Commit Graph

5 Commits

Author SHA1 Message Date
knukn
fe75cb35ca feat(client): support agent_name injection to enable isolated memory and custom prompts (#1253)
* feat(client): 添加agent_name参数支持自定义代理名称

允许在初始化DeerFlowClient时指定代理名称,该名称将用于中间件构建和系统提示模板

* test: add coverage for agent_name parameter in DeerFlowClient

* fix(client): address PR review comments for agent_name injection

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Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-03-23 17:44:21 +08:00
haoliangxu
3af709097e fix: normalize structured LLM content in serialization and memory updater (#1215)
* fix: normalize ToolMessage structured content in serialization

When models return ToolMessage content as a list of content blocks
(e.g. [{"type": "text", "text": "..."}]), the UI previously displayed
the raw Python repr string instead of the extracted text.

Replace str(msg.content) with the existing _extract_text() helper in
both _serialize_message() and stream() to properly normalize
list-of-blocks content to plain text.

Fixes #1149

Also fixes the same root cause as #1188 (characters displayed one per
line when tool response content is returned as structured blocks).

Added 11 regression tests covering string, list-of-blocks, mixed,
empty, and fallback content types.

* fix(memory): extract text from structured LLM responses in memory updater

When LLMs return response content as list of content blocks
(e.g. [{"type": "text", "text": "..."}]) instead of plain strings,
str() produces Python repr which breaks JSON parsing in the memory
updater. This caused memory updates to silently fail.

Changes:
- Add _extract_text() helper in updater.py for safe content normalization
- Use _extract_text() instead of str(response.content) in update_memory()
- Fix format_conversation_for_update() to handle plain strings in list content
- Fix subagent executor fallback path to extract text from list content
- Replace print() with structured logging (logger.info/warning/error)
- Add 13 regression tests covering _extract_text, format_conversation,
  and update_memory with structured LLM responses

* fix: address Copilot review - defensive text extraction + logger.exception

- client.py _extract_text: use block.get('text') + isinstance check (prevent KeyError/TypeError)
- prompt.py format_conversation_for_update: same defensive check for dict text blocks
- executor.py: type-safe text extraction in both code paths, fallback to placeholder instead of str(raw_content)
- updater.py: use logger.exception() instead of logger.error() for traceback preservation

* Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* fix: preserve chunked structured content without spurious newlines

* fix: restore backend unit test compatibility

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Co-authored-by: Exploreunive <Exploreunive@users.noreply.github.com>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-03-22 17:29:29 +08:00
haoliangxu
06cba217c3 feat: track token usage per conversation turn (#1218)
* feat: track token usage per conversation turn

Add token usage tracking to the streaming API so consumers can monitor
cost per turn without additional API calls.

Changes:

1. _serialize_message now includes usage_metadata for AI messages in
   values events, exposing input_tokens/output_tokens/total_tokens
   from LangChain's native metadata.

2. stream() accumulates token usage across all AI messages in a turn
   and emits the cumulative totals in the end event:
   {usage: {input_tokens: N, output_tokens: N, total_tokens: N}}

3. Each messages-tuple AI event with text content now includes a
   per-message usage_metadata field for granular tracking.

This enables the frontend to display token consumption per turn,
support cost-aware UX, and let users monitor API spending.

10 tests added covering serialization passthrough and cumulative
aggregation logic.

Co-Authored-By: OpenClaw <noreply@openclaw.ai>

* fix: address Copilot review - use Mapping access for usage_metadata

- Replace getattr(usage, 'input_tokens', 0) with usage.get('input_tokens', 0)
  since LangChain usage_metadata is a dict, not an object
- Remove unused 'import pytest' (fixes Ruff F401)
- Add proper stream() integration tests for cumulative usage in end event
  and per-message usage_metadata in messages-tuple events

