows **how** the agent reached its final answer—not just the answer itself—and is useful for debugging and monitoring. In the Claude Agent SDK, you can follow a run through its streamed messages and events. Traces may contain prompts or other sensitive data, so handle them accordingly."
"stringValue": "An **agent trace** is the connected record of one agent task from start to finish\u2014not just a single LLM request.\n\nFor example, a task might produce a trace containing:\n1. The agent\u2019s model call\n2. A weather-tool call and its result\n3. A follow-up model call that writes the answer\n\nThose steps are linked as spans in a timeline, often with timestamps, status, token usage, and cost. Traces help you understand what the agent did, where it failed or spent time, and which calls contributed to the cost.\n\nA trace may contain prompts and tool inputs or outputs, depending on the instrumentation, but it **doesn\u2019t mean the agent\u2019s hidden chain-of-thought is being recorded**."
}
},
{
@ -517,19 +517,19 @@
{
"key": "llm.token_count.prompt",
"value": {
"intValue": "172"
"intValue": "323"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "665"
"intValue": "671"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "837"
"intValue": "994"
}
},
{
@ -547,13 +547,13 @@
{
"key": "llm.cost.total",
"value": {
"doubleValue": 0.013987999999999999
"doubleValue": 0.014712000000000001
}
},
{
"key": "session.id",
"value": {
"stringValue": "7b36c5c7-8eb5-45ad-8ffc-2966f64389b7"
"stringValue": "0e5bb47c-6b08-4a1b-bde4-9ab142589572"
}
},
{

748
litellm-rust/crates/traces/tests/fixtures/claude_agent_sdk_swarm.json vendored
View file

File diff suppressed because it is too large Load diff

76
litellm-rust/crates/traces/tests/fixtures/crewai_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "ce3d1e9d-9ad8-4e9c-baa1-790de8270f2a"
}
},
{
"key": "service.name",
"value": {
"stringValue": "crewai-simple"
"stringValue": "474ef02c-24f0-4fe4-84e3-568c08278bf7"
}
},
{
@ -38,6 +32,12 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
@ -49,13 +49,13 @@
},
"spans": [
{
"traceId": "110c44d444b7742cfa57fc70cef424d8",
"spanId": "29ed447ec5f9b6e8",
"parentSpanId": "97f30ba7a431f8e7",
"traceId": "9423efa0faa4a873d896222c6b33aacc",
"spanId": "742c3f48055313df",
"parentSpanId": "3af673c4fc56a4d3",
"name": "ChatCompletion",
"kind": 1,
"startTimeUnixNano": "1791012936617707896",
"endTimeUnixNano": "1791012938235971272",
"startTimeUnixNano": "1791061313593128000",
"endTimeUnixNano": "1791061315819218000",
"attributes": [
{
"key": "llm.system",
@ -66,7 +66,7 @@
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"role\":\"system\",\"content\":\"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}],\"model\":\"openai/gpt-6-luna\"}"
"stringValue": "{\"messages\": [{\"role\": \"system\", \"content\": \"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"}, {\"role\": \"user\", \"content\": \"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}], \"model\": \"openai/gpt-6-luna\"}"
}
},
{
@ -78,7 +78,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"id\":\"chatcmpl-EUoZMwGeJM9ul6B0NsHufReuUYcEa\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a chronological record of an AI agent’s actions and observations while completing a task—such as its inputs, tool calls, tool results, and final response. It helps explain, debug, and evaluate the agent’s behavior.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791012936,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":86,\"prompt_tokens\":73,\"total_tokens\":159,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":27,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
"stringValue": "{\"id\":\"chatcmpl-EV19e7sf5xeFVr05xBNOmEygQ8mTd\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a chronological record of an AI agent\u2019s execution: the steps it took, such as receiving input, calling tools, observing results, and producing an answer. Traces help developers understand, debug, and evaluate an agent\u2019s behavior.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791061314,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":122,\"prompt_tokens\":73,\"total_tokens\":195,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":61,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
}
},
{
@ -90,7 +90,7 @@
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\"}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\"}"
}
},
{
@ -126,7 +126,7 @@
{
"key": "llm.token_count.total",
"value": {
"intValue": "159"
"intValue": "195"
}
},
{
@ -138,7 +138,7 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "86"
"intValue": "122"
}
},
{
@ -162,7 +162,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "27"
"intValue": "61"
}
},
{
@ -180,7 +180,7 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a chronological record of an AI agent’s actions and observations while completing a task—such as its inputs, tool calls, tool results, and final response. It helps explain, debug, and evaluate the agent’s behavior."
"stringValue": "An **agent trace** is a chronological record of an AI agent\u2019s execution: the steps it took, such as receiving input, calling tools, observing results, and producing an answer. Traces help developers understand, debug, and evaluate an agent\u2019s behavior."
}
},
{
@ -210,18 +210,18 @@
},
"spans": [
{
"traceId": "110c44d444b7742cfa57fc70cef424d8",
"spanId": "97f30ba7a431f8e7",
"parentSpanId": "b2637dab2e2fc2ca",
"traceId": "9423efa0faa4a873d896222c6b33aacc",
"spanId": "3af673c4fc56a4d3",
"parentSpanId": "1edfe2e36e867117",
"name": "research_agent._execute_core",
"kind": 1,
"startTimeUnixNano": "1791012936360020229",
"endTimeUnixNano": "1791012938250502005",
"startTimeUnixNano": "1791061313403894000",
"endTimeUnixNano": "1791061315825553000",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"agent\":{\"entity_type\":\"agent\",\"id\":\"af3484b5-92a5-4a01-acf7-0e759af2f968\",\"role\":\"research_agent\",\"goal\":\"Answer questions clearly\",\"backstory\":\"You explain technical concepts.\",\"cache\":true,\"verbose\":false,\"max_rpm\":null,\"allow_delegation\":false,\"tools\":[],\"max_iter\":25,\"tool_failure_policy\":null,\"i18n\":{\"prompt_file\":null},\"cache_handler\":null,\"tools_results\":[],\"max_tokens\":null,\"knowledge\":null,\"knowledge_sources\":null,\"knowledge_storage\":null,\"security_config\":{\"fingerprint\":{\"metadata\":{}}},\"checkpoint\":null,\"adapted_agent\":false,\"knowledge_config\":null,\"apps\":null,\"mcps\":null,\"memory\":null,\"skills\":null,\"execution_context\":null,\"checkpoint_kickoff_event_id\":null,\"max_execution_time\":null,\"use_system_prompt\":true,\"system_template\":null,\"prompt_template\":null,\"response_template\":null,\"allow_code_execution\":false,\"respect_context_window\":true,\"max_retry_limit\":2,\"multimodal\":false,\"inject_date\":false,\"date_format\":\"%Y-%m-%d\",\"code_execution_mode\":\"safe\",\"planning_config\":null,\"planning\":false,\"reasoning\":false,\"max_reasoning_attempts\":null,\"embedder\":null,\"agent_knowledge_context\":null,\"crew_knowledge_context\":null,\"knowledge_search_query\":null,\"from_repository\":null,\"guardrail_max_retries\":3,\"a2a\":null,\"key\":\"4dc9e33a8c7925f0f322ab0a8886aef0\"},\"context\":\"\",\"tools\":[]}"
"stringValue": "{\"agent\": {\"entity_type\": \"agent\", \"id\": \"85ad8f68-68d9-4510-9367-af4d6697cb24\", \"role\": \"research_agent\", \"goal\": \"Answer questions clearly\", \"backstory\": \"You explain technical concepts.\", \"cache\": true, \"verbose\": false, \"max_rpm\": null, \"allow_delegation\": false, \"tools\": [], \"max_iter\": 25, \"tool_failure_policy\": null, \"i18n\": {\"prompt_file\": null}, \"cache_handler\": null, \"tools_results\": [], \"max_tokens\": null, \"knowledge\": null, \"knowledge_sources\": null, \"knowledge_storage\": null, \"security_config\": {\"fingerprint\": {\"metadata\": {}}}, \"checkpoint\": null, \"adapted_agent\": false, \"knowledge_config\": null, \"apps\": null, \"mcps\": null, \"memory\": null, \"skills\": null, \"execution_context\": null, \"checkpoint_kickoff_event_id\": null, \"max_execution_time\": null, \"use_system_prompt\": true, \"system_template\": null, \"prompt_template\": null, \"response_template\": null, \"allow_code_execution\": false, \"respect_context_window\": true, \"max_retry_limit\": 2, \"multimodal\": false, \"inject_date\": false, \"date_format\": \"%Y-%m-%d\", \"code_execution_mode\": \"safe\", \"planning_config\": null, \"planning\": false, \"reasoning\": false, \"max_reasoning_attempts\": null, \"embedder\": null, \"agent_knowledge_context\": null, \"crew_knowledge_context\": null, \"knowledge_search_query\": null, \"from_repository\": null, \"guardrail_max_retries\": 3, \"a2a\": null, \"key\": \"4dc9e33a8c7925f0f322ab0a8886aef0\"}, \"context\": \"\", \"tools\": []}"
}
},
{
@ -239,7 +239,7 @@
{
"key": "task_id",
"value": {
"stringValue": "583ee87b-ad8f-42b0-9354-78036e8c3875"
"stringValue": "be50aff3-19df-4772-99d7-5bd96218c0f6"
}
},
{
@ -257,13 +257,13 @@
{
"key": "crew_id",
"value": {
"stringValue": "75706cf9-6de1-475e-8e6b-dc145c5d54d6"
"stringValue": "e829105c-c95a-4f8e-aea4-4a98b6c84b13"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"description\":\"What is an agent trace?\",\"name\":\"What is an agent trace?\",\"expected_output\":\"A short answer\",\"summary\":\"What is an agent trace?...\",\"raw\":\"An **agent trace** is a chronological record of an AI agent’s actions and observations while completing a task—such as its inputs, tool calls, tool results, and final response. It helps explain, debug, and evaluate the agent’s behavior.\",\"pydantic\":null,\"json_dict\":null,\"agent\":\"research_agent\",\"output_format\":\"raw\",\"messages\":[{\"role\":\"system\",\"content\":\"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}],\"tool_failures\":[]}"
"stringValue": "{\"description\": \"What is an agent trace?\", \"name\": \"What is an agent trace?\", \"expected_output\": \"A short answer\", \"summary\": \"What is an agent trace?...\", \"raw\": \"An **agent trace** is a chronological record of an AI agent\u2019s execution: the steps it took, such as receiving input, calling tools, observing results, and producing an answer. Traces help developers understand, debug, and evaluate an agent\u2019s behavior.\", \"pydantic\": null, \"json_dict\": null, \"agent\": \"research_agent\", \"output_format\": \"raw\", \"messages\": [{\"role\": \"system\", \"content\": \"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"}, {\"role\": \"user\", \"content\": \"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}], \"tool_failures\": []}"
}
},
{
@ -285,17 +285,17 @@
"flags": 256
},
{
"traceId": "110c44d444b7742cfa57fc70cef424d8",
"spanId": "b2637dab2e2fc2ca",
"traceId": "9423efa0faa4a873d896222c6b33aacc",
"spanId": "1edfe2e36e867117",
"name": "research_crew.kickoff",
"kind": 1,
"startTimeUnixNano": "1791012936330034002",
"endTimeUnixNano": "1791012938279104760",
"startTimeUnixNano": "1791061313393493000",
"endTimeUnixNano": "1791061315827947000",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"question\":\"What is an agent trace?\"}"
"stringValue": "{\"question\": \"What is an agent trace?\"}"
}
},
{
@ -313,31 +313,31 @@
{
"key": "crew_id",
"value": {
"stringValue": "75706cf9-6de1-475e-8e6b-dc145c5d54d6"
"stringValue": "e829105c-c95a-4f8e-aea4-4a98b6c84b13"
}
},
{
"key": "crew_inputs",
"value": {
"stringValue": "{\"question\":\"What is an agent trace?\"}"
"stringValue": "{\"question\": \"What is an agent trace?\"}"
}
},
{
"key": "crew_agents",
"value": {
"stringValue": "[{\"key\":\"4dc9e33a8c7925f0f322ab0a8886aef0\",\"id\":\"af3484b5-92a5-4a01-acf7-0e759af2f968\",\"role\":\"research_agent\",\"goal\":\"Answer questions clearly\",\"backstory\":\"You explain technical concepts.\",\"verbose?\":false,\"max_iter\":25,\"max_rpm\":null,\"delegation_enabled\":false,\"tools_names\":[]}]"
"stringValue": "[{\"key\": \"4dc9e33a8c7925f0f322ab0a8886aef0\", \"id\": \"85ad8f68-68d9-4510-9367-af4d6697cb24\", \"role\": \"research_agent\", \"goal\": \"Answer questions clearly\", \"backstory\": \"You explain technical concepts.\", \"verbose?\": false, \"max_iter\": 25, \"max_rpm\": null, \"delegation_enabled\": false, \"tools_names\": []}]"
}
},
{
"key": "crew_tasks",
"value": {
"stringValue": "[{\"id\":\"583ee87b-ad8f-42b0-9354-78036e8c3875\",\"description\":\"{question}\",\"expected_output\":\"A short answer\",\"async_execution?\":false,\"human_input?\":false,\"agent_role\":\"research_agent\",\"agent_key\":\"4dc9e33a8c7925f0f322ab0a8886aef0\",\"context\":null,\"tools_names\":[]}]"
"stringValue": "[{\"id\": \"be50aff3-19df-4772-99d7-5bd96218c0f6\", \"description\": \"{question}\", \"expected_output\": \"A short answer\", \"async_execution?\": false, \"human_input?\": false, \"agent_role\": \"research_agent\", \"agent_key\": \"4dc9e33a8c7925f0f322ab0a8886aef0\", \"context\": null, \"tools_names\": []}]"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"raw\":\"An **agent trace** is a chronological record of an AI agent’s actions and observations while completing a task—such as its inputs, tool calls, tool results, and final response. It helps explain, debug, and evaluate the agent’s behavior.\",\"pydantic\":null,\"json_dict\":null,\"tasks_output\":[{\"description\":\"What is an agent trace?\",\"name\":\"What is an agent trace?\",\"expected_output\":\"A short answer\",\"summary\":\"What is an agent trace?...\",\"raw\":\"An **agent trace** is a chronological record of an AI agent’s actions and observations while completing a task—such as its inputs, tool calls, tool results, and final response. It helps explain, debug, and evaluate the agent’s behavior.\",\"pydantic\":null,\"json_dict\":null,\"agent\":\"research_agent\",\"output_format\":\"raw\",\"messages\":[{\"role\":\"system\",\"content\":\"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"},{\"role\":\"user\",\"content\":\"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}],\"tool_failures\":[]}],\"token_usage\":{\"total_tokens\":159,\"prompt_tokens\":73,\"cached_prompt_tokens\":0,\"completion_tokens\":86,\"reasoning_tokens\":27,\"cache_creation_tokens\":0,\"successful_requests\":1}}"
"stringValue": "{\"raw\": \"An **agent trace** is a chronological record of an AI agent\u2019s execution: the steps it took, such as receiving input, calling tools, observing results, and producing an answer. Traces help developers understand, debug, and evaluate an agent\u2019s behavior.\", \"pydantic\": null, \"json_dict\": null, \"tasks_output\": [{\"description\": \"What is an agent trace?\", \"name\": \"What is an agent trace?\", \"expected_output\": \"A short answer\", \"summary\": \"What is an agent trace?...\", \"raw\": \"An **agent trace** is a chronological record of an AI agent\u2019s execution: the steps it took, such as receiving input, calling tools, observing results, and producing an answer. Traces help developers understand, debug, and evaluate an agent\u2019s behavior.\", \"pydantic\": null, \"json_dict\": null, \"agent\": \"research_agent\", \"output_format\": \"raw\", \"messages\": [{\"role\": \"system\", \"content\": \"You are research_agent. You explain technical concepts.\\nYour personal goal is: Answer questions clearly\"}, {\"role\": \"user\", \"content\": \"\\nCurrent Task: What is an agent trace?\\n\\nThis is the expected criteria for your final answer: A short answer\\nyou MUST return the actual complete content as the final answer, not a summary.\\n\\nProvide your complete response:\"}], \"tool_failures\": []}], \"token_usage\": {\"total_tokens\": 195, \"prompt_tokens\": 73, \"cached_prompt_tokens\": 0, \"completion_tokens\": 122, \"reasoning_tokens\": 61, \"cache_creation_tokens\": 0, \"successful_requests\": 1}}"
}
},
{

1022
litellm-rust/crates/traces/tests/fixtures/crewai_swarm.json vendored
View file

File diff suppressed because one or more lines are too long

98
litellm-rust/crates/traces/tests/fixtures/deepagents_simple.json vendored
View file

File diff suppressed because one or more lines are too long

542
litellm-rust/crates/traces/tests/fixtures/deepagents_swarm.json vendored
View file

File diff suppressed because one or more lines are too long

401
litellm-rust/crates/traces/tests/fixtures/google_adk_billed_failure.json vendored Normal file
View file

File diff suppressed because one or more lines are too long

576
litellm-rust/crates/traces/tests/fixtures/google_adk_retry.json vendored Normal file
View file

@ -0,0 +1,576 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.42.1"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.63b1"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "cdd888fb2417b10d57e2b2cee4dd428c",
"spanId": "977a62497f2dda9d",
"parentSpanId": "1a4892aa3fae3307",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061574509820000",
"endTimeUnixNano": "1791061576009220000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "503"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "4d1e094d-24e7-49f6-8d33-656a7d7712ae"
}
}
],
"status": {
"code": 2
},
"flags": 256
}
]
}
]
},
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.42.1"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.63b1"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "cdd888fb2417b10d57e2b2cee4dd428c",
"spanId": "2856caaa423beba1",
"parentSpanId": "1a4892aa3fae3307",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061576507921000",
"endTimeUnixNano": "1791061577761649000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "38acfc26-d8f3-4adc-9528-0196c28b8640"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.google_adk",
"version": "1.0.2"
},
"spans": [
{
"traceId": "cdd888fb2417b10d57e2b2cee4dd428c",
"spanId": "1a4892aa3fae3307",
"parentSpanId": "dff96cfbe5a5cb73",
"name": "call_llm",
"kind": 1,
"startTimeUnixNano": "1791061573142809000",
"endTimeUnixNano": "1791061577770765000",
"attributes": [
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "generate_content"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "gen_ai.system",
"value": {
"stringValue": "gcp.vertex.agent"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
"key": "gcp.vertex.agent.invocation_id",
"value": {
"stringValue": "e-de6dbbd8-e192-415a-a15c-24e216046237"
}
},
{
"key": "gcp.vertex.agent.session_id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "gcp.vertex.agent.event_id",
"value": {
"stringValue": "e5d9e1dd-20e8-4715-96db-dcd96a5f4e35"
}
},
{
"key": "gcp.vertex.agent.llm_request",
"value": {
"stringValue": "{\"model\": \"openai/openai/gpt-6-luna\", \"config\": {\"system_instruction\": \"You are an agent. Your internal name is \\\"research_agent\\\".\", \"labels\": {\"adk_agent_name\": \"research_agent\"}}, \"contents\": [{\"parts\": [{\"text\": \"Reply with one short sentence about agent traces.\"}], \"role\": \"user\"}]}"
}
},
{
"key": "gcp.vertex.agent.llm_response",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"Agent traces show the steps an agent takes to complete a task.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":47,\"prompt_token_count\":32,\"thoughts_token_count\":25,\"total_token_count\":79}}"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "32"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "72"
}
},
{
"key": "gen_ai.usage.cache_read.input_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.reasoning.output_tokens",
"value": {
"intValue": "25"
}
},
{
"key": "gen_ai.response.finish_reasons",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "stop"
}
]
}
}
},
{
"key": "llm.provider",
"value": {
"stringValue": "google"
}
},
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"openai/openai/gpt-6-luna\",\"contents\":[{\"parts\":[{\"text\":\"Reply with one short sentence about agent traces.\"}],\"role\":\"user\"}],\"config\":{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}},\"live_connect_config\":{\"input_audio_transcription\":{},\"output_audio_transcription\":{}}}"
}
},
{
"key": "input.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "llm.model_name",
"value": {
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}}"
}
},
{
"key": "llm.input_messages.0.message.role",
"value": {
"stringValue": "system"
}
},
{
"key": "llm.input_messages.0.message.content",
"value": {
"stringValue": "You are an agent. Your internal name is \"research_agent\"."
}
},
{
"key": "llm.input_messages.1.message.role",
"value": {
"stringValue": "user"
}
},
{
"key": "llm.input_messages.1.message.contents.0.message_content.text",
"value": {
"stringValue": "Reply with one short sentence about agent traces."
}
},
{
"key": "llm.input_messages.1.message.contents.0.message_content.type",
"value": {
"stringValue": "text"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"Agent traces show the steps an agent takes to complete a task.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":47,\"prompt_token_count\":32,\"thoughts_token_count\":25,\"total_token_count\":79}}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "79"
}
},
{
"key": "llm.token_count.prompt",
"value": {
"intValue": "32"
}
},
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "25"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "47"
}
},
{
"key": "llm.output_messages.0.message.role",
"value": {
"stringValue": "model"
}
},
{
"key": "llm.output_messages.0.message.contents.0.message_content.text",
"value": {
"stringValue": "Agent traces show the steps an agent takes to complete a task."
}
},
{
"key": "llm.output_messages.0.message.contents.0.message_content.type",
"value": {
"stringValue": "text"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "LLM"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "cdd888fb2417b10d57e2b2cee4dd428c",
"spanId": "dff96cfbe5a5cb73",
"parentSpanId": "1effa85408e9e4f7",
"name": "agent_run [research_agent]",
"kind": 1,
"startTimeUnixNano": "1791061573129211000",
"endTimeUnixNano": "1791061577770883000",
"attributes": [
{
"key": "agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "gen_ai.agent.description",
"value": {
"stringValue": ""
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"Agent traces show the steps an agent takes to complete a task.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":47,\"prompt_token_count\":32,\"thoughts_token_count\":25,\"total_token_count\":79},\"invocation_id\":\"e-de6dbbd8-e192-415a-a15c-24e216046237\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"\"},\"id\":\"e5d9e1dd-20e8-4715-96db-dcd96a5f4e35\",\"timestamp\":1791061573.14271}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "AGENT"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "cdd888fb2417b10d57e2b2cee4dd428c",
"spanId": "1effa85408e9e4f7",
"name": "invocation [research_app]",
"kind": 1,
"startTimeUnixNano": "1791061573075243000",
"endTimeUnixNano": "1791061577771095000",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"user_id\": \"debug_user_id\", \"session_id\": \"debug_session_id\", \"invocation_id\": null, \"new_message\": {\"parts\": [{\"text\": \"Reply with one short sentence about agent traces.\"}], \"role\": \"user\"}, \"state_delta\": null, \"run_config\": {\"save_input_blobs_as_artifacts\": false, \"support_cfc\": false, \"streaming_mode\": \"StreamingMode.NONE\", \"output_audio_transcription\": {}, \"input_audio_transcription\": {}, \"save_live_blob\": false, \"save_live_audio\": false, \"max_llm_calls\": 500, \"include_thoughts_from_other_agents\": false}, \"yield_user_message\": false, \"abort_signal\": null}"
}
},
{
"key": "input.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"Agent traces show the steps an agent takes to complete a task.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":47,\"prompt_token_count\":32,\"thoughts_token_count\":25,\"total_token_count\":79},\"invocation_id\":\"e-de6dbbd8-e192-415a-a15c-24e216046237\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"research_agent@1\"},\"id\":\"e5d9e1dd-20e8-4715-96db-dcd96a5f4e35\",\"timestamp\":1791061573.14271}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "CHAIN"
}
}
],
"status": {
"code": 1
},
"flags": 256
}
]
}
]
}
]
}

136
litellm-rust/crates/traces/tests/fixtures/google_adk_simple.json vendored
View file

@ -21,21 +21,77 @@
"stringValue": "1.42.1"
}
},
{
"key": "service.name",
"value": {
"stringValue": "google-adk-simple"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.63b1"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "247bc18e08a66f0b54a024928fd70ccb",
"spanId": "6c636a3189b1dc12",
"parentSpanId": "69c1827a773fcdf9",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061383698325000",
"endTimeUnixNano": "1791061386697226000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "0a5203cf-e0f4-4131-b2ec-310740539396"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.google_adk",
@ -43,13 +99,13 @@
},
"spans": [
{
"traceId": "df61d220386ef57406d1eebb19dd6599",
"spanId": "cb6d07f7e2960614",
"parentSpanId": "52ac80deae53913e",
"traceId": "247bc18e08a66f0b54a024928fd70ccb",
"spanId": "69c1827a773fcdf9",
"parentSpanId": "74dcd13708e96513",
"name": "call_llm",
"kind": 1,
"startTimeUnixNano": "1791012833504174130",
"endTimeUnixNano": "1791012836291324943",
"startTimeUnixNano": "1791061382042896000",
"endTimeUnixNano": "1791061386709208000",
"attributes": [
{
"key": "session.id",
@ -90,13 +146,13 @@
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "litellm_proxy/openai/gpt-6-luna"
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
"key": "gcp.vertex.agent.invocation_id",
"value": {
"stringValue": "e-01b1f385-a6b8-4122-a535-1cdfc38d3c6f"
"stringValue": "e-87a3570c-297c-4a96-8f12-2edcc67b26ff"
}
},
{
@ -108,19 +164,19 @@
{
"key": "gcp.vertex.agent.event_id",
"value": {
"stringValue": "eb613b2d-4ceb-4949-89a5-730ca9a71b9a"
"stringValue": "02b35dc6-0c26-4ecb-81a3-e2ca9947b98e"
}
},
{
"key": "gcp.vertex.agent.llm_request",
"value": {
"stringValue": "{\"model\":\"litellm_proxy/openai/gpt-6-luna\",\"config\":{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}},\"contents\":[{\"parts\":[{\"text\":\"What is an agent trace?\"}],\"role\":\"user\"}]}"
"stringValue": "{\"model\": \"openai/openai/gpt-6-luna\", \"config\": {\"system_instruction\": \"You are an agent. Your internal name is \\\"research_agent\\\".\", \"labels\": {\"adk_agent_name\": \"research_agent\"}}, \"contents\": [{\"parts\": [{\"text\": \"What is an agent trace?\"}], \"role\": \"user\"}]}"
}
},
{
"key": "gcp.vertex.agent.llm_response",
"value": {
"stringValue": "{\"model_version\":\"litellm_proxy/openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent’s run: the sequence of steps it took and what happened at each step. It may include the input, tool calls and their results, intermediate outputs, timing, and errors.\\n\\nTraces help developers debug, evaluate, and monitor an agent. They don’t necessarily contain the agent’s private reasoning; a trace can record observable actions and brief summaries instead.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":177,\"prompt_token_count\":29,\"thoughts_token_count\":85,\"total_token_count\":206}}"
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a chronological record of an AI agent\u2019s work on a task. It may include the user\u2019s request, the agent\u2019s actions, tool calls and their results, and the final response.\\n\\nTraces help developers debug behavior, evaluate performance, and audit what happened. They don\u2019t necessarily include the agent\u2019s full internal reasoning; often they contain only observable steps and outputs.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":168,\"prompt_token_count\":29,\"thoughts_token_count\":80,\"total_token_count\":197}}"
}
},
{
@ -132,7 +188,7 @@
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "262"
"intValue": "248"
}
},
{
@ -144,7 +200,7 @@
{
"key": "gen_ai.usage.reasoning.output_tokens",
"value": {
"intValue": "85"
"intValue": "80"
}
},
{
@ -168,7 +224,7 @@
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"litellm_proxy/openai/gpt-6-luna\",\"contents\":[{\"parts\":[{\"text\":\"What is an agent trace?\"}],\"role\":\"user\"}],\"config\":{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}},\"live_connect_config\":{\"input_audio_transcription\":{},\"output_audio_transcription\":{}}}"
"stringValue": "{\"model\":\"openai/openai/gpt-6-luna\",\"contents\":[{\"parts\":[{\"text\":\"What is an agent trace?\"}],\"role\":\"user\"}],\"config\":{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}},\"live_connect_config\":{\"input_audio_transcription\":{},\"output_audio_transcription\":{}}}"
}
},
{
@ -180,7 +236,7 @@
{
"key": "llm.model_name",
"value": {
"stringValue": "litellm_proxy/openai/gpt-6-luna"
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
@ -222,7 +278,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"litellm_proxy/openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent’s run: the sequence of steps it took and what happened at each step. It may include the input, tool calls and their results, intermediate outputs, timing, and errors.\\n\\nTraces help developers debug, evaluate, and monitor an agent. They don’t necessarily contain the agent’s private reasoning; a trace can record observable actions and brief summaries instead.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":177,\"prompt_token_count\":29,\"thoughts_token_count\":85,\"total_token_count\":206}}"
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a chronological record of an AI agent\u2019s work on a task. It may include the user\u2019s request, the agent\u2019s actions, tool calls and their results, and the final response.\\n\\nTraces help developers debug behavior, evaluate performance, and audit what happened. They don\u2019t necessarily include the agent\u2019s full internal reasoning; often they contain only observable steps and outputs.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":168,\"prompt_token_count\":29,\"thoughts_token_count\":80,\"total_token_count\":197}}"
}
},
{
@ -234,7 +290,7 @@
{
"key": "llm.token_count.total",
"value": {
"intValue": "206"
"intValue": "197"
}
},
{
@ -246,13 +302,13 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "85"
"intValue": "80"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "177"
"intValue": "168"
}
},
{
@ -264,7 +320,7 @@
{
"key": "llm.output_messages.0.message.contents.0.message_content.text",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s run: the sequence of steps it took and what happened at each step. It may include the input, tool calls and their results, intermediate outputs, timing, and errors.\n\nTraces help developers debug, evaluate, and monitor an agent. They don’t necessarily contain the agent’s private reasoning; a trace can record observable actions and brief summaries instead."
"stringValue": "An **agent trace** is a chronological record of an AI agent\u2019s work on a task. It may include the user\u2019s request, the agent\u2019s actions, tool calls and their results, and the final response.\n\nTraces help developers debug behavior, evaluate performance, and audit what happened. They don\u2019t necessarily include the agent\u2019s full internal reasoning; often they contain only observable steps and outputs."
}
},
{
@ -273,12 +329,6 @@
"stringValue": "text"
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "chatcmpl-EUoXinBzYIEZupgGwtpL85Z1kunV8"
}
},
{
"key": "openinference.span.kind",
"value": {
@ -292,13 +342,13 @@
"flags": 256
},
{
"traceId": "df61d220386ef57406d1eebb19dd6599",
"spanId": "52ac80deae53913e",
"parentSpanId": "b304248bca94d81a",
"traceId": "247bc18e08a66f0b54a024928fd70ccb",
"spanId": "74dcd13708e96513",
"parentSpanId": "e6d2e44b3d2d9d6d",
"name": "agent_run [research_agent]",
"kind": 1,
"startTimeUnixNano": "1791012833480201860",
"endTimeUnixNano": "1791012836291519273",
"startTimeUnixNano": "1791061382027984000",
"endTimeUnixNano": "1791061386709354000",
"attributes": [
{
"key": "agent.name",
@ -345,7 +395,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"litellm_proxy/openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent’s run: the sequence of steps it took and what happened at each step. It may include the input, tool calls and their results, intermediate outputs, timing, and errors.\\n\\nTraces help developers debug, evaluate, and monitor an agent. They don’t necessarily contain the agent’s private reasoning; a trace can record observable actions and brief summaries instead.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":177,\"prompt_token_count\":29,\"thoughts_token_count\":85,\"total_token_count\":206},\"invocation_id\":\"e-01b1f385-a6b8-4122-a535-1cdfc38d3c6f\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"\"},\"id\":\"eb613b2d-4ceb-4949-89a5-730ca9a71b9a\",\"timestamp\":1791012833.504099}"
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a chronological record of an AI agent\u2019s work on a task. It may include the user\u2019s request, the agent\u2019s actions, tool calls and their results, and the final response.\\n\\nTraces help developers debug behavior, evaluate performance, and audit what happened. They don\u2019t necessarily include the agent\u2019s full internal reasoning; often they contain only observable steps and outputs.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":168,\"prompt_token_count\":29,\"thoughts_token_count\":80,\"total_token_count\":197},\"invocation_id\":\"e-87a3570c-297c-4a96-8f12-2edcc67b26ff\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"\"},\"id\":\"02b35dc6-0c26-4ecb-81a3-e2ca9947b98e\",\"timestamp\":1791061382.04284}"
}
},
{
@ -367,17 +417,17 @@
"flags": 256
},
{
"traceId": "df61d220386ef57406d1eebb19dd6599",
"spanId": "b304248bca94d81a",
"traceId": "247bc18e08a66f0b54a024928fd70ccb",
"spanId": "e6d2e44b3d2d9d6d",
"name": "invocation [research_app]",
"kind": 1,
"startTimeUnixNano": "1791012833360078724",
"endTimeUnixNano": "1791012836291729271",
"startTimeUnixNano": "1791061381946497000",
"endTimeUnixNano": "1791061386709575000",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"user_id\":\"debug_user_id\",\"session_id\":\"debug_session_id\",\"invocation_id\":null,\"new_message\":{\"parts\":[{\"text\":\"What is an agent trace?\"}],\"role\":\"user\"},\"state_delta\":null,\"run_config\":{\"save_input_blobs_as_artifacts\":false,\"support_cfc\":false,\"streaming_mode\":\"StreamingMode.NONE\",\"output_audio_transcription\":{},\"input_audio_transcription\":{},\"save_live_blob\":false,\"save_live_audio\":false,\"max_llm_calls\":500,\"include_thoughts_from_other_agents\":false},\"yield_user_message\":false,\"abort_signal\":null}"
"stringValue": "{\"user_id\": \"debug_user_id\", \"session_id\": \"debug_session_id\", \"invocation_id\": null, \"new_message\": {\"parts\": [{\"text\": \"What is an agent trace?\"}], \"role\": \"user\"}, \"state_delta\": null, \"run_config\": {\"save_input_blobs_as_artifacts\": false, \"support_cfc\": false, \"streaming_mode\": \"StreamingMode.NONE\", \"output_audio_transcription\": {}, \"input_audio_transcription\": {}, \"save_live_blob\": false, \"save_live_audio\": false, \"max_llm_calls\": 500, \"include_thoughts_from_other_agents\": false}, \"yield_user_message\": false, \"abort_signal\": null}"
}
},
{
@ -401,7 +451,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"litellm_proxy/openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent’s run: the sequence of steps it took and what happened at each step. It may include the input, tool calls and their results, intermediate outputs, timing, and errors.\\n\\nTraces help developers debug, evaluate, and monitor an agent. They don’t necessarily contain the agent’s private reasoning; a trace can record observable actions and brief summaries instead.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":177,\"prompt_token_count\":29,\"thoughts_token_count\":85,\"total_token_count\":206},\"invocation_id\":\"e-01b1f385-a6b8-4122-a535-1cdfc38d3c6f\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"research_agent@1\"},\"id\":\"eb613b2d-4ceb-4949-89a5-730ca9a71b9a\",\"timestamp\":1791012833.504099}"
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a chronological record of an AI agent\u2019s work on a task. It may include the user\u2019s request, the agent\u2019s actions, tool calls and their results, and the final response.\\n\\nTraces help developers debug behavior, evaluate performance, and audit what happened. They don\u2019t necessarily include the agent\u2019s full internal reasoning; often they contain only observable steps and outputs.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":168,\"prompt_token_count\":29,\"thoughts_token_count\":80,\"total_token_count\":197},\"invocation_id\":\"e-87a3570c-297c-4a96-8f12-2edcc67b26ff\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"research_agent@1\"},\"id\":\"02b35dc6-0c26-4ecb-81a3-e2ca9947b98e\",\"timestamp\":1791061382.04284}"
}
},
{

480
litellm-rust/crates/traces/tests/fixtures/google_adk_stream.json vendored Normal file
View file

