Address PR review feedback by avoiding an async method reference as the OpenAI client api_key.
Initialize the client with a placeholder key, refresh the Copilot token before each chat/chat_stream call, and update the runtime client api_key before dispatch.
Add a regression test that verifies the client api_key is refreshed to a real string before chat requests.
Generated with GitHub Copilot, GPT-5.4.
Implement the real GitHub device flow and Copilot token exchange for the GitHub Copilot provider.
Also route github-copilot models through a dedicated backend and strip the provider prefix before API requests.
Add focused regression coverage for provider wiring and model normalization.
Generated with GitHub Copilot, GPT-5.4.
Some LLM providers (OpenAI-compat, Azure, vLLM, Ollama) reject requests
with consecutive same-role messages or trailing assistant messages. Add
_enforce_role_alternation() to merge consecutive same-role user/assistant
messages and strip trailing assistant messages before sending to the API.
Replace the flatten/unflatten approach (merging extra_content.google.*
into provider_specific_fields then reconstructing) with direct pass-through:
parse extra_content as-is, store on ToolCallRequest.extra_content, serialize
back untouched. This is lossless, requires no hardcoded field names, and
covers all three parsing branches (str, dict, SDK object) plus streaming.
Handle string and dict-shaped responses from OpenAI-compatible backends so non-standard providers no longer crash on missing choices fields. Add regression tests to keep SDK, dict, and plain-text parsing paths aligned.
Register Mistral as a first-class provider with LiteLLM routing,
MISTRAL_API_KEY env var, and https://api.mistral.ai/v1 default base.
Includes schema field, registry entry, and tests.
Preserve the provider and agent-loop streaming primitives plus the CLI experiment scaffolding so this work can be resumed later without blocking urgent bug fixes on main.
Made-with: Cursor
Resolve conflict in context.py: keep main's build_messages which already
merges runtime context into user message (achieving the same cache goal).
The real value-add from this PR is the second cache breakpoint in
litellm_provider.py.
Made-with: Cursor
When an OpenAI-compatible API returns a non-JSON response (e.g. plain
text "unsupported model: xxx" with HTTP 200), the OpenAI SDK raises a
JSONDecodeError whose message is the unhelpful "Expecting value: line 1
column 1 (char 0)". Extract the original response body from
JSONDecodeError.doc (or APIError.response.text) so users see the actual
error message from the API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the static provider-level supports_vision check with a
reactive fallback: when a model returns an image-unsupported error,
strip image_url blocks from messages and retry once. This avoids
maintaining an inaccurate vision capability table and correctly
handles gateway/unknown model scenarios.
Also extract _safe_chat() to deduplicate try/except boilerplate
in chat_with_retry().
- Add field to ProviderSpec (default True)
- Add and methods in LiteLLMProvider
- Filter image_url content blocks in before sending to non-vision models
- Reverts session-layer filtering from original PR (wrong layer)
This fixes the issue where switching from Claude (vision-capable) to
non-vision models (e.g., Baidu Qianfan) causes API errors due to
unsupported image_url content blocks.
The provider layer is the correct place for this filtering because:
1. It has access to model/provider capabilities
2. It only affects non-vision models
3. It preserves session layer purity (storage should not know about model capabilities)
With custom_llm_provider kwarg handling routing, the openrouter/ prefix
caused model names like anthropic/claude-sonnet-4-6 to become
openrouter/anthropic/claude-sonnet-4-6, which OpenRouter API rejects.
Add native Ollama support so local models (e.g. nemotron-3-nano) can be
used without an API key. Adds ProviderSpec with ollama_chat LiteLLM
prefix, ProvidersConfig field, and skips API key validation for local
providers.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Kunal Karmakar
Xubin Ren
chengyongru
Tejas1Koli
RongLei
pikaxinge
Ziyan Lin
Flo
comadreja
longyongshen
Yohei Nishikubo
Desmond Sow
Matt von Rohr
siyuan.qsyandClaude Opus 4.6
Javis486
Jiajun Xie
coldxiangyu
Paresh Mathur
gaoyiman
HuangMinlong
WhalerO
Jerome Sonnet (letzdoo)andClaude Opus 4.6