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chore: add config.yml
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mubashir1osmani
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152
tests/e2e/gateway/litellm-config.yml
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152
tests/e2e/gateway/litellm-config.yml
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#jThis default config file aims to support most popular model providers out of the box
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#In general, the model name used by the client will be the same as the ones from the provider (For example, you will use "anthropic.claude-3-5-sonnet-20240620-v1:0" when you're calling LiteLLM just like you would when calling Amazon Bedrock directly)
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#In the case where there are model name conflicts, a prefix will be used (For example, the Azure and the openAI model names conflict, so when you are using Azure, you will use "azure/gpt-4o-realtime-preview-2024-10-01")
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#Some model providers require additional user-specific configuration (such as Azure which requires you to specify your own api_base with your resource name, and your api_version).
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#In this case, the provider is commented out, and you should uncomment it and provide your specific info
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#For more detailed information about each provider, refer to the docs: https://docs.litellm.ai/docs/providers
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#If you are not interested in a particular provider, just remove it from your config.yaml, and redeploy, and it will no longer show up in your LiteLLM deployment
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#If a particular provider is not working, double check your .env file, and make sure you have provided a valid api key for that provider, and then redeploy
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#Full details on guardrails here: https://docs.litellm.ai/docs/proxy/guardrails/bedrock
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general_settings:
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store_prompts_in_spend_logs: true
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master_key: "sk-1234"
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proxy_batch_write_at: 60
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database_connection_pool_limit: 10
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# disable_error_logs: True
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forward_client_headers_to_llm_api: false
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maximum_spend_logs_retention_period: "60d" # GSE-13389: Cleanup logs older than 60 days
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maximum_spend_logs_cleanup_cron: "0 1 * * *" # 01:00 UTC daily = 18:00 PDT
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database_url: os.environ/DATABASE_URL
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control_plane_url: os.environ/CONTROL_PLANE_URL
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alerts: ["email"]
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# fallbacks: [{"gpt-4": ["anthropic.claude-3-5-sonnet-20240620-v1:0"]}] #Configure fallbacks for context window exeeded errors (In this example, we will fall back to Claude Sonnet if over 8000 tokens, which is gpt-4's limit)
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# default_fallbacks: ["anthropic.claude-3-haiku-20240307-v1:0"] #Configure fallbacks for any error for every model (the above fallback configurations override this one)
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# environment_variables:
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# STORE_MODEL_IN_DB: 'True'
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# LITELLM_LOG: "DEBUG"
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litellm_settings:
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drop_params: True
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request_timeout: 600
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num_retries: 3
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json_logs: true
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store_audit_logs: True
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cache: true
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cache_params:
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type: redis
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host: redis
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port: 6379
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password: os.environ/REDIS_PASSWORD
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namespace: litellm.caching
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ttl: 16600
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# max_budget: 1000000000.0 # (float) sets max budget in dollars across the entire proxy across all API keys. Note, the budget does not apply to the master key. That is the only exception.
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# namespace: "litellm.caching.caching"
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# ttl: 15
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# budget_duration: 1mo # (str) frequency of budget reset - You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"), months ("1mo").
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# max_internal_user_budget: 1000000000.0 # (float) sets default budget in dollars for each internal user. (Doesn't apply to Admins. Doesn't apply to Teams. Doesn't apply to master key)
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# internal_user_budget_duration: "1mo" # (str) frequency of budget reset - You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"), months ("1mo").
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# success_callback: ["s3_v2"]
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# failure_callback: ["s3_v2"]
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# service_callback: ["datadog"]
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callbacks: ["arize_phoenix", "datadog", "smtp_email", "prometheus", "otel"]
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require_auth_for_metrics_endpoint: false
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cache: true
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cache_params:
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type: redis
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host: redis
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port: 6379
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password: os.environ/REDIS_PASSWORD
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namespace: litellm.caching
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ttl: 16600
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#type: redis-semantic
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#similarity_threshold: 0.8 # similarity threshold for semantic cache
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#redis_semantic_cache_embedding_model: text-embedding-ada-002 # only works with text-embedding-ada-002 for now... https://github.com/BerriAI/litellm/issues/4001
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#ttl: Optional[float]
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#default_in_memory_ttl: Optional[float]
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#default_in_redis_ttl: Optional[float]
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model_list:
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- model_name: gpt-5.5
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litellm_params:
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model: openai/gpt-5.5
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api_key: os.environ/OPENAI_API_KEY
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- model_name: claude-haiku-4-5
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litellm_params:
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model: anthropic/claude-haiku-4-5
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api_key: os.environ/ANTHROPIC_API_KEY
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# Same underlying model via Vertex AI — distinct routing/auth path
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# # (service-account JSON), so it gets its own model_name.
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- model_name: gemini-2.5-flash-vertex
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litellm_params:
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model: vertex_ai/gemini-2.5-flash
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vertex_project: os.environ/VERTEXAI_PROJECT
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vertex_location: us-central1
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vertex_credentials: os.environ/VERTEXAI_CREDENTIALS
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- model_name: gemini-2.5-flash
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litellm_params:
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model: gemini/gemini-2.5-flash
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api_key: os.environ/GEMINI_API_KEY
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# load balancing to a different deployment, if gemini gets rate limited.
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- model_name: gemini-2.5-flash
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litellm_params:
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model: gemini/gemini-2.5-flash
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api_key: os.environ/GEMINI_API_KEY
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# embedding models
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- model_name: openai-text-embedding-3-small
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litellm_params:
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model: openai/text-embedding-3-small
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api_key: os.environ/OPENAI_API_KEY
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- model_name: gemini-2-embedding
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litellm_params:
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model: gemini/gemini-2-embedding
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api_key: os.environ/GEMINI_API_KEY
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# realtime models
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- model_name: openai-realtime
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litellm_params:
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model: openai/realtime-2
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api_key: os.environ/OPENAI_API_KEY
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model_info:
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mode: realtime
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mcp_servers:
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deepwiki_mcp:
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url: "https://mcp.deepwiki.com/mcp"
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auth_type: none
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description: "just a test"
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atlassian:
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url: "https://mcp.atlassian.com/v1/mcp"
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auth_type: oauth2
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authorization_url: https://auth.atlassian.com/authorize
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guardrails:
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- guardrail_name: "presidio-pii"
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litellm_params:
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guardrail: presidio
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mode: pre_call
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presidio_analyzer_api_base: os.environ/PRESIDIO_ANALYZER_API_BASE
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presidio_anonymizer_api_base: os.environ/PRESIDIO_ANONYMIZER_API_BASE
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default_on: false
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pii_entities_config:
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EMAIL_ADDRESS: BLOCK
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CREDIT_CARD: BLOCK
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US_SSN: BLOCK
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PHONE_NUMBER: BLOCK
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7
tests/e2e/pytest.ini
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7
tests/e2e/pytest.ini
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[pytest]
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# Config when any e2e suite under tests/e2e/ is run directly, e.g.
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# uv run pytest tests/e2e/spend_tracking/ -v
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# The e2e marker is also registered in conftest.py for runs rooted elsewhere.
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addopts = --strict-markers --strict-config
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markers =
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e2e: live test that requires a running proxy and real provider keys
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