github/deer-flow
1
0
Fork 0
mirror of https://github.com/bytedance/deer-flow.git synced 2026-09-15 00:19:14 +00:00
Code Issues Packages Projects Releases Wiki Activity
deer-flow/backend/packages/harness/deerflow/models/AGENTS.md
Ryker_Feng ccff5f5ce7
docs: govern agent guidance size (#4799) 
* docs: govern agent guidance size

* refactor: split agent guidance by code scope

* Clarify virtual path handling in AGENTS.md

Updated the translation section to clarify the role of `LocalSandboxProvider` and the handling of virtual paths in the tool layer.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-08-13 21:49:04 +08:00

1.8 KiB
Raw Blame History

Model Factory (packages/harness/deerflow/models/factory.py)

  • create_chat_model(name, thinking_enabled) instantiates LLM from config via reflection
  • Supports thinking_enabled flag with per-model when_thinking_enabled overrides
  • Supports vLLM-style thinking toggles via when_thinking_enabled.extra_body.chat_template_kwargs.enable_thinking for Qwen reasoning models, while normalizing legacy thinking configs for backward compatibility
  • Supports supports_vision flag for image understanding models
  • Config values starting with $ resolved as environment variables
  • Missing provider modules surface actionable install hints from reflection resolvers (for example uv add langchain-google-genai)

vLLM Provider (packages/harness/deerflow/models/vllm_provider.py)

  • VllmChatModel subclasses langchain_openai:ChatOpenAI for vLLM 0.19.0 OpenAI-compatible endpoints
  • Preserves vLLM's non-standard assistant reasoning field on full responses, streaming deltas, and follow-up tool-call turns
  • Designed for configs that enable thinking through extra_body.chat_template_kwargs.enable_thinking on vLLM 0.19.0 Qwen reasoning models, while accepting the older thinking alias
  • cumulative_stream_usage is an opt-in model setting (default false) for endpoints that repeat cumulative token totals on each streaming chunk. The provider converts snapshots to deltas only when a stable completion id is present, isolates interleaved streams by id, and leaves the original usage untouched otherwise. Per-model tracking is lock-protected and cleared on the trailing empty-choices frame whether or not that frame carries usage. A soft cap of 1024 ids evicts only entries idle for at least one hour; active streams may temporarily exceed the cap so eviction cannot corrupt their deltas. Regression coverage lives in tests/test_vllm_provider.py.