* docs: document Claude Code default models and env var overrides
- Update config example with current Claude Code 2.1.x model names
- Add section documenting default models (sonnet/haiku) that Claude Code requests
- Document env var overrides (ANTHROPIC_DEFAULT_SONNET_MODEL, etc.)
- Show how model_name alias can route to any provider (Bedrock, Vertex, etc.)
* Update docs
Removed warning about changing model names in Claude Code versions.
* docs: add 1M context support and improve Claude Code quickstart guide
- Add comprehensive 1M context window documentation
- Document [1m] suffix usage and shell escaping requirements
- Clarify that LiteLLM config should NOT include [1m] in model names
- Add standalone claude_code_1m_context.md guide
- Improve model selection documentation with environment variables
- Add section on default models used by Claude Code v2.1.14
- Add troubleshooting for 1M context issues
- Reorganize to emphasize environment variables approach
Addresses GitHub issue #14444
* docs: reorder model selection options - prioritize --model over env vars
- Move command line/session model selection to Option 1 (most reliable)
- Move environment variables to Option 2
- Add note that env vars may be cached from previous session
- Emphasize that --model always uses exact model specified
* docs: reorganize 1M context section - separate command line from env vars
- Split 1M context examples into two clear sections
- Show command line usage first (--model and /model)
- Show environment variables as alternative approach
- Improves readability and emphasizes most reliable method
* docs: remove misleading default models section from website tutorial
- Remove 'Default Models Used by Claude Code' section (misleading)
- Remove claim that config must match exact default model names
- Update config comment to be more general
- Add claude-opus-4-5-20251101 to example config
- Keep authentication section as-is
* docs: correct model selection in website tutorial
- Remove incorrect claim that Claude Code automatically uses proxy models
- Add explicit model selection examples with --model and /model
- Show environment variables as alternative approach
- Remove misleading comment about 'multiple configured'
* docs: add 1M context section to website tutorial
- Add section on using [1m] suffix for 1 million token context
- Include warning about shell escaping (quotes required)
- Explain how Claude Code handles [1m] internally
- Add /context verification command
- Note that LiteLLM config should NOT include [1m]
* docs: add tip about using .env for API keys
- Add note that ANTHROPIC_API_KEY can be stored in .env file
- Clarifies alternative to exporting environment variables
* fix(proxy_setting_endpoints.py): add new GET /in_product_nudges route
allows for context-based nudges
* feat: initial commit, adding in-product nudge for claude code usage
helps us talk to more litellm x claude code users
* fix: link out to google form for claude code in-product nudge
* feat(index.json): add new guide
* feat(index.json): add new claude code guides
* fix: link out
* fix: remove baselines
* fix: safely handle unmapped call type
* docs: cleanup links for ai coding tools
* docs(claude_non_anthropic_models.md): add tutorial showing non anthropic model connection to claude code
* docs: link to non-anthropic model tutorial for claude code
* Prompt Management API - new API to interact with Prompt Management integrations (no PR required) (#17800)
* feat: initial commit adding prompt management api
* feat: initial commit adding prompt management api
* fix: refactoring to make sure get prompt is async
* fix: additional fixes
* fix: partially working generic api prompt management
* fix(generic_guardrail_api.py): add 'structured_messages' support
allows guardrail provider to know if text is from system or user
* fix(generic_guardrail_api.md): document 'structured_messages' parameter
give api provider a way to distinguish between user and system messages
* feat(anthropic/): return openai chat completion format structured messages when calls made via `/v1/messages` on Anthropic
* feat(responses/guardrail_translation): support 'structured_messages' param for guardrails
structured openai chat completion spec messages, for guardrail checks when using /v1/responses api
allows guardrail checks to work consistently across APIs
* fix(unified_guardrail.py): support during_call event type for unified guardrails
allows guardrails overriding apply_guardrails to work 'during_call'
* feat(generic_guardrail_api.py): support new 'tool_calls' field for generic guardrail api
returns the tool calls emitted by the LLM API to the user
* fix(generic_guardrail_api.py): working anthropic /v1/messages tool call response
send llm tool calls to guardrail api when called via `/v1/messages` API
* fix(responses/): run generic_guardrail_api on responses api tool call responses
* fix: fix tests
* test: fix tests
* fix: fix tests
* fix(unified_guardrail.py): correctly map a v1/messages call to the anthropic unified guardrail
* fix: add more rigorous call type checks
