* Add doc for adding model pricing and context window
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
* Refactor model pricing documentation to include sample spec and examples
Co-authored-by: krrishdholakia <krrishdholakia@gmail.com>
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Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* Add fallback in sort to prevent NoneType and str comparison
* Hide Default Team Settings from Proxy Admin Viewers
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Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* fix: prevent duplicate spend logs in Responses API for non-OpenAI providers
Fixes#15740
This fixes a logging duplication bug where using kwargs.pop() removed
the litellm_logging_obj before passing kwargs to internal acompletion()
calls, causing duplicate spend log entries for providers without native
Responses API support (Anthropic, Gemini, etc).
By changing from pop() to get(), the logging object is preserved and
reused across the internal completion call, preventing duplicate entries
and maintaining correct cost tracking.
* test: add test for logging object preservation in responses API
Verify that litellm_logging_obj is preserved in kwargs when calling
responses(), ensuring no duplicate spend log entries are created.
- Add conditional check for 'global' vertex_location
- Use aiplatform.googleapis.com (no prefix) for global endpoint
- Apply fix to both v1 and v1beta1 APIs
- Matches existing behavior in regular completion calls
Fixes context caching 404 errors when using global location.
Regular completion already handles global correctly, this brings
context caching in line with that behavior.
Related: #11190, #9234
Co-authored-by: prawaan-singh <prawaan.singh@thoughtspot.com>
- Update Hosted Proxy links to point to enterprise docs
- Remove "(Preview)" label from Hosted Proxy
- Fix "Supported LLM Providers" link to point to docs instead of GitHub anchor
Fixes cross-platform Docker build issue where `prisma generate` fails
when building for amd64 platform from macOS. The Prisma CLI requires
Node.js and npm to be available in the runtime environment.
The Python prisma package (v0.11.0) uses nodeenv to bootstrap Node.js
if not found. However, the downloaded npm v10 fails with a
"sizeCalculation" error in minimal Chainguard environments during
cross-platform builds. Providing system nodejs/npm resolves this.
Changes:
- Added nodejs and npm to runtime dependencies (Dockerfile:51)
- This enables prisma generate to run successfully during the build
Error without fix:
npm error cannot set sizeCalculation without setting maxSize or maxEntrySize
subprocess.CalledProcessError: Command '[...nodeenv/bin/npm', 'install',
'prisma@5.4.2']' returned non-zero exit status 1.
Testing:
docker buildx build --platform linux/amd64 -t litellm:test .
Co-authored-by: Claude <noreply@anthropic.com>
* fix: fix getting mcp servers
* fix(litellm_logging.py): handle list objects for final response in standard logging payload
Fixes issue where mcp tool call response wouldn't show up
* fix(litellm_responses_transformation/): remove invalid item error for unmapped objects - breaks stream and there's no real value to this as outside of a few of them, not all can be mapped to chat completions
resolves error for web search calls via chat completions to responses api
* Litellm dev 11 22 2025 p1 (#16975)
* fix(model_armor.py): return response after applying changes
* fix: initial commit adding guardrail span logging to otel on post-call runs
sends it as a separate span right now, need to include in the same llm request/response span
* fix(opentelemetry.py): include guardrail in received request log + set input/ouput fields on parent otel span instead of nesting it
allows request/response to be seen easily on observability tools
* fix(model_armor.py): working model armor logging on post call events
* fix: fix exception message
* fix(opentelemetry.py): add backwards compatibility for litellm_request
allow users building on the spec change to use previous spec
* feat(teams.py): param for disabling guardrails by team
allows use-case where you don't run global guardrails for team - only run team-specific guardrails
* feat(custom_guardrail.py): add support for disabling global guardrails
only run guardrails requested for in the request/key/team
* feat: support adding disable_global_guardrails to metadata if present in key/team metadata
* feat(create_key_button.tsx): new disable global guardrails field
* feat(key_edit_view.tsx): support disabling global guardrails on key edit
* feat(teams.tsx): add disable global guardrails on create team on UI
* feat(team_info.tsx): allow disabling global guardrails on team update