---------

Co-authored-by: Exploreunive <Exploreunive@users.noreply.github.com>
Co-authored-by: OpenClaw <noreply@openclaw.ai>
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-03-21 10:29:52 +08:00
Simon Su
ceab7fac14 fix: improve MiniMax code plan integration (#1169)
This PR improves MiniMax Code Plan integration in DeerFlow by fixing three issues in the current flow: stream errors were not clearly surfaced in the UI, the frontend could not display the actual provider model ID, and MiniMax reasoning output could leak into final assistant content as inline <think>...</think>. The change adds a MiniMax-specific adapter, exposes real model IDs end-to-end, and adds a frontend fallback for historical messages.
Co-authored-by: Willem Jiang <willem.jiang@gmail.com>
2026-03-20 17:18:59 +08:00
DanielWalnut
76803b826f refactor: split backend into harness (deerflow.*) and app (app.*) (#1131)
* refactor: extract shared utils to break harness→app cross-layer imports

Move _validate_skill_frontmatter to src/skills/validation.py and
CONVERTIBLE_EXTENSIONS + convert_file_to_markdown to src/utils/file_conversion.py.
This eliminates the two reverse dependencies from client.py (harness layer)
into gateway/routers/ (app layer), preparing for the harness/app package split.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: split backend/src into harness (deerflow.*) and app (app.*)

Physically split the monolithic backend/src/ package into two layers:

- **Harness** (`packages/harness/deerflow/`): publishable agent framework
  package with import prefix `deerflow.*`. Contains agents, sandbox, tools,
  models, MCP, skills, config, and all core infrastructure.

- **App** (`app/`): unpublished application code with import prefix `app.*`.
  Contains gateway (FastAPI REST API) and channels (IM integrations).

Key changes:
- Move 13 harness modules to packages/harness/deerflow/ via git mv
- Move gateway + channels to app/ via git mv
- Rename all imports: src.* → deerflow.* (harness) / app.* (app layer)
- Set up uv workspace with deerflow-harness as workspace member
- Update langgraph.json, config.example.yaml, all scripts, Docker files
- Add build-system (hatchling) to harness pyproject.toml
- Add PYTHONPATH=. to gateway startup commands for app.* resolution
- Update ruff.toml with known-first-party for import sorting
- Update all documentation to reflect new directory structure

Boundary rule enforced: harness code never imports from app.
All 429 tests pass. Lint clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: add harness→app boundary check test and update docs

Add test_harness_boundary.py that scans all Python files in
packages/harness/deerflow/ and fails if any `from app.*` or
`import app.*` statement is found. This enforces the architectural
rule that the harness layer never depends on the app layer.

Update CLAUDE.md to document the harness/app split architecture,
import conventions, and the boundary enforcement test.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add config versioning with auto-upgrade on startup

When config.example.yaml schema changes, developers' local config.yaml
files can silently become outdated. This adds a config_version field and
auto-upgrade mechanism so breaking changes (like src.* → deerflow.*
renames) are applied automatically before services start.

- Add config_version: 1 to config.example.yaml
- Add startup version check warning in AppConfig.from_file()
- Add scripts/config-upgrade.sh with migration registry for value replacements
- Add `make config-upgrade` target
- Auto-run config-upgrade in serve.sh and start-daemon.sh before starting services
- Add config error hints in service failure messages

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix comments

* fix: update src.* import in test_sandbox_tools_security to deerflow.*

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: handle empty config and search parent dirs for config.example.yaml

Address Copilot review comments on PR #1131:
- Guard against yaml.safe_load() returning None for empty config files
- Search parent directories for config.example.yaml instead of only
  looking next to config.yaml, fixing detection in common setups

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: correct skills root path depth and config_version type coercion

- loader.py: fix get_skills_root_path() to use 5 parent levels (was 3)
  after harness split, file lives at packages/harness/deerflow/skills/
  so parent×3 resolved to backend/packages/harness/ instead of backend/
- app_config.py: coerce config_version to int() before comparison in
  _check_config_version() to prevent TypeError when YAML stores value
  as string (e.g. config_version: "1")
- tests: add regression tests for both fixes

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: update test imports from src.* to deerflow.*/app.* after harness refactor

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

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Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-14 22:55:52 +08:00