Keep tool-call assistant messages valid across provider sanitization and avoid trailing user-only history after model errors. This prevents follow-up requests from sending broken tool chains back to the gateway.
Resolved conflict in azure_openai_provider.py by keeping main's
Responses API implementation (role alternation not needed for the
Responses API input format).
Made-with: Cursor
- Add reasoning_content fallback from reasoning in _parse dict branch
- Add content fallback from msg.reasoning in _parse SDK object branch
- Add reasoning_content fallback in _parse SDK object branch
- Add reasoning fallback in _parse_chunks dict branch
- Add reasoning fallback in _parse_chunks SDK object branch
This ensures StepFun Plan API works correctly in both streaming and
non-streaming modes, for both dict and SDK object response formats.
StepFun Plan API returns response content in the 'reasoning' field when
the model is in thinking mode and 'content' is empty. OpenAICompatProvider
previously only checked 'content' and 'reasoning_content', missing this field.
This patch adds a fallback: if content is empty and 'reasoning' is present,
extract text from reasoning to populate content, ensuring StepFun models
(step-3.5-flash, step-3.5-flash-2603) work correctly with tool calls.
Co-authored-by: moranfong <moranfong@gmail.com>
Extends reasoning_effort to accept 'adaptive' in addition to
low/medium/high. When set, uses Anthropic's type: 'adaptive'
thinking API instead of a fixed budget, letting the model decide
when and how much to think per turn.
Also auto-enables interleaved thinking between tool calls on
claude-sonnet-4-6 and claude-opus-4-6.
Usage:
"reasoning_effort": "adaptive" in agents.defaults config
Merge the three retry-after header parsers (base, OpenAI, Anthropic)
into a single _extract_retry_after_from_headers on LLMProvider that
handles retry-after-ms, case-insensitive lookup, and HTTP date.
Remove the per-provider _parse_retry_after_headers duplicates and
their now-unused email.utils / time imports. Add test for retry-after-ms.
Made-with: Cursor
- Add byteplus and byteplus_coding_plan to thinking param providers
- Only send extra_body when reasoning_effort is explicitly set
- Use setdefault().update() to avoid clobbering existing extra_body
- Add 7 regression tests for thinking params
Made-with: Cursor
- Use import importlib.util (not bare importlib) for find_spec
- Warn and fall back to standard openai instead of crashing with
ImportError when LANGFUSE_SECRET_KEY is set but langfuse is missing
Made-with: Cursor
The streaming path in OpenAICompatProvider.chat_stream() was passing
reasoning_content deltas through on_content_delta(), causing model
internal reasoning to be displayed to the user alongside the actual
response content.
reasoning_content is already collected separately in _parse_chunks()
and stored in LLMResponse.reasoning_content for session history.
It should never be forwarded to the user-facing stream.
Enable GPT-5 models (gpt-5, gpt-5.4, gpt-5.4-mini, etc.) to work
correctly with the OpenAI-compatible provider by:
- Setting `supports_max_completion_tokens=True` on the OpenAI provider
spec so `max_completion_tokens` is sent instead of the deprecated
`max_tokens` parameter that GPT-5 rejects.
- Adding `_supports_temperature()` to conditionally omit the
`temperature` parameter for reasoning models (o1/o3/o4) and when
`reasoning_effort` is active, matching the existing Azure provider
behaviour.
Both changes are backward-compatible: older GPT-4 models continue to
work as before since `max_completion_tokens` is accepted by all recent
OpenAI models and temperature is only omitted when reasoning is active.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
reasoning_content and extra_content were accidentally dropped from
_ALLOWED_MSG_KEYS.
Also fix session/manager.py to include reasoning_content when building
LLM messages from session history, so the field is not lost across
turns.
Without this fix, providers such as Kimi, emit reasoning_content in
assistant messages will have it stripped on the next request, breaking
multi-turn thinking mode.
Fixes: https://github.com/HKUDS/nanobot/issues/2777
Signed-off-by: Lingao Meng <menglingao@xiaomi.com>
Extract reasoning_content from both non-streaming and streaming responses
in OpenAICompatProvider. Accumulate chunks during streaming and merge into
LLMResponse, enabling reasoning chain display for models like MiMo and DeepSeek-R1.
Signed-off-by: Lingao Meng <menglingao@xiaomi.com>
Register xiaomi_mimo as an OpenAI-compatible provider with its API base URL,
add xiaomi_mimo to the provider config schema, and document it in README.
Signed-off-by: Lingao Meng <menglingao@xiaomi.com>
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.
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