* fix(python): convert MCP tool results to LangChain content blocks
Previously, tool results were converted using `str(tool_result.content)`,
which produced Python repr strings like
`[TextContent(type='text', text='...')]` instead of the actual content.
Now properly maps MCP content types to LangChain format:
- TextContent → plain string or {"type": "text", ...}
- ImageContent → {"type": "image", "source_type": "base64", ...}
- AudioContent → {"type": "audio", "source_type": "base64", ...}
- EmbeddedResource → text or file block depending on resource type
Single TextContent results (the most common case) are returned as
plain strings for backward compatibility.
* test(python): cover langchain tool content conversion
* style(python): format langchain adapter
* test(python): cover langchain block tool messages
* chore: update imports
* refactor(mcpagent): update agent creation logic and imports
- Replaced `create_tool_calling_agent` with `create_agent` for improved agent instantiation.
- Simplified the `_create_agent` method by removing the `AgentExecutor` and directly returning the created agent.
- Adjusted type annotations for `_agent_executor` to initialize as `None` instead of using a union type.
* feat(mcpagent): migrate to LangChain 1.0.0 and simplify agent methods
- Updated MCPAgent to utilize LangChain 1.0.0's `create_agent()` for improved agent instantiation.
- Introduced new methods `astream_simplified()` and `run_v2()` that leverage the built-in `astream()` for internal agent loop handling.
- Retained legacy methods `stream()` and `run()` for backward compatibility with manual execution.
- Enhanced documentation to reflect changes and provide clearer usage examples.
* feat(mcpagent): implement dynamic tool updates during agent execution
- Enhanced the MCPAgent to support dynamic tool updates when using server_manager mode.
- Implemented logic to check for tool changes before and during execution, allowing for immediate updates and safe restarts.
- Introduced a maximum restart limit to prevent infinite loops during tool updates.
- Updated documentation to reflect the new behavior and usage of dynamic tool management.
* refactor(mcpagent): clean up imports and streamline logging
- Removed unused imports and organized existing ones for better clarity in `mcpagent.py`.
- Simplified logging statements for consistency and improved readability.
- Enhanced the overall structure of the code by eliminating unnecessary whitespace.
- Updated unit tests to reflect changes in imports and ensure compatibility with the refactored code.
* feat(mcpagent): enhance agent execution with step tracking and middleware
- Introduced `ModelCallLimitMiddleware` to enforce a maximum number of model calls during agent execution.
- Updated the `_consume_and_return` method to return a tuple of the final result and the number of steps taken.
- Enhanced telemetry tracking to include steps taken and execution time for better performance insights.
- Simplified agent creation logic by integrating middleware directly into the `create_agent` function.
- Improved logging for agent execution details, including steps and tool usage.
* fix(mcpagent): add null check for node output messages extraction
- Updated the MCPAgent to include a null check for `node_output` before attempting to access the "messages" key, preventing potential errors during message extraction.
* minor
* feat(mcpagent): filter out ToolMessage from history
- Updated the MCPAgent to exclude ToolMessage instances from being added to the message history, ensuring that only relevant messages are recorded.
- This change helps maintain a cleaner history by avoiding intermediate tool execution details.
* fix(mcpagent): set max_steps assignment
* ruff
* docs
* e2e tests for agent
* key in tests
* fix manager test and split ci tests
* telemetry off for tests
* fix server manager test
* feat(mcpagent): delegate query streaming to remote agent in remote mode
- recreated functionality to the MCPAgent to yield results from a remote agent when operating in remote mode, enhancing the agent's capability to handle queries efficiently.
* fix: agent unit tests
* Print inputs
* rollback, wrong repo
* fix streaming output
* stricter streaming test
* pass observability callbacks
* fix tests mock
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Co-authored-by: pietrozullo <pietro.zullo@gmail.com>
Pietro Zullo