@ -0,0 +1,480 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.42.1"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.63b1"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "59fb70ffc358dfe30a5e0d4615e4ca7f",
"spanId": "fc3bcda50363d82c",
"parentSpanId": "90b23ff6b4199a4b",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061463015671000",
"endTimeUnixNano": "1791061465614543000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "73d0b177-86e5-4e11-9704-bff2667a0a75"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.google_adk",
"version": "1.0.2"
},
"spans": [
{
"traceId": "59fb70ffc358dfe30a5e0d4615e4ca7f",
"spanId": "90b23ff6b4199a4b",
"parentSpanId": "a8c9e8a324ce58ff",
"name": "call_llm",
"kind": 1,
"startTimeUnixNano": "1791061461482820000",
"endTimeUnixNano": "1791061465621507000",
"attributes": [
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "generate_content"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "gen_ai.system",
"value": {
"stringValue": "gcp.vertex.agent"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
"key": "gcp.vertex.agent.invocation_id",
"value": {
"stringValue": "e-135e3af9-1874-442b-a1f3-d0601dd85c31"
}
},
{
"key": "gcp.vertex.agent.session_id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "gcp.vertex.agent.event_id",
"value": {
"stringValue": "a4c320a1-6fae-4364-9978-5082db9799ef"
}
},
{
"key": "gcp.vertex.agent.llm_request",
"value": {
"stringValue": "{\"model\": \"openai/openai/gpt-6-luna\", \"config\": {\"system_instruction\": \"You are an agent. Your internal name is \\\"research_agent\\\".\", \"labels\": {\"adk_agent_name\": \"research_agent\"}}, \"contents\": [{\"parts\": [{\"text\": \"What is an agent trace?\"}], \"role\": \"user\"}]}"
}
},
{
"key": "gcp.vertex.agent.llm_response",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\\n\\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\u2019t have to include the model\u2019s private reasoning; it can record only observable steps.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":184,\"prompt_token_count\":29,\"thoughts_token_count\":84,\"total_token_count\":213}}"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "29"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "268"
}
},
{
"key": "gen_ai.usage.cache_read.input_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.reasoning.output_tokens",
"value": {
"intValue": "84"
}
},
{
"key": "gen_ai.response.finish_reasons",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "stop"
}
]
}
}
},
{
"key": "llm.provider",
"value": {
"stringValue": "google"
}
},
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"openai/openai/gpt-6-luna\",\"contents\":[{\"parts\":[{\"text\":\"What is an agent trace?\"}],\"role\":\"user\"}],\"config\":{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}},\"live_connect_config\":{\"input_audio_transcription\":{},\"output_audio_transcription\":{}}}"
}
},
{
"key": "input.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "llm.model_name",
"value": {
"stringValue": "openai/openai/gpt-6-luna"
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"system_instruction\":\"You are an agent. Your internal name is \\\"research_agent\\\".\",\"labels\":{\"adk_agent_name\":\"research_agent\"}}"
}
},
{
"key": "llm.input_messages.0.message.role",
"value": {
"stringValue": "system"
}
},
{
"key": "llm.input_messages.0.message.content",
"value": {
"stringValue": "You are an agent. Your internal name is \"research_agent\"."
}
},
{
"key": "llm.input_messages.1.message.role",
"value": {
"stringValue": "user"
}
},
{
"key": "llm.input_messages.1.message.contents.0.message_content.text",
"value": {
"stringValue": "What is an agent trace?"
}
},
{
"key": "llm.input_messages.1.message.contents.0.message_content.type",
"value": {
"stringValue": "text"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\\n\\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\u2019t have to include the model\u2019s private reasoning; it can record only observable steps.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":184,\"prompt_token_count\":29,\"thoughts_token_count\":84,\"total_token_count\":213}}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "213"
}
},
{
"key": "llm.token_count.prompt",
"value": {
"intValue": "29"
}
},
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "84"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "184"
}
},
{
"key": "llm.output_messages.0.message.role",
"value": {
"stringValue": "model"
}
},
{
"key": "llm.output_messages.0.message.contents.0.message_content.text",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\n\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\u2019t have to include the model\u2019s private reasoning; it can record only observable steps."
}
},
{
"key": "llm.output_messages.0.message.contents.0.message_content.type",
"value": {
"stringValue": "text"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "LLM"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "59fb70ffc358dfe30a5e0d4615e4ca7f",
"spanId": "a8c9e8a324ce58ff",
"parentSpanId": "3f575c3cdbccf331",
"name": "agent_run [research_agent]",
"kind": 1,
"startTimeUnixNano": "1791061461465979000",
"endTimeUnixNano": "1791061465621633000",
"attributes": [
{
"key": "agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "gen_ai.agent.description",
"value": {
"stringValue": ""
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\\n\\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\u2019t have to include the model\u2019s private reasoning; it can record only observable steps.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":184,\"prompt_token_count\":29,\"thoughts_token_count\":84,\"total_token_count\":213},\"invocation_id\":\"e-135e3af9-1874-442b-a1f3-d0601dd85c31\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"research_agent@1\"},\"id\":\"a4c320a1-6fae-4364-9978-5082db9799ef\",\"timestamp\":1791061465.604408}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "AGENT"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "59fb70ffc358dfe30a5e0d4615e4ca7f",
"spanId": "3f575c3cdbccf331",
"name": "invocation [research_app]",
"kind": 1,
"startTimeUnixNano": "1791061461360524000",
"endTimeUnixNano": "1791061465621830000",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"user_id\": \"debug_user_id\", \"session_id\": \"debug_session_id\", \"invocation_id\": null, \"new_message\": {\"parts\": [{\"text\": \"What is an agent trace?\"}], \"role\": \"user\"}, \"state_delta\": null, \"run_config\": {\"save_input_blobs_as_artifacts\": false, \"support_cfc\": false, \"streaming_mode\": \"StreamingMode.SSE\", \"output_audio_transcription\": {}, \"input_audio_transcription\": {}, \"save_live_blob\": false, \"save_live_audio\": false, \"max_llm_calls\": 500, \"include_thoughts_from_other_agents\": false}, \"yield_user_message\": false, \"abort_signal\": null}"
}
},
{
"key": "input.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "user.id",
"value": {
"stringValue": "debug_user_id"
}
},
{
"key": "session.id",
"value": {
"stringValue": "debug_session_id"
}
},
{
"key": "output.value",
"value": {
"stringValue": "{\"model_version\":\"openai/gpt-6-luna\",\"content\":{\"parts\":[{\"text\":\"An **agent trace** is a record of an AI agent\u2019s execution: what it received, the actions it took (such as tool calls), the results it got back, and how the task ended.\\n\\nTraces help developers debug failures, understand behavior, and evaluate performance. They may include timestamps, inputs and outputs, tool errors, or state changes. A trace doesn\u2019t have to include the model\u2019s private reasoning; it can record only observable steps.\"}],\"role\":\"model\"},\"partial\":false,\"finish_reason\":\"STOP\",\"usage_metadata\":{\"cached_content_token_count\":0,\"candidates_token_count\":184,\"prompt_token_count\":29,\"thoughts_token_count\":84,\"total_token_count\":213},\"invocation_id\":\"e-135e3af9-1874-442b-a1f3-d0601dd85c31\",\"author\":\"research_agent\",\"actions\":{\"state_delta\":{},\"artifact_delta\":{},\"requested_auth_configs\":{},\"requested_tool_confirmations\":{}},\"node_info\":{\"path\":\"research_agent@1\"},\"id\":\"a4c320a1-6fae-4364-9978-5082db9799ef\",\"timestamp\":1791061465.604408}"
}
},
{
"key": "output.mime_type",
"value": {
"stringValue": "application/json"
}
},
{
"key": "openinference.span.kind",
"value": {
"stringValue": "CHAIN"
}
}
],
"status": {
"code": 1
},
"flags": 256
}
]
}
]
}
]
}

751
litellm-rust/crates/traces/tests/fixtures/google_adk_swarm.json vendored
View file

File diff suppressed because it is too large Load diff

70
litellm-rust/crates/traces/tests/fixtures/langchain_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "857d5035-73a2-443d-a3b3-beda907e8e08"
}
},
{
"key": "service.name",
"value": {
"stringValue": "langchain-simple"
"stringValue": "3ecc2492-218f-4536-8bb8-b2c1e5ee234b"
}
},
{
@ -38,6 +32,12 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
@ -49,18 +49,18 @@
},
"spans": [
{
"traceId": "fff422e2eaff0db64132f26efe387a6c",
"spanId": "de7f6f2c980f1dd9",
"parentSpanId": "462247f1c7f18034",
"traceId": "d9a080b530fb7f3d642b1608d157ba70",
"spanId": "1965258dc3bdbc38",
"parentSpanId": "3d757465aa89a16c",
"name": "ChatOpenAI",
"kind": 1,
"startTimeUnixNano": "1791012713671619840",
"endTimeUnixNano": "1791012718164809984",
"startTimeUnixNano": "1791061313991373056",
"endTimeUnixNano": "1791061316664692992",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"HumanMessage\"],\"kwargs\":{\"content\":\"What is an agent trace?\",\"type\":\"human\",\"id\":\"83d6b4d7-b3ca-4058-a516-7c884a44ac2e\"}}]]}"
"stringValue": "{\"messages\": [[{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"What is an agent trace?\", \"type\": \"human\", \"id\": \"52e7344a-4b38-4789-8147-2a277f6c775f\"}}]]}"
}
},
{
@ -72,7 +72,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"generations\":[[{\"text\":\"An **agent trace** is a record of an AI agent’s run: what it received, which actions or tools it used, what results came back, and what it ultimately produced.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather API.\\n3. API returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces help developers debug and evaluate agents. They usually capture observable steps and tool interactions—not necessarily the agent’s private internal reasoning. The exact contents depend on the system.\",\"generation_info\":{\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ChatGeneration\",\"message\":{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"AIMessage\"],\"kwargs\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, which actions or tools it used, what results came back, and what it ultimately produced.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather API.\\n3. API returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces help developers debug and evaluate agents. They usually capture observable steps and tool interactions—not necessarily the agent’s private internal reasoning. The exact contents depend on the system.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":233,\"prompt_tokens\":12,\"total_tokens\":245,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":118,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoVmBRgBU4JGnF3vUCi895jENkSZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"id\":\"lc_run--01a100ad-34c7-7293-9fc1-65444c4a94ce-0\",\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":233,\"total_tokens\":245,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":118}},\"tool_calls\":[],\"invalid_tool_calls\":[]}}}]],\"llm_output\":{\"token_usage\":{\"completion_tokens\":233,\"prompt_tokens\":12,\"total_tokens\":245,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":118,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoVmBRgBU4JGnF3vUCi895jENkSZ\",\"service_tier\":\"default\"},\"run\":null,\"type\":\"LLMResult\"}"
"stringValue": "{\"generations\": [[{\"text\": \"An **agent trace** is a recorded timeline of what an AI agent did to complete a task. It may include:\\n\\n- The user\u2019s request and the agent\u2019s intermediate reasoning or decisions\\n- Calls to tools, APIs, or other agents, including their inputs and results\\n- Model responses, errors, and retries\\n- Timing, token usage, and other metadata\\n\\nTraces help developers understand how an agent reached an outcome, find failures or bottlenecks, and evaluate its behavior. In many systems, a trace is made up of smaller **spans**, each representing one step, such as a model call or tool call.\", \"generation_info\": {\"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ChatGeneration\", \"message\": {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"An **agent trace** is a recorded timeline of what an AI agent did to complete a task. It may include:\\n\\n- The user\u2019s request and the agent\u2019s intermediate reasoning or decisions\\n- Calls to tools, APIs, or other agents, including their inputs and results\\n- Model responses, errors, and retries\\n- Timing, token usage, and other metadata\\n\\nTraces help developers understand how an agent reached an outcome, find failures or bottlenecks, and evaluate its behavior. In many systems, a trace is made up of smaller **spans**, each representing one step, such as a model call or tool call.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 191, \"prompt_tokens\": 12, \"total_tokens\": 203, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 55, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19ewJlEkpZw5ySD3IcmX746sKS3\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"lc_run--01a10392-c9c7-7263-a600-2b79f287e58b-0\", \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 191, \"total_tokens\": 203, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 55}}, \"tool_calls\": [], \"invalid_tool_calls\": []}}}]], \"llm_output\": {\"token_usage\": {\"completion_tokens\": 191, \"prompt_tokens\": 12, \"total_tokens\": 203, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 55, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19ewJlEkpZw5ySD3IcmX746sKS3\", \"service_tier\": \"default\"}, \"run\": null, \"type\": \"LLMResult\"}"
}
},
{
@ -102,13 +102,13 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s run: what it received, which actions or tools it used, what results came back, and what it ultimately produced.\n\nFor example:\n\n1. User asks for the weather.\n2. Agent calls a weather API.\n3. API returns the forecast.\n4. Agent summarizes it for the user.\n\nTraces help developers debug and evaluate agents. They usually capture observable steps and tool interactions—not necessarily the agent’s private internal reasoning. The exact contents depend on the system."
"stringValue": "An **agent trace** is a recorded timeline of what an AI agent did to complete a task. It may include:\n\n- The user\u2019s request and the agent\u2019s intermediate reasoning or decisions\n- Calls to tools, APIs, or other agents, including their inputs and results\n- Model responses, errors, and retries\n- Timing, token usage, and other metadata\n\nTraces help developers understand how an agent reached an outcome, find failures or bottlenecks, and evaluate its behavior. In many systems, a trace is made up of smaller **spans**, each representing one step, such as a model call or tool call."
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"model_name\":\"openai/gpt-6-luna\",\"stream\":false,\"_type\":\"openai-chat\",\"stop\":null}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"model_name\": \"openai/gpt-6-luna\", \"stream\": false, \"_type\": \"openai-chat\", \"stop\": null}"
}
},
{
@ -138,13 +138,13 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "233"
"intValue": "191"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "245"
"intValue": "203"
}
},
{
@ -156,7 +156,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "118"
"intValue": "55"
}
},
{
@ -180,7 +180,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_chat_model\",\"lc_agent_name\":\"research_agent\",\"langgraph_step\":1,\"langgraph_node\":\"model\",\"langgraph_triggers\":[\"branch:to:model\"],\"langgraph_path\":[\"__pregel_pull\",\"model\"],\"langgraph_checkpoint_ns\":\"model:5d073867-3e88-7b69-7e22-507bf15134a9\",\"checkpoint_ns\":\"model:5d073867-3e88-7b69-7e22-507bf15134a9\",\"ls_provider\":\"openai\",\"ls_model_name\":\"openai/gpt-6-luna\",\"ls_model_type\":\"chat\",\"ls_temperature\":null,\"lc_versions\":{\"langchain-core\":\"1.6.6\",\"langchain\":\"1.4.3\",\"langchain-openai\":\"1.6.7\"}}"
"stringValue": "{\"ls_integration\": \"langchain_chat_model\", \"lc_agent_name\": \"research_agent\", \"langgraph_step\": 1, \"langgraph_node\": \"model\", \"langgraph_triggers\": [\"branch:to:model\"], \"langgraph_path\": [\"__pregel_pull\", \"model\"], \"langgraph_checkpoint_ns\": \"model:cc9bfebe-a965-ae0a-e6f2-9e3e3c0d6f14\", \"checkpoint_ns\": \"model:cc9bfebe-a965-ae0a-e6f2-9e3e3c0d6f14\", \"ls_provider\": \"openai\", \"ls_model_name\": \"openai/gpt-6-luna\", \"ls_model_type\": \"chat\", \"ls_temperature\": null, \"lc_versions\": {\"langchain-core\": \"1.6.6\", \"langchain\": \"1.4.3\", \"langchain-openai\": \"1.6.7\"}}"
}
},
{
@ -196,18 +196,18 @@
"flags": 256
},
{
"traceId": "fff422e2eaff0db64132f26efe387a6c",
"spanId": "462247f1c7f18034",
"parentSpanId": "b2609fcd461d1097",
"traceId": "d9a080b530fb7f3d642b1608d157ba70",
"spanId": "3d757465aa89a16c",
"parentSpanId": "2c19b55e7c4b5fd8",
"name": "model",
"kind": 1,
"startTimeUnixNano": "1791012713671066112",
"endTimeUnixNano": "1791012718166048000",
"startTimeUnixNano": "1791061313991044096",
"endTimeUnixNano": "1791061316665323008",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"83d6b4d7-b3ca-4058-a516-7c884a44ac2e\"}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"52e7344a-4b38-4789-8147-2a277f6c775f\"}}]}"
}
},
{
@ -219,7 +219,7 @@
{
"key": "output.value",
"value": {
"stringValue": "[{\"graph\":null,\"update\":{\"messages\":[{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, which actions or tools it used, what results came back, and what it ultimately produced.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather API.\\n3. API returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces help developers debug and evaluate agents. They usually capture observable steps and tool interactions—not necessarily the agent’s private internal reasoning. The exact contents depend on the system.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":233,\"prompt_tokens\":12,\"total_tokens\":245,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":118,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoVmBRgBU4JGnF3vUCi895jENkSZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":\"research_agent\",\"id\":\"lc_run--01a100ad-34c7-7293-9fc1-65444c4a94ce-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":233,\"total_tokens\":245,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":118}}}}]},\"resume\":null,\"goto\":[]}]"
"stringValue": "[{\"graph\": null, \"update\": {\"messages\": [{\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a recorded timeline of what an AI agent did to complete a task. It may include:\\n\\n- The user\u2019s request and the agent\u2019s intermediate reasoning or decisions\\n- Calls to tools, APIs, or other agents, including their inputs and results\\n- Model responses, errors, and retries\\n- Timing, token usage, and other metadata\\n\\nTraces help developers understand how an agent reached an outcome, find failures or bottlenecks, and evaluate its behavior. In many systems, a trace is made up of smaller **spans**, each representing one step, such as a model call or tool call.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 191, \"prompt_tokens\": 12, \"total_tokens\": 203, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 55, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19ewJlEkpZw5ySD3IcmX746sKS3\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": \"research_agent\", \"id\": \"lc_run--01a10392-c9c7-7263-a600-2b79f287e58b-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 191, \"total_tokens\": 203, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 55}}}}]}, \"resume\": null, \"goto\": []}]"
}
},
{
@ -243,7 +243,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_create_agent\",\"lc_agent_name\":\"research_agent\",\"langgraph_step\":1,\"langgraph_node\":\"model\",\"langgraph_triggers\":[\"branch:to:model\"],\"langgraph_path\":[\"__pregel_pull\",\"model\"],\"langgraph_checkpoint_ns\":\"model:5d073867-3e88-7b69-7e22-507bf15134a9\"}"
"stringValue": "{\"ls_integration\": \"langchain_create_agent\", \"lc_agent_name\": \"research_agent\", \"langgraph_step\": 1, \"langgraph_node\": \"model\", \"langgraph_triggers\": [\"branch:to:model\"], \"langgraph_path\": [\"__pregel_pull\", \"model\"], \"langgraph_checkpoint_ns\": \"model:cc9bfebe-a965-ae0a-e6f2-9e3e3c0d6f14\"}"
}
},
{
@ -259,17 +259,17 @@
"flags": 256
},
{
"traceId": "fff422e2eaff0db64132f26efe387a6c",
"spanId": "b2609fcd461d1097",
"traceId": "d9a080b530fb7f3d642b1608d157ba70",
"spanId": "2c19b55e7c4b5fd8",
"name": "research_agent",
"kind": 1,
"startTimeUnixNano": "1791012713670352896",
"endTimeUnixNano": "1791012718166877952",
"startTimeUnixNano": "1791061313989900032",
"endTimeUnixNano": "1791061316666109184",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"role\":\"user\",\"content\":\"What is an agent trace?\"}]}"
"stringValue": "{\"messages\": [{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]}"
}
},
{
@ -281,7 +281,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"83d6b4d7-b3ca-4058-a516-7c884a44ac2e\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, which actions or tools it used, what results came back, and what it ultimately produced.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather API.\\n3. API returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces help developers debug and evaluate agents. They usually capture observable steps and tool interactions—not necessarily the agent’s private internal reasoning. The exact contents depend on the system.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":233,\"prompt_tokens\":12,\"total_tokens\":245,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":118,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoVmBRgBU4JGnF3vUCi895jENkSZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":\"research_agent\",\"id\":\"lc_run--01a100ad-34c7-7293-9fc1-65444c4a94ce-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":233,\"total_tokens\":245,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":118}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"52e7344a-4b38-4789-8147-2a277f6c775f\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a recorded timeline of what an AI agent did to complete a task. It may include:\\n\\n- The user\u2019s request and the agent\u2019s intermediate reasoning or decisions\\n- Calls to tools, APIs, or other agents, including their inputs and results\\n- Model responses, errors, and retries\\n- Timing, token usage, and other metadata\\n\\nTraces help developers understand how an agent reached an outcome, find failures or bottlenecks, and evaluate its behavior. In many systems, a trace is made up of smaller **spans**, each representing one step, such as a model call or tool call.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 191, \"prompt_tokens\": 12, \"total_tokens\": 203, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 55, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19ewJlEkpZw5ySD3IcmX746sKS3\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": \"research_agent\", \"id\": \"lc_run--01a10392-c9c7-7263-a600-2b79f287e58b-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 191, \"total_tokens\": 203, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 55}}}}]}"
}
},
{
@ -299,7 +299,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_create_agent\",\"lc_agent_name\":\"research_agent\"}"
"stringValue": "{\"ls_integration\": \"langchain_create_agent\", \"lc_agent_name\": \"research_agent\"}"
}
},
{

504
litellm-rust/crates/traces/tests/fixtures/langchain_swarm.json vendored
View file

