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deer-flow/tests/integration/test_tool_interceptor_integration.py

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feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT
"""
Integration tests for tool-specific interrupts feature (Issue #572).
Tests the complete flow of selective tool interrupts including:
- Tool wrapping with interrupt logic
- Agent creation with interrupt configuration
- Tool execution with user feedback
- Resume mechanism after interrupt
"""
from typing import Any
security: add log injection attack prevention with input sanitization (#667) * security: add log injection attack prevention with input sanitization - Created src/utils/log_sanitizer.py to sanitize user-controlled input before logging - Prevents log injection attacks using newlines, tabs, carriage returns, etc. - Escapes dangerous characters: \n, \r, \t, \0, \x1b - Provides specialized functions for different input types: - sanitize_log_input: general purpose sanitization - sanitize_thread_id: for user-provided thread IDs - sanitize_user_content: for user messages (more aggressive truncation) - sanitize_agent_name: for agent identifiers - sanitize_tool_name: for tool names - sanitize_feedback: for user interrupt feedback - create_safe_log_message: template-based safe message creation - Updated src/server/app.py to sanitize all user input in logging: - Thread IDs from request parameter - Message content from user - Agent names and node information - Tool names and feedback - Updated src/agents/tool_interceptor.py to sanitize: - Tool names during execution - User feedback during interrupt handling - Tool input data - Added 29 comprehensive unit tests covering: - Classic newline injection attacks - Carriage return injection - Tab and null character injection - HTML/ANSI escape sequence injection - Combined multi-character attacks - Truncation and length limits Fixes potential log forgery vulnerability where malicious users could inject fake log entries via unsanitized input containing control characters.
2025-10-27 20:57:23 +08:00
from unittest.mock import AsyncMock, MagicMock, Mock, call, patch
feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
security: add log injection attack prevention with input sanitization (#667) * security: add log injection attack prevention with input sanitization - Created src/utils/log_sanitizer.py to sanitize user-controlled input before logging - Prevents log injection attacks using newlines, tabs, carriage returns, etc. - Escapes dangerous characters: \n, \r, \t, \0, \x1b - Provides specialized functions for different input types: - sanitize_log_input: general purpose sanitization - sanitize_thread_id: for user-provided thread IDs - sanitize_user_content: for user messages (more aggressive truncation) - sanitize_agent_name: for agent identifiers - sanitize_tool_name: for tool names - sanitize_feedback: for user interrupt feedback - create_safe_log_message: template-based safe message creation - Updated src/server/app.py to sanitize all user input in logging: - Thread IDs from request parameter - Message content from user - Agent names and node information - Tool names and feedback - Updated src/agents/tool_interceptor.py to sanitize: - Tool names during execution - User feedback during interrupt handling - Tool input data - Added 29 comprehensive unit tests covering: - Classic newline injection attacks - Carriage return injection - Tab and null character injection - HTML/ANSI escape sequence injection - Combined multi-character attacks - Truncation and length limits Fixes potential log forgery vulnerability where malicious users could inject fake log entries via unsanitized input containing control characters.
2025-10-27 20:57:23 +08:00
import pytest
feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
from langchain_core.messages import HumanMessage
security: add log injection attack prevention with input sanitization (#667) * security: add log injection attack prevention with input sanitization - Created src/utils/log_sanitizer.py to sanitize user-controlled input before logging - Prevents log injection attacks using newlines, tabs, carriage returns, etc. - Escapes dangerous characters: \n, \r, \t, \0, \x1b - Provides specialized functions for different input types: - sanitize_log_input: general purpose sanitization - sanitize_thread_id: for user-provided thread IDs - sanitize_user_content: for user messages (more aggressive truncation) - sanitize_agent_name: for agent identifiers - sanitize_tool_name: for tool names - sanitize_feedback: for user interrupt feedback - create_safe_log_message: template-based safe message creation - Updated src/server/app.py to sanitize all user input in logging: - Thread IDs from request parameter - Message content from user - Agent names and node information - Tool names and feedback - Updated src/agents/tool_interceptor.py to sanitize: - Tool names during execution - User feedback during interrupt handling - Tool input data - Added 29 comprehensive unit tests covering: - Classic newline injection attacks - Carriage return injection - Tab and null character injection - HTML/ANSI escape sequence injection - Combined multi-character attacks - Truncation and length limits Fixes potential log forgery vulnerability where malicious users could inject fake log entries via unsanitized input containing control characters.
