mirror of
https://github.com/BerriAI/litellm.git
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806b8be451
- Cache _get_agent_object_permission via user_api_key_cache (sentinel for no-permission rows) so MCP requests from agent keys don't hit the DB on every tool-list / tool-call. - Re-raise HTTPException in handle_sse_mcp so 401 + WWW-Authenticate challenges (and other HTTP errors) propagate to SSE clients instead of being swallowed as 500. - Normalise booleans in _validate_token_response so admin rules written as JSON-style "true" / "false" match upstream responses that return Python True / False. - Treat configured JWT issuer claim mappings as advisory: when a mapped field is absent or empty, leave the normalised claim unset instead of raising, matching the global litellm_jwtauth path. Co-authored-by: Claude <noreply@anthropic.com> |
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| .. | ||
| _experimental | ||
| agent_endpoints | ||
| analytics_endpoints | ||
| anthropic_endpoints | ||
| auth | ||
| batches_endpoints | ||
| client | ||
| common_utils | ||
| config_management_endpoints | ||
| container_endpoints | ||
| credential_endpoints | ||
| custom_hooks | ||
| db | ||
| discovery_endpoints | ||
| example_config_yaml | ||
| fine_tuning_endpoints | ||
| google_endpoints | ||
| guardrails | ||
| health_check_utils | ||
| health_endpoints | ||
| hooks | ||
| image_endpoints | ||
| 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 | ||
| search_endpoints | ||
| spend_tracking | ||
| swagger | ||
| 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 | ||
| _logging.py | ||
| _new_new_secret_config.yaml | ||
| _new_secret_config.yaml | ||
| _super_secret_config.yaml | ||
| _types.py | ||
| cached_logo.jpg | ||
| caching_routes.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 | ||
| enterprise | ||
| health_check.py | ||
| lambda.py | ||
| litellm_pre_call_utils.py | ||
| llamaguard_prompt.txt | ||
| logo.jpg | ||
| mcp_registry.json | ||
| mcp_tools.py | ||
| model_config.yaml | ||
| openapi.json | ||
| openapi_registry.json | ||
| post_call_rules.py | ||
| prisma_migration.py | ||
| prometheus_cleanup.py | ||
| proxy_cli.py | ||
| proxy_config.yaml | ||
| proxy_server.py | ||
| README.md | ||
| route_llm_request.py | ||
| schema.prisma | ||
| start.sh | ||
| utils.py | ||
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