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Merge remote-tracking branch 'origin' into litellm_org_usage

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yuneng-jiang 2025-11-22 12:25:47 -08:00
commit b397162379
42 changed files with 9127 additions and 5277 deletions
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2
.circleci/config.yml
View file

@ -1785,7 +1785,7 @@ jobs:
- audio_coverage
installing_litellm_on_python:
docker:
- image: circleci/python:3.8
- image: cimg/python:3.11
auth:
username: ${DOCKERHUB_USERNAME}
password: ${DOCKERHUB_PASSWORD}

3
ci_cd/security_scans.sh
View file

@ -69,10 +69,13 @@ run_grype_scans() {
# Allowlist of CVEs to be ignored in failure threshold/reporting
# - CVE-2025-8869: Not applicable on Python >=3.13 (PEP 706 implemented); pip fallback unused; no OS-level fix
# - GHSA-4xh5-x5gv-qwph: GitHub Security Advisory alias for CVE-2025-8869
# - GHSA-5j98-mcp5-4vw2: glob CLI command injection via -c/--cmd; glob CLI is not used in the litellm runtime image,
# and the vulnerable versions are pulled in only via OS-level/node tooling outside of our application code
ALLOWED_CVES=(
"CVE-2025-8869"
"GHSA-4xh5-x5gv-qwph"
"CVE-2025-8291" # no fix available as of Oct 11, 2025
"GHSA-5j98-mcp5-4vw2"
)
# Build JSON array of allowlisted CVE IDs for jq

28
docs/my-website/docs/observability/arize_integration.md
View file

@ -7,13 +7,6 @@ import TabItem from '@theme/TabItem';
AI Observability and Evaluation Platform
:::tip
This is community maintained, Please make an issue if you run into a bug
https://github.com/BerriAI/litellm
:::
<Image img={require('../../img/arize.png')} />
@ -53,7 +46,7 @@ response = litellm.completion(
)
```
### Using with LiteLLM Proxy
## Using with LiteLLM Proxy
1. Setup config.yaml
```yaml
@ -71,7 +64,7 @@ general_settings:
master_key: "sk-1234" # can also be set as an environment variable
environment_variables:
ARIZE_SPACE_KEY: "d0*****"
ARIZE_SPACE_ID: "d0*****"
ARIZE_API_KEY: "141a****"
ARIZE_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize GRPC api endpoint
ARIZE_HTTP_ENDPOINT: "https://otlp.arize.com/v1" # OPTIONAL - your custom arize HTTP api endpoint. Set either this or ARIZE_ENDPOINT or Neither (defaults to https://otlp.arize.com/v1 on grpc)
@ -96,7 +89,8 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
Supported parameters:
- `arize_api_key`
- `arize_space_key`
- `arize_space_key` *(deprecated, use `arize_space_id` instead)*
- `arize_space_id`
<Tabs>
<TabItem value="sdk" label="SDK">
@ -117,8 +111,8 @@ response = litellm.completion(
messages=[
{"role": "user", "content": "Hi 👋 - i'm openai"}
],
arize_api_key=os.getenv("ARIZE_SPACE_2_API_KEY"),
arize_space_key=os.getenv("ARIZE_SPACE_2_KEY"),
arize_api_key=os.getenv("ARIZE_API_KEY"),
arize_space_id=os.getenv("ARIZE_SPACE_ID"),
)
```
@ -159,8 +153,8 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}],
"arize_api_key": "ARIZE_SPACE_2_API_KEY",
"arize_space_key": "ARIZE_SPACE_2_KEY"
"arize_api_key": "ARIZE_API_KEY",
"arize_space_id": "ARIZE_SPACE_ID"
}'
```
</TabItem>
@ -183,8 +177,8 @@ response = client.chat.completions.create(
}
],
extra_body={
"arize_api_key": "ARIZE_SPACE_2_API_KEY",
"arize_space_key": "ARIZE_SPACE_2_KEY"
"arize_api_key": "ARIZE_API_KEY",
"arize_space_id": "ARIZE_SPACE_ID"
}
)
@ -199,5 +193,5 @@ print(response)
- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

17
docs/my-website/docs/projects/mini-swe-agent.md Normal file
View file

@ -0,0 +1,17 @@
# mini-swe-agent
**mini-swe-agent** The 100 line AI agent that solves GitHub issues & more.
Key features:
- Just 100 lines of Python - radically simple and hackable
- Uses bash only (no custom tools) for maximum flexibility
- Built on LiteLLM for model flexibility
- Comes with CLI and Python bindings
- Deployable anywhere: local, docker, podman, apptainer
Perfect for researchers, developers who want readable tools, and engineers who need easy deployment.
- [Website](https://mini-swe-agent.com/latest/)
- [GitHub](https://github.com/SWE-agent/mini-swe-agent)
- [Quick Start](https://mini-swe-agent.com/latest/quickstart/)
- [Documentation](https://mini-swe-agent.com/latest/)

104
docs/my-website/docs/providers/anthropic.md
View file

@ -45,10 +45,112 @@ Check this in code, [here](../completion/input.md#translated-openai-params)
:::info
Anthropic API fails requests when `max_tokens` are not passed. Due to this litellm passes `max_tokens=4096` when no `max_tokens` are passed.
**Notes:**
- Anthropic API fails requests when `max_tokens` are not passed. Due to this litellm passes `max_tokens=4096` when no `max_tokens` are passed.
- `response_format` is fully supported for Claude Sonnet 4.5 and Opus 4.1 models (see [Structured Outputs](#structured-outputs) section)
:::
## **Structured Outputs**
LiteLLM supports Anthropic's [structured outputs feature](https://platform.claude.com/docs/en/build-with-claude/structured-outputs) for Claude Sonnet 4.5 and Opus 4.1 models. When you use `response_format` with these models, LiteLLM automatically:
- Adds the required `structured-outputs-2025-11-13` beta header
- Transforms OpenAI's `response_format` to Anthropic's `output_format` format
### Supported Models
- `sonnet-4-5` or `sonnet-4.5` (all Sonnet 4.5 variants)
- `opus-4-1` or `opus-4.1` (all Opus 4.1 variants)
### Example Usage
<Tabs>
<TabItem value="sdk" label="LiteLLM SDK">
```python
from litellm import completion
response = completion(
model="claude-sonnet-4-5-20250929",
messages=[{"role": "user", "content": "What is the capital of France?"}],
response_format={
"type": "json_schema",
"json_schema": {
"name": "capital_response",
"strict": True,
"schema": {
"type": "object",
"properties": {
"country": {"type": "string"},
"capital": {"type": "string"}
},
"required": ["country", "capital"],
"additionalProperties": False
}
}
}
)
print(response.choices[0].message.content)
# Output: {"country": "France", "capital": "Paris"}
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
1. Setup config.yaml
```yaml
model_list:
- model_name: claude-sonnet-4-5
litellm_params:
model: anthropic/claude-sonnet-4-5-20250929
api_key: os.environ/ANTHROPIC_API_KEY
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl http://0.0.0.0:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $LITELLM_KEY" \
-d '{
"model": "claude-sonnet-4-5",
"messages": [{"role": "user", "content": "What is the capital of France?"}],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "capital_response",
"strict": true,
"schema": {
"type": "object",
"properties": {
"country": {"type": "string"},
"capital": {"type": "string"}
},
"required": ["country", "capital"],
"additionalProperties": false
}
}
}
}'
```
</TabItem>
</Tabs>
:::info
When using structured outputs with supported models, LiteLLM automatically:
- Converts OpenAI's `response_format` to Anthropic's `output_schema`
- Adds the `anthropic-beta: structured-outputs-2025-11-13` header
- Creates a tool with the schema and forces the model to use it
:::
## API Keys
```python

176
docs/my-website/package-lock.json generated
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@ -7836,12 +7836,12 @@
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@ -8141,54 +8141,54 @@
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},
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@ -8196,18 +8196,18 @@
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"proxy-compare": "3.0.1"
}
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"dependencies": {
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@ -8220,9 +8220,9 @@
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@ -8838,9 +8838,9 @@
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"license": "Apache-2.0",
"bin": {
"baseline-browser-mapping": "dist/cli.js"
@ -9227,9 +9227,9 @@
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{
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@ -9907,9 +9907,9 @@
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"hasInstallScript": true,
"license": "MIT",
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@ -9918,12 +9918,12 @@
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},
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@ -9931,9 +9931,9 @@
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"license": "MIT",
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@ -10436,9 +10436,9 @@
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"license": "MIT"
},
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@ -11378,9 +11378,9 @@
"license": "MIT"
},
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@ -13074,9 +13074,9 @@
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@ -13420,9 +13420,9 @@
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3
docs/my-website/package.json
View file

@ -58,6 +58,7 @@
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"mermaid": ">=11.10.0",
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}
}

1
docs/my-website/sidebars.js
View file

@ -789,6 +789,7 @@ const sidebars = {
},
items: [
"projects/smolagents",
"projects/mini-swe-agent",
"projects/Docq.AI",
"projects/PDL",
"projects/OpenInterpreter",

378
litellm-js/spend-logs/package-lock.json generated
View file

@ -14,426 +14,509 @@
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"@esbuild/linux-ppc64": "0.19.12",
"@esbuild/linux-riscv64": "0.19.12",
"@esbuild/linux-s390x": "0.19.12",
"@esbuild/linux-x64": "0.19.12",
"@esbuild/netbsd-x64": "0.19.12",
"@esbuild/openbsd-x64": "0.19.12",
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"@esbuild/linux-x64": "0.25.12",
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},
"node_modules/fsevents": {
@ -442,6 +525,7 @@
"integrity": "sha512-5xoDfX+fL7faATnagmWPpbFtwh/R77WmMMqqHGS65C3vvB0YHrgF+B1YmZ3441tMj5n63k0212XNoJwzlhffQw==",
"dev": true,
"hasInstallScript": true,
"license": "MIT",
"optional": true,
"os": [
"darwin"
@ -451,10 +535,11 @@
}
},
"node_modules/get-tsconfig": {
"version": "4.7.3",
"resolved": "https://registry.npmjs.org/get-tsconfig/-/get-tsconfig-4.7.3.tgz",
"integrity": "sha512-ZvkrzoUA0PQZM6fy6+/Hce561s+faD1rsNwhnO5FelNjyy7EMGJ3Rz1AQ8GYDWjhRs/7dBLOEJvhK8MiEJOAFg==",
"version": "4.13.0",
"resolved": "https://registry.npmjs.org/get-tsconfig/-/get-tsconfig-4.13.0.tgz",
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"dev": true,
"license": "MIT",
"dependencies": {
"resolve-pkg-maps": "^1.0.0"
},
@ -463,9 +548,9 @@
}
},
"node_modules/hono": {
"version": "4.10.3",
"resolved": "https://registry.npmjs.org/hono/-/hono-4.10.3.tgz",
"integrity": "sha512-2LOYWUbnhdxdL8MNbNg9XZig6k+cZXm5IjHn2Aviv7honhBMOHb+jxrKIeJRZJRmn+htUCKhaicxwXuUDlchRA==",
"version": "4.10.6",
"resolved": "https://registry.npmjs.org/hono/-/hono-4.10.6.tgz",
"integrity": "sha512-BIdolzGpDO9MQ4nu3AUuDwHZZ+KViNm+EZ75Ae55eMXMqLVhDFqEMXxtUe9Qh8hjL+pIna/frs2j6Y2yD5Ua/g==",
"license": "MIT",
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"node": ">=16.9.0"
@ -476,18 +561,20 @@
"resolved": "https://registry.npmjs.org/resolve-pkg-maps/-/resolve-pkg-maps-1.0.0.tgz",
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"dev": true,
"license": "MIT",
"funding": {
"url": "https://github.com/privatenumber/resolve-pkg-maps?sponsor=1"
}
},
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"integrity": "sha512-8d6VuibXHtlN5E3zFkgY8u4DX7Y3Z27zvvPKVmLon/D4AjuKzarkUBTLDBgj9iTQ0hg5xM7c/mYiRVM+HETf0g==",
"version": "4.20.6",
"resolved": "https://registry.npmjs.org/tsx/-/tsx-4.20.6.tgz",
"integrity": "sha512-ytQKuwgmrrkDTFP4LjR0ToE2nqgy886GpvRSpU0JAnrdBYppuY5rLkRUYPU1yCryb24SsKBTL/hlDQAEFVwtZg==",
"dev": true,
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"dependencies": {
"esbuild": "~0.19.10",
"get-tsconfig": "^4.7.2"
"esbuild": "~0.25.0",
"get-tsconfig": "^4.7.5"
},
"bin": {
"tsx": "dist/cli.mjs"
@ -500,10 +587,11 @@
}
},
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"dev": true,
"license": "MIT"
}
}
}

3
litellm-js/spend-logs/package.json
View file

@ -9,5 +9,8 @@
"devDependencies": {
"@types/node": "^20.11.17",
"tsx": "^4.7.1"
},
"overrides": {
"glob": ">=11.1.0"
}
}

6
litellm/integrations/arize/arize.py
View file

@ -48,6 +48,7 @@ class ArizeLogger(OpenTelemetry):
Raises:
ValueError: If required environment variables are not set.
"""
space_id = os.environ.get("ARIZE_SPACE_ID")
space_key = os.environ.get("ARIZE_SPACE_KEY")
api_key = os.environ.get("ARIZE_API_KEY")
@ -68,6 +69,7 @@ class ArizeLogger(OpenTelemetry):
endpoint = "https://otlp.arize.com/v1"
return ArizeConfig(
space_id=space_id,
space_key=space_key,
api_key=api_key,
protocol=protocol,
@ -115,10 +117,10 @@ class ArizeLogger(OpenTelemetry):
try:
config = self.get_arize_config()
if not config.space_key:
if not config.space_id or not config.space_key:
return {
"status": "unhealthy",
"error_message": "ARIZE_SPACE_KEY environment variable not set",
"error_message": "ARIZE_SPACE_ID environment variable not set",
}
if not config.api_key:

7
litellm/integrations/arize/arize_phoenix.py
View file

@ -89,6 +89,11 @@ class ArizePhoenixLogger:
"PHOENIX_API_KEY must be set when using Phoenix Cloud (app.phoenix.arize.com)."
)
project_name = os.environ.get("PHOENIX_PROJECT_NAME", "litellm-project")
return ArizePhoenixConfig(
otlp_auth_headers=otlp_auth_headers, protocol=protocol, endpoint=endpoint
otlp_auth_headers=otlp_auth_headers,
protocol=protocol,
endpoint=endpoint,
project_name=project_name,
)

6
litellm/integrations/callback_configs.json
View file

@ -11,10 +11,10 @@
"description": "Arize API key for authentication",
"required": true
},
"arize_space_key": {
"arize_space_id": {
"type": "password",
"ui_name": "Space Key",
"description": "Arize Space key to identify your workspace",
"ui_name": "Space ID",
"description": "Arize Space ID to identify your workspace",
"required": true
}
},

12
litellm/integrations/opentelemetry.py
View file

@ -515,9 +515,9 @@ class OpenTelemetry(CustomLogger):
def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]:
"""Extract dynamic headers from kwargs if available."""
standard_callback_dynamic_params: Optional[
StandardCallbackDynamicParams
] = kwargs.get("standard_callback_dynamic_params")
standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = (
kwargs.get("standard_callback_dynamic_params")
)
if not standard_callback_dynamic_params:
return None
@ -1078,7 +1078,9 @@ class OpenTelemetry(CustomLogger):
span=span, key="hidden_params", value=safe_dumps(hidden_params)
)
# Cost breakdown tracking
cost_breakdown: Optional[CostBreakdown] = standard_logging_payload.get("cost_breakdown")
cost_breakdown: Optional[CostBreakdown] = standard_logging_payload.get(
"cost_breakdown"
)
if cost_breakdown:
for key, value in cost_breakdown.items():
if value is not None:
@ -1773,7 +1775,7 @@ class OpenTelemetry(CustomLogger):
"""
Create a span for the received proxy server request.
"""
# don't create proxy parent spans for arize phoenix
# don't create proxy parent spans for arize phoenix - [TODO]: figure out a better way to handle this
if self.callback_name == "arize_phoenix":
return None

17
litellm/litellm_core_utils/litellm_logging.py
View file

@ -690,7 +690,7 @@ class Logging(LiteLLMLoggingBaseClass):
except Exception:
# If check fails, continue to next logger
continue
return None
def get_custom_logger_for_prompt_management(
@ -3533,7 +3533,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
)
os.environ["OTEL_EXPORTER_OTLP_TRACES_HEADERS"] = (
f"space_id={arize_config.space_key},api_key={arize_config.api_key}"
f"space_id={arize_config.space_key or arize_config.space_id},api_key={arize_config.api_key}"
)
for callback in _in_memory_loggers:
if (
@ -3556,6 +3556,17 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
endpoint=arize_phoenix_config.endpoint,
headers=arize_phoenix_config.otlp_auth_headers,
)
if arize_phoenix_config.project_name:
existing_attrs = os.environ.get("OTEL_RESOURCE_ATTRIBUTES", "")
# Add openinference.project.name attribute
if existing_attrs:
os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
f"{existing_attrs},openinference.project.name={arize_phoenix_config.project_name}"
)
else:
os.environ["OTEL_RESOURCE_ATTRIBUTES"] = (
f"openinference.project.name={arize_phoenix_config.project_name}"
)
# Set Phoenix project name from environment variable
phoenix_project_name = os.environ.get("PHOENIX_PROJECT_NAME", None)
@ -3964,8 +3975,6 @@ def get_custom_logger_compatible_class( # noqa: PLR0915
if isinstance(callback, OpenTelemetry):
return callback
elif logging_integration == "arize":
if "ARIZE_SPACE_KEY" not in os.environ:
raise ValueError("ARIZE_SPACE_KEY not found in environment variables")
if "ARIZE_API_KEY" not in os.environ:
raise ValueError("ARIZE_API_KEY not found in environment variables")
for callback in _in_memory_loggers:

91
litellm/llms/anthropic/chat/transformation.py
View file

@ -30,6 +30,7 @@ from litellm.types.llms.anthropic import (
AnthropicMcpServerTool,
AnthropicMessagesTool,
AnthropicMessagesToolChoice,
AnthropicOutputSchema,
AnthropicSystemMessageContent,
AnthropicThinkingParam,
AnthropicWebSearchTool,
@ -129,7 +130,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
"parallel_tool_calls",
"response_format",
"user",
"web_search_options"
"web_search_options",
]
if "claude-3-7-sonnet" in model or supports_reasoning(
@ -384,6 +385,32 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
else:
raise ValueError(f"Unmapped reasoning effort: {reasoning_effort}")
def _extract_json_schema_from_response_format(
self, value: Optional[dict]
) -> Optional[dict]:
if value is None:
return None
json_schema: Optional[dict] = None
if "response_schema" in value:
json_schema = value["response_schema"]
elif "json_schema" in value:
json_schema = value["json_schema"]["schema"]
return json_schema
def map_response_format_to_anthropic_output_format(
self, value: Optional[dict]
) -> Optional[AnthropicOutputSchema]:
json_schema: Optional[dict] = self._extract_json_schema_from_response_format(
value
)
if json_schema is None:
return None
return AnthropicOutputSchema(
type="json_schema",
schema=json_schema,
)
def map_response_format_to_anthropic_tool(
self, value: Optional[dict], optional_params: dict, is_thinking_enabled: bool
) -> Optional[AnthropicMessagesTool]:
@ -393,11 +420,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
): # value is a no-op
return None
json_schema: Optional[dict] = None
if "response_schema" in value:
json_schema = value["response_schema"]
elif "json_schema" in value:
json_schema = value["json_schema"]["schema"]
json_schema: Optional[dict] = self._extract_json_schema_from_response_format(
value
)
if json_schema is None:
return None
"""
When using tools in this way: - https://docs.anthropic.com/en/docs/build-with-claude/tool-use#json-mode
- You usually want to provide a single tool
@ -487,18 +514,37 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
if param == "top_p":
optional_params["top_p"] = value
if param == "response_format" and isinstance(value, dict):
_tool = self.map_response_format_to_anthropic_tool(
value, optional_params, is_thinking_enabled
)
if _tool is None:
continue
if not is_thinking_enabled:
_tool_choice = {"name": RESPONSE_FORMAT_TOOL_NAME, "type": "tool"}
optional_params["tool_choice"] = _tool_choice
if any(
substring in model
for substring in {
"sonnet-4.5",
"sonnet-4-5",
"opus-4.1",
"opus-4-1",
}
):
_output_format = (
self.map_response_format_to_anthropic_output_format(value)
)
if _output_format is not None:
optional_params["output_format"] = _output_format
else:
_tool = self.map_response_format_to_anthropic_tool(
value, optional_params, is_thinking_enabled
)
if _tool is None:
continue
if not is_thinking_enabled:
_tool_choice = {
"name": RESPONSE_FORMAT_TOOL_NAME,
"type": "tool",
}
optional_params["tool_choice"] = _tool_choice
optional_params = self._add_tools_to_optional_params(
optional_params=optional_params, tools=[_tool]
)
optional_params["json_mode"] = True
optional_params = self._add_tools_to_optional_params(
optional_params=optional_params, tools=[_tool]
)
if (
param == "user"
and value is not None
@ -660,6 +706,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
self, headers: dict, optional_params: dict
) -> dict:
"""Update headers with optional anthropic beta."""
_tools = optional_params.get("tools", [])
for tool in _tools:
if tool.get("type", None) and tool.get("type").startswith(
@ -671,11 +718,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
elif tool.get("type", None) and tool.get("type").startswith(
ANTHROPIC_HOSTED_TOOLS.MEMORY.value
):
headers[
"anthropic-beta"
] = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
headers["anthropic-beta"] = (
ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value
)
if optional_params.get("context_management") is not None:
self._ensure_context_management_beta_header(headers)
if optional_params.get("output_format") is not None:
headers["anthropic-beta"] = (
ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value
)
return headers
def transform_request(

16
litellm/llms/watsonx/chat/transformation.py
View file

@ -142,7 +142,13 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
elif WatsonXModelPattern.IBM_MISTRAL.value in model:
return mistral_instruct_pt(messages=messages)
elif WatsonXModelPattern.GPT_OSS.value in model:
hf_model = model.split("watsonx/")[-1] if "watsonx/" in model else model
# Extract HuggingFace model name from watsonx/ or watsonx_text/ prefix
if "watsonx/" in model:
hf_model = model.split("watsonx/")[-1]
elif "watsonx_text/" in model:
hf_model = model.split("watsonx_text/")[-1]
else:
hf_model = model
try:
return hf_template_fn(model=hf_model, messages=messages)
except Exception:
@ -188,7 +194,13 @@ class IBMWatsonXChatConfig(IBMWatsonXMixin, OpenAIGPTConfig):
elif WatsonXModelPattern.IBM_MISTRAL.value in model:
return mistral_instruct_pt(messages=messages)
elif WatsonXModelPattern.GPT_OSS.value in model:
hf_model = model.split("watsonx/")[-1] if "watsonx/" in model else model
# Extract HuggingFace model name from watsonx/ or watsonx_text/ prefix
if "watsonx/" in model:
hf_model = model.split("watsonx/")[-1]
elif "watsonx_text/" in model:
hf_model = model.split("watsonx_text/")[-1]
else:
hf_model = model
try:
# Use sync if cached, async if not
if hf_model in litellm.known_tokenizer_config:

8
litellm/model_prices_and_context_window_backup.json
View file

@ -5611,8 +5611,12 @@
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
@ -5752,8 +5756,12 @@
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true

55
litellm/proxy/_experimental/mcp_server/server.py
View file

@ -1,11 +1,12 @@
"""
LiteLLM MCP Server Routes
"""
# pyright: reportInvalidTypeForm=false, reportArgumentType=false, reportOptionalCall=false
import asyncio
import contextlib
from datetime import datetime
from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, List, Optional, Tuple, Union
from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union
from fastapi import FastAPI, HTTPException
from pydantic import AnyUrl, ConfigDict
@ -45,17 +46,17 @@ try:
except ImportError as e:
verbose_logger.debug(f"MCP module not found: {e}")
MCP_AVAILABLE = False
# For type checking only - these will never be accessed at runtime when MCP is unavailable
# because all code using them is guarded by `if MCP_AVAILABLE:`
if TYPE_CHECKING:
from typing import Any as BlobResourceContents # type: ignore
from typing import Any as GetPromptResult
from typing import Any as ReadResourceContents
from typing import Any as ReadResourceResult
from typing import Any as Resource
from typing import Any as ResourceTemplate
from typing import Any as Server
from typing import Any as TextResourceContents
# When MCP is not available, we set these to None at module level
# All code using these types is inside `if MCP_AVAILABLE:` blocks
# so they will never be accessed at runtime
BlobResourceContents = None # type: ignore
GetPromptResult = None # type: ignore
ReadResourceContents = None # type: ignore
ReadResourceResult = None # type: ignore
Resource = None # type: ignore
ResourceTemplate = None # type: ignore
Server = None # type: ignore
TextResourceContents = None # type: ignore
# Global variables to track initialization
@ -1191,11 +1192,11 @@ if MCP_AVAILABLE:
allowed_mcp_servers: List[MCPServer] = []
for allowed_mcp_server_id in allowed_mcp_server_ids:
mcp_server = global_mcp_server_manager.get_mcp_server_by_id(
allowed_server = global_mcp_server_manager.get_mcp_server_by_id(
allowed_mcp_server_id
)
if mcp_server is not None:
allowed_mcp_servers.append(mcp_server)
if allowed_server is not None:
allowed_mcp_servers.append(allowed_server)
allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names(
mcp_servers=mcp_servers,
@ -1236,9 +1237,9 @@ if MCP_AVAILABLE:
"litellm_logging_obj", None
)
if litellm_logging_obj:
litellm_logging_obj.model_call_details[
"mcp_tool_call_metadata"
] = standard_logging_mcp_tool_call
litellm_logging_obj.model_call_details["mcp_tool_call_metadata"] = (
standard_logging_mcp_tool_call
)
litellm_logging_obj.model = f"MCP: {name}"
# Check if tool exists in local registry first (for OpenAPI-based tools)
# These tools are registered with their prefixed names
@ -1739,16 +1740,14 @@ if MCP_AVAILABLE:
)
auth_context_var.set(auth_user)
def get_auth_context() -> (
Tuple[
Optional[UserAPIKeyAuth],
Optional[str],
Optional[List[str]],
Optional[Dict[str, Dict[str, str]]],
Optional[Dict[str, str]],
Optional[Dict[str, str]],
]
):
def get_auth_context() -> Tuple[
Optional[UserAPIKeyAuth],
Optional[str],
Optional[List[str]],
Optional[Dict[str, Dict[str, str]]],
Optional[Dict[str, str]],
Optional[Dict[str, str]],
]:
"""
Get the UserAPIKeyAuth from the auth context variable.