2025-10-27 20:57:23 +08:00
from langchain_core.tools import tool
feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
from src.agents.agents import create_agent
from src.agents.tool_interceptor import ToolInterceptor, wrap_tools_with_interceptor
from src.config.configuration import Configuration
from src.server.chat_request import ChatRequest
class TestToolInterceptorIntegration:
"""Integration tests for tool interceptor with agent workflow."""
def test_agent_creation_with_tool_interrupts(self):
"""Test creating an agent with tool interrupts configured."""
@tool
def search_tool(query: str) -> str:
"""Search the web."""
return f"Search results for: {query}"
@tool
def db_tool(query: str) -> str:
"""Query database."""
return f"DB results for: {query}"
tools = [search_tool, db_tool]
# Create agent with interrupts on db_tool only
with patch("src.agents.agents.langchain_create_agent") as mock_create, \
feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
patch("src.agents.agents.get_llm_by_type") as mock_llm:
mock_create.return_value = MagicMock()
mock_llm.return_value = MagicMock()
agent = create_agent(
agent_name="test_agent",
agent_type="researcher",
tools=tools,
prompt_template="researcher",
interrupt_before_tools=["db_tool"],
)
# Verify langchain_create_agent was called with wrapped tools
feat: implement tool-specific interrupts for create_react_agent (#572) (#659) * 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
2025-10-26 09:47:03 +08:00
assert mock_create.called
call_args = mock_create.call_args
wrapped_tools = call_args.kwargs["tools"]
# Should have wrapped the tools
assert len(wrapped_tools) == 2
assert wrapped_tools[0].name == "search_tool"
assert wrapped_tools[1].name == "db_tool"
def test_configuration_with_tool_interrupts(self):
"""Test Configuration object with interrupt_before_tools."""
config = Configuration(
interrupt_before_tools=["db_tool", "api_write_tool"],
max_step_num=3,
max_search_results=5,
)
assert config.interrupt_before_tools == ["db_tool", "api_write_tool"]
assert config.max_step_num == 3
assert config.max_search_results == 5
def test_configuration_default_no_interrupts(self):
"""Test Configuration defaults to no interrupts."""
config = Configuration()
assert config.interrupt_before_tools == []
def test_chat_request_with_tool_interrupts(self):
"""Test ChatRequest with interrupt_before_tools."""
request = ChatRequest(
messages=[{"role": "user", "content": "Search for X"}],
interrupt_before_tools=["db_tool", "payment_api"],
)
assert request.interrupt_before_tools == ["db_tool", "payment_api"]
def test_chat_request_interrupt_feedback_with_tool_interrupts(self):
"""Test ChatRequest with both interrupt_before_tools and interrupt_feedback."""
request = ChatRequest(
messages=[{"role": "user", "content": "Research topic"}],
interrupt_before_tools=["db_tool"],
interrupt_feedback="approved",
)
assert request.interrupt_before_tools == ["db_tool"]
assert request.interrupt_feedback == "approved"
def test_multiple_tools_selective_interrupt(self):
"""Test that only specified tools trigger interrupts."""
@tool
def tool_a(x: str) -> str:
"""Tool A"""
return f"A: {x}"
@tool
def tool_b(x: str) -> str:
"""Tool B"""
return f"B: {x}"
@tool
def tool_c(x: str) -> str:
"""Tool C"""
return f"C: {x}"
tools = [tool_a, tool_b, tool_c]
interceptor = ToolInterceptor(["tool_b"])
# Wrap all tools
wrapped_tools = wrap_tools_with_interceptor(tools, ["tool_b"])
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
# tool_a should not interrupt
mock_interrupt.return_value = "approved"
result_a = wrapped_tools[0].invoke("test")
mock_interrupt.assert_not_called()
# tool_b should interrupt
result_b = wrapped_tools[1].invoke("test")
mock_interrupt.assert_called()
# tool_c should not interrupt
mock_interrupt.reset_mock()
result_c = wrapped_tools[2].invoke("test")
mock_interrupt.assert_not_called()
def test_interrupt_with_user_approval(self):
"""Test interrupt flow with user approval."""
@tool
def sensitive_tool(action: str) -> str:
"""A sensitive tool."""
return f"Executed: {action}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
interceptor = ToolInterceptor(["sensitive_tool"])
wrapped = ToolInterceptor.wrap_tool(sensitive_tool, interceptor)
result = wrapped.invoke("delete_data")
mock_interrupt.assert_called()
assert "Executed: delete_data" in str(result)
def test_interrupt_with_user_rejection(self):
"""Test interrupt flow with user rejection."""
@tool
def sensitive_tool(action: str) -> str:
"""A sensitive tool."""
return f"Executed: {action}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "rejected"
interceptor = ToolInterceptor(["sensitive_tool"])
wrapped = ToolInterceptor.wrap_tool(sensitive_tool, interceptor)
result = wrapped.invoke("delete_data")
mock_interrupt.assert_called()
assert isinstance(result, dict)
assert "error" in result
assert result["status"] == "rejected"
def test_interrupt_message_contains_tool_info(self):
"""Test that interrupt message contains tool name and input."""
@tool
def db_query_tool(query: str) -> str:
"""Database query tool."""
return f"Query result: {query}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
interceptor = ToolInterceptor(["db_query_tool"])
wrapped = ToolInterceptor.wrap_tool(db_query_tool, interceptor)
wrapped.invoke("SELECT * FROM users")
# Verify interrupt was called with meaningful message
mock_interrupt.assert_called()
interrupt_message = mock_interrupt.call_args[0][0]
assert "db_query_tool" in interrupt_message
assert "SELECT * FROM users" in interrupt_message
def test_tool_wrapping_preserves_functionality(self):
"""Test that tool wrapping preserves original tool functionality."""
@tool
def simple_tool(text: str) -> str:
"""Process text."""
return f"Processed: {text}"
interceptor = ToolInterceptor([]) # No interrupts
wrapped = ToolInterceptor.wrap_tool(simple_tool, interceptor)
result = wrapped.invoke({"text": "hello"})
assert "hello" in str(result)
def test_tool_wrapping_preserves_tool_metadata(self):
"""Test that tool wrapping preserves tool name and description."""
@tool
def my_special_tool(x: str) -> str:
"""This is my special tool description."""
return f"Result: {x}"
interceptor = ToolInterceptor([])
wrapped = ToolInterceptor.wrap_tool(my_special_tool, interceptor)
assert wrapped.name == "my_special_tool"
assert "special tool" in wrapped.description.lower()
def test_multiple_interrupts_in_sequence(self):
"""Test handling multiple tool interrupts in sequence."""
@tool
def tool_one(x: str) -> str:
"""Tool one."""
return f"One: {x}"
@tool
def tool_two(x: str) -> str:
"""Tool two."""
return f"Two: {x}"
@tool
def tool_three(x: str) -> str:
"""Tool three."""
return f"Three: {x}"
tools = [tool_one, tool_two, tool_three]
wrapped_tools = wrap_tools_with_interceptor(
tools, ["tool_one", "tool_two"]
)
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
# First interrupt
result_one = wrapped_tools[0].invoke("first")
assert mock_interrupt.call_count == 1
# Second interrupt
result_two = wrapped_tools[1].invoke("second")
assert mock_interrupt.call_count == 2
# Third (no interrupt)
result_three = wrapped_tools[2].invoke("third")
assert mock_interrupt.call_count == 2
assert "One: first" in str(result_one)
assert "Two: second" in str(result_two)
assert "Three: third" in str(result_three)
def test_empty_interrupt_list_no_interrupts(self):
"""Test that empty interrupt list doesn't trigger interrupts."""
@tool
def test_tool(x: str) -> str:
"""Test tool."""
return f"Result: {x}"
wrapped_tools = wrap_tools_with_interceptor([test_tool], [])
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
wrapped_tools[0].invoke("test")
mock_interrupt.assert_not_called()
def test_none_interrupt_list_no_interrupts(self):
"""Test that None interrupt list doesn't trigger interrupts."""
@tool
def test_tool(x: str) -> str:
"""Test tool."""
return f"Result: {x}"
wrapped_tools = wrap_tools_with_interceptor([test_tool], None)
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
wrapped_tools[0].invoke("test")
mock_interrupt.assert_not_called()
def test_case_sensitive_tool_name_matching(self):
"""Test that tool name matching is case-sensitive."""
@tool
def MyTool(x: str) -> str:
"""A tool."""
return f"Result: {x}"
interceptor_lower = ToolInterceptor(["mytool"])
interceptor_exact = ToolInterceptor(["MyTool"])
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
# Case mismatch - should NOT interrupt
wrapped_lower = ToolInterceptor.wrap_tool(MyTool, interceptor_lower)
result_lower = wrapped_lower.invoke("test")
mock_interrupt.assert_not_called()
# Case match - should interrupt
wrapped_exact = ToolInterceptor.wrap_tool(MyTool, interceptor_exact)
result_exact = wrapped_exact.invoke("test")
mock_interrupt.assert_called()
def test_tool_error_handling(self):
"""Test handling of tool errors during execution."""