* fix(anthropic_endpoints/endpoints.py): initialize logging object at the beginning of endpoint
ensures call id + trace id are emitted to guardrail api
* feat(anthropic/chat/guardrail_translation): support streaming guardrails
sample on every 5 chunks
* fix(openai/chat/guardrail_translation): support openai streaming guardrails
* fix: initial commit fixing output guardrails for responses api
* feat(openai/responses/guardrail_translation): handler.py - fix output checks on responses api
* fix(openai/responses/guardrail_translation/handler.py): ensure responses api guardrails work on streaming
* test: update tests
* test: update tests
* fix: support multiple kinds of input to the guardrail api
* feat(guardrail_translation/handler.py): support extracting tool calls from openai chat completions for guardrail api's
* feat(generic_guardrail_api.py): support extracting + returning modified tool calls on generic_guardrails_api
allows guardrail api to analyze tool call being sent to provider - to run any analysis on it
* fix(guardrails.py): support anthropic /v1/messages tool calls
* feat(responses_api/): extract tool calls for guardrail processing
* docs(generic_guardrail_api.md): document tools param support
* docs: generic_guardrail_api.md
improve documentation
* fix(unified_guardrail.py): correctly map a v1/messages call to the anthropic unified guardrail
* fix: add more rigorous call type checks
* fix(anthropic_endpoints/endpoints.py): initialize logging object at the beginning of endpoint
ensures call id + trace id are emitted to guardrail api
* feat(anthropic/chat/guardrail_translation): support streaming guardrails
sample on every 5 chunks
* fix(openai/chat/guardrail_translation): support openai streaming guardrails
* fix: initial commit fixing output guardrails for responses api
* feat(openai/responses/guardrail_translation): handler.py - fix output checks on responses api
* fix(openai/responses/guardrail_translation/handler.py): ensure responses api guardrails work on streaming
* test: update tests
* test: update tests
* test: update tests
* fix(bedrock_guardrails.py): fix post call streaming iterator logic
* fix: fix return
* fix(bedrock_guardrails.py): fix
* refactor(generic_guardrail_api.py): refactor to update to new guardrail api logic
* refactor: refactor llm api integrations to support passing in text as a list[str] instead of one at a time
* refactor: fix linting errors
* refactor: pass request type to guardrail api
allows request vs. response processing to occur
* feat: pass user api key dict information to the guardrail api
* fix: pass user api key dict information to the guardrail api
* feat: pass litellm call id + trace id, if present
* docs: update docs
* feat(generic_guardrail_api.py): new generic api for guardrails
Allows guardrail providers to work with litellm for guardrails without needing to make a PR to LiteLLM
* docs(generic_guardrail_api.md): document new generic guardrail api
* Fix: Improve PII detection and guardrail API integration
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* feat: correctly extract raw request from guardrail api
* docs(generic_guardrail_api.md): document this is a beta feature
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Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix: use fastuuid helper across the codebase
First batch of changes, simple drop in replacement.
* second batch of changes
* fixed: script mistake on helper file
* fix: cli auth with SSO okta
* fix: add LITTELM_CLI_SERVICE_ACCOUNT_NAME
* fix: get_litellm_cli_user_api_key_auth
* use existing_key CLI
* fix: use existing key
* test auth commands
* test_cli_sso_callback_regenerate_vs_create_flow
* feat: add CLI Token Utilities
* fix: get_stored_api_key
* move file
* fix: get_valid_models
* fix config.yaml
* TestCLITokenUtils
* TestGetValidModelsWithCLI
* fix: tie user id to keys created through CLI
* fix: add teams interface to CLI
* add /keys/update to the list client commands
* fix /sso/cli/poll to return the user_id
* fix: working TeamsManagementClient
* fix CLI Login command
* fixes for auth
* Potential fix for code scanning alert no. 3400: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* ruff fix
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Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix: add follow_redirects=True,
* test_pass_through_with_httpbin_redirect
* cook book veo video
* docs Veo Video Generation with Google AI Studio
* add veo-3.0-generate-preview cost tracking details
* track vertex_video_models
* Add new model provider Novita AI (#7582)
* feat: add new model provider Novita AI
* feat: use deepseek r1 model for examples in Novita AI docs
* fix: fix tests
* fix: fix tests for novita
* fix: fix novita transformation
* ci: fix ci yaml
* fix: fix novita transformation and test (#10056)
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Co-authored-by: Jason <ggbbddjm@gmail.com>
This commit updates the Grafana dashboard configuration to include a datasource template variable. This allows users to dynamically select the datasource directly within the Grafana dashboard, improving flexibility and user experience.
Cesar Garcia