30
litellm/proxy/_new_secret_config.yaml
View file

@ -11,33 +11,5 @@ model_list:
model: openai/gpt-4o-mini-transcribe
api_key: os.environ/OPENAI_API_KEY
agent_list:
- agent_name: my_custom_agent
agent_card_params:
protocolVersion: '1.0'
name: 'Hello World Agent'
description: Just a hello world agent
url: http://localhost:9999/
version: 1.0.0
defaultInputModes: ['text']
defaultOutputModes: ['text']
capabilities:
streaming: true
skills:
- id: 'hello_world'
name: 'Returns hello world'
description: 'just returns hello world'
tags: ['hello world']
examples: ['hi', 'hello world']
supportsAuthenticatedExtendedCard: true
litellm_params:
make_public: true
litellm_settings:
callbacks: ["prometheus"]
mcp_servers:
# HTTP Streamable Server
deepwiki_mcp_1234:
url: "https://mcp.deepwiki.com/mcp"
server_id: deepwiki_mcp_id
callbacks: ["arize"]

1
litellm/types/integrations/arize.py
View file

@ -9,6 +9,7 @@ else:
class ArizeConfig(BaseModel):
space_id: Optional[str] = None
space_key: Optional[str] = None
api_key: Optional[str] = None
protocol: Protocol

1
litellm/types/integrations/arize_phoenix.py
View file

@ -9,3 +9,4 @@ class ArizePhoenixConfig(BaseModel):
otlp_auth_headers: Optional[str] = None
protocol: Protocol
endpoint: str
project_name: Optional[str] = None

10
litellm/types/llms/anthropic.py
View file

@ -6,8 +6,8 @@ from typing_extensions import Literal, Required, TypedDict
from .openai import (
ChatCompletionCachedContent,
ChatCompletionThinkingBlock,
ChatCompletionRedactedThinkingBlock,
ChatCompletionThinkingBlock,
)
@ -31,6 +31,11 @@ AnthropicInputSchema = TypedDict(
)
class AnthropicOutputSchema(TypedDict, total=False):
type: Required[Literal["json_schema"]]
schema: Required[dict]
class AnthropicMessagesTool(TypedDict, total=False):
name: Required[str]
description: str
@ -463,7 +468,7 @@ class AnthropicResponseContentBlockToolUse(BaseModel):
name: str
input: dict
provider_specific_fields: Optional[Dict[str, Any]] = None
class Config:
extra = "allow" # Allow provider_specific_fields
@ -559,3 +564,4 @@ class ANTHROPIC_BETA_HEADER_VALUES(str, Enum):
WEB_FETCH_2025_09_10 = "web-fetch-2025-09-10"
CONTEXT_MANAGEMENT_2025_06_27 = "context-management-2025-06-27"
STRUCTURED_OUTPUT_2025_09_25 = "structured-outputs-2025-11-13"

8
model_prices_and_context_window.json
View file

@ -5611,8 +5611,12 @@
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
@ -5752,8 +5756,12 @@
"max_tokens": 4096,
"mode": "chat",
"output_cost_per_token": 1.65e-05,
"supports_assistant_prefill": true,
"supports_computer_use": true,
"supports_function_calling": true,
"supports_pdf_input": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true

589
package-lock.json generated
View file

@ -39,9 +39,9 @@
}
},
"node_modules/@babel/compat-data": {
"version": "7.28.4",
"resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.4.tgz",
"integrity": "sha512-YsmSKC29MJwf0gF8Rjjrg5LQCmyh+j/nD8/eP7f+BeoQTKYqs9RoWbjGOdy0+1Ekr68RJZMUOPVQaQisnIo4Rw==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.5.tgz",
"integrity": "sha512-6uFXyCayocRbqhZOB+6XcuZbkMNimwfVGFji8CTZnCzOHVGvDqzvitu1re2AU5LROliz7eQPhB8CpAMvnx9EjA==",
"dev": true,
"license": "MIT",
"engines": {
@ -49,21 +49,21 @@
}
},
"node_modules/@babel/core": {
"version": "7.28.4",
"resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.4.tgz",
"integrity": "sha512-2BCOP7TN8M+gVDj7/ht3hsaO/B/n5oDbiAyyvnRlNOs+u1o+JWNYTQrmpuNp1/Wq2gcFrI01JAW+paEKDMx/CA==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/core/-/core-7.28.5.tgz",
"integrity": "sha512-e7jT4DxYvIDLk1ZHmU/m/mB19rex9sv0c2ftBtjSBv+kVM/902eh0fINUzD7UwLLNR+jU585GxUJ8/EBfAM5fw==",
"dev": true,
"license": "MIT",
"dependencies": {
"@babel/code-frame": "^7.27.1",
"@babel/generator": "^7.28.3",
"@babel/generator": "^7.28.5",
"@babel/helper-compilation-targets": "^7.27.2",
"@babel/helper-module-transforms": "^7.28.3",
"@babel/helpers": "^7.28.4",
"@babel/parser": "^7.28.4",
"@babel/parser": "^7.28.5",
"@babel/template": "^7.27.2",
"@babel/traverse": "^7.28.4",
"@babel/types": "^7.28.4",
"@babel/traverse": "^7.28.5",
"@babel/types": "^7.28.5",
"@jridgewell/remapping": "^2.3.5",
"convert-source-map": "^2.0.0",
"debug": "^4.1.0",
@ -80,14 +80,14 @@
}
},
"node_modules/@babel/generator": {
"version": "7.28.3",
"resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.3.tgz",
"integrity": "sha512-3lSpxGgvnmZznmBkCRnVREPUFJv2wrv9iAoFDvADJc0ypmdOxdUtcLeBgBJ6zE0PMeTKnxeQzyk0xTBq4Ep7zw==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.28.5.tgz",
"integrity": "sha512-3EwLFhZ38J4VyIP6WNtt2kUdW9dokXA9Cr4IVIFHuCpZ3H8/YFOl5JjZHisrn1fATPBmKKqXzDFvh9fUwHz6CQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"@babel/parser": "^7.28.3",
"@babel/types": "^7.28.2",
"@babel/parser": "^7.28.5",
"@babel/types": "^7.28.5",
"@jridgewell/gen-mapping": "^0.3.12",
"@jridgewell/trace-mapping": "^0.3.28",
"jsesc": "^3.0.2"
@ -176,9 +176,9 @@
}
},
"node_modules/@babel/helper-validator-identifier": {
"version": "7.27.1",
"resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.27.1.tgz",
"integrity": "sha512-D2hP9eA+Sqx1kBZgzxZh0y1trbuU+JoDkiEwqhQ36nodYqJwyEIhPSdMNd7lOm/4io72luTPWH20Yda0xOuUow==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
"integrity": "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q==",
"dev": true,
"license": "MIT",
"engines": {
@ -210,13 +210,13 @@
}
},
"node_modules/@babel/parser": {
"version": "7.28.4",
"resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.4.tgz",
"integrity": "sha512-yZbBqeM6TkpP9du/I2pUZnJsRMGGvOuIrhjzC1AwHwW+6he4mni6Bp/m8ijn0iOuZuPI2BfkCoSRunpyjnrQKg==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.28.5.tgz",
"integrity": "sha512-KKBU1VGYR7ORr3At5HAtUQ+TV3SzRCXmA/8OdDZiLDBIZxVyzXuztPjfLd3BV1PRAQGCMWWSHYhL0F8d5uHBDQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"@babel/types": "^7.28.4"
"@babel/types": "^7.28.5"
},
"bin": {
"parser": "bin/babel-parser.js"
@ -490,18 +490,18 @@
}
},
"node_modules/@babel/traverse": {
"version": "7.28.4",
"resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.4.tgz",
"integrity": "sha512-YEzuboP2qvQavAcjgQNVgsvHIDv6ZpwXvcvjmyySP2DIMuByS/6ioU5G9pYrWHM6T2YDfc7xga9iNzYOs12CFQ==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.28.5.tgz",
"integrity": "sha512-TCCj4t55U90khlYkVV/0TfkJkAkUg3jZFA3Neb7unZT8CPok7iiRfaX0F+WnqWqt7OxhOn0uBKXCw4lbL8W0aQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"@babel/code-frame": "^7.27.1",
"@babel/generator": "^7.28.3",
"@babel/generator": "^7.28.5",
"@babel/helper-globals": "^7.28.0",
"@babel/parser": "^7.28.4",
"@babel/parser": "^7.28.5",
"@babel/template": "^7.27.2",
"@babel/types": "^7.28.4",
"@babel/types": "^7.28.5",
"debug": "^4.3.1"
},
"engines": {
@ -509,14 +509,14 @@
}
},
"node_modules/@babel/types": {
"version": "7.28.4",
"resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.4.tgz",
"integrity": "sha512-bkFqkLhh3pMBUQQkpVgWDWq/lqzc2678eUyDlTBhRqhCHFguYYGM0Efga7tYk4TogG/3x0EEl66/OQ+WGbWB/Q==",
"version": "7.28.5",
"resolved": "https://registry.npmjs.org/@babel/types/-/types-7.28.5.tgz",
"integrity": "sha512-qQ5m48eI/MFLQ5PxQj4PFaprjyCTLI37ElWMmNs0K8Lk3dVeOdNpB3ks8jc7yM5CDmVC73eMVk/trk3fgmrUpA==",
"dev": true,
"license": "MIT",
"dependencies": {
"@babel/helper-string-parser": "^7.27.1",
"@babel/helper-validator-identifier": "^7.27.1"
"@babel/helper-validator-identifier": "^7.28.5"
},
"engines": {
"node": ">=6.9.0"
@ -529,6 +529,29 @@
"dev": true,
"license": "MIT"
},
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"resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.11.tgz",
"integrity": "sha512-RfqAvLnMl313r7c9oclB1HhUEAezcpLjz95wFH4LVuhk9JF/r22qmVP9AMmOU4vMX7Q8pN8jwNg/CSpdFnMjTQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"is-core-module": "^2.16.0",
"is-core-module": "^2.16.1",
"path-parse": "^1.0.7",
"supports-preserve-symlinks-flag": "^1.0.0"
},
@ -5022,6 +5068,19 @@
"node": ">=8"
}
},
"node_modules/test-exclude/node_modules/minimatch": {
"version": "3.1.2",
"resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz",
"integrity": "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==",
"dev": true,
"license": "ISC",
"dependencies": {
"brace-expansion": "^1.1.7"
},
"engines": {
"node": "*"
}
},
"node_modules/tmpl": {
"version": "1.0.5",
"resolved": "https://registry.npmjs.org/tmpl/-/tmpl-1.0.5.tgz",
@ -5045,7 +5104,8 @@
"node_modules/toggle-selection": {
"version": "1.0.6",
"resolved": "https://registry.npmjs.org/toggle-selection/-/toggle-selection-1.0.6.tgz",
"integrity": "sha512-BiZS+C1OS8g/q2RRbJmy59xpyghNBqrr6k5L/uKBGRsTfxmu3ffiRnd8mlGPUVayg8pvfi5urfnu8TU7DVOkLQ=="
"integrity": "sha512-BiZS+C1OS8g/q2RRbJmy59xpyghNBqrr6k5L/uKBGRsTfxmu3ffiRnd8mlGPUVayg8pvfi5urfnu8TU7DVOkLQ==",
"license": "MIT"
},
"node_modules/type-detect": {
"version": "4.0.8",
@ -5071,16 +5131,16 @@
}
},
"node_modules/undici-types": {
"version": "7.10.0",
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.10.0.tgz",
"integrity": "sha512-t5Fy/nfn+14LuOc2KNYg75vZqClpAiqscVvMygNnlsHBFpSXdJaYtXMcdNLpl/Qvc3P2cB3s6lOV51nqsFq4ag==",
"version": "7.16.0",
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.16.0.tgz",
"integrity": "sha512-Zz+aZWSj8LE6zoxD+xrjh4VfkIG8Ya6LvYkZqtUQGJPZjYl53ypCaUwWqo7eI0x66KBGeRo+mlBEkMSeSZ38Nw==",
"dev": true,
"license": "MIT"
},
"node_modules/update-browserslist-db": {
"version": "1.1.3",
"resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.1.3.tgz",
"integrity": "sha512-UxhIZQ+QInVdunkDAaiazvvT/+fXL5Osr0JZlJulepYu6Jd7qJtDZjlur0emRlT71EN3ScPoE7gvsuIKKNavKw==",
"version": "1.1.4",
"resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.1.4.tgz",
"integrity": "sha512-q0SPT4xyU84saUX+tomz1WLkxUbuaJnR1xWt17M7fJtEJigJeWUNGUqrauFXsHnqev9y9JTRGwk13tFBuKby4A==",
"dev": true,
"funding": [
{
@ -5228,13 +5288,6 @@
"url": "https://github.com/chalk/wrap-ansi?sponsor=1"
}
},
"node_modules/wrappy": {
"version": "1.0.2",
"resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
"integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==",
"dev": true,
"license": "ISC"
},
"node_modules/write-file-atomic": {
"version": "4.0.2",
"resolved": "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-4.0.2.tgz",

3
package.json
View file

@ -9,5 +9,8 @@
"@testing-library/react": "^14.3.1",
"@types/react-copy-to-clipboard": "^5.0.7",
"jest": "^29.7.0"
},
"overrides": {
"glob": ">=11.1.0"
}
}

2
pyproject.toml
View file

@ -159,7 +159,7 @@ requires = ["poetry-core", "wheel"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
version = "1.80.3"
version = "1.80.4"
version_files = [
"pyproject.toml:^version"
]

39
tests/llm_translation/test_anthropic_completion.py
View file

@ -1304,7 +1304,12 @@ def test_anthropic_websearch(optional_params: dict):
litellm._turn_on_debug()
params = {
"model": "anthropic/claude-sonnet-4-5-20250929",
"messages": [{"role": "user", "content": "What is the current weather in Tokyo right now?. Make sure to search the web for an answer"}],
"messages": [
{
"role": "user",
"content": "What is the current weather in Tokyo right now?. Make sure to search the web for an answer",
}
],
**optional_params,
}
@ -1331,7 +1336,9 @@ def test_anthropic_text_editor():
"content": "There'''s a syntax error in my primes.py file. Can you help me fix it?",
}
],
"tools": [{"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}],
"tools": [
{"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"}
],
}
try:
@ -1692,6 +1699,7 @@ def test_anthropic_via_responses_api():
print(f"✓ All {len(events_seen)} events matched expected structure")
print(f"✓ Received {text_delta_count} text delta chunks")
def test_anthropic_strict_parameter_passthrough():
"""Test that the strict parameter in tool parameters is passed through to Anthropic input_schema"""
args = {
@ -1730,6 +1738,7 @@ def test_anthropic_strict_parameter_passthrough():
assert "input_schema" in tool
assert tool["input_schema"]["strict"] is True
def test_anthropic_strict_not_present():
"""Test that the strict parameter in tool parameters is passed through to Anthropic input_schema"""
args = {
@ -1766,3 +1775,29 @@ def test_anthropic_strict_not_present():
tool = mapped_params["tools"][0]
assert "input_schema" in tool
assert "strict" not in tool["input_schema"]
def test_anthropic_structured_output_chat_completion_api():
response = litellm.completion(
model="claude-sonnet-4-5-20250929",
messages=[{"role": "user", "content": "What is the capital of France?"}],
response_format={
"type": "json_schema",
"json_schema": {
"name": "final_output",
"strict": True,
"schema": {
"description": 'Progress report for the thinking process\n\nThis model represents a snapshot of the agent\'s current progress during\nthe thinking process, providing a brief description of the current activity.\n\nAttributes:\n agent_doing: Brief description of what the agent is currently doing.\n Should be kept under 10 words. Example: "Learning about home automation"',
"properties": {
"agent_doing": {"title": "Agent Doing", "type": "string"}
},
"required": ["agent_doing"],
"title": "ThinkingStep",
"type": "object",
"additionalProperties": False,
},
},
},
)
assert response is not None
print(f"response: {response}")

13
tests/local_testing/test_basic_python_version.py
View file

@ -38,10 +38,21 @@ def test_litellm_proxy_server():
def test_package_dependencies():
"""
Test that all optional dependencies are correctly specified in extras.
"""
try:
import tomli
import pathlib
import litellm
# Try to import tomllib (Python 3.11+) or tomli (older versions)
try:
import tomllib as tomli
except ImportError:
try:
import tomli
except ImportError:
pytest.skip("tomli/tomllib not available - skipping dependency check")
# Get the litellm package root path
litellm_path = pathlib.Path(litellm.__file__).parent.parent

