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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 - **App** (`app/`): unpublished application code with import prefix `app.*`.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on startup When config.example.yaml schema changes, developers' local config.yaml files can silently become outdated.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 intercepting clarification requests and presenting them to the user."""
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from collections.abc import Callable
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from typing import 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 langchain_core.messages import ToolMessage
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from langgraph.graph import END
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from langgraph.prebuilt.tool_node import ToolCallRequest
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from langgraph.types import Command
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class ClarificationMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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pass
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class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
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"""Intercepts clarification tool calls and interrupts execution to present questions to the user.
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When the model calls the `ask_clarification` tool, this middleware:
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1. Intercepts the tool call before execution
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2. Extracts the clarification question and metadata
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3. Formats a user-friendly message
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4. Returns a Command that interrupts execution and presents the question
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5. Waits for user response before continuing
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This replaces the tool-based approach where clarification continued the conversation flow.
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"""
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state_schema = ClarificationMiddlewareState
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def _is_chinese(self, text: str) -> bool:
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"""Check if text contains Chinese characters.
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Args:
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text: Text to check
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Returns:
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True if text contains Chinese characters
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"""
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return any("\u4e00" <= char <= "\u9fff" for char in text)
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def _format_clarification_message(self, args: dict) -> str:
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"""Format the clarification arguments into a user-friendly message.
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Args:
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args: The tool call arguments containing clarification details
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Returns:
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Formatted message string
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"""
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question = args.get("question", "")
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clarification_type = args.get("clarification_type", "missing_info")
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context = args.get("context")
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options = args.get("options", [])
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# Type-specific icons
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type_icons = {
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"missing_info": "❓",
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"ambiguous_requirement": "🤔",
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"approach_choice": "🔀",
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"risk_confirmation": "⚠️",
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"suggestion": "💡",
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}
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icon = type_icons.get(clarification_type, "❓")
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# Build the message naturally
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message_parts = []
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# Add icon and question together for a more natural flow
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if context:
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# If there's context, present it first as background
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message_parts.append(f"{icon} {context}")
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message_parts.append(f"\n{question}")
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else:
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# Just the question with icon
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message_parts.append(f"{icon} {question}")
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# Add options in a cleaner format
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if options and len(options) > 0:
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message_parts.append("") # blank line for spacing
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for i, option in enumerate(options, 1):
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message_parts.append(f" {i}. {option}")
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return "\n".join(message_parts)
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def _handle_clarification(self, request: ToolCallRequest) -> Command:
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"""Handle clarification request and return command to interrupt execution.
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Args:
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request: Tool call request
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Returns:
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Command that interrupts execution with the formatted clarification message
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"""
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# Extract clarification arguments
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args = request.tool_call.get("args", {})
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question = args.get("question", "")
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print("[ClarificationMiddleware] Intercepted clarification request")
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print(f"[ClarificationMiddleware] Question: {question}")
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# Format the clarification message
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formatted_message = self._format_clarification_message(args)
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# Get the tool call ID
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tool_call_id = request.tool_call.get("id", "")
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# Create a ToolMessage with the formatted question
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# This will be added to the message history
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tool_message = ToolMessage(
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content=formatted_message,
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tool_call_id=tool_call_id,
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name="ask_clarification",
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)
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# Return a Command that:
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# 1. Adds the formatted tool message
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# 2. Interrupts execution by going to __end__
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# Note: We don't add an extra AIMessage here - the frontend will detect
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# and display ask_clarification tool messages directly
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return Command(
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update={"messages": [tool_message]},
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goto=END,
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)
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@override
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def wrap_tool_call(
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self,
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request: ToolCallRequest,
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handler: Callable[[ToolCallRequest], ToolMessage | Command],
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) -> ToolMessage | Command:
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"""Intercept ask_clarification tool calls and interrupt execution (sync version).
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Args:
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request: Tool call request
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handler: Original tool execution handler
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Returns:
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Command that interrupts execution with the formatted clarification message
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"""
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# Check if this is an ask_clarification tool call
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if request.tool_call.get("name") != "ask_clarification":
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# Not a clarification call, execute normally
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return handler(request)
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return self._handle_clarification(request)
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@override
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async def awrap_tool_call(
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self,
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request: ToolCallRequest,
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handler: Callable[[ToolCallRequest], ToolMessage | Command],
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) -> ToolMessage | Command:
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"""Intercept ask_clarification tool calls and interrupt execution (async version).
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Args:
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request: Tool call request
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handler: Original tool execution handler (async)
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Returns:
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Command that interrupts execution with the formatted clarification message
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"""
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# Check if this is an ask_clarification tool call
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if request.tool_call.get("name") != "ask_clarification":
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# Not a clarification call, execute normally
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return await handler(request)
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return self._handle_clarification(request)
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