@tool
def error_tool(x: str) -> str:
"""A tool that raises an error."""
raise ValueError(f"Intentional error: {x}")
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
interceptor = ToolInterceptor(["error_tool"])
wrapped = ToolInterceptor.wrap_tool(error_tool, interceptor)
with pytest.raises(ValueError) as exc_info:
wrapped.invoke("test")
assert "Intentional error: test" in str(exc_info.value)
def test_approval_keywords_comprehensive(self):
"""Test all approved keywords are recognized."""
approval_keywords = [
"approved",
"approve",
"yes",
"proceed",
"continue",
"ok",
"okay",
"accepted",
"accept",
"[approved]",
"APPROVED",
"Proceed with this action",
"[ACCEPTED] I approve",
]
for keyword in approval_keywords:
result = ToolInterceptor._parse_approval(keyword)
assert (
result is True
), f"Keyword '{keyword}' should be approved but got {result}"
def test_rejection_keywords_comprehensive(self):
"""Test that rejection keywords are recognized."""
rejection_keywords = [
"no",
"reject",
"cancel",
"decline",
"stop",
"abort",
"maybe",
"later",
"random text",
"",
]
for keyword in rejection_keywords:
result = ToolInterceptor._parse_approval(keyword)
assert (
result is False
), f"Keyword '{keyword}' should be rejected but got {result}"
def test_interrupt_with_complex_tool_input(self):
"""Test interrupt with complex tool input types."""
@tool
def complex_tool(data: str) -> str:
"""A tool with complex input."""
return f"Processed: {data}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = "approved"
interceptor = ToolInterceptor(["complex_tool"])
wrapped = ToolInterceptor.wrap_tool(complex_tool, interceptor)
complex_input = {
"data": "complex data with nested info"
}
result = wrapped.invoke(complex_input)
mock_interrupt.assert_called()
assert "Processed" in str(result)
def test_configuration_from_runnable_config(self):
"""Test Configuration.from_runnable_config with interrupt_before_tools."""
from langchain_core.runnables import RunnableConfig
config = RunnableConfig(
configurable={
"interrupt_before_tools": ["db_tool"],
"max_step_num": 5,
}
)
configuration = Configuration.from_runnable_config(config)
assert configuration.interrupt_before_tools == ["db_tool"]
assert configuration.max_step_num == 5
def test_tool_interceptor_initialization_logging(self):
"""Test that ToolInterceptor initialization is logged."""
with patch("src.agents.tool_interceptor.logger") as mock_logger:
interceptor = ToolInterceptor(["tool_a", "tool_b"])
mock_logger.info.assert_called()
def test_wrap_tools_with_interceptor_logging(self):
"""Test that tool wrapping is logged."""
@tool
def test_tool(x: str) -> str:
"""Test."""
return x
with patch("src.agents.tool_interceptor.logger") as mock_logger:
wrapped = wrap_tools_with_interceptor([test_tool], ["test_tool"])
# Check that at least one info log was called
assert mock_logger.info.called or mock_logger.debug.called
def test_interrupt_resolution_with_empty_feedback(self):
"""Test interrupt resolution with empty feedback."""
@tool
def test_tool(x: str) -> str:
"""Test."""
return f"Result: {x}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = ""
interceptor = ToolInterceptor(["test_tool"])
wrapped = ToolInterceptor.wrap_tool(test_tool, interceptor)
result = wrapped.invoke("test")
# Empty feedback should be treated as rejection
assert isinstance(result, dict)
assert result["status"] == "rejected"
def test_interrupt_resolution_with_none_feedback(self):
"""Test interrupt resolution with None feedback."""
@tool
def test_tool(x: str) -> str:
"""Test."""
return f"Result: {x}"
with patch("src.agents.tool_interceptor.interrupt") as mock_interrupt:
mock_interrupt.return_value = None
interceptor = ToolInterceptor(["test_tool"])
wrapped = ToolInterceptor.wrap_tool(test_tool, interceptor)
result = wrapped.invoke("test")
# None feedback should be treated as rejection
assert isinstance(result, dict)
assert result["status"] == "rejected"