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https://gitee.com/wanwujie/deer-flow
synced 2026-04-24 22:54:46 +08:00
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 - **App** (`app/`): unpublished application code with import prefix `app.*`.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on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated.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> --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
DanielWalnut
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"""Middleware for memory mechanism."""
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import re
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from typing import Any, override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langgraph.runtime import Runtime
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from deerflow.agents.memory.queue import get_memory_queue
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from deerflow.config.memory_config import get_memory_config
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class MemoryMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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pass
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def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
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"""Filter messages to keep only user inputs and final assistant responses.
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This filters out:
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- Tool messages (intermediate tool call results)
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- AI messages with tool_calls (intermediate steps, not final responses)
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- The <uploaded_files> block injected by UploadsMiddleware into human messages
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(file paths are session-scoped and must not persist in long-term memory).
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The user's actual question is preserved; only turns whose content is entirely
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the upload block (nothing remains after stripping) are dropped along with
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their paired assistant response.
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Only keeps:
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- Human messages (with the ephemeral upload block removed)
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- AI messages without tool_calls (final assistant responses), unless the
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paired human turn was upload-only and had no real user text.
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Args:
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messages: List of all conversation messages.
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Returns:
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Filtered list containing only user inputs and final assistant responses.
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"""
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_UPLOAD_BLOCK_RE = re.compile(r"<uploaded_files>[\s\S]*?</uploaded_files>\n*", re.IGNORECASE)
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filtered = []
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skip_next_ai = False
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for msg in messages:
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msg_type = getattr(msg, "type", None)
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if msg_type == "human":
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content = getattr(msg, "content", "")
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if isinstance(content, list):
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content = " ".join(p.get("text", "") for p in content if isinstance(p, dict))
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content_str = str(content)
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if "<uploaded_files>" in content_str:
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# Strip the ephemeral upload block; keep the user's real question.
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stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
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if not stripped:
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# Nothing left — the entire turn was upload bookkeeping;
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# skip it and the paired assistant response.
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skip_next_ai = True
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continue
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# Rebuild the message with cleaned content so the user's question
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# is still available for memory summarisation.
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from copy import copy
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clean_msg = copy(msg)
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clean_msg.content = stripped
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filtered.append(clean_msg)
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skip_next_ai = False
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else:
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filtered.append(msg)
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skip_next_ai = False
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elif msg_type == "ai":
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tool_calls = getattr(msg, "tool_calls", None)
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if not tool_calls:
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if skip_next_ai:
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skip_next_ai = False
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continue
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filtered.append(msg)
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# Skip tool messages and AI messages with tool_calls
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return filtered
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class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
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"""Middleware that queues conversation for memory update after agent execution.
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This middleware:
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1. After each agent execution, queues the conversation for memory update
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2. Only includes user inputs and final assistant responses (ignores tool calls)
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3. The queue uses debouncing to batch multiple updates together
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4. Memory is updated asynchronously via LLM summarization
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"""
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state_schema = MemoryMiddlewareState
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def __init__(self, agent_name: str | None = None):
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"""Initialize the MemoryMiddleware.
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Args:
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agent_name: If provided, memory is stored per-agent. If None, uses global memory.
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"""
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super().__init__()
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self._agent_name = agent_name
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@override
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def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
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"""Queue conversation for memory update after agent completes.
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Args:
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state: The current agent state.
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runtime: The runtime context.
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Returns:
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None (no state changes needed from this middleware).
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"""
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config = get_memory_config()
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if not config.enabled:
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return None
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# Get thread ID from runtime context
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thread_id = runtime.context.get("thread_id")
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if not thread_id:
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print("MemoryMiddleware: No thread_id in context, skipping memory update")
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return None
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# Get messages from state
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messages = state.get("messages", [])
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if not messages:
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print("MemoryMiddleware: No messages in state, skipping memory update")
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return None
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# Filter to only keep user inputs and final assistant responses
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filtered_messages = _filter_messages_for_memory(messages)
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# Only queue if there's meaningful conversation
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# At minimum need one user message and one assistant response
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user_messages = [m for m in filtered_messages if getattr(m, "type", None) == "human"]
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assistant_messages = [m for m in filtered_messages if getattr(m, "type", None) == "ai"]
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if not user_messages or not assistant_messages:
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return None
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# Queue the filtered conversation for memory update
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queue = get_memory_queue()
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queue.add(thread_id=thread_id, messages=filtered_messages, agent_name=self._agent_name)
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return None
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