44
tests/local_testing/test_router_utils.py
View file

@ -458,6 +458,50 @@ def test_router_get_deployment_credentials():
assert credentials["api_key"] == "123"
def test_router_get_deployment_credentials_with_provider():
"""
Test that get_deployment_credentials_with_provider returns credentials with provider info.
"""
router = Router(
model_list=[
{
"model_name": "gpt-4o",
"litellm_params": {
"model": "gpt-4o",
"api_key": "sk-test-123",
"api_base": "https://api.openai.com/v1",
},
"model_info": {"id": "openai-deployment-1"},
},
{
"model_name": "claude-3",
"litellm_params": {
"model": "anthropic/claude-3-sonnet",
"api_key": "sk-ant-123",
},
"model_info": {"id": "anthropic-deployment-1"},
},
]
)
# Test getting credentials by model_id
credentials = router.get_deployment_credentials_with_provider(model_id="openai-deployment-1")
assert credentials is not None
assert credentials["api_key"] == "sk-test-123"
assert credentials["custom_llm_provider"] == "openai"
assert credentials["api_base"] == "https://api.openai.com/v1"
# Test getting credentials by model_group_name (model_name)
credentials2 = router.get_deployment_credentials_with_provider(model_id="claude-3")
assert credentials2 is not None
assert credentials2["api_key"] == "sk-ant-123"
assert credentials2["custom_llm_provider"] == "anthropic"
# Test with non-existent model
credentials3 = router.get_deployment_credentials_with_provider(model_id="non-existent")
assert credentials3 is None
def test_router_get_deployment_model_info():
router = Router(
model_list=[

44
tests/proxy_admin_ui_tests/package-lock.json generated
View file

@ -14,12 +14,13 @@
}
},
"node_modules/@playwright/test": {
"version": "1.47.2",
"resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.47.2.tgz",
"integrity": "sha512-jTXRsoSPONAs8Za9QEQdyjFn+0ZQFjCiIztAIF6bi1HqhBzG9Ma7g1WotyiGqFSBRZjIEqMdT8RUlbk1QVhzCQ==",
"version": "1.56.1",
"resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.56.1.tgz",
"integrity": "sha512-vSMYtL/zOcFpvJCW71Q/OEGQb7KYBPAdKh35WNSkaZA75JlAO8ED8UN6GUNTm3drWomcbcqRPFqQbLae8yBTdg==",
"dev": true,
"license": "Apache-2.0",
"dependencies": {
"playwright": "1.47.2"
"playwright": "1.56.1"
},
"bin": {
"playwright": "cli.js"
@ -29,12 +30,13 @@
}
},
"node_modules/@types/node": {
"version": "22.5.5",
"resolved": "https://registry.npmjs.org/@types/node/-/node-22.5.5.tgz",
"integrity": "sha512-Xjs4y5UPO/CLdzpgR6GirZJx36yScjh73+2NlLlkFRSoQN8B0DpfXPdZGnvVmLRLOsqDpOfTNv7D9trgGhmOIA==",
"version": "22.19.1",
"resolved": "https://registry.npmjs.org/@types/node/-/node-22.19.1.tgz",
"integrity": "sha512-LCCV0HdSZZZb34qifBsyWlUmok6W7ouER+oQIGBScS8EsZsQbrtFTUrDX4hOl+CS6p7cnNC4td+qrSVGSCTUfQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"undici-types": "~6.19.2"
"undici-types": "~6.21.0"
}
},
"node_modules/fsevents": {
@ -43,6 +45,7 @@
"integrity": "sha512-xiqMQR4xAeHTuB9uWm+fFRcIOgKBMiOBP+eXiyT7jsgVCq1bkVygt00oASowB7EdtpOHaaPgKt812P9ab+DDKA==",
"dev": true,
"hasInstallScript": true,
"license": "MIT",
"optional": true,
"os": [
"darwin"
@ -52,12 +55,13 @@
}
},
"node_modules/playwright": {
"version": "1.47.2",
"resolved": "https://registry.npmjs.org/playwright/-/playwright-1.47.2.tgz",
"integrity": "sha512-nx1cLMmQWqmA3UsnjaaokyoUpdVaaDhJhMoxX2qj3McpjnsqFHs516QAKYhqHAgOP+oCFTEOCOAaD1RgD/RQfA==",
"version": "1.56.1",
"resolved": "https://registry.npmjs.org/playwright/-/playwright-1.56.1.tgz",
"integrity": "sha512-aFi5B0WovBHTEvpM3DzXTUaeN6eN0qWnTkKx4NQaH4Wvcmc153PdaY2UBdSYKaGYw+UyWXSVyxDUg5DoPEttjw==",
"dev": true,
"license": "Apache-2.0",
"dependencies": {
"playwright-core": "1.47.2"
"playwright-core": "1.56.1"
},
"bin": {
"playwright": "cli.js"
@ -70,10 +74,11 @@
}
},
"node_modules/playwright-core": {
"version": "1.47.2",
"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.47.2.tgz",
"integrity": "sha512-3JvMfF+9LJfe16l7AbSmU555PaTl2tPyQsVInqm3id16pdDfvZ8TTZ/pyzmkbDrZTQefyzU7AIHlZqQnxpqHVQ==",
"version": "1.56.1",
"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.56.1.tgz",
"integrity": "sha512-hutraynyn31F+Bifme+Ps9Vq59hKuUCz7H1kDOcBs+2oGguKkWTU50bBWrtz34OUWmIwpBTWDxaRPXrIXkgvmQ==",
"dev": true,
"license": "Apache-2.0",
"bin": {
"playwright-core": "cli.js"
},
@ -82,10 +87,11 @@
}
},
"node_modules/undici-types": {
"version": "6.19.8",
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.19.8.tgz",
"integrity": "sha512-ve2KP6f/JnbPBFyobGHuerC9g1FYGn/F8n1LWTwNxCEzd6IfqTwUQcNXgEtmmQ6DlRrC1hrSrBnCZPokRrDHjw==",
"dev": true
"version": "6.21.0",
"resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
"integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
"dev": true,
"license": "MIT"
}
}
}

3
tests/proxy_admin_ui_tests/package.json
View file

@ -10,5 +10,8 @@
"devDependencies": {
"@playwright/test": "^1.47.2",
"@types/node": "^22.5.5"
},
"overrides": {
"glob": ">=11.1.0"
}
}

1870
tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json generated
View file

File diff suppressed because it is too large Load diff

3
tests/proxy_admin_ui_tests/ui_unit_tests/package.json
View file

@ -22,5 +22,8 @@
"@ant-design/icons": "^5.0.0",
"react": "^18.2.0",
"react-dom": "^18.2.0"
},
"overrides": {
"glob": ">=11.1.0"
}
}

4
tests/proxy_unit_tests/test_user_api_key_auth.py
View file

@ -827,6 +827,10 @@ async def test_user_api_key_auth_websocket():
mock_websocket = MagicMock(spec=WebSocket)
mock_websocket.query_params = {"model": "some_model"}
mock_websocket.headers = {"authorization": "Bearer some_api_key"}
# Mock the scope attribute that user_api_key_auth_websocket accesses
mock_websocket.scope = {"headers": [(b"authorization", b"Bearer some_api_key")]}
# Mock the url attribute
mock_websocket.url = URL(url="/ws")
# Mock the return value of `user_api_key_auth` when it's called within the `user_api_key_auth_websocket` function
with patch(

233
tests/test_litellm/integrations/dotprompt/test_prompt_manager.py
View file

@ -548,89 +548,6 @@ def test_prompt_main():
pass
@pytest.mark.asyncio
async def test_dotprompt_auto_detection_with_model_only():
"""
Test that dotprompt prompts can be auto-detected when passing model="gpt-4" and prompt_id,
without needing to specify model="dotprompt/gpt-4".
"""
from litellm.integrations.dotprompt import DotpromptManager
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
prompt_dir = Path(__file__).parent
dotprompt_manager = DotpromptManager(prompt_directory=str(prompt_dir))
# Register the dotprompt manager in callbacks
original_callbacks = litellm.callbacks.copy()
litellm.callbacks = [dotprompt_manager]
try:
# Mock the HTTP handler to avoid actual API calls
client = AsyncHTTPHandler()
# Create a proper mock response
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 200
mock_response.headers = {"content-type": "application/json"}
mock_response.json.return_value = {
"id": "chatcmpl-test-123",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Test response",
},
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 10,
"total_tokens": 20,
},
}
with patch.object(client, "post", return_value=mock_response) as mock_post:
# Call with model="gpt-4" (no "dotprompt/" prefix) and prompt_id
response = await litellm.acompletion(
model="gpt-4",
prompt_id="chat_prompt",
prompt_variables={"user_message": "Hello world"},
messages=[{"role": "user", "content": "This will be ignored"}],
client=client,
api_key="test-api-key",
)
mock_post.assert_called_once()
# Get request body from the call
request_body = mock_post.call_args.kwargs.get("json") or json.loads(mock_post.call_args.kwargs.get("data", "{}"))
# Verify the prompt was auto-detected and used
# The chat_prompt.prompt has metadata: model: gpt-4, temperature: 0.7, max_tokens: 150
assert request_body["model"] == "gpt-4"
# Verify the messages were transformed using the prompt template
# chat_prompt template: "User: {{user_message}}"
messages = request_body["messages"]
assert len(messages) >= 1
# The first message should be from the prompt template with the variable substituted
# Template is: "User: {{user_message}}" with user_message="Hello world"
first_message_content = messages[0]["content"]
assert "Hello world" in first_message_content
# Verify response was returned
assert response is not None
finally:
# Restore original callbacks
litellm.callbacks = original_callbacks
@pytest.mark.asyncio
async def test_dotprompt_with_prompt_version():
@ -638,131 +555,35 @@ async def test_dotprompt_with_prompt_version():
Test that dotprompt can load and use specific prompt versions.
Versions are stored as separate files with .v{version}.prompt naming convention.
"""
from litellm.integrations.dotprompt import DotpromptManager
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
from litellm.integrations.dotprompt.prompt_manager import PromptManager
prompt_dir = Path(__file__).parent
dotprompt_manager = DotpromptManager(prompt_directory=str(prompt_dir))
prompt_manager = PromptManager(prompt_directory=str(prompt_dir))
# Register the dotprompt manager in callbacks
original_callbacks = litellm.callbacks.copy()
litellm.callbacks = [dotprompt_manager]
# Test version 1
v1_prompt = prompt_manager.get_prompt(prompt_id="chat_prompt", version=1)
assert v1_prompt is not None
assert v1_prompt.model == "gpt-3.5-turbo"
try:
# Test version 1
client = AsyncHTTPHandler()
# Create a proper mock response for version 1
mock_response_v1 = Mock(spec=httpx.Response)
mock_response_v1.status_code = 200
mock_response_v1.headers = {"content-type": "application/json"}
mock_response_v1.json.return_value = {
"id": "chatcmpl-test-v1-123",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-3.5-turbo",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Test response v1",
},
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 10,
"total_tokens": 20,
},
}
with patch.object(client, "post", return_value=mock_response_v1) as mock_post:
response = await litellm.acompletion(
model="gpt-3.5-turbo",
prompt_id="chat_prompt",
prompt_version=1,
prompt_variables={"user_message": "Test v1"},
messages=[],
client=client,
api_key="test-api-key",
)
mock_post.assert_called_once()
request_body = mock_post.call_args.kwargs.get("json") or json.loads(mock_post.call_args.kwargs.get("data", "{}"))
# Verify version 1 prompt was used
# chat_prompt.v1.prompt has: model: gpt-3.5-turbo, temperature: 0.5, max_tokens: 100
assert request_body["model"] == "gpt-3.5-turbo"
# Verify the message contains "Version 1:" prefix from v1 template
messages = request_body["messages"]
assert len(messages) >= 1
first_message_content = messages[0]["content"]
assert "Version 1:" in first_message_content
assert "Test v1" in first_message_content
# Verify response was returned
assert response is not None
# Test version 2
client = AsyncHTTPHandler()
# Create a proper mock response for version 2
mock_response_v2 = Mock(spec=httpx.Response)
mock_response_v2.status_code = 200
mock_response_v2.headers = {"content-type": "application/json"}
mock_response_v2.json.return_value = {
"id": "chatcmpl-test-v2-123",
"object": "chat.completion",
"created": 1700000000,
"model": "gpt-4",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Test response v2",
},
"finish_reason": "stop",
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 10,
"total_tokens": 20,
},
}
with patch.object(client, "post", return_value=mock_response_v2) as mock_post:
response = await litellm.acompletion(
model="gpt-4",
prompt_id="chat_prompt",
prompt_version=2,
prompt_variables={"user_message": "Test v2"},
messages=[],
client=client,
api_key="test-api-key",
)
mock_post.assert_called_once()
request_body = mock_post.call_args.kwargs.get("json") or json.loads(mock_post.call_args.kwargs.get("data", "{}"))
# Verify version 2 prompt was used
# chat_prompt.v2.prompt has: model: gpt-4, temperature: 0.9, max_tokens: 200
assert request_body["model"] == "gpt-4"
# Verify the message contains "Version 2:" prefix from v2 template
messages = request_body["messages"]
assert len(messages) >= 1
first_message_content = messages[0]["content"]
assert "Version 2:" in first_message_content
assert "Test v2" in first_message_content
# Verify response was returned
assert response is not None
# Verify version 1 content
v1_rendered = prompt_manager.render(
prompt_id="chat_prompt",
prompt_variables={"user_message": "Test v1"},
version=1
)
assert "Version 1:" in v1_rendered
assert "Test v1" in v1_rendered
finally:
# Restore original callbacks
litellm.callbacks = original_callbacks
# Test version 2
v2_prompt = prompt_manager.get_prompt(prompt_id="chat_prompt", version=2)
assert v2_prompt is not None
assert v2_prompt.model == "gpt-4"
# Verify version 2 content
v2_rendered = prompt_manager.render(
prompt_id="chat_prompt",
prompt_variables={"user_message": "Test v2"},
version=2
)
assert "Version 2:" in v2_rendered
assert "Test v2" in v2_rendered

44
tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py
View file

@ -469,6 +469,7 @@ def test_anthropic_chat_transform_request_includes_context_management():
def test_transform_parsed_response_includes_context_management_metadata():
import httpx
from litellm.types.utils import ModelResponse
config = AnthropicConfig()
@ -513,4 +514,45 @@ def test_transform_parsed_response_includes_context_management_metadata():
assert result.__dict__.get("context_management") == context_management_payload
provider_fields = result.choices[0].message.provider_specific_fields
assert provider_fields and provider_fields["context_management"] == context_management_payload
assert (
provider_fields
and provider_fields["context_management"] == context_management_payload
)
def test_anthropic_structured_output_beta_header():
from litellm.types.utils import CallTypes
from litellm.utils import return_raw_request
response = return_raw_request(
endpoint=CallTypes.completion,
kwargs={
"model": "claude-sonnet-4-5-20250929",
"messages": [{"role": "user", "content": "What is the capital of France?"}],
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "final_output",
"strict": True,
"schema": {
"description": 'Progress report for the thinking process\n\nThis model represents a snapshot of the agent\'s current progress during\nthe thinking process, providing a brief description of the current activity.\n\nAttributes:\n agent_doing: Brief description of what the agent is currently doing.\n Should be kept under 10 words. Example: "Learning about home automation"',
"properties": {
"agent_doing": {"title": "Agent Doing", "type": "string"}
},
"required": ["agent_doing"],
"title": "ThinkingStep",
"type": "object",
"additionalProperties": False,
},
},
},
},
)
assert response is not None
print(f"response: {response}")
print(f"raw_request_headers: {response['raw_request_headers']}")
assert (
"structured-outputs-2025-11-13"
in response["raw_request_headers"]["anthropic-beta"]
)

345
tests/test_litellm/llms/docker_model_runner/test_docker_model_runner_chat_transformation.py
View file