File diff suppressed because one or more lines are too long

70
litellm-rust/crates/traces/tests/fixtures/langgraph_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "ea95702f-4404-4f09-b8b6-19515d10fb73"
}
},
{
"key": "service.name",
"value": {
"stringValue": "langgraph-simple"
"stringValue": "5ae85caa-bb64-4218-94f6-0066f5d01eba"
}
},
{
@ -38,6 +32,12 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
@ -49,18 +49,18 @@
},
"spans": [
{
"traceId": "af9e61052268f1da3133f29cace994e7",
"spanId": "258df8df18b0c4d0",
"parentSpanId": "6e090feb0298b338",
"traceId": "a2b7bdfc7ce2168e05f7298ee11ca7d3",
"spanId": "be5e5e817f6a71a5",
"parentSpanId": "91fbc8f8c9a10da7",
"name": "ChatOpenAI",
"kind": 1,
"startTimeUnixNano": "1791012813994045952",
"endTimeUnixNano": "1791012817659385088",
"startTimeUnixNano": "1791061316831124992",
"endTimeUnixNano": "1791061320049913856",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"HumanMessage\"],\"kwargs\":{\"content\":\"What is an agent trace?\",\"type\":\"human\",\"id\":\"c2ac14fc-4fb2-4fc9-9814-729cacc38b74\"}}]]}"
"stringValue": "{\"messages\": [[{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"What is an agent trace?\", \"type\": \"human\", \"id\": \"2a09435d-ae05-4259-9d4a-5a788d07464d\"}}]]}"
}
},
{
@ -72,7 +72,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"generations\":[[{\"text\":\"An **agent trace** is a record of an AI agent’s run: what it received, what steps it took, which tools or services it called, what results came back, and how it produced its final response.\\n\\nA trace might include:\\n\\n- The user’s request and relevant inputs\\n- The agent’s steps or decisions\\n- Tool calls and their results\\n- Errors, retries, and timing\\n- The final output\\n\\nTraces help developers understand, debug, and evaluate an agent’s behavior. They don’t necessarily contain the model’s private internal reasoning; often they show only observable steps, such as tool calls and outputs.\",\"generation_info\":{\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ChatGeneration\",\"message\":{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"AIMessage\"],\"kwargs\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, what steps it took, which tools or services it called, what results came back, and how it produced its final response.\\n\\nA trace might include:\\n\\n- The user’s request and relevant inputs\\n- The agent’s steps or decisions\\n- Tool calls and their results\\n- Errors, retries, and timing\\n- The final output\\n\\nTraces help developers understand, debug, and evaluate an agent’s behavior. They don’t necessarily contain the model’s private internal reasoning; often they show only observable steps, such as tool calls and outputs.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":195,\"prompt_tokens\":12,\"total_tokens\":207,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":59,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXOfkaG93LZigoStAzPDsRzU4wZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"id\":\"lc_run--01a100ae-bcaa-7321-ae7a-432658009c65-0\",\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":195,\"total_tokens\":207,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":59}},\"tool_calls\":[],\"invalid_tool_calls\":[]}}}]],\"llm_output\":{\"token_usage\":{\"completion_tokens\":195,\"prompt_tokens\":12,\"total_tokens\":207,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":59,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXOfkaG93LZigoStAzPDsRzU4wZ\",\"service_tier\":\"default\"},\"run\":null,\"type\":\"LLMResult\"}"
"stringValue": "{\"generations\": [[{\"text\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It commonly includes the agent\u2019s inputs and outputs, tool calls and their results, and timing or errors.\\n\\nFor example, a trace might show: *user asks a question \u2192 agent searches the web \u2192 receives results \u2192 summarizes them.*\\n\\nTraces help people debug, evaluate, and audit an agent. They usually capture observable actions\u2014not necessarily the model\u2019s private internal reasoning.\", \"generation_info\": {\"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ChatGeneration\", \"message\": {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It commonly includes the agent\u2019s inputs and outputs, tool calls and their results, and timing or errors.\\n\\nFor example, a trace might show: *user asks a question \u2192 agent searches the web \u2192 receives results \u2192 summarizes them.*\\n\\nTraces help people debug, evaluate, and audit an agent. They usually capture observable actions\u2014not necessarily the model\u2019s private internal reasoning.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 187, \"prompt_tokens\": 12, \"total_tokens\": 199, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 83, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19hpMRm3ctM96rCV2Noc7Dp5Wcm\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"lc_run--01a10392-d4df-72b2-aa09-53e30f1c2e3c-0\", \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 187, \"total_tokens\": 199, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 83}}, \"tool_calls\": [], \"invalid_tool_calls\": []}}}]], \"llm_output\": {\"token_usage\": {\"completion_tokens\": 187, \"prompt_tokens\": 12, \"total_tokens\": 199, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 83, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19hpMRm3ctM96rCV2Noc7Dp5Wcm\", \"service_tier\": \"default\"}, \"run\": null, \"type\": \"LLMResult\"}"
}
},
{
@ -102,13 +102,13 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s run: what it received, what steps it took, which tools or services it called, what results came back, and how it produced its final response.\n\nA trace might include:\n\n- The user’s request and relevant inputs\n- The agent’s steps or decisions\n- Tool calls and their results\n- Errors, retries, and timing\n- The final output\n\nTraces help developers understand, debug, and evaluate an agent’s behavior. They don’t necessarily contain the model’s private internal reasoning; often they show only observable steps, such as tool calls and outputs."
"stringValue": "An **agent trace** is a record of what an AI agent did during a task, step by step. It commonly includes the agent\u2019s inputs and outputs, tool calls and their results, and timing or errors.\n\nFor example, a trace might show: *user asks a question \u2192 agent searches the web \u2192 receives results \u2192 summarizes them.*\n\nTraces help people debug, evaluate, and audit an agent. They usually capture observable actions\u2014not necessarily the model\u2019s private internal reasoning."
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"model_name\":\"openai/gpt-6-luna\",\"stream\":false,\"_type\":\"openai-chat\",\"stop\":null}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"model_name\": \"openai/gpt-6-luna\", \"stream\": false, \"_type\": \"openai-chat\", \"stop\": null}"
}
},
{
@ -138,13 +138,13 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "195"
"intValue": "187"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "207"
"intValue": "199"
}
},
{
@ -156,7 +156,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "59"
"intValue": "83"
}
},
{
@ -180,7 +180,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_chat_model\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"call_model:9cdca8af-0105-48c1-a90c-e733d0eda7d0\",\"checkpoint_ns\":\"call_model:9cdca8af-0105-48c1-a90c-e733d0eda7d0\",\"ls_provider\":\"openai\",\"ls_model_name\":\"openai/gpt-6-luna\",\"ls_model_type\":\"chat\",\"ls_temperature\":null,\"lc_versions\":{\"langchain-core\":\"1.6.6\",\"langchain-openai\":\"1.6.7\"}}"
"stringValue": "{\"ls_integration\": \"langchain_chat_model\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"call_model:4756011d-1080-f746-eefc-23cf69bce566\", \"checkpoint_ns\": \"call_model:4756011d-1080-f746-eefc-23cf69bce566\", \"ls_provider\": \"openai\", \"ls_model_name\": \"openai/gpt-6-luna\", \"ls_model_type\": \"chat\", \"ls_temperature\": null, \"lc_versions\": {\"langchain-core\": \"1.6.6\", \"langchain-openai\": \"1.6.7\"}}"
}
},
{
@ -196,18 +196,18 @@
"flags": 256
},
{
"traceId": "af9e61052268f1da3133f29cace994e7",
"spanId": "6e090feb0298b338",
"parentSpanId": "a5857e5f6e1fee75",
"traceId": "a2b7bdfc7ce2168e05f7298ee11ca7d3",
"spanId": "91fbc8f8c9a10da7",
"parentSpanId": "4ed2d903d4625b4a",
"name": "call_model",
"kind": 1,
"startTimeUnixNano": "1791012813993732096",
"endTimeUnixNano": "1791012817660307968",
"startTimeUnixNano": "1791061316830797056",
"endTimeUnixNano": "1791061320050427904",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"c2ac14fc-4fb2-4fc9-9814-729cacc38b74\"}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"2a09435d-ae05-4259-9d4a-5a788d07464d\"}}]}"
}
},
{
@ -219,7 +219,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, what steps it took, which tools or services it called, what results came back, and how it produced its final response.\\n\\nA trace might include:\\n\\n- The user’s request and relevant inputs\\n- The agent’s steps or decisions\\n- Tool calls and their results\\n- Errors, retries, and timing\\n- The final output\\n\\nTraces help developers understand, debug, and evaluate an agent’s behavior. They don’t necessarily contain the model’s private internal reasoning; often they show only observable steps, such as tool calls and outputs.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":195,\"prompt_tokens\":12,\"total_tokens\":207,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":59,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXOfkaG93LZigoStAzPDsRzU4wZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-bcaa-7321-ae7a-432658009c65-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":195,\"total_tokens\":207,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":59}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It commonly includes the agent\u2019s inputs and outputs, tool calls and their results, and timing or errors.\\n\\nFor example, a trace might show: *user asks a question \u2192 agent searches the web \u2192 receives results \u2192 summarizes them.*\\n\\nTraces help people debug, evaluate, and audit an agent. They usually capture observable actions\u2014not necessarily the model\u2019s private internal reasoning.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 187, \"prompt_tokens\": 12, \"total_tokens\": 199, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 83, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19hpMRm3ctM96rCV2Noc7Dp5Wcm\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10392-d4df-72b2-aa09-53e30f1c2e3c-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 187, \"total_tokens\": 199, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 83}}}}]}"
}
},
{
@ -243,7 +243,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"call_model:9cdca8af-0105-48c1-a90c-e733d0eda7d0\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"call_model:4756011d-1080-f746-eefc-23cf69bce566\"}"
}
},
{
@ -259,17 +259,17 @@
"flags": 256
},
{
"traceId": "af9e61052268f1da3133f29cace994e7",
"spanId": "a5857e5f6e1fee75",
"traceId": "a2b7bdfc7ce2168e05f7298ee11ca7d3",
"spanId": "4ed2d903d4625b4a",
"name": "research_agent",
"kind": 1,
"startTimeUnixNano": "1791012813992832000",
"endTimeUnixNano": "1791012817661214976",
"startTimeUnixNano": "1791061316829106944",
"endTimeUnixNano": "1791061320050825984",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"role\":\"user\",\"content\":\"What is an agent trace?\"}]}"
"stringValue": "{\"messages\": [{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]}"
}
},
{
@ -281,7 +281,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"c2ac14fc-4fb2-4fc9-9814-729cacc38b74\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of an AI agent’s run: what it received, what steps it took, which tools or services it called, what results came back, and how it produced its final response.\\n\\nA trace might include:\\n\\n- The user’s request and relevant inputs\\n- The agent’s steps or decisions\\n- Tool calls and their results\\n- Errors, retries, and timing\\n- The final output\\n\\nTraces help developers understand, debug, and evaluate an agent’s behavior. They don’t necessarily contain the model’s private internal reasoning; often they show only observable steps, such as tool calls and outputs.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":195,\"prompt_tokens\":12,\"total_tokens\":207,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":59,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXOfkaG93LZigoStAzPDsRzU4wZ\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-bcaa-7321-ae7a-432658009c65-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":12,\"output_tokens\":195,\"total_tokens\":207,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":59}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"2a09435d-ae05-4259-9d4a-5a788d07464d\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It commonly includes the agent\u2019s inputs and outputs, tool calls and their results, and timing or errors.\\n\\nFor example, a trace might show: *user asks a question \u2192 agent searches the web \u2192 receives results \u2192 summarizes them.*\\n\\nTraces help people debug, evaluate, and audit an agent. They usually capture observable actions\u2014not necessarily the model\u2019s private internal reasoning.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 187, \"prompt_tokens\": 12, \"total_tokens\": 199, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 83, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV19hpMRm3ctM96rCV2Noc7Dp5Wcm\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10392-d4df-72b2-aa09-53e30f1c2e3c-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 12, \"output_tokens\": 187, \"total_tokens\": 199, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 83}}}}]}"
}
},
{
@ -299,7 +299,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\"}"
"stringValue": "{\"ls_integration\": \"langgraph\"}"
}
},
{

