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https://gitee.com/wanwujie/deer-flow
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* feat: implement tool-specific interrupts for create_react_agent (#572) Add selective tool interrupt capability allowing interrupts before specific tools rather than all tools. Users can now configure which tools trigger interrupts via the interrupt_before_tools parameter. Changes: - Create ToolInterceptor class to handle tool-specific interrupt logic - Add interrupt_before_tools parameter to create_agent() function - Extend Configuration with interrupt_before_tools field - Add interrupt_before_tools to ChatRequest API - Update nodes.py to pass interrupt configuration to agents - Update app.py workflow to support tool interrupt configuration - Add comprehensive unit tests for tool interceptor Features: - Selective tool interrupts: interrupt only specific tools by name - Approval keywords: recognize user approval (approved, proceed, accept, etc.) - Backward compatible: optional parameter, existing code unaffected - Flexible: works with default tools and MCP-powered tools - Works with existing resume mechanism for seamless workflow Example usage: request = ChatRequest( messages=[...], interrupt_before_tools=['db_tool', 'sensitive_api'] ) * test: add comprehensive integration tests for tool-specific interrupts (#572) Add 24 integration tests covering all aspects of the tool interceptor feature: Test Coverage: - Agent creation with tool interrupts - Configuration support (with/without interrupts) - ChatRequest API integration - Multiple tools with selective interrupts - User approval/rejection flows - Tool wrapping and functionality preservation - Error handling and edge cases - Approval keyword recognition - Complex tool inputs - Logging and monitoring All tests pass with 100% coverage of tool interceptor functionality. Tests verify: ✓ Selective tool interrupts work correctly ✓ Only specified tools trigger interrupts ✓ Non-matching tools execute normally ✓ User feedback is properly parsed ✓ Tool functionality is preserved after wrapping ✓ Error handling works as expected ✓ Configuration options are properly respected ✓ Logging provides useful debugging info * fix: mock get_llm_by_type in agent creation test Fix test_agent_creation_with_tool_interrupts which was failing because get_llm_by_type() was being called before create_react_agent was mocked. Changes: - Add mock for get_llm_by_type in test - Use context manager composition for multiple patches - Test now passes and validates tool wrapping correctly All 24 integration tests now pass successfully. * refactor: use mock assertion methods for consistent and clearer error messages Update integration tests to use mock assertion methods instead of direct attribute checking for consistency and clearer error messages: Changes: - Replace 'assert mock_interrupt.called' with 'mock_interrupt.assert_called()' - Replace 'assert not mock_interrupt.called' with 'mock_interrupt.assert_not_called()' Benefits: - Consistent with pytest-mock and unittest.mock best practices - Clearer error messages when assertions fail - Better IDE autocompletion support - More professional test code All 42 tests pass with improved assertion patterns. * refactor: use default_factory for interrupt_before_tools consistency Improve consistency between ChatRequest and Configuration implementations: Changes: - ChatRequest.interrupt_before_tools: Use Field(default_factory=list) instead of Optional[None] - Remove unnecessary 'or []' conversion in app.py line 505 - Aligns with Configuration.interrupt_before_tools implementation pattern - No functional changes - all tests still pass Benefits: - Consistent field definition across codebase - Simpler and cleaner code - Reduced chance of None/empty list bugs - Better alignment with Pydantic best practices All 42 tests passing. * refactor: improve tool input formatting in interrupt messages Enhance tool input representation for better readability in interrupt messages: Changes: - Add json import for better formatting - Create _format_tool_input() static method with JSON serialization - Use JSON formatting for dicts, lists, tuples with indent=2 - Fall back to str() for non-serializable types - Handle None input specially (returns 'No input') - Improve interrupt message formatting with better spacing Benefits: - Complex tool inputs now display as readable JSON - Nested structures are properly indented and visible - Better user experience when reviewing tool inputs before approval - Handles edge cases gracefully with fallbacks - Improved logging output for debugging Example improvements: Before: {'query': 'SELECT...', 'limit': 10, 'nested': {'key': 'value'}} After: { "query": "SELECT...", "limit": 10, "nested": { "key": "value" } } All 42 tests still passing. * test: add comprehensive unit tests for tool input formatting
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import logging
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from typing import List, Optional
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from langgraph.prebuilt import create_react_agent
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from src.config.agents import AGENT_LLM_MAP
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from src.llms.llm import get_llm_by_type
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from src.prompts import apply_prompt_template
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from src.agents.tool_interceptor import wrap_tools_with_interceptor
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logger = logging.getLogger(__name__)
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# Create agents using configured LLM types
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def create_agent(
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agent_name: str,
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agent_type: str,
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tools: list,
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prompt_template: str,
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pre_model_hook: callable = None,
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interrupt_before_tools: Optional[List[str]] = None,
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):
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"""Factory function to create agents with consistent configuration.
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Args:
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agent_name: Name of the agent
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agent_type: Type of agent (researcher, coder, etc.)
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tools: List of tools available to the agent
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prompt_template: Name of the prompt template to use
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pre_model_hook: Optional hook to preprocess state before model invocation
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interrupt_before_tools: Optional list of tool names to interrupt before execution
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Returns:
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A configured agent graph
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"""
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# Wrap tools with interrupt logic if specified
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processed_tools = tools
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if interrupt_before_tools:
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logger.info(
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f"Creating agent '{agent_name}' with tool-specific interrupts: {interrupt_before_tools}"
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)
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processed_tools = wrap_tools_with_interceptor(tools, interrupt_before_tools)
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return create_react_agent(
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name=agent_name,
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model=get_llm_by_type(AGENT_LLM_MAP[agent_type]),
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tools=processed_tools,
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prompt=lambda state: apply_prompt_template(
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prompt_template, state, locale=state.get("locale", "en-US")
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),
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pre_model_hook=pre_model_hook,
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)
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