@ -1,8 +1,8 @@
"""
Unit tests for Docker Model Runner configuration.
Unit tests for Docker Model Runner transformation.
This test validates that litellm.completion correctly routes requests to Docker Model Runner
with the proper URL structure and request body.
This test validates that the DockerModelRunnerChatConfig correctly transforms
requests to the proper URL, headers, and body format.
"""
import os
@ -13,183 +13,192 @@ sys.path.insert(
)
import json
from unittest.mock import Mock, patch
from typing import cast
import httpx
import pytest
import litellm
from litellm import completion
from litellm.llms.docker_model_runner.chat.transformation import (
DockerModelRunnerChatConfig,
)
from litellm.types.llms.openai import AllMessageValues
class TestDockerModelRunnerIntegration:
"""Integration test for Docker Model Runner"""
class TestDockerModelRunnerTransformation:
"""
Unit tests for Docker Model Runner transformation layer.
"""
def test_completion_hits_correct_url_and_body(self):
def test_get_complete_url_with_default_api_base(self):
"""
Test that litellm.completion with docker_model_runner provider:
1. Hits the correct URL: {api_base}/v1/chat/completions where api_base includes engine path
2. Sends the correct request body with messages and parameters
Test that get_complete_url returns the correct URL with default api_base.
"""
# Mock _get_httpx_client to return a mock HTTPHandler
with patch("litellm.llms.custom_httpx.llm_http_handler._get_httpx_client") as mock_get_client:
# Create a mock HTTPHandler instance
mock_http_handler = Mock()
mock_get_client.return_value = mock_http_handler
# Create mock response
mock_response = Mock(spec=httpx.Response)
mock_response.json.return_value = {
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"model": "llama-3.1",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I help you today?"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
}
}
mock_response.status_code = 200
mock_response.headers = httpx.Headers({"content-type": "application/json"})
mock_response.text = json.dumps(mock_response.json.return_value)
# Capture the request
captured_kwargs = {}
def mock_post(**kwargs):
captured_kwargs.update(kwargs)
return mock_response
mock_http_handler.post.side_effect = mock_post
config = DockerModelRunnerChatConfig()
url = config.get_complete_url(
api_base=None,
api_key=None,
model="llama-3.1",
optional_params={},
litellm_params={},
stream=False
)
assert url == "http://localhost:22088/engines/llama.cpp/v1/chat/completions"
# Make the completion call with engine in api_base
response = completion(
model="docker_model_runner/llama-3.1",
messages=[{"role": "user", "content": "Hello, how are you?"}],
api_base="http://localhost:22088/engines/llama.cpp",
temperature=0.7,
max_tokens=100
)
# Verify the request was made
assert mock_http_handler.post.called
url = captured_kwargs.get('url', '')
data = captured_kwargs.get('data', '')
print("URL For request", url)
print("request body for request", data)
# Should hit {api_base}/v1/chat/completions where api_base includes engine
assert "/engines/llama.cpp/v1/chat/completions" in url
assert "http://localhost:22088" in url
# Verify the request body
request_data = json.loads(data) if isinstance(data, str) else data
print("Parsed request data:", json.dumps(request_data, indent=4))
# Check messages
assert "messages" in request_data
assert len(request_data["messages"]) == 1
assert request_data["messages"][0]["role"] == "user"
assert request_data["messages"][0]["content"] == "Hello, how are you?"
# Check parameters
assert request_data["temperature"] == 0.7
assert request_data["max_tokens"] == 100
# Verify response
assert response.choices[0].message.content == "Hello! How can I help you today?"
def test_completion_with_custom_engine_and_host(self):
def test_get_complete_url_with_custom_api_base(self):
"""
Test that litellm.completion works with custom engine and host:
1. Uses model-runner.docker.internal as host
2. Specifies a different engine in the api_base
3. Model name is sent in the request body
Test that get_complete_url correctly appends /v1/chat/completions to custom api_base.
"""
# Mock _get_httpx_client to return a mock HTTPHandler
with patch("litellm.llms.custom_httpx.llm_http_handler._get_httpx_client") as mock_get_client:
# Create a mock HTTPHandler instance
mock_http_handler = Mock()
mock_get_client.return_value = mock_http_handler
# Create mock response
mock_response = Mock(spec=httpx.Response)
mock_response.json.return_value = {
"id": "chatcmpl-456",
"object": "chat.completion",
"created": 1677652288,
"model": "mistral-7b",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "Bonjour! How can I assist you?"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 25,
"total_tokens": 40
}
}
mock_response.status_code = 200
mock_response.headers = httpx.Headers({"content-type": "application/json"})
mock_response.text = json.dumps(mock_response.json.return_value)
# Capture the request
captured_kwargs = {}
def mock_post(**kwargs):
captured_kwargs.update(kwargs)
return mock_response
mock_http_handler.post.side_effect = mock_post
config = DockerModelRunnerChatConfig()
url = config.get_complete_url(
api_base="http://localhost:22088/engines/llama.cpp",
api_key=None,
model="llama-3.1",
optional_params={},
litellm_params={},
stream=False
)
assert url == "http://localhost:22088/engines/llama.cpp/v1/chat/completions"
assert "/engines/llama.cpp/v1/chat/completions" in url
assert "http://localhost:22088" in url
# Make the completion call with custom engine and host
response = completion(
model="docker_model_runner/mistral-7b",
messages=[{"role": "user", "content": "Hello!"}],
api_base="http://model-runner.docker.internal/engines/custom-engine",
temperature=0.5,
max_tokens=200
)
def test_get_complete_url_with_custom_engine_and_host(self):
"""
Test that get_complete_url works with custom engine and host.
"""
config = DockerModelRunnerChatConfig()
url = config.get_complete_url(
api_base="http://model-runner.docker.internal/engines/custom-engine",
api_key=None,
model="mistral-7b",
optional_params={},
litellm_params={},
stream=False
)
assert "model-runner.docker.internal" in url
assert "/engines/custom-engine/v1/chat/completions" in url
assert url == "http://model-runner.docker.internal/engines/custom-engine/v1/chat/completions"
# Verify the request was made
assert mock_http_handler.post.called
url = captured_kwargs.get('url', '')
data = captured_kwargs.get('data', '')
print("URL For request", url)
print("request body for request", data)
# Should hit the custom host and engine
assert "model-runner.docker.internal" in url
assert "/engines/custom-engine/v1/chat/completions" in url
def test_get_complete_url_removes_trailing_slash(self):
"""
Test that get_complete_url removes trailing slashes from api_base.
"""
config = DockerModelRunnerChatConfig()
url = config.get_complete_url(
api_base="http://localhost:22088/engines/llama.cpp/",
api_key=None,
model="llama-3.1",
optional_params={},
litellm_params={},
stream=False
)
# Should not have double slashes
assert "/v1/chat/completions" in url
assert "//v1" not in url
# Verify the request body contains the model name
request_data = json.loads(data) if isinstance(data, str) else data
print("Parsed request data:", json.dumps(request_data, indent=4))
# Check that model name is in the request body
assert request_data["model"] == "mistral-7b"
# Check messages
assert "messages" in request_data
assert len(request_data["messages"]) == 1
assert request_data["messages"][0]["role"] == "user"
assert request_data["messages"][0]["content"] == "Hello!"
# Check parameters
assert request_data["temperature"] == 0.5
assert request_data["max_tokens"] == 200
# Verify response
assert response.choices[0].message.content == "Bonjour! How can I assist you?"
def test_transform_request_body(self):
"""
Test that transform_request creates the correct request body with messages and parameters.
"""
config = DockerModelRunnerChatConfig()
messages = cast(list[AllMessageValues], [{"role": "user", "content": "Hello, how are you?"}])
optional_params = {
"temperature": 0.7,
"max_tokens": 100
}
request_data = config.transform_request(
model="llama-3.1",
messages=messages,
optional_params=optional_params,
litellm_params={},
headers={}
)
# Check messages
assert "messages" in request_data
assert len(request_data["messages"]) == 1
assert request_data["messages"][0]["role"] == "user"
assert request_data["messages"][0]["content"] == "Hello, how are you?"
# Check parameters
assert request_data["temperature"] == 0.7
assert request_data["max_tokens"] == 100
# Check model name is in request
assert request_data["model"] == "llama-3.1"
def test_validate_environment_returns_headers(self):
"""
Test that validate_environment returns the correct headers.
"""
config = DockerModelRunnerChatConfig()
headers = config.validate_environment(
headers={},
model="llama-3.1",
messages=cast(list[AllMessageValues], [{"role": "user", "content": "Hello"}]),
optional_params={},
litellm_params={},
api_key="test-key",
api_base="http://localhost:22088/engines/llama.cpp"
)
# Should have Authorization header with Bearer token
assert "Authorization" in headers
assert "Bearer" in headers["Authorization"]
def test_map_openai_params(self):
"""
Test that map_openai_params correctly maps OpenAI parameters.
"""
config = DockerModelRunnerChatConfig()
non_default_params = {
"temperature": 0.5,
"max_tokens": 200,
"top_p": 0.9
}
optional_params = {}
result = config.map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model="mistral-7b",
drop_params=False
)
# Check that parameters are mapped correctly
assert result["temperature"] == 0.5
assert result["max_tokens"] == 200
assert result["top_p"] == 0.9
def test_map_max_completion_tokens_to_max_tokens(self):
"""
Test that max_completion_tokens is mapped to max_tokens.
"""
config = DockerModelRunnerChatConfig()
non_default_params = {
"max_completion_tokens": 150
}
optional_params = {}
result = config.map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model="llama-3.1",
drop_params=False
)
# max_completion_tokens should be mapped to max_tokens
assert result["max_tokens"] == 150
assert "max_completion_tokens" not in result

1
tests/test_litellm/llms/openai/realtime/test_openai_realtime_handler.py
View file

@ -168,6 +168,7 @@ async def test_async_realtime_url_contains_model():
async def __aexit__(self, exc_type, exc, tb):
return None
shared_context = get_shared_realtime_ssl_context()
with patch("websockets.connect", return_value=DummyAsyncContextManager(mock_backend_ws)) as mock_ws_connect, \
patch("litellm.llms.openai.realtime.handler.RealTimeStreaming") as mock_realtime_streaming:

10180
ui/litellm-dashboard/package-lock.json generated
View file

File diff suppressed because it is too large Load diff

2
ui/litellm-dashboard/package.json
View file

@ -78,7 +78,7 @@
"webpack-dev-server": ">=5.2.1",
"mermaid": ">=11.10.0",
"js-yaml": ">=4.1.1",
"glob": ">=10.5.0"
"glob": ">=11.1.0"
},
"engines": {
"node": ">=18.17.0",

2
ui/litellm-dashboard/src/components/callback_info_helpers.tsx
View file

@ -17,7 +17,7 @@ export const CALLBACK_CONFIGS: CallbackConfig[] = [
supports_key_team_logging: true,
dynamic_params: {
arize_api_key: "password",
arize_space_key: "password",
arize_space_id: "password",
},
description: "Arize Logging Integration",
},