234
litellm-rust/crates/traces/tests/fixtures/langgraph_swarm.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "b392f5f6-8bde-4100-8615-85406c69532f"
}
},
{
"key": "service.name",
"value": {
"stringValue": "langgraph-swarm"
"stringValue": "7008af59-8740-44f0-afdc-ff9d33f91deb"
}
},
{
@ -38,6 +32,12 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
@ -49,18 +49,18 @@
},
"spans": [
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "fe69d78d633b09ba",
"parentSpanId": "f1d7b2e38e4a297a",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "08253eda5ea23268",
"parentSpanId": "0c8d577859bdf794",
"name": "ChatOpenAI",
"kind": 1,
"startTimeUnixNano": "1791012830407246080",
"endTimeUnixNano": "1791012832857249024",
"startTimeUnixNano": "1791061349479399936",
"endTimeUnixNano": "1791061352921875968",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"SystemMessage\"],\"kwargs\":{\"content\":\"Gather the key facts about the user's question.\",\"type\":\"system\"}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"HumanMessage\"],\"kwargs\":{\"content\":\"What is an agent trace?\",\"type\":\"human\",\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}}]]}"
"stringValue": "{\"messages\": [[{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"SystemMessage\"], \"kwargs\": {\"content\": \"Gather the key facts about the user's question.\", \"type\": \"system\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"What is an agent trace?\", \"type\": \"human\", \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}]]}"
}
},
{
@ -72,7 +72,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"generations\":[[{\"text\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"generation_info\":{\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ChatGeneration\",\"message\":{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"AIMessage\"],\"kwargs\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}},\"tool_calls\":[],\"invalid_tool_calls\":[]}}}]],\"llm_output\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\"},\"run\":null,\"type\":\"LLMResult\"}"
"stringValue": "{\"generations\": [[{\"text\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"generation_info\": {\"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ChatGeneration\", \"message\": {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}, \"tool_calls\": [], \"invalid_tool_calls\": []}}}]], \"llm_output\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\"}, \"run\": null, \"type\": \"LLMResult\"}"
}
},
{
@ -114,13 +114,13 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\n\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\n\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning."
"stringValue": "An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\n\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\n\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system."
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"model_name\":\"openai/gpt-6-luna\",\"stream\":false,\"_type\":\"openai-chat\",\"stop\":null}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"model_name\": \"openai/gpt-6-luna\", \"stream\": false, \"_type\": \"openai-chat\", \"stop\": null}"
}
},
{
@ -150,13 +150,13 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "191"
"intValue": "223"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "216"
"intValue": "248"
}
},
{
@ -168,7 +168,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "88"
"intValue": "96"
}
},
{
@ -192,7 +192,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_chat_model\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f|call_model:c7cf85d6-2beb-b779-6379-65712fdb9128\",\"checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f\",\"ls_provider\":\"openai\",\"ls_model_name\":\"openai/gpt-6-luna\",\"ls_model_type\":\"chat\",\"ls_temperature\":null,\"lc_versions\":{\"langchain-core\":\"1.6.6\",\"langchain-openai\":\"1.6.7\"}}"
"stringValue": "{\"ls_integration\": \"langchain_chat_model\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545|call_model:8efbb99d-9a7f-0fed-6a01-200ce39b60d2\", \"checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545\", \"ls_provider\": \"openai\", \"ls_model_name\": \"openai/gpt-6-luna\", \"ls_model_type\": \"chat\", \"ls_temperature\": null, \"lc_versions\": {\"langchain-core\": \"1.6.6\", \"langchain-openai\": \"1.6.7\"}}"
}
},
{
@ -208,18 +208,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "f1d7b2e38e4a297a",
"parentSpanId": "ace3bc964d644662",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "0c8d577859bdf794",
"parentSpanId": "310d05438d077e5d",
"name": "call_model",
"kind": 1,
"startTimeUnixNano": "1791012830406982912",
"endTimeUnixNano": "1791012832857625856",
"startTimeUnixNano": "1791061349479172096",
"endTimeUnixNano": "1791061352922414080",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}]}"
}
},
{
@ -231,7 +231,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -255,7 +255,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f|call_model:c7cf85d6-2beb-b779-6379-65712fdb9128\",\"checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545|call_model:8efbb99d-9a7f-0fed-6a01-200ce39b60d2\", \"checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545\"}"
}
},
{
@ -271,18 +271,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "ace3bc964d644662",
"parentSpanId": "6976ae7fb7fc8e90",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "310d05438d077e5d",
"parentSpanId": "ecb303041155e83e",
"name": "search_agent",
"kind": 1,
"startTimeUnixNano": "1791012830406665984",
"endTimeUnixNano": "1791012832857971968",
"startTimeUnixNano": "1791061349478894080",
"endTimeUnixNano": "1791061352922853120",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}]}"
}
},
{
@ -294,7 +294,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -318,7 +318,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":1,\"langgraph_node\":\"search\",\"langgraph_triggers\":[\"branch:to:search\"],\"langgraph_path\":[\"__pregel_pull\",\"search\"],\"langgraph_checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f\",\"checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 1, \"langgraph_node\": \"search\", \"langgraph_triggers\": [\"branch:to:search\"], \"langgraph_path\": [\"__pregel_pull\", \"search\"], \"langgraph_checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545\", \"checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545\"}"
}
},
{
@ -334,18 +334,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "6976ae7fb7fc8e90",
"parentSpanId": "5926c6bcedd87dc2",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "ecb303041155e83e",
"parentSpanId": "87feccfee88f28d6",
"name": "search",
"kind": 1,
"startTimeUnixNano": "1791012830406498048",
"endTimeUnixNano": "1791012832858153216",
"startTimeUnixNano": "1791061349478753024",
"endTimeUnixNano": "1791061352922967808",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}]}"
}
},
{
@ -357,7 +357,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -381,7 +381,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":1,\"langgraph_node\":\"search\",\"langgraph_triggers\":[\"branch:to:search\"],\"langgraph_path\":[\"__pregel_pull\",\"search\"],\"langgraph_checkpoint_ns\":\"search:c41a211e-f71e-79ef-4d79-d39203e3129f\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 1, \"langgraph_node\": \"search\", \"langgraph_triggers\": [\"branch:to:search\"], \"langgraph_path\": [\"__pregel_pull\", \"search\"], \"langgraph_checkpoint_ns\": \"search:fe96c9fa-704e-2e93-8e3d-a91748c5e545\"}"
}
},
{
@ -395,20 +395,72 @@
"code": 1
},
"flags": 256
},
}
]
}
]
},
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.45.0"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "7008af59-8740-44f0-afdc-ff9d33f91deb"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "openinference.instrumentation.langchain",
"version": "0.1.78"
},
"spans": [
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "835d6ca1079a36b7",
"parentSpanId": "f06939809a5142de",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "b2650845221be5b5",
"parentSpanId": "01a05fd6b3434433",
"name": "ChatOpenAI",
"kind": 1,
"startTimeUnixNano": "1791012832859083008",
"endTimeUnixNano": "1791012835106898176",
"startTimeUnixNano": "1791061352924045056",
"endTimeUnixNano": "1791061354661287168",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[[{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"SystemMessage\"],\"kwargs\":{\"content\":\"Write a concise answer from the facts above.\",\"type\":\"system\"}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"HumanMessage\"],\"kwargs\":{\"content\":\"What is an agent trace?\",\"type\":\"human\",\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"AIMessage\"],\"kwargs\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}},\"tool_calls\":[],\"invalid_tool_calls\":[]}}]]}"
"stringValue": "{\"messages\": [[{\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"SystemMessage\"], \"kwargs\": {\"content\": \"Write a concise answer from the facts above.\", \"type\": \"system\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"HumanMessage\"], \"kwargs\": {\"content\": \"What is an agent trace?\", \"type\": \"human\", \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}, \"tool_calls\": [], \"invalid_tool_calls\": []}}]]}"
}
},
{
@ -420,7 +472,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"generations\":[[{\"text\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"generation_info\":{\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ChatGeneration\",\"message\":{\"lc\":1,\"type\":\"constructor\",\"id\":[\"langchain\",\"schema\",\"messages\",\"AIMessage\"],\"kwargs\":{\"content\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"id\":\"lc_run--01a100af-065b-7190-833c-c11b443bf075-0\",\"usage_metadata\":{\"input_tokens\":125,\"output_tokens\":124,\"total_tokens\":249,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":62}},\"tool_calls\":[],\"invalid_tool_calls\":[]}}}]],\"llm_output\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\"},\"run\":null,\"type\":\"LLMResult\"}"
"stringValue": "{\"generations\": [[{\"text\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"generation_info\": {\"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ChatGeneration\", \"message\": {\"lc\": 1, \"type\": \"constructor\", \"id\": [\"langchain\", \"schema\", \"messages\", \"AIMessage\"], \"kwargs\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"id\": \"lc_run--01a10393-61dc-79b2-a2fb-94ed44373aaf-0\", \"usage_metadata\": {\"input_tokens\": 149, \"output_tokens\": 96, \"total_tokens\": 245, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 46}}, \"tool_calls\": [], \"invalid_tool_calls\": []}}}]], \"llm_output\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\"}, \"run\": null, \"type\": \"LLMResult\"}"
}
},
{
@ -462,7 +514,7 @@
{
"key": "llm.input_messages.2.message.content",
"value": {
"stringValue": "An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\n\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\n\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning."
"stringValue": "An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\n\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\n\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system."
}
},
{
@ -474,13 +526,13 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning."
"stringValue": "An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents."
}
},
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"model_name\":\"openai/gpt-6-luna\",\"stream\":false,\"_type\":\"openai-chat\",\"stop\":null}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"model_name\": \"openai/gpt-6-luna\", \"stream\": false, \"_type\": \"openai-chat\", \"stop\": null}"
}
},
{
@ -504,19 +556,19 @@
{
"key": "llm.token_count.prompt",
"value": {
"intValue": "125"
"intValue": "149"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "124"
"intValue": "96"
}
},
{
"key": "llm.token_count.total",
"value": {
"intValue": "249"
"intValue": "245"
}
},
{
@ -528,7 +580,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "62"
"intValue": "46"
}
},
{
@ -552,7 +604,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langchain_chat_model\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727|call_model:442a608e-daef-0597-707c-ce4c36787c47\",\"checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727\",\"ls_provider\":\"openai\",\"ls_model_name\":\"openai/gpt-6-luna\",\"ls_model_type\":\"chat\",\"ls_temperature\":null,\"lc_versions\":{\"langchain-core\":\"1.6.6\",\"langchain-openai\":\"1.6.7\"}}"
"stringValue": "{\"ls_integration\": \"langchain_chat_model\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f|call_model:eb6b2c4b-85cd-9fc6-69fd-ee7c39fb138b\", \"checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f\", \"ls_provider\": \"openai\", \"ls_model_name\": \"openai/gpt-6-luna\", \"ls_model_type\": \"chat\", \"ls_temperature\": null, \"lc_versions\": {\"langchain-core\": \"1.6.6\", \"langchain-openai\": \"1.6.7\"}}"
}
},
{
@ -568,18 +620,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "f06939809a5142de",
"parentSpanId": "bd184870f763c317",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "01a05fd6b3434433",
"parentSpanId": "cc1c518bb4a917b2",
"name": "call_model",
"kind": 1,
"startTimeUnixNano": "1791012832858917888",
"endTimeUnixNano": "1791012835107453184",
"startTimeUnixNano": "1791061352923860992",
"endTimeUnixNano": "1791061354661565952",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -591,7 +643,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100af-065b-7190-833c-c11b443bf075-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":125,\"output_tokens\":124,\"total_tokens\":249,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":62}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-61dc-79b2-a2fb-94ed44373aaf-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 149, \"output_tokens\": 96, \"total_tokens\": 245, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 46}}}}]}"
}
},
{
@ -615,7 +667,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":1,\"langgraph_node\":\"call_model\",\"langgraph_triggers\":[\"branch:to:call_model\"],\"langgraph_path\":[\"__pregel_pull\",\"call_model\"],\"langgraph_checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727|call_model:442a608e-daef-0597-707c-ce4c36787c47\",\"checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 1, \"langgraph_node\": \"call_model\", \"langgraph_triggers\": [\"branch:to:call_model\"], \"langgraph_path\": [\"__pregel_pull\", \"call_model\"], \"langgraph_checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f|call_model:eb6b2c4b-85cd-9fc6-69fd-ee7c39fb138b\", \"checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f\"}"
}
},
{
@ -631,18 +683,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "bd184870f763c317",
"parentSpanId": "223d6d53ba0d9ccf",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "cc1c518bb4a917b2",
"parentSpanId": "1d5fb238ad612007",
"name": "writer_agent",
"kind": 1,
"startTimeUnixNano": "1791012832858571008",
"endTimeUnixNano": "1791012835107939072",
"startTimeUnixNano": "1791061352923495168",
"endTimeUnixNano": "1791061354661936896",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -654,7 +706,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100af-065b-7190-833c-c11b443bf075-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":125,\"output_tokens\":124,\"total_tokens\":249,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":62}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-61dc-79b2-a2fb-94ed44373aaf-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 149, \"output_tokens\": 96, \"total_tokens\": 245, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 46}}}}]}"
}
},
{
@ -678,7 +730,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":2,\"langgraph_node\":\"write\",\"langgraph_triggers\":[\"branch:to:write\"],\"langgraph_path\":[\"__pregel_pull\",\"write\"],\"langgraph_checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727\",\"checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 2, \"langgraph_node\": \"write\", \"langgraph_triggers\": [\"branch:to:write\"], \"langgraph_path\": [\"__pregel_pull\", \"write\"], \"langgraph_checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f\", \"checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f\"}"
}
},
{
@ -694,18 +746,18 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "223d6d53ba0d9ccf",
"parentSpanId": "5926c6bcedd87dc2",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "1d5fb238ad612007",
"parentSpanId": "87feccfee88f28d6",
"name": "write",
"kind": 1,
"startTimeUnixNano": "1791012832858390016",
"endTimeUnixNano": "1791012835108278016",
"startTimeUnixNano": "1791061352923243008",
"endTimeUnixNano": "1791061354662168064",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}]}"
}
},
{
@ -717,7 +769,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100af-065b-7190-833c-c11b443bf075-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":125,\"output_tokens\":124,\"total_tokens\":249,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":62}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-61dc-79b2-a2fb-94ed44373aaf-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 149, \"output_tokens\": 96, \"total_tokens\": 245, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 46}}}}]}"
}
},
{
@ -741,7 +793,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\",\"langgraph_step\":2,\"langgraph_node\":\"write\",\"langgraph_triggers\":[\"branch:to:write\"],\"langgraph_path\":[\"__pregel_pull\",\"write\"],\"langgraph_checkpoint_ns\":\"write:810699d0-d241-c77b-aa0e-a027ab212727\"}"
"stringValue": "{\"ls_integration\": \"langgraph\", \"langgraph_step\": 2, \"langgraph_node\": \"write\", \"langgraph_triggers\": [\"branch:to:write\"], \"langgraph_path\": [\"__pregel_pull\", \"write\"], \"langgraph_checkpoint_ns\": \"write:6adb767f-ed1c-ab7e-f61d-53c0f979ae7f\"}"
}
},
{
@ -757,17 +809,17 @@
"flags": 256
},
{
"traceId": "2790928deea2b5a1cc09cae41cdc7b9b",
"spanId": "5926c6bcedd87dc2",
"traceId": "bb011b339b47b44ab98494833b7a430c",
"spanId": "87feccfee88f28d6",
"name": "research_agent",
"kind": 1,
"startTimeUnixNano": "1791012830405728000",
"endTimeUnixNano": "1791012835108648960",
"startTimeUnixNano": "1791061349477843968",
"endTimeUnixNano": "1791061354662377216",
"attributes": [
{
"key": "input.value",
"value": {
"stringValue": "{\"messages\":[{\"role\":\"user\",\"content\":\"What is an agent trace?\"}]}"
"stringValue": "{\"messages\": [{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]}"
}
},
{
@ -779,7 +831,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"messages\":[{\"type\":\"human\",\"data\":{\"content\":\"What is an agent trace?\",\"additional_kwargs\":{},\"response_metadata\":{},\"type\":\"human\",\"name\":null,\"id\":\"944b9b8c-2bfb-4dc5-8b76-08bc9c4c8028\"}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of the steps an AI agent took to handle a task. It may include the input it received, actions or tool calls it made, results it got back, and the final response.\\n\\nFor example: *user asks for the weather → agent calls a weather tool → tool returns the forecast → agent summarizes it.*\\n\\nTraces help people debug and evaluate an agent’s behavior. They don’t necessarily reveal the model’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":191,\"prompt_tokens\":25,\"total_tokens\":216,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":88,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXeuCY2EjB5O5kTBNjfaCrtAq8U\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100ae-fcc7-77c2-b3dd-8522b20f04e5-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":25,\"output_tokens\":191,\"total_tokens\":216,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":88}}}},{\"type\":\"ai\",\"data\":{\"content\":\"An **agent trace** is a record of how an AI agent handled a task—such as the steps it took, tools it used, and results it received. Traces help with debugging and evaluation, but don’t necessarily show the agent’s full internal reasoning.\",\"additional_kwargs\":{\"refusal\":null},\"response_metadata\":{\"token_usage\":{\"completion_tokens\":124,\"prompt_tokens\":125,\"total_tokens\":249,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":62,\"rejected_prediction_tokens\":0,\"text_tokens\":null},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"image_tokens\":null,\"text_tokens\":null,\"cache_creation_tokens\":0}},\"model_provider\":\"openai\",\"model_name\":\"openai/gpt-6-luna\",\"system_fingerprint\":null,\"id\":\"chatcmpl-EUoXh6pPDCqqY08c8to8pkmEfoP6m\",\"service_tier\":\"default\",\"finish_reason\":\"stop\",\"logprobs\":null},\"type\":\"ai\",\"name\":null,\"id\":\"lc_run--01a100af-065b-7190-833c-c11b443bf075-0\",\"tool_calls\":[],\"invalid_tool_calls\":[],\"usage_metadata\":{\"input_tokens\":125,\"output_tokens\":124,\"total_tokens\":249,\"input_token_details\":{\"audio\":0,\"cache_read\":0,\"cache_creation\":0},\"output_token_details\":{\"audio\":0,\"reasoning\":62}}}}]}"
"stringValue": "{\"messages\": [{\"type\": \"human\", \"data\": {\"content\": \"What is an agent trace?\", \"additional_kwargs\": {}, \"response_metadata\": {}, \"type\": \"human\", \"name\": null, \"id\": \"f6b15395-342f-44c5-bfd1-fcaa3cd8c878\"}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of what an AI agent did during a task. It commonly includes the user\u2019s request, the agent\u2019s actions or tool calls, the results it received, and its final response. Some traces also record timestamps, errors, and other execution details.\\n\\nFor example, a trace might show that an agent searched the web, opened two pages, extracted information, and then answered a question.\\n\\nTraces are useful for debugging, evaluating, and monitoring agents. They don\u2019t necessarily include the agent\u2019s private internal reasoning; what\u2019s recorded depends on the system.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 223, \"prompt_tokens\": 25, \"total_tokens\": 248, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 96, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1ADGQYQLXMTkiPH7xDAmDKTcU1J\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-5467-77d0-aad0-f90b45a1d6af-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 25, \"output_tokens\": 223, \"total_tokens\": 248, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 96}}}}, {\"type\": \"ai\", \"data\": {\"content\": \"An **agent trace** is a chronological record of an AI agent\u2019s task: its actions or tool calls, the results it receives, and its final response. Traces help people debug and evaluate agents.\", \"additional_kwargs\": {\"refusal\": null}, \"response_metadata\": {\"token_usage\": {\"completion_tokens\": 96, \"prompt_tokens\": 149, \"total_tokens\": 245, \"completion_tokens_details\": {\"accepted_prediction_tokens\": 0, \"audio_tokens\": 0, \"reasoning_tokens\": 46, \"rejected_prediction_tokens\": 0, \"text_tokens\": null}, \"prompt_tokens_details\": {\"audio_tokens\": 0, \"cache_write_tokens\": 0, \"cached_tokens\": 0, \"image_tokens\": null, \"text_tokens\": null, \"cache_creation_tokens\": 0}}, \"model_provider\": \"openai\", \"model_name\": \"openai/gpt-6-luna\", \"system_fingerprint\": null, \"id\": \"chatcmpl-EV1AHQQGzXMvkUbS86kQTl6mTAMf6\", \"service_tier\": \"default\", \"finish_reason\": \"stop\", \"logprobs\": null}, \"type\": \"ai\", \"name\": null, \"id\": \"lc_run--01a10393-61dc-79b2-a2fb-94ed44373aaf-0\", \"tool_calls\": [], \"invalid_tool_calls\": [], \"usage_metadata\": {\"input_tokens\": 149, \"output_tokens\": 96, \"total_tokens\": 245, \"input_token_details\": {\"audio\": 0, \"cache_read\": 0, \"cache_creation\": 0}, \"output_token_details\": {\"audio\": 0, \"reasoning\": 46}}}}]}"
}
},
{
@ -797,7 +849,7 @@
{
"key": "metadata",
"value": {
"stringValue": "{\"ls_integration\":\"langgraph\"}"
"stringValue": "{\"ls_integration\": \"langgraph\"}"
}
},
{

1834
litellm-rust/crates/traces/tests/fixtures/langsmith_deep_agent_export.json vendored
View file

File diff suppressed because one or more lines are too long

188
litellm-rust/crates/traces/tests/fixtures/llamaindex_simple.json vendored
View file

File diff suppressed because one or more lines are too long

460
litellm-rust/crates/traces/tests/fixtures/llamaindex_swarm.json vendored
View file

File diff suppressed because one or more lines are too long

465
litellm-rust/crates/traces/tests/fixtures/mastra_simple.json vendored Normal file
View file

@ -0,0 +1,465 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "host.name",
"value": {
"stringValue": "fixture-host"
}
},
{
"key": "host.arch",
"value": {
"stringValue": "arm64"
}
},
{
"key": "host.id",
"value": {
"stringValue": "00000000-0000-0000-0000-000000000000"
}
},
{
"key": "process.pid",
"value": {
"intValue": "83271"
}
},
{
"key": "process.executable.name",
"value": {
"stringValue": "node"
}
},
{
"key": "process.executable.path",
"value": {
"stringValue": "/home/user/.local/share/fnm/node-versions/v25.8.1/installation/bin/node"
}
},
{
"key": "process.command_args",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "/home/user/.local/share/fnm/node-versions/v25.8.1/installation/bin/node"
},
{
"stringValue": "--env-file=.env"
},
{
"stringValue": "/home/user/dev/litellm-lens-example/mastra/simple/main.ts"
}
]
}
}
},
{
"key": "process.runtime.version",
"value": {
"stringValue": "25.8.1"
}
},
{
"key": "process.runtime.name",
"value": {
"stringValue": "nodejs"
}
},
{
"key": "process.runtime.description",
"value": {
"stringValue": "Node.js"
}
},
{
"key": "process.command",
"value": {
"stringValue": "/home/user/dev/litellm-lens-example/mastra/simple/main.ts"
}
},
{
"key": "process.owner",
"value": {
"stringValue": "user"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:node"
}
},
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "nodejs"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "2.11.0"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "6573bbd66c74e1ceb09fd173fe4cdc45",
"spanId": "453659faa8fb4313",
"parentSpanId": "8992d8314e703e8c",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061319981000000",
"endTimeUnixNano": "1791061322404980084",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "afc4951d-7141-4476-b4d5-529b294e56b7"
}
}
],
"status": {},
"flags": 257
}
]
},
{
"scope": {
"name": "@mastra/otel-bridge",
"version": "1.0.0"
},
"spans": [
{
"traceId": "6573bbd66c74e1ceb09fd173fe4cdc45",
"spanId": "dc2c2cdc64224139",
"parentSpanId": "8992d8314e703e8c",
"name": "chat openai/gpt-6-luna",
"kind": 3,
"startTimeUnixNano": "1791061319977000000",
"endTimeUnixNano": "1791061322412000000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "chat"
}
},
{
"key": "mastra.span.type",
"value": {
"stringValue": "model_inference"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of an AI agent\u2019s steps during a task\u2014such as the inputs it received, actions or tool calls it made, results it got back, and its final response. Traces help people debug, evaluate, and audit agent behavior.\"}]}]"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "litellm.chat"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "23"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "142"
}
},
{
"key": "gen_ai.usage.reasoning_tokens",
"value": {
"intValue": "78"
}
},
{
"key": "gen_ai.usage.cache_read.input_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.agent.id",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "mastra.completion_start_time",
"value": {
"stringValue": "2026-10-03T21:02:02.408Z"
}
},
{
"key": "gen_ai.response.finish_reasons",
"value": {
"stringValue": "[\"stop\"]"
}
},
{
"key": "gen_ai.response.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "mastra.metadata.runId",
"value": {
"stringValue": "c832c9e4-df32-442c-b296-4f4e62489aa2"
}
}
],
"status": {
"code": 1
},
"flags": 257
},
{
"traceId": "6573bbd66c74e1ceb09fd173fe4cdc45",
"spanId": "8992d8314e703e8c",
"parentSpanId": "10b79de481b78d1b",
"name": "agent_step research_agent",
"kind": 1,
"startTimeUnixNano": "1791061319974000000",
"endTimeUnixNano": "1791061322412000000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "agent_step"
}
},
{
"key": "mastra.span.type",
"value": {
"stringValue": "model_step"
}
},
{
"key": "mastra.model_step.input",
"value": {
"stringValue": "[{\"role\":\"system\",\"content\":\"Answer the question concisely.\"},{\"role\":\"user\",\"content\":\"What is an agent trace?\"}]"
}
},
{
"key": "mastra.model_step.output",
"value": {
"stringValue": "{\"text\":\"An **agent trace** is a record of an AI agent\u2019s steps during a task\u2014such as the inputs it received, actions or tool calls it made, results it got back, and its final response. Traces help people debug, evaluate, and audit agent behavior.\",\"toolCalls\":[]}"
}
},
{
"key": "mastra.model_step.step_index",
"value": {
"intValue": "0"
}
},
{
"key": "mastra.model_step.is_continued",
"value": {
"boolValue": false
}
},
{
"key": "mastra.metadata.runId",
"value": {
"stringValue": "c832c9e4-df32-442c-b296-4f4e62489aa2"
}
},
{
"key": "mastra.metadata.headers",
"value": {
"stringValue": "{\"content-encoding\":\"gzip\",\"content-length\":\"508\",\"content-security-policy\":\"frame-ancestors 'none'\",\"content-type\":\"application/json\",\"date\":\"Sat, 03 Oct 2026 21:01:58 GMT\",\"llm_provider-access-control-expose-headers\":\"X-Request-ID, CF-Ray, CF-Ray\",\"llm_provider-alt-svc\":\"h3=\\\":443\\\"; ma=86400\",\"llm_provider-cf-cache-status\":\"DYNAMIC\",\"llm_provider-cf-ray\":\"a44ee2a238354c71-SJC\",\"llm_provider-connection\":\"keep-alive\",\"llm_provider-content-encoding\":\"br\",\"llm_provider-content-type\":\"application/json\",\"llm_provider-date\":\"Sat, 03 Oct 2026 21:02:02 GMT\",\"llm_provider-openai-organization\":\"personal-1e898z\",\"llm_provider-openai-processing-ms\":\"2281\",\"llm_provider-openai-project\":\"proj_WG9WJ3ACNqlcabNE2emRzoqT\",\"llm_provider-openai-version\":\"2020-10-01\",\"llm_provider-server\":\"cloudflare\",\"llm_provider-strict-transport-security\":\"max-age=31536000; includeSubDomains; preload\",\"llm_provider-transfer-encoding\":\"chunked\",\"llm_provider-x-content-type-options\":\"nosniff\",\"llm_provider-x-openai-proxy-wasm\":\"v0.1\",\"llm_provider-x-ratelimit-limit-requests\":\"30000\",\"llm_provider-x-ratelimit-limit-tokens\":\"180000000\",\"llm_provider-x-ratelimit-remaining-requests\":\"29999\",\"llm_provider-x-ratelimit-remaining-tokens\":\"179999985\",\"llm_provider-x-ratelimit-reset-requests\":\"2ms\",\"llm_provider-x-ratelimit-reset-tokens\":\"0s\",\"llm_provider-x-request-id\":\"req_3bdaa67f622f4d60b679a0949bb6de56\",\"vary\":\"Accept-Encoding\",\"x-content-type-options\":\"nosniff\",\"x-frame-options\":\"DENY\",\"x-litellm-attempted-fallbacks\":\"0\",\"x-litellm-attempted-retries\":\"0\",\"x-litellm-call-id\":\"afc4951d-7141-4476-b4d5-529b294e56b7\",\"x-litellm-callback-duration-ms\":\"0.0\",\"x-litellm-key-spend\":\"7.329999999999999e-05\",\"x-litellm-model-api-base\":\"https://api.openai.com\",\"x-litellm-model-group\":\"openai/gpt-6-luna\",\"x-litellm-model-id\":\"e68fbd1ce26aa7a89b059c9a6df12007e624c6a2106cd3e6af492ab7a259f117\",\"x-litellm-model-name\":\"openai/gpt-6-luna\",\"x-litellm-overhead-duration-ms\":\"2.7521\",\"x-litellm-response-cost\":\"7.329999999999999e-05\",\"x-litellm-response-cost-discount-amount\":\"0.0\",\"x-litellm-response-cost-input\":\"2.3e-06\",\"x-litellm-response-cost-margin-amount\":\"0.0\",\"x-litellm-response-cost-margin-percent\":\"0.0\",\"x-litellm-response-cost-original\":\"7.329999999999999e-05\",\"x-litellm-response-cost-output\":\"7.099999999999999e-05\",\"x-litellm-response-cost-reasoning\":\"3.9e-05\",\"__truncated\":\"7 more keys omitted\"}"
}
},
{
"key": "mastra.metadata.modelMetadata",
"value": {
"stringValue": "{\"modelId\":\"openai/gpt-6-luna\",\"modelVersion\":\"v4\",\"modelProvider\":\"litellm.chat\"}"
}
}
],
"status": {
"code": 1
},
"flags": 257
},
{
"traceId": "6573bbd66c74e1ceb09fd173fe4cdc45",
"spanId": "10b79de481b78d1b",
"parentSpanId": "60009089ee1451e9",
"name": "model_generation openai/gpt-6-luna",
"kind": 1,
"startTimeUnixNano": "1791061319969000000",
"endTimeUnixNano": "1791061322415000000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "model_generation"
}
},
{
"key": "mastra.span.type",
"value": {
"stringValue": "model_generation"
}
},
{
"key": "mastra.model_generation.input",
"value": {
"stringValue": "{\"messages\":[{\"role\":\"system\",\"content\":\"Answer the question concisely.\"},{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"What is an agent trace?\",\"providerOptions\":{\"mastra\":{\"createdAt\":1791061319965}}}]}]}"
}
},
{
"key": "mastra.model_generation.output",
"value": {
"stringValue": "{\"files\":[],\"reasoning\":[],\"sources\":[],\"text\":\"An **agent trace** is a record of an AI agent\u2019s steps during a task\u2014such as the inputs it received, actions or tool calls it made, results it got back, and its final response. Traces help people debug, evaluate, and audit agent behavior.\",\"warnings\":[]}"
}
},
{
"key": "mastra.metadata.runId",
"value": {
"stringValue": "c832c9e4-df32-442c-b296-4f4e62489aa2"
}
}
],
"status": {
"code": 1
},
"flags": 257
},
{
"traceId": "6573bbd66c74e1ceb09fd173fe4cdc45",
"spanId": "60009089ee1451e9",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791061319961000000",
"endTimeUnixNano": "1791061322433000000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "mastra.span.type",
"value": {
"stringValue": "agent_run"
}
},
{
"key": "mastra.agent_run.input",
"value": {
"stringValue": "What is an agent trace?"
}
},
{
"key": "mastra.agent_run.output",
"value": {
"stringValue": "{\"text\":\"An **agent trace** is a record of an AI agent\u2019s steps during a task\u2014such as the inputs it received, actions or tool calls it made, results it got back, and its final response. Traces help people debug, evaluate, and audit agent behavior.\",\"files\":[]}"
}
},
{
"key": "gen_ai.agent.id",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.system_instructions",
"value": {
"stringValue": "Answer the question concisely."
}
},
{
"key": "mastra.metadata.runId",
"value": {
"stringValue": "c832c9e4-df32-442c-b296-4f4e62489aa2"
}
}
],
"status": {
"code": 1
},
"flags": 257
}
]
}
]
}
]
}

