fix(#3189): prevent write_file streaming timeout on long reports (#3195)

* fix(#3189): prevent write_file streaming timeout on long reports

Adds a layered defense against StreamChunkTimeoutError caused by oversized
single-shot write_file tool calls:

- factory: default stream_chunk_timeout to 240s for OpenAI-compatible
  clients (overridable via ModelConfig.stream_chunk_timeout in config.yaml)
- sandbox/tools: server-side 80 KB length guard on non-append write_file
  calls (configurable via DEERFLOW_WRITE_FILE_MAX_BYTES env var, 0 disables);
  rejects oversized payloads with a structured error pointing the model at
  str_replace or append=True
- middleware: classify StreamChunkTimeoutError as transient but cap retries
  at 1 via per-exception _RETRY_BUDGET_OVERRIDES (same-payload retry on a
  chunk-gap timeout buffers the same way upstream; full 3-attempt loop
  would stack 6-12 min of dead air)
- middleware: surface an actionable user-facing message for stream-drop
  exceptions instead of leaking the raw langchain stack
- prompts: add a routing-style File Editing Workflow hint to both lead_agent
  and general_purpose subagent prompts, pointing the model at str_replace
  for incremental edits (mirrors Claude Code's Edit / Codex's apply_patch)
- tests: behavioural coverage for size guard, retry budget override,
  stream-drop user message, factory default injection

Refs #3189

* fix(#3189): drop stream_chunk_timeout for non-OpenAI providers

Address CR feedback on PR #3195:

- factory: pop `stream_chunk_timeout` from kwargs for any model_use_path other than `langchain_openai:ChatOpenAI` instead of returning early. `ModelConfig.stream_chunk_timeout` is part of the shared schema, so a user-supplied value on a non-OpenAI provider would otherwise be forwarded to its constructor and raise `TypeError: unexpected keyword argument`.

- factory: rewrite docstring to describe the actual `exclude_none=True` behaviour (explicit null is excluded and falls back to the default) instead of the misleading "None falling out via exclude_none=True keeps its value".

- tests: add regression coverage asserting the kwarg is stripped before reaching a non-OpenAI provider's constructor.

Refs: bytedance#3189

* fix(#3189): restrict stream-drop user copy to StreamChunkTimeoutError only

Per CR on #3195: narrow _STREAM_DROP_EXCEPTIONS to StreamChunkTimeoutError. Generic httpx RemoteProtocolError / ReadError fall back to the standard 'temporarily unavailable' copy, since they routinely fire on transient network blips where the 'split the output' guidance is misleading. Retry/backoff classification is unchanged — both remain transient/retriable. Tests updated to reflect new copy, plus a symmetric regression test for ReadError.

---------

Co-authored-by: Willem Jiang <willem.jiang@gmail.com>

backend/packages/harness/deerflow/agents/lead_agent/prompt.py

@@ -542,6 +542,14 @@ combined with a FastAPI gateway for REST API access [citation:FastAPI](https://f
 {subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
 - Progressive Loading: Load resources incrementally as referenced in skills
 - Output Files: Final deliverables must be in `/mnt/user-data/outputs`
+- File Editing Workflow: When revising an existing file, prefer
+  `str_replace` over `write_file` — it sends only the diff and avoids
+  re-emitting the whole file (mirrors Claude Code's Edit and Codex's
+  apply_patch). When writing long new content from scratch, split it
+  into sections: the first `write_file` call creates the file, then use
+  `write_file` with append=True to extend it section by section. This
+  keeps each tool call small and avoids mid-stream chunk-gap timeouts
+  on oversized single-shot writes. (See issue #3189.)  
 - Clarity: Be direct and helpful, avoid unnecessary meta-commentary
 - Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `![Image Description](image_path)\n\n` or "```mermaid" to display images in response or Markdown files
 - Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance