mirror of
https://github.com/BerriAI/litellm.git
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da2f5a498f
* feat(agents): support Databricks Agent Platform agents in the agent gateway Add a databricks_agent provider that speaks the MLflow ResponsesAgent wire (Responses-shaped input, output_text items, output_text.delta SSE) to Model Serving endpoints (/invocations or the unified /serving-endpoints/responses) and Databricks Apps (/responses), with a PAT or OAuth M2M client credentials Register it through the agents config block or the Admin UI agent catalog and call it through /a2a/<agent> (message/send, message/stream) or model: a2a/<agent> chat completions. The chat route now runs every completion-bridge agent through its own provider, and attaches the minted Databricks OAuth token for both bridge and URL agents. Consumed OAuth settings never reach the provider call * fix(agents): harden databricks_agent input, output, and OAuth handling Non-text content parts answer 400 unless drop_params is set, a reply without an output list answers 502, flat OAuth fields resolve an os.environ api_base, the bridge debug log no longer echoes the request model, routing tests stub only the HTTP boundary, and the catalog description fits the dropdown * fix(agents): merge agent and client extra_headers on the a2a chat route, and reject an empty databricks_oauth block * fix(agents): bridge only databricks_agent agents on the a2a chat route, keep the card url over a client api_base, and require workspace_url for a Databricks App * refactor(agents): read the a2a chat bridge providers from a constants set instead of branching on a provider name * fix(databricks_agent): let the configured credential win, reject malformed client extra_headers, and keep a missing endpoint name empty * test(agents): drive the flat OAuth a2a send through the real bridge * fix(agents): keep an empty databricks_oauth block ignored on url agents, as before this change * fix(agents): accept bare App hosts, env service principals, and os.environ refs on Databricks agents * fix(databricks_agent): address review caveats on chat bridging, params, and auth Map max_tokens and max_completion_tokens to max_output_tokens and pass temperature, top_p, and user, so /v1/messages works without drop_params. Send A2A message metadata only with string values, answering 400 or dropping the rest under drop_params. Join message items with a blank line in both stream and non-stream replies. Cache the env service principal token until shortly before it expires. Keep databricks_agent replies out of the response cache. On the chat bridge, let request fields win over the agent's stored params while pinning the agent's api_base and api_key, and apply the agent's static_headers between client and agent extra_headers, as /a2a does. Resolve os.environ/ only for databricks_agent api_base and api_key instead of every bridge provider. Ignore an unusable databricks_oauth block on URL agents on chat, as at the merge base, instead of answering 400. * test(internal_context): list the databricks agent token cache as process-local * fix(databricks_agent): keep the agent's endpoint, input, and target out of client control extra_body model and input answer 400, or are dropped under drop_params. A client api_base or base_url never replaces the agent's card URL on the bridge. Chat to a URL agent keeps its merge-base call, and an incomplete databricks_oauth block on a URL agent keeps its merge-base ValueError. * test(databricks): expect the merge-base 400 for a client api_base on agent chat * fix(databricks): keep agent-owned inputs and identity headers out of a chat caller's reach * fix(databricks): bound the agent-over-caller merge to two levels so it needs no recursion --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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| .. | ||
| _experimental | ||
| a2a | ||
| agent_endpoints | ||
| analytics_endpoints | ||
| anthropic_endpoints | ||
| auth | ||
| batches_endpoints | ||
| client | ||
| common_utils | ||
| config_management_endpoints | ||
| config_resolvers | ||
| container_endpoints | ||
| credential_endpoints | ||
| custom_hooks | ||
| db | ||
| decisions_endpoints | ||
| discovery_endpoints | ||
| enterprise_billing | ||
| example_config_yaml | ||
| fine_tuning_endpoints | ||
| google_endpoints | ||
| guardrails | ||
| health_check_utils | ||
| health_endpoints | ||
| hooks | ||
| image_endpoints | ||
| lens | ||
| list_api | ||
| logging_endpoints | ||
| management | ||
| management_endpoints | ||
| management_helpers | ||
| memory | ||
| middleware | ||
| ocr_endpoints | ||
| openai_evals_endpoints | ||
| openai_files_endpoints | ||
| pass_through_endpoints | ||
| policy_engine | ||
| prompts | ||
| public_endpoints | ||
| rag_endpoints | ||
| realtime_endpoints | ||
| rerank_endpoints | ||
| response_api_endpoints | ||
| response_polling | ||
| roi_calculator | ||
| search_endpoints | ||
| shutdown | ||
| spend_tracking | ||
| swagger | ||
| telemetry | ||
| test_prompts | ||
| types_utils | ||
| ui_crud_endpoints | ||
| vector_store_endpoints | ||
| vector_store_files_endpoints | ||
| vertex_ai_endpoints | ||
| video_endpoints | ||
| workflows | ||
| .gitignore | ||
| __init__.py | ||
| _lazy_features.py | ||
| _lazy_openapi_snapshot.json | ||
| _lazy_openapi_snapshot.py | ||
| _new_new_secret_config.yaml | ||
| _new_secret_config.yaml | ||
| _super_secret_config.yaml | ||
| _types.py | ||
| admin_mcp.py | ||
| bug_report_config.py | ||
| cached_logo.jpg | ||
| caching_routes.py | ||
| collector.py | ||
| common_request_processing.py | ||
| compliance_checks.py | ||
| custom_auth_auto.py | ||
| custom_prompt_management.py | ||
| custom_sso.py | ||
| custom_validate.py | ||
| dd_span_tagger.py | ||
| dev_config.yaml | ||
| enterprise | ||
| health_check.py | ||
| lambda.py | ||
| litellm_pre_call_utils.py | ||
| llamaguard_prompt.txt | ||
| logo.png | ||
| logo_dark.png | ||
| logo_monogram.png | ||
| logo_monogram_dark.png | ||
| mcp_registry.json | ||
| mcp_tools.py | ||
| model_config.yaml | ||
| model_insights_tasks.json | ||
| moyai_endpoints.py | ||
| native_compaction.py | ||
| openapi.json | ||
| openapi_registry.json | ||
| plugin_routes.py | ||
| post_call_rules.py | ||
| prisma_migration.py | ||
| prometheus_cleanup.py | ||
| prometheus_metrics_server.py | ||
| proxy_cli.py | ||
| proxy_config.yaml | ||
| proxy_server.py | ||
| read_model_list.py | ||
| README.md | ||
| route_llm_request.py | ||
| route_priority.py | ||
| schema.prisma | ||
| start.sh | ||
| tracing_endpoints.py | ||
| tracing_runtime.py | ||
| utils.py | ||
| wildcard_config.yaml | ||
litellm-proxy
A local, fast, and lightweight OpenAI-compatible server to call 100+ LLM APIs.
usage
$ uv tool install litellm
$ litellm --model ollama/codellama
#INFO: Ollama running on http://0.0.0.0:8000
replace openai base
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
See how to call Huggingface,Bedrock,TogetherAI,Anthropic, etc.
Folder Structure
Routes
proxy_server.py- all openai-compatible routes -/v1/chat/completion,/v1/embedding+ model info routes -/v1/models,/v1/model/info,/v1/model_group_inforoutes.health_endpoints/-/health,/health/liveliness,/health/readinessmanagement_endpoints/key_management_endpoints.py- all/key/*routesmanagement_endpoints/team_endpoints.py- all/team/*routesmanagement_endpoints/internal_user_endpoints.py- all/user/*routesmanagement_endpoints/ui_sso.py- all/sso/*routes