2061
litellm-rust/crates/traces/tests/fixtures/mastra_swarm.json vendored Normal file
View file

File diff suppressed because it is too large Load diff

138
litellm-rust/crates/traces/tests/fixtures/openai_agents_simple.json vendored
View file

File diff suppressed because one or more lines are too long

662
litellm-rust/crates/traces/tests/fixtures/openai_agents_swarm.json vendored
View file

File diff suppressed because one or more lines are too long

104
litellm-rust/crates/traces/tests/fixtures/opentelemetry_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "acc856a2-7413-4bfb-aba1-4ac97d22b519"
}
},
{
"key": "service.name",
"value": {
"stringValue": "opentelemetry-simple"
"stringValue": "070c0b51-82ca-4b60-baa3-95fcae85ee51"
}
},
{
@ -38,10 +32,72 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "c9e85daf52f338211b3d8ee18906c14f",
"spanId": "0dd2cca62dcb29a5",
"parentSpanId": "a73b3b1f5911f321",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061316015649000",
"endTimeUnixNano": "1791061319566768000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "170a81eb-86c9-44fe-b63d-8700627f07a7"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.openai",
@ -49,13 +105,13 @@
},
"spans": [
{
"traceId": "d0eecfc62e38855ffa4993587fdaeda3",
"spanId": "a206ce51f5f4f91a",
"parentSpanId": "d478cf6e508d09e3",
"traceId": "c9e85daf52f338211b3d8ee18906c14f",
"spanId": "a73b3b1f5911f321",
"parentSpanId": "771415ea54fc15af",
"name": "ChatCompletion",
"kind": 1,
"startTimeUnixNano": "1791012993296096478",
"endTimeUnixNano": "1791012997843009292",
"startTimeUnixNano": "1791061316006161000",
"endTimeUnixNano": "1791061319570167000",
"attributes": [
{
"key": "llm.system",
@ -66,7 +122,7 @@
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"messages\":[{\"role\":\"user\",\"content\":\"What is an agent trace?\"}]}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"messages\": [{\"role\": \"user\", \"content\": \"What is an agent trace?\"}]}"
}
},
{
@ -78,7 +134,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"id\":\"chatcmpl-EUoaHKAH0u5nc0HxhHSAtRbHTI9nn\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a record of the steps an AI agent takes to handle a request. It can include the request, the agent’s actions (such as calling a search or database tool), the results of those actions, and the final response.\\n\\nFor example:\\n\\n1. User asks for tomorrow’s weather.\\n2. Agent calls a weather service.\\n3. The service returns the forecast.\\n4. Agent summarizes it for the user.\\n\\nTraces help developers understand how an agent behaved, diagnose errors, and measure performance. They don’t necessarily include the model’s private internal reasoning.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791012993,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":222,\"prompt_tokens\":12,\"total_tokens\":234,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":96,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
"stringValue": "{\"id\":\"chatcmpl-EV19gSXpccLloSr3XAbDLESfYKYBD\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a record of an AI agent\u2019s execution: the steps it took while working on a task.\\n\\nA trace might include:\\n- The user\u2019s request\\n- The agent\u2019s actions, such as calling a search tool or running code\\n- Tool results or other observations\\n- The agent\u2019s final response\\n- Timing, errors, or other debugging details\\n\\nFor example: *receive a question \u2192 search the web \u2192 read results \u2192 summarize them*.\\n\\nTraces help developers understand, debug, and evaluate an agent\u2019s behavior. They usually capture observable actions and outputs\u2014not necessarily the agent\u2019s private internal reasoning.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791061316,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":192,\"prompt_tokens\":12,\"total_tokens\":204,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":57,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
}
},
{
@ -90,7 +146,7 @@
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\"}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\"}"
}
},
{
@ -114,7 +170,7 @@
{
"key": "llm.token_count.total",
"value": {
"intValue": "234"
"intValue": "204"
}
},
{
@ -126,7 +182,7 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "222"
"intValue": "192"
}
},
{
@ -150,7 +206,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "96"
"intValue": "57"
}
},
{
@ -168,7 +224,7 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of the steps an AI agent takes to handle a request. It can include the request, the agent’s actions (such as calling a search or database tool), the results of those actions, and the final response.\n\nFor example:\n\n1. User asks for tomorrow’s weather.\n2. Agent calls a weather service.\n3. The service returns the forecast.\n4. Agent summarizes it for the user.\n\nTraces help developers understand how an agent behaved, diagnose errors, and measure performance. They don’t necessarily include the model’s private internal reasoning."
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution: the steps it took while working on a task.\n\nA trace might include:\n- The user\u2019s request\n- The agent\u2019s actions, such as calling a search tool or running code\n- Tool results or other observations\n- The agent\u2019s final response\n- Timing, errors, or other debugging details\n\nFor example: *receive a question \u2192 search the web \u2192 read results \u2192 summarize them*.\n\nTraces help developers understand, debug, and evaluate an agent\u2019s behavior. They usually capture observable actions and outputs\u2014not necessarily the agent\u2019s private internal reasoning."
}
},
{
@ -197,12 +253,12 @@
},
"spans": [
{
"traceId": "d0eecfc62e38855ffa4993587fdaeda3",
"spanId": "d478cf6e508d09e3",
"traceId": "c9e85daf52f338211b3d8ee18906c14f",
"spanId": "771415ea54fc15af",
"name": "research_agent",
"kind": 1,
"startTimeUnixNano": "1791012993279400805",
"endTimeUnixNano": "1791012997843043918",
"startTimeUnixNano": "1791061315990190000",
"endTimeUnixNano": "1791061319570213000",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -225,7 +281,7 @@
{
"key": "output.value",
"value": {
"stringValue": "An **agent trace** is a record of the steps an AI agent takes to handle a request. It can include the request, the agent’s actions (such as calling a search or database tool), the results of those actions, and the final response.\n\nFor example:\n\n1. User asks for tomorrow’s weather.\n2. Agent calls a weather service.\n3. The service returns the forecast.\n4. Agent summarizes it for the user.\n\nTraces help developers understand how an agent behaved, diagnose errors, and measure performance. They don’t necessarily include the model’s private internal reasoning."
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution: the steps it took while working on a task.\n\nA trace might include:\n- The user\u2019s request\n- The agent\u2019s actions, such as calling a search tool or running code\n- Tool results or other observations\n- The agent\u2019s final response\n- Timing, errors, or other debugging details\n\nFor example: *receive a question \u2192 search the web \u2192 read results \u2192 summarize them*.\n\nTraces help developers understand, debug, and evaluate an agent\u2019s behavior. They usually capture observable actions and outputs\u2014not necessarily the agent\u2019s private internal reasoning."
}
}
],

226
litellm-rust/crates/traces/tests/fixtures/opentelemetry_swarm.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "2f0e5505-868d-4055-8cc7-da99aabeac5d"
}
},
{
"key": "service.name",
"value": {
"stringValue": "opentelemetry-swarm"
"stringValue": "c949636c-f59d-48d0-8239-a92b83ddc0ff"
}
},
{
@ -38,10 +32,72 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "649da60c202b3c6e",
"parentSpanId": "251f89cfa368db92",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061358177721000",
"endTimeUnixNano": "1791061361394236000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "b7d9d8c9-6c02-4b60-bea6-f706d8539157"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.openai",
@ -49,13 +105,13 @@
},
"spans": [
{
"traceId": "e868a26f268dc15240585d6c3e536ab2",
"spanId": "a663c32e7657f1df",
"parentSpanId": "86ca0e090d648d0c",
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "251f89cfa368db92",
"parentSpanId": "0e1f3d821df03d59",
"name": "ChatCompletion",
"kind": 1,
"startTimeUnixNano": "1791013009573764272",
"endTimeUnixNano": "1791013012992167966",
"startTimeUnixNano": "1791061358166907000",
"endTimeUnixNano": "1791061361398541000",
"attributes": [
{
"key": "llm.system",
@ -66,7 +122,7 @@
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"messages\":[{\"role\":\"user\",\"content\":\"List key facts about: What is an agent trace?\"}]}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"messages\": [{\"role\": \"user\", \"content\": \"List key facts about: What is an agent trace?\"}]}"
}
},
{
@ -78,7 +134,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"id\":\"chatcmpl-EUoaXDlWrV7o7T6eQBfbmCGblGv7W\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\\n- The exact detail varies by system: a trace may omit some events or sensitive data.\\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791013009,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":292,\"prompt_tokens\":17,\"total_tokens\":309,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":112,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
"stringValue": "{\"id\":\"chatcmpl-EV1AMlFMAux4TPXGalT8n8WFpz9de\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\\n- Traces often show events in order and may group them into nested steps or spans.\\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791061358,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":262,\"prompt_tokens\":17,\"total_tokens\":279,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":121,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
}
},
{
@ -90,7 +146,7 @@
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\"}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\"}"
}
},
{
@ -114,7 +170,7 @@
{
"key": "llm.token_count.total",
"value": {
"intValue": "309"
"intValue": "279"
}
},
{
@ -126,7 +182,7 @@
{
"key": "llm.token_count.completion",
"value": {
"intValue": "292"
"intValue": "262"
}
},
{
@ -150,7 +206,7 @@
{
"key": "llm.token_count.completion_details.reasoning",
"value": {
"intValue": "112"
"intValue": "121"
}
},
{
@ -168,7 +224,7 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\n- The exact detail varies by system: a trace may omit some events or sensitive data.\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies."
"stringValue": "- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\n- Traces often show events in order and may group them into nested steps or spans.\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection."
}
},
{
@ -197,13 +253,13 @@
},
"spans": [
{
"traceId": "e868a26f268dc15240585d6c3e536ab2",
"spanId": "86ca0e090d648d0c",
"parentSpanId": "e3fc3c9c37fe686f",
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "0e1f3d821df03d59",
"parentSpanId": "fddaf2da465baf71",
"name": "search_agent",
"kind": 1,
"startTimeUnixNano": "1791013009564659675",
"endTimeUnixNano": "1791013012992208258",
"startTimeUnixNano": "1791061358150691000",
"endTimeUnixNano": "1791061361398579000",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -226,7 +282,7 @@
{
"key": "output.value",
"value": {
"stringValue": "- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\n- The exact detail varies by system: a trace may omit some events or sensitive data.\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies."
"stringValue": "- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\n- Traces often show events in order and may group them into nested steps or spans.\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection."
}
}
],
@ -261,13 +317,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "2f0e5505-868d-4055-8cc7-da99aabeac5d"
}
},
{
"key": "service.name",
"value": {
"stringValue": "opentelemetry-swarm"
"stringValue": "c949636c-f59d-48d0-8239-a92b83ddc0ff"
}
},
{
@ -275,10 +325,72 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "0ec1817127832b0c",
"parentSpanId": "7ba1e22608c20ae9",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061361399259000",
"endTimeUnixNano": "1791061363203340000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "e84366c5-5d88-4ec0-bc54-6b5000ae11d1"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "openinference.instrumentation.openai",
@ -286,13 +398,13 @@
},
"spans": [
{
"traceId": "e868a26f268dc15240585d6c3e536ab2",
"spanId": "88f6d1b77297aea1",
"parentSpanId": "d659d1129befe46c",
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "7ba1e22608c20ae9",
"parentSpanId": "7756eefae3c9e2e9",
"name": "ChatCompletion",
"kind": 1,
"startTimeUnixNano": "1791013012992568678",
"endTimeUnixNano": "1791013014841844220",
"startTimeUnixNano": "1791061361398942000",
"endTimeUnixNano": "1791061363203834000",
"attributes": [
{
"key": "llm.system",
@ -303,7 +415,7 @@
{
"key": "input.value",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\",\"messages\":[{\"role\":\"user\",\"content\":\"Using these notes, answer 'What is an agent trace?':\\n- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\\n- The exact detail varies by system: a trace may omit some events or sensitive data.\\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies.\"}]}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\", \"messages\": [{\"role\": \"user\", \"content\": \"Using these notes, answer 'What is an agent trace?':\\n- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\\n- Traces often show events in order and may group them into nested steps or spans.\\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection.\"}]}"
}
},
{
@ -315,7 +427,7 @@
{
"key": "output.value",
"value": {
"stringValue": "{\"id\":\"chatcmpl-EUoabltkqCx58uxj30PtrZOFbex43\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a record of an AI agent’s execution: the events involved in handling a task. It may include the user’s request, model and tool calls, results, intermediate actions, errors, and final response, often with timestamps, durations, identifiers, and other metadata.\\n\\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. Their level of detail varies, and they aren’t necessarily a complete record of the agent’s internal reasoning—they capture observable execution events, not a guaranteed transcript of private thought. Because traces may contain sensitive information, they should be protected with appropriate access controls, redaction, and retention policies.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791013013,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":137,\"prompt_tokens\":190,\"total_tokens\":327,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":0,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
"stringValue": "{\"id\":\"chatcmpl-EV1APW9BhnXpYoujMXnLb4ElRb9gM\",\"choices\":[{\"finish_reason\":\"stop\",\"index\":0,\"message\":{\"content\":\"An **agent trace** is a record of an AI agent\u2019s execution as it works toward a task. It can include inputs and outputs, tool calls and results, handoffs, errors, timing, and other metadata, often organized as ordered or nested steps.\\n\\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. They capture only what the system records\u2014not necessarily the agent\u2019s internal reasoning. Their contents and formats vary by platform, and they may contain sensitive information that should be protected.\",\"role\":\"assistant\",\"annotations\":[],\"provider_specific_fields\":{\"refusal\":null}},\"provider_specific_fields\":{}}],\"created\":1791061361,\"model\":\"openai/gpt-6-luna\",\"object\":\"chat.completion\",\"service_tier\":\"default\",\"usage\":{\"completion_tokens\":107,\"prompt_tokens\":151,\"total_tokens\":258,\"completion_tokens_details\":{\"accepted_prediction_tokens\":0,\"audio_tokens\":0,\"reasoning_tokens\":0,\"rejected_prediction_tokens\":0},\"prompt_tokens_details\":{\"audio_tokens\":0,\"cache_write_tokens\":0,\"cached_tokens\":0,\"cache_creation_tokens\":0}}}"
}
},
{
@ -327,7 +439,7 @@
{
"key": "llm.invocation_parameters",
"value": {
"stringValue": "{\"model\":\"openai/gpt-6-luna\"}"
"stringValue": "{\"model\": \"openai/gpt-6-luna\"}"
}
},
{
@ -339,7 +451,7 @@
{
"key": "llm.input_messages.0.message.content",
"value": {
"stringValue": "Using these notes, answer 'What is an agent trace?':\n- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\n- The exact detail varies by system: a trace may omit some events or sensitive data.\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies."
"stringValue": "Using these notes, answer 'What is an agent trace?':\n- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\n- Traces often show events in order and may group them into nested steps or spans.\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection."
}
},
{
@ -351,19 +463,19 @@
{
"key": "llm.token_count.total",
"value": {
"intValue": "327"
"intValue": "258"
}
},
{
"key": "llm.token_count.prompt",
"value": {
"intValue": "190"
"intValue": "151"
}
},
{
"key": "llm.token_count.completion",
"value": {
"intValue": "137"
"intValue": "107"
}
},
{
@ -405,7 +517,7 @@
{
"key": "llm.output_messages.0.message.content",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s execution: the events involved in handling a task. It may include the user’s request, model and tool calls, results, intermediate actions, errors, and final response, often with timestamps, durations, identifiers, and other metadata.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. Their level of detail varies, and they aren’t necessarily a complete record of the agent’s internal reasoning—they capture observable execution events, not a guaranteed transcript of private thought. Because traces may contain sensitive information, they should be protected with appropriate access controls, redaction, and retention policies."
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution as it works toward a task. It can include inputs and outputs, tool calls and results, handoffs, errors, timing, and other metadata, often organized as ordered or nested steps.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. They capture only what the system records\u2014not necessarily the agent\u2019s internal reasoning. Their contents and formats vary by platform, and they may contain sensitive information that should be protected."
}
},
{
@ -434,13 +546,13 @@
},
"spans": [
{
"traceId": "e868a26f268dc15240585d6c3e536ab2",
"spanId": "d659d1129befe46c",
"parentSpanId": "e3fc3c9c37fe686f",
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "7756eefae3c9e2e9",
"parentSpanId": "fddaf2da465baf71",
"name": "writer_agent",
"kind": 1,
"startTimeUnixNano": "1791013012992247633",
"endTimeUnixNano": "1791013014841889262",
"startTimeUnixNano": "1791061361398626000",
"endTimeUnixNano": "1791061363203851000",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -457,13 +569,13 @@
{
"key": "input.value",
"value": {
"stringValue": "Using these notes, answer 'What is an agent trace?':\n- **An agent trace is a record of an AI agent’s execution**—the sequence of events involved in handling a task.\n- It may include the user’s request, model calls, tool calls and their results, intermediate actions, errors, and the final response.\n- Traces often capture **timestamps, durations, identifiers, and metadata**, and may organize events as nested steps or spans.\n- They help developers **debug behavior, measure performance, evaluate results, and audit tool use**.\n- The exact detail varies by system: a trace may omit some events or sensitive data.\n- A trace is **not necessarily the agent’s full internal reasoning**. It records observable execution events, not a guaranteed transcript of private thought.\n- Traces can contain sensitive information, so they should be handled with appropriate access controls, redaction, and retention policies."
"stringValue": "Using these notes, answer 'What is an agent trace?':\n- **An agent trace** is a record of an AI agent\u2019s execution as it works toward a task.\n- It may include the agent\u2019s inputs and outputs, tool calls and results, handoffs to other agents, errors, timing, and other metadata.\n- Traces often show events in order and may group them into nested steps or spans.\n- They help developers debug behavior, measure performance, evaluate results, and audit tool use.\n- A trace is **not necessarily a transcript of the agent\u2019s internal reasoning**; it records only what the system captures.\n- Trace contents and formats vary by platform, and traces may contain sensitive information that needs protection."
}
},
{
"key": "output.value",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s execution: the events involved in handling a task. It may include the user’s request, model and tool calls, results, intermediate actions, errors, and final response, often with timestamps, durations, identifiers, and other metadata.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. Their level of detail varies, and they aren’t necessarily a complete record of the agent’s internal reasoning—they capture observable execution events, not a guaranteed transcript of private thought. Because traces may contain sensitive information, they should be protected with appropriate access controls, redaction, and retention policies."
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution as it works toward a task. It can include inputs and outputs, tool calls and results, handoffs, errors, timing, and other metadata, often organized as ordered or nested steps.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. They capture only what the system records\u2014not necessarily the agent\u2019s internal reasoning. Their contents and formats vary by platform, and they may contain sensitive information that should be protected."
}
}
],
@ -471,12 +583,12 @@
"flags": 256
},
{
"traceId": "e868a26f268dc15240585d6c3e536ab2",
"spanId": "e3fc3c9c37fe686f",
"traceId": "233c54429123cf3eecb0c0cdd275daea",
"spanId": "fddaf2da465baf71",
"name": "research_agent",
"kind": 1,
"startTimeUnixNano": "1791013009564625842",
"endTimeUnixNano": "1791013014841904929",
"startTimeUnixNano": "1791061358150661000",
"endTimeUnixNano": "1791061363203862000",
"attributes": [
{
"key": "gen_ai.agent.name",
@ -499,7 +611,7 @@
{
"key": "output.value",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s execution: the events involved in handling a task. It may include the user’s request, model and tool calls, results, intermediate actions, errors, and final response, often with timestamps, durations, identifiers, and other metadata.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. Their level of detail varies, and they aren’t necessarily a complete record of the agent’s internal reasoning—they capture observable execution events, not a guaranteed transcript of private thought. Because traces may contain sensitive information, they should be protected with appropriate access controls, redaction, and retention policies."
"stringValue": "An **agent trace** is a record of an AI agent\u2019s execution as it works toward a task. It can include inputs and outputs, tool calls and results, handoffs, errors, timing, and other metadata, often organized as ordered or nested steps.\n\nTraces help developers debug behavior, measure performance, evaluate results, and audit tool use. They capture only what the system records\u2014not necessarily the agent\u2019s internal reasoning. Their contents and formats vary by platform, and they may contain sensitive information that should be protected."
}
}
],

335
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_billed_failure.json vendored Normal file
View file

File diff suppressed because one or more lines are too long

428
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_retry.json vendored Normal file
View file

@ -0,0 +1,428 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.44.0"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "f4d7cce9-2e1d-428d-bd43-6a7cf51804ef"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.65b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "213790ddf7f01594d52d54818775b98d",
"spanId": "10cd8cbd5f72b220",
"parentSpanId": "b730242cc7698d6f",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061622553239000",
"endTimeUnixNano": "1791061623933306000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "503"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "bbff9fcc-930d-47fe-b0eb-0ab1eba0eff0"
}
}
],
"status": {
"code": 2
},
"flags": 256
},
{
"traceId": "213790ddf7f01594d52d54818775b98d",
"spanId": "c80b675f9912b72c",
"parentSpanId": "b730242cc7698d6f",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061624396320000",
"endTimeUnixNano": "1791061625776764000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "b1b0392c-9022-4369-88ce-7d8bdc57c90c"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "pydantic-ai",
"version": "2.53.0"
},
"spans": [
{
"traceId": "213790ddf7f01594d52d54818775b98d",
"spanId": "b730242cc7698d6f",
"parentSpanId": "c820226f9e157926",
"name": "chat openai/gpt-6-luna",
"kind": 3,
"startTimeUnixNano": "1791061622542709000",
"endTimeUnixNano": "1791061625782151000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "chat"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "litellm"
}
},
{
"key": "gen_ai.system",
"value": {
"stringValue": "litellm"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "server.port",
"value": {
"intValue": "4002"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a10397-7f05-701f-abe9-6d45efbbf059"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a10397-7f05-701f-abe9-6d469e9ac27e"
}
},
{
"key": "model_request_parameters",
"value": {
"stringValue": "{\"function_tools\":[],\"native_tools\":[],\"tool_visibility\":{},\"revealed_tool_names\":[],\"deferred_capability_ids\":[],\"output_mode\":\"text\",\"output_object\":null,\"output_tools\":[],\"prompted_output_template\":null,\"allow_text_output\":true,\"allow_image_output\":false,\"instruction_parts\":null,\"thinking\":null}"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"Agent traces record an agent\u2019s actions and decisions over time.\"}],\"finish_reason\":\"stop\"}]"
}
},
{
"key": "logfire.json_schema",
"value": {
"stringValue": "{\"type\":\"object\",\"properties\":{\"gen_ai.input.messages\":{\"type\":\"array\"},\"gen_ai.output.messages\":{\"type\":\"array\"},\"model_request_parameters\":{\"type\":\"object\"}}}"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "15"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "48"
}
},
{
"key": "gen_ai.usage.details.accepted_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.details.audio_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.details.reasoning_tokens",
"value": {
"intValue": "27"
}
},
{
"key": "gen_ai.usage.details.rejected_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.response.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "operation.cost",
"value": {
"doubleValue": 2.55e-5
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "chatcmpl-EV1Ee6ZsIx2AYZLIIcwgTLCt8rYAp"
}
},
{
"key": "gen_ai.response.finish_reasons",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "stop"
}
]
}
}
}
],
"status": {},
"flags": 256
},
{
"traceId": "213790ddf7f01594d52d54818775b98d",
"spanId": "c820226f9e157926",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791061622536769000",
"endTimeUnixNano": "1791061625782963000",
"attributes": [
{
"key": "model_name",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "agent_name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a10397-7f05-701f-abe9-6d45efbbf059"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a10397-7f05-701f-abe9-6d469e9ac27e"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "logfire.msg",
"value": {
"stringValue": "research_agent run"
}
},
{
"key": "final_result",
"value": {
"stringValue": "Agent traces record an agent\u2019s actions and decisions over time."
}
},
{
"key": "gen_ai.aggregated_usage.input_tokens",
"value": {
"intValue": "15"
}
},
{
"key": "gen_ai.aggregated_usage.output_tokens",
"value": {
"intValue": "48"
}
},
{
"key": "gen_ai.aggregated_usage.details.accepted_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.aggregated_usage.details.audio_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.aggregated_usage.details.reasoning_tokens",
"value": {
"intValue": "27"
}
},
{
"key": "gen_ai.aggregated_usage.details.rejected_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "pydantic_ai.all_messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"Reply with one short sentence about agent traces.\"}]},{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"Agent traces record an agent\u2019s actions and decisions over time.\"}],\"finish_reason\":\"stop\"}]"
}
},
{
"key": "logfire.json_schema",
"value": {
"stringValue": "{\"type\":\"object\",\"properties\":{\"pydantic_ai.all_messages\":{\"type\":\"array\"},\"final_result\":{\"type\":\"object\"}}}"
}
}
],
"status": {},
"flags": 256
}
]
}
]
}
]
}

116
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "4fcc6fdf-bb26-4716-b9a0-0b0ac37a9e0f"
}
},
{
"key": "service.name",
"value": {
"stringValue": "pydantic-ai-simple"
"stringValue": "c7824b78-804b-4722-8c04-f6621505c8a8"
}
},
{
@ -38,10 +32,72 @@
"value": {
"stringValue": "0.65b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "70fd997379ef81db013f15d676cd7f31",
"spanId": "b58c9553116bade1",
"parentSpanId": "22f9ea473ed15dac",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061437813731000",
"endTimeUnixNano": "1791061440894014000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "3fc2877e-2f63-4526-bd0c-bfaa4f310152"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "pydantic-ai",
@ -49,13 +105,13 @@
},
"spans": [
{
"traceId": "7cc3e93f259ad31a906a564a9c2417c8",
"spanId": "aaf2942c7cf1704d",
"parentSpanId": "b0ff4929e310ba70",
"traceId": "70fd997379ef81db013f15d676cd7f31",
"spanId": "22f9ea473ed15dac",
"parentSpanId": "0b8d18eebfafe2fa",
"name": "chat openai/gpt-6-luna",
"kind": 3,
"startTimeUnixNano": "1791012714817527952",
"endTimeUnixNano": "1791012718673878573",
"startTimeUnixNano": "1791061437802112000",
"endTimeUnixNano": "1791061440910441000",
"attributes": [
{
"key": "gen_ai.operation.name",
@ -78,7 +134,7 @@
{
"key": "server.address",
"value": {
"stringValue": "host.docker.internal"
"stringValue": "localhost"
}
},
{
@ -102,13 +158,13 @@
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a100ad-392b-750b-9c31-255f744ad43a"
"stringValue": "01a10394-ad5c-738a-bf42-55814270d120"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a100ad-392b-750b-9c31-2560ada06282"
"stringValue": "01a10394-ad5c-738a-bf42-5582cd333c4e"
}
},
{
@ -126,7 +182,7 @@
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of an AI agent’s activity during a task. It can show the sequence of events, such as:\\n\\n1. The request or input the agent received \\n2. The actions it took, including tool calls \\n3. The results or observations it got back \\n4. The final response or outcome \\n\\nFor example: *“User asks for the weather → agent calls a weather service → receives the forecast → replies with it.”*\\n\\nTraces help people debug, evaluate, and understand an agent’s behavior. They don’t necessarily include the agent’s private reasoning; often they’re just a structured log of inputs, actions, and results.\"}],\"finish_reason\":\"stop\"}]"
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of what an AI agent did during a particular run. It often includes the agent\u2019s inputs and outputs, the steps it took, tool calls and their results, and any errors or timing information.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather tool.\\n3. Tool returns the forecast.\\n4. Agent replies to the user.\\n\\nTraces help developers debug, evaluate, and monitor agents. They don\u2019t necessarily include the model\u2019s private internal reasoning; they usually record observable events and outputs.\"}],\"finish_reason\":\"stop\"}]"
}
},
{
@ -144,7 +200,7 @@
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "228"
"intValue": "224"
}
},
{
@ -162,7 +218,7 @@
{
"key": "gen_ai.usage.details.reasoning_tokens",
"value": {
"intValue": "84"
"intValue": "102"
}
},
{
@ -180,13 +236,13 @@
{
"key": "operation.cost",
"value": {
"doubleValue": 0.0001152
"doubleValue": 0.0001132
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "chatcmpl-EUoVnXwnEJ35Q9wg5reUDTwXBeUZ2"
"stringValue": "chatcmpl-EV1BeXSXQ8VvyNzL3B0tyykPxE888"
}
},
{
@ -206,12 +262,12 @@
"flags": 256
},
{
"traceId": "7cc3e93f259ad31a906a564a9c2417c8",
"spanId": "b0ff4929e310ba70",
"traceId": "70fd997379ef81db013f15d676cd7f31",
"spanId": "0b8d18eebfafe2fa",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791012714810870859",
"endTimeUnixNano": "1791012718674832747",
"startTimeUnixNano": "1791061437793838000",
"endTimeUnixNano": "1791061440912912000",
"attributes": [
{
"key": "model_name",
@ -234,13 +290,13 @@
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a100ad-392b-750b-9c31-255f744ad43a"
"stringValue": "01a10394-ad5c-738a-bf42-55814270d120"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a100ad-392b-750b-9c31-2560ada06282"
"stringValue": "01a10394-ad5c-738a-bf42-5582cd333c4e"
}
},
{
@ -258,7 +314,7 @@
{
"key": "final_result",
"value": {
"stringValue": "An **agent trace** is a record of an AI agent’s activity during a task. It can show the sequence of events, such as:\n\n1. The request or input the agent received \n2. The actions it took, including tool calls \n3. The results or observations it got back \n4. The final response or outcome \n\nFor example: *“User asks for the weather → agent calls a weather service → receives the forecast → replies with it.”*\n\nTraces help people debug, evaluate, and understand an agent’s behavior. They don’t necessarily include the agent’s private reasoning; often they’re just a structured log of inputs, actions, and results."
"stringValue": "An **agent trace** is a record of what an AI agent did during a particular run. It often includes the agent\u2019s inputs and outputs, the steps it took, tool calls and their results, and any errors or timing information.\n\nFor example:\n\n1. User asks for the weather.\n2. Agent calls a weather tool.\n3. Tool returns the forecast.\n4. Agent replies to the user.\n\nTraces help developers debug, evaluate, and monitor agents. They don\u2019t necessarily include the model\u2019s private internal reasoning; they usually record observable events and outputs."
}
},
{
@ -270,7 +326,7 @@
{
"key": "gen_ai.aggregated_usage.output_tokens",
"value": {
"intValue": "228"
"intValue": "224"
}
},
{
@ -288,7 +344,7 @@
{
"key": "gen_ai.aggregated_usage.details.reasoning_tokens",
"value": {
"intValue": "84"
"intValue": "102"
}
},
{
@ -300,7 +356,7 @@
{
"key": "pydantic_ai.all_messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]},{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of an AI agent’s activity during a task. It can show the sequence of events, such as:\\n\\n1. The request or input the agent received \\n2. The actions it took, including tool calls \\n3. The results or observations it got back \\n4. The final response or outcome \\n\\nFor example: *“User asks for the weather → agent calls a weather service → receives the forecast → replies with it.”*\\n\\nTraces help people debug, evaluate, and understand an agent’s behavior. They don’t necessarily include the agent’s private reasoning; often they’re just a structured log of inputs, actions, and results.\"}],\"finish_reason\":\"stop\"}]"
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]},{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of what an AI agent did during a particular run. It often includes the agent\u2019s inputs and outputs, the steps it took, tool calls and their results, and any errors or timing information.\\n\\nFor example:\\n\\n1. User asks for the weather.\\n2. Agent calls a weather tool.\\n3. Tool returns the forecast.\\n4. Agent replies to the user.\\n\\nTraces help developers debug, evaluate, and monitor agents. They don\u2019t necessarily include the model\u2019s private internal reasoning; they usually record observable events and outputs.\"}],\"finish_reason\":\"stop\"}]"
}
},
{

383
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_stream.json vendored Normal file
View file

@ -0,0 +1,383 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.44.0"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "ab58f52e-4016-4af6-9591-cf695ecf7ad5"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.65b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "f3f8852c4f360b03ce2a36e6d3ed6f51",
"spanId": "a383ecf3f8beb7a6",
"parentSpanId": "ab941fc1b528a0aa",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061491440301000",
"endTimeUnixNano": "1791061494417800000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "94605f85-b0da-4596-8538-9072fce7119e"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "pydantic-ai",
"version": "2.53.0"
},
"spans": [
{
"traceId": "f3f8852c4f360b03ce2a36e6d3ed6f51",
"spanId": "ab941fc1b528a0aa",
"parentSpanId": "3d68d31cca0b440f",
"name": "chat openai/gpt-6-luna",
"kind": 3,
"startTimeUnixNano": "1791061491429076000",
"endTimeUnixNano": "1791061494419171000",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "chat"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "litellm"
}
},
{
"key": "gen_ai.system",
"value": {
"stringValue": "litellm"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "server.port",
"value": {
"intValue": "4002"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a10395-7eda-7547-aa1b-802d534ffa83"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a10395-7eda-7547-aa1b-802ea21e8858"
}
},
{
"key": "model_request_parameters",
"value": {
"stringValue": "{\"function_tools\":[],\"native_tools\":[],\"tool_visibility\":{},\"revealed_tool_names\":[],\"deferred_capability_ids\":[],\"output_mode\":\"text\",\"output_object\":null,\"output_tools\":[],\"prompted_output_template\":null,\"allow_text_output\":true,\"allow_image_output\":false,\"instruction_parts\":null,\"thinking\":null}"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a chronological record of an AI agent\u2019s activity while completing a task. It may include:\\n\\n- The task or input it received\\n- Actions it took, such as tool calls\\n- Results or observations it received\\n- Changes in state or decisions\\n- The final response or outcome\\n\\nFor example: *User asks for the weather \u2192 agent calls a weather API \u2192 API returns the forecast \u2192 agent summarizes it.*\\n\\nA trace helps people debug or evaluate an agent. It doesn\u2019t necessarily include the agent\u2019s private reasoning; often it records only observable steps and tool interactions.\"}],\"finish_reason\":\"stop\"}]"
}
},
{
"key": "logfire.json_schema",
"value": {
"stringValue": "{\"type\":\"object\",\"properties\":{\"gen_ai.input.messages\":{\"type\":\"array\"},\"gen_ai.output.messages\":{\"type\":\"array\"},\"model_request_parameters\":{\"type\":\"object\"}}}"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "12"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "221"
}
},
{
"key": "gen_ai.usage.details.accepted_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.details.audio_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.details.reasoning_tokens",
"value": {
"intValue": "93"
}
},
{
"key": "gen_ai.usage.details.rejected_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.response.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "operation.cost",
"value": {
"doubleValue": 0.0001117
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "chatcmpl-EV1CVbDkWDEsratBuf9bhvEG3Av51"
}
},
{
"key": "gen_ai.response.finish_reasons",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "stop"
}
]
}
}
},
{
"key": "gen_ai.client.operation.time_to_first_chunk",
"value": {
"doubleValue": 1.7067450829781592
}
}
],
"status": {},
"flags": 256
},
{
"traceId": "f3f8852c4f360b03ce2a36e6d3ed6f51",
"spanId": "3d68d31cca0b440f",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791061491422577000",
"endTimeUnixNano": "1791061494419566000",
"attributes": [
{
"key": "model_name",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "agent_name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.agent.call.id",
"value": {
"stringValue": "01a10395-7eda-7547-aa1b-802d534ffa83"
}
},
{
"key": "gen_ai.conversation.id",
"value": {
"stringValue": "01a10395-7eda-7547-aa1b-802ea21e8858"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "logfire.msg",
"value": {
"stringValue": "research_agent run"
}
},
{
"key": "final_result",
"value": {
"stringValue": "An **agent trace** is a chronological record of an AI agent\u2019s activity while completing a task. It may include:\n\n- The task or input it received\n- Actions it took, such as tool calls\n- Results or observations it received\n- Changes in state or decisions\n- The final response or outcome\n\nFor example: *User asks for the weather \u2192 agent calls a weather API \u2192 API returns the forecast \u2192 agent summarizes it.*\n\nA trace helps people debug or evaluate an agent. It doesn\u2019t necessarily include the agent\u2019s private reasoning; often it records only observable steps and tool interactions."
}
},
{
"key": "gen_ai.aggregated_usage.input_tokens",
"value": {
"intValue": "12"
}
},
{
"key": "gen_ai.aggregated_usage.output_tokens",
"value": {
"intValue": "221"
}
},
{
"key": "gen_ai.aggregated_usage.details.accepted_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.aggregated_usage.details.audio_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.aggregated_usage.details.reasoning_tokens",
"value": {
"intValue": "93"
}
},
{
"key": "gen_ai.aggregated_usage.details.rejected_prediction_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "pydantic_ai.all_messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]},{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a chronological record of an AI agent\u2019s activity while completing a task. It may include:\\n\\n- The task or input it received\\n- Actions it took, such as tool calls\\n- Results or observations it received\\n- Changes in state or decisions\\n- The final response or outcome\\n\\nFor example: *User asks for the weather \u2192 agent calls a weather API \u2192 API returns the forecast \u2192 agent summarizes it.*\\n\\nA trace helps people debug or evaluate an agent. It doesn\u2019t necessarily include the agent\u2019s private reasoning; often it records only observable steps and tool interactions.\"}],\"finish_reason\":\"stop\"}]"
}
},
{
"key": "logfire.json_schema",
"value": {
"stringValue": "{\"type\":\"object\",\"properties\":{\"pydantic_ai.all_messages\":{\"type\":\"array\"},\"final_result\":{\"type\":\"object\"}}}"
}
}
],
"status": {},
"flags": 256
}
]
}
]
}
]
}

610
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_swarm.json vendored
View file

File diff suppressed because it is too large Load diff

1695
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_swarm_stream.json vendored Normal file
View file

File diff suppressed because it is too large Load diff

752
litellm-rust/crates/traces/tests/fixtures/pydantic_ai_token_limit_swarm.json vendored Normal file
View file

File diff suppressed because one or more lines are too long

376
litellm-rust/crates/traces/tests/fixtures/strands_billed_failure.json vendored Normal file
View file

File diff suppressed because one or more lines are too long

410
litellm-rust/crates/traces/tests/fixtures/strands_retry.json vendored Normal file
View file

@ -0,0 +1,410 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "python"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "1.45.0"
}
},
{
"key": "service.instance.id",
"value": {
"stringValue": "aa1cf838-c2c4-4a73-b7fa-0526a8c668be"
}
},
{
"key": "telemetry.auto.version",
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "d1eb10a194f225edbe397b5d11775b00",
"spanId": "8364872d770021af",
"parentSpanId": "0c44072c55ef7e09",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061413860747000",
"endTimeUnixNano": "1791061414742658000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "503"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "cc203841-e4f8-42da-bd36-cdbe2bd9a4c0"
}
}
],
"status": {
"code": 2
},
"flags": 256
},
{
"traceId": "d1eb10a194f225edbe397b5d11775b00",
"spanId": "99aad174e55e03b0",
"parentSpanId": "0c44072c55ef7e09",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061415155273000",
"endTimeUnixNano": "1791061416293001000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "400e4cf8-4aa5-4c4a-b998-b6f9aa175b56"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "strands.telemetry.tracer"
},
"spans": [
{
"traceId": "d1eb10a194f225edbe397b5d11775b00",
"spanId": "0c44072c55ef7e09",
"parentSpanId": "dfd461361cc66975",
"name": "chat",
"kind": 1,
"startTimeUnixNano": "1791061413626174000",
"endTimeUnixNano": "1791061416293142000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T21:03:33.626175+00:00"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "chat"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "strands-agents"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\": \"assistant\", \"parts\": [{\"type\": \"text\", \"content\": \"Agent traces reveal the steps an AI takes to complete a task.\"}], \"finish_reason\": \"end_turn\"}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T21:03:36.293124+00:00"
}
},
{
"key": "gen_ai.usage.prompt_tokens",
"value": {
"intValue": "112"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "112"
}
},
{
"key": "gen_ai.usage.completion_tokens",
"value": {
"intValue": "16"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "16"
}
},
{
"key": "gen_ai.usage.total_tokens",
"value": {
"intValue": "128"
}
},
{
"key": "gen_ai.server.time_to_first_token",
"value": {
"intValue": "2564"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "d1eb10a194f225edbe397b5d11775b00",
"spanId": "dfd461361cc66975",
"parentSpanId": "de906be3407b1984",
"name": "execute_event_loop_cycle",
"kind": 1,
"startTimeUnixNano": "1791061413626094000",
"endTimeUnixNano": "1791061416293347000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T21:03:33.626095+00:00"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "execute_event_loop_cycle"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "strands-agents"
}
},
{
"key": "event_loop.cycle_id",
"value": {
"stringValue": "0cbf2094-36ac-4ef7-bcb9-ff068bb73db4"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T21:03:36.293340+00:00"
}
}
],
"status": {
"code": 1
},
"flags": 256
},
{
"traceId": "d1eb10a194f225edbe397b5d11775b00",
"spanId": "de906be3407b1984",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791061413625732000",
"endTimeUnixNano": "1791061416293449000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T21:03:33.625738+00:00"
}
},
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "strands-agents"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "research_agent"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\": \"assistant\", \"parts\": [{\"type\": \"text\", \"content\": \"Agent traces reveal the steps an AI takes to complete a task.\\n\"}], \"finish_reason\": \"end_turn\"}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T21:03:36.293440+00:00"
}
},
{
"key": "gen_ai.usage.prompt_tokens",
"value": {
"intValue": "112"
}
},
{
"key": "gen_ai.usage.completion_tokens",
"value": {
"intValue": "16"
}
},
{
"key": "gen_ai.usage.input_tokens",
"value": {
"intValue": "112"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "16"
}
},
{
"key": "gen_ai.usage.total_tokens",
"value": {
"intValue": "128"
}
},
{
"key": "gen_ai.usage.cache_read.input_tokens",
"value": {
"intValue": "0"
}
},
{
"key": "gen_ai.usage.cache_creation.input_tokens",
"value": {
"intValue": "0"
}
}
],
"status": {
"code": 1
},
"flags": 256
}
]
}
]
}
]
}

142
litellm-rust/crates/traces/tests/fixtures/strands_simple.json vendored
View file

@ -24,13 +24,7 @@
{
"key": "service.instance.id",
"value": {
"stringValue": "75522799-6b4e-4d3c-8fea-48e93bb7bff3"
}
},
{
"key": "service.name",
"value": {
"stringValue": "strands-simple"
"stringValue": "cef9613f-cfb6-4664-8e51-f3be7d834c7a"
}
},
{
@ -38,28 +32,90 @@
"value": {
"stringValue": "0.66b0"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:python3"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "d59ddcb97fb9bced94931df97e06d02f",
"spanId": "3d99b5070f22df12",
"parentSpanId": "3cb702edbce111e7",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061361049433000",
"endTimeUnixNano": "1791061363567498000",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "25cb4dbf-9676-4b83-a427-bb7e3655dcc3"
}
}
],
"status": {},
"flags": 256
}
]
},
{
"scope": {
"name": "strands.telemetry.tracer"
},
"spans": [
{
"traceId": "5afc8d017bfcdf56f0be86ad343f713f",
"spanId": "834876741d8e93ed",
"parentSpanId": "096b2d49390ffd61",
"traceId": "d59ddcb97fb9bced94931df97e06d02f",
"spanId": "3cb702edbce111e7",
"parentSpanId": "af8eeb87690dc466",
"name": "chat",
"kind": 1,
"startTimeUnixNano": "1791012992659355096",
"endTimeUnixNano": "1791012995125157327",
"startTimeUnixNano": "1791061360813404000",
"endTimeUnixNano": "1791061363567665000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T07:36:32.659356+00:00"
"stringValue": "2026-10-03T21:02:40.813405+00:00"
}
},
{
@ -83,25 +139,19 @@
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]}]"
}
},
{
"key": "gen_ai.response.id",
"value": {
"stringValue": "chatcmpl-EUoaGl9d9qhhE1TSGyh8uIZzZdnZU"
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"What is an agent trace?\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of an AI agent’s execution: the steps it took to handle a task, such as its reasoning or decisions, tool calls, responses from those tools, and any errors or retries.\\n\\nUnlike a chat transcript, which mainly shows messages, a trace can reveal the agent’s actions and how the task progressed. Traces are useful for debugging, evaluating performance, and understanding what happened during a run. The exact details recorded depend on the system.\"}],\"finish_reason\":\"end_turn\"}]"
"stringValue": "[{\"role\": \"assistant\", \"parts\": [{\"type\": \"text\", \"content\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It may include the agent\u2019s inputs and outputs, tool calls, tool results, timing, and errors.\\n\\nTraces help developers understand, debug, and evaluate an agent\u2019s behavior. They don\u2019t necessarily include the model\u2019s private chain-of-thought; they usually capture observable actions and results.\"}], \"finish_reason\": \"end_turn\"}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T07:36:35.125123+00:00"
"stringValue": "2026-10-03T21:02:43.567641+00:00"
}
},
{
@ -119,25 +169,25 @@
{
"key": "gen_ai.usage.completion_tokens",
"value": {
"intValue": "164"
"intValue": "158"
}
},
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "164"
"intValue": "158"
}
},
{
"key": "gen_ai.usage.total_tokens",
"value": {
"intValue": "273"
"intValue": "267"
}
},
{
"key": "gen_ai.server.time_to_first_token",
"value": {
"intValue": "1340"
"intValue": "1886"
}
}
],
@ -147,18 +197,18 @@
"flags": 256
},
{
"traceId": "5afc8d017bfcdf56f0be86ad343f713f",
"spanId": "096b2d49390ffd61",
"parentSpanId": "b0349b560f73a073",
"traceId": "d59ddcb97fb9bced94931df97e06d02f",
"spanId": "af8eeb87690dc466",
"parentSpanId": "14de27d8d8573810",
"name": "execute_event_loop_cycle",
"kind": 1,
"startTimeUnixNano": "1791012992659255386",
"endTimeUnixNano": "1791012995125388413",
"startTimeUnixNano": "1791061360813321000",
"endTimeUnixNano": "1791061363567872000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T07:36:32.659256+00:00"
"stringValue": "2026-10-03T21:02:40.813323+00:00"
}
},
{
@ -176,19 +226,19 @@
{
"key": "event_loop.cycle_id",
"value": {
"stringValue": "70cc05bb-79e5-4357-a4e6-a8be2e70da9d"
"stringValue": "000ad6d9-df30-4bf3-9884-3af13de6c1fc"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]}]"
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"What is an agent trace?\"}]}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T07:36:35.125376+00:00"
"stringValue": "2026-10-03T21:02:43.567865+00:00"
}
}
],
@ -198,17 +248,17 @@
"flags": 256
},
{
"traceId": "5afc8d017bfcdf56f0be86ad343f713f",
"spanId": "b0349b560f73a073",
"traceId": "d59ddcb97fb9bced94931df97e06d02f",
"spanId": "14de27d8d8573810",
"name": "invoke_agent research_agent",
"kind": 1,
"startTimeUnixNano": "1791012992659008009",
"endTimeUnixNano": "1791012995125532248",
"startTimeUnixNano": "1791061360812989000",
"endTimeUnixNano": "1791061363567984000",
"attributes": [
{
"key": "gen_ai.event.start_time",
"value": {
"stringValue": "2026-10-03T07:36:32.659013+00:00"
"stringValue": "2026-10-03T21:02:40.812995+00:00"
}
},
{
@ -238,19 +288,19 @@
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"What is an agent trace?\"}]}]"
"stringValue": "[{\"role\": \"user\", \"parts\": [{\"type\": \"text\", \"content\": \"What is an agent trace?\"}]}]"
}
},
{
"key": "gen_ai.output.messages",
"value": {
"stringValue": "[{\"role\":\"assistant\",\"parts\":[{\"type\":\"text\",\"content\":\"An **agent trace** is a record of an AI agent’s execution: the steps it took to handle a task, such as its reasoning or decisions, tool calls, responses from those tools, and any errors or retries.\\n\\nUnlike a chat transcript, which mainly shows messages, a trace can reveal the agent’s actions and how the task progressed. Traces are useful for debugging, evaluating performance, and understanding what happened during a run. The exact details recorded depend on the system.\\n\"}],\"finish_reason\":\"end_turn\"}]"
"stringValue": "[{\"role\": \"assistant\", \"parts\": [{\"type\": \"text\", \"content\": \"An **agent trace** is a record of what an AI agent did during a task, step by step. It may include the agent\u2019s inputs and outputs, tool calls, tool results, timing, and errors.\\n\\nTraces help developers understand, debug, and evaluate an agent\u2019s behavior. They don\u2019t necessarily include the model\u2019s private chain-of-thought; they usually capture observable actions and results.\\n\"}], \"finish_reason\": \"end_turn\"}]"
}
},
{
"key": "gen_ai.event.end_time",
"value": {
"stringValue": "2026-10-03T07:36:35.125518+00:00"
"stringValue": "2026-10-03T21:02:43.567976+00:00"
}
},
{
@ -262,7 +312,7 @@
{
"key": "gen_ai.usage.completion_tokens",
"value": {
"intValue": "164"
"intValue": "158"
}
},
{
@ -274,13 +324,13 @@
{
"key": "gen_ai.usage.output_tokens",
"value": {
"intValue": "164"
"intValue": "158"
}
},
{
"key": "gen_ai.usage.total_tokens",
"value": {
"intValue": "273"
"intValue": "267"
}
},
{

888
litellm-rust/crates/traces/tests/fixtures/strands_swarm.json vendored
View file

File diff suppressed because it is too large Load diff

397
litellm-rust/crates/traces/tests/fixtures/vercel_ai_sdk_billed_failure.json vendored Normal file
View file

@ -0,0 +1,397 @@
{
"resourceSpans": [
{
"resource": {
"attributes": [
{
"key": "host.name",
"value": {
"stringValue": "fixture-host"
}
},
{
"key": "host.arch",
"value": {
"stringValue": "arm64"
}
},
{
"key": "host.id",
"value": {
"stringValue": "00000000-0000-0000-0000-000000000000"
}
},
{
"key": "process.pid",
"value": {
"intValue": "85828"
}
},
{
"key": "process.executable.name",
"value": {
"stringValue": "node"
}
},
{
"key": "process.executable.path",
"value": {
"stringValue": "/home/user/.local/share/fnm/node-versions/v25.8.1/installation/bin/node"
}
},
{
"key": "process.command_args",
"value": {
"arrayValue": {
"values": [
{
"stringValue": "/home/user/.local/share/fnm/node-versions/v25.8.1/installation/bin/node"
},
{
"stringValue": "--env-file=.env"
},
{
"stringValue": "/home/user/dev/litellm-lens-example/vercel-ai-sdk-js/validate-attempts.ts"
},
{
"stringValue": "response-loss"
}
]
}
}
},
{
"key": "process.runtime.version",
"value": {
"stringValue": "25.8.1"
}
},
{
"key": "process.runtime.name",
"value": {
"stringValue": "nodejs"
}
},
{
"key": "process.runtime.description",
"value": {
"stringValue": "Node.js"
}
},
{
"key": "process.command",
"value": {
"stringValue": "/home/user/dev/litellm-lens-example/vercel-ai-sdk-js/validate-attempts.ts"
}
},
{
"key": "process.owner",
"value": {
"stringValue": "user"
}
},
{
"key": "service.name",
"value": {
"stringValue": "unknown_service:node"
}
},
{
"key": "telemetry.sdk.language",
"value": {
"stringValue": "nodejs"
}
},
{
"key": "telemetry.sdk.name",
"value": {
"stringValue": "opentelemetry"
}
},
{
"key": "telemetry.sdk.version",
"value": {
"stringValue": "2.11.0"
}
}
]
},
"scopeSpans": [
{
"scope": {
"name": "litellm.gateway.client"
},
"spans": [
{
"traceId": "da0164cd74393d84076fef5253c18441",
"spanId": "5a53a9fc62fda14c",
"parentSpanId": "3a9bf8b19ee9e8ce",
"name": "gateway.request",
"kind": 3,
"startTimeUnixNano": "1791061438583000000",
"endTimeUnixNano": "1791061439641048667",
"attributes": [
{
"key": "litellm.gateway.attempt",
"value": {
"boolValue": true
}
},
{
"key": "http.request.method",
"value": {
"stringValue": "POST"
}
},
{
"key": "url.full",
"value": {
"stringValue": "http://localhost:4002/v1/chat/completions"
}
},
{
"key": "server.address",
"value": {
"stringValue": "localhost"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
},
{
"key": "litellm.call_id",
"value": {
"stringValue": "5d4283ed-39e1-4530-9200-24585098215f"
}
},
{
"key": "error.type",
"value": {
"stringValue": "Error"
}
}
],
"status": {
"code": 2
},
"flags": 257
}
]
},
{
"scope": {
"name": "gen_ai"
},
"spans": [
{
"traceId": "da0164cd74393d84076fef5253c18441",
"spanId": "3a9bf8b19ee9e8ce",
"parentSpanId": "a1466bebc369ae63",
"name": "chat openai/gpt-6-luna",
"kind": 3,
"startTimeUnixNano": "1791061438576000000",
"endTimeUnixNano": "1791061439644540125",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "chat"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "litellm.chat"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
}
],
"events": [
{
"timeUnixNano": "1791061439644530875",
"name": "exception",
"attributes": [
{
"key": "exception.type",
"value": {
"stringValue": "AI_APICallError"
}
},
{
"key": "exception.message",
"value": {
"stringValue": "Failed to process successful response"
}
},
{
"key": "exception.stacktrace",
"value": {
"stringValue": "AI_APICallError: Failed to process successful response\n at Object.pull (file:///home/user/dev/litellm-lens-example/node_modules/@ai-sdk/provider-utils/dist/index.js:3711:13)\n at process.processTicksAndRejections (node:internal/process/task_queues:104:5)"
}
}
]
}
],
"status": {
"message": "Failed to process successful response",
"code": 2
},
"flags": 257
},
{
"traceId": "da0164cd74393d84076fef5253c18441",
"spanId": "a1466bebc369ae63",
"parentSpanId": "d0ad2c5d6c006eae",
"name": "step 1",
"kind": 1,
"startTimeUnixNano": "1791061438576000000",
"endTimeUnixNano": "1791061439644670417",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "agent_step"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
}
],
"events": [
{
"timeUnixNano": "1791061439644668209",
"name": "exception",
"attributes": [
{
"key": "exception.type",
"value": {
"stringValue": "AI_APICallError"
}
},
{
"key": "exception.message",
"value": {
"stringValue": "Failed to process successful response"
}
},
{
"key": "exception.stacktrace",
"value": {
"stringValue": "AI_APICallError: Failed to process successful response\n at Object.pull (file:///home/user/dev/litellm-lens-example/node_modules/@ai-sdk/provider-utils/dist/index.js:3711:13)\n at process.processTicksAndRejections (node:internal/process/task_queues:104:5)"
}
}
]
}
],
"status": {
"message": "Failed to process successful response",
"code": 2
},
"flags": 257
},
{
"traceId": "da0164cd74393d84076fef5253c18441",
"spanId": "d0ad2c5d6c006eae",
"name": "invoke_agent openai/gpt-6-luna",
"kind": 1,
"startTimeUnixNano": "1791061438572000000",
"endTimeUnixNano": "1791061439644129708",
"attributes": [
{
"key": "gen_ai.operation.name",
"value": {
"stringValue": "invoke_agent"
}
},
{
"key": "gen_ai.provider.name",
"value": {
"stringValue": "litellm.chat"
}
},
{
"key": "gen_ai.request.model",
"value": {
"stringValue": "openai/gpt-6-luna"
}
},
{
"key": "gen_ai.agent.name",
"value": {
"stringValue": "vercel_response-loss"
}
},
{
"key": "gen_ai.input.messages",
"value": {
"stringValue": "[{\"role\":\"user\",\"parts\":[{\"type\":\"text\",\"content\":\"Reply with one short sentence about agent traces.\"}]}]"
}
},
{
"key": "http.response.status_code",
"value": {
"intValue": "200"
}
}
],
"events": [
{
"timeUnixNano": "1791061439644125500",
"name": "exception",
"attributes": [
{
"key": "exception.type",
"value": {
"stringValue": "AI_APICallError"
}
},
{
"key": "exception.message",
"value": {
"stringValue": "Failed to process successful response"
}
},
{
"key": "exception.stacktrace",
"value": {
"stringValue": "AI_APICallError: Failed to process successful response\n at Object.pull (file:///home/user/dev/litellm-lens-example/node_modules/@ai-sdk/provider-utils/dist/index.js:3711:13)\n at process.processTicksAndRejections (node:internal/process/task_queues:104:5)"
}
}
]
}
],
"status": {
"message": "Failed to process successful response",
"code": 2
},
"flags": 257
}
]
}
]
}
]
}

Some files were not shown because too many files have changed in this diff Show more