Feat/aworld cli mm text2agent

[tallate]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @tallate, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a powerful meta-learning and version control system for agents and swarms within the aworld-cli ecosystem. It empowers agents to autonomously learn from past executions, optimize their configurations, and manage their versions effectively. Concurrently, the CLI's user interface is significantly upgraded with interactive input features and enhanced visualization tools, making agent development and interaction more intuitive. The addition of an automated agent code generation skill further streamlines the creation of new agents, complemented by a suite of new example agents and skills demonstrating advanced functionalities across various domains.

Highlights

• Meta-Learning Framework: A comprehensive meta-learning framework has been introduced, enabling agents to analyze execution trajectories, evaluate performance with reward functions, and automatically optimize their configurations for improved task completion.
• Agent & Swarm Version Control: A robust version control system for managing agent and swarm definitions is now implemented, supporting both markdown-based DSL and Python code. This includes dedicated context tools for registry interaction, allowing for tracking and dynamic registration of agent and swarm versions.
• Interactive CLI Enhancements: The aworld-cli user experience has been significantly upgraded with new interactive input methods (multi-select, composite menus), new commands for skill and agent management, and visual rendering of team topologies, making CLI interaction more intuitive.
• Automated Agent Code Generation: A new text2agent skill has been added to automate the generation of complete Python agent code files based on user requirements, streamlining the agent development process and replacing older manual creation mechanisms.
• New Example Agents & Skills: Several new example agents (e.g., SearchReasoningAgent, GaiaAgent) and skills (e.g., pptx for presentation automation, coding_search for image search, analyze_and_generate for team generation) have been added to showcase diverse and advanced capabilities.

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[gemini-code-assist]

Code Review

This pull request introduces a substantial set of new features, centered around a "text2agent" capability, a more interactive command-line interface, version-controlled agent and swarm registries, and a meta-learning framework. The changes are extensive and well-structured. My review focuses on improving code quality by addressing issues such as misleading validation logic in example files, the use of bare except blocks which can hide bugs, direct printing of stack traces to the console, and hardcoded configuration values. The suggested changes will enhance the robustness, debuggability, and maintainability of the new features.

I am having trouble creating individual review comments. Click here to see my feedback.

aworld-cli/Sum1To100.java (174-181)

The validation logic in validateAllMethods is incomplete and potentially misleading. It omits method3MathFormula from the test set, even though it's used as the ground truth. Furthermore, the labels for the methods are incorrect (e.g., "方法3" is mapped to method4Recursion), which could cause confusion when debugging. The validation should be comprehensive and the output clear.

        TestMethod[] methods = {
            new TestMethod("方法1: For循环", Sum1To100::method1ForLoop),
            new TestMethod("方法2: While循环", Sum1To100::method2WhileLoop),
            new TestMethod("方法3: 数学公式", Sum1To100::method3MathFormula),
            new TestMethod("方法4: 递归", Sum1To100::method4Recursion),
            new TestMethod("方法5: Stream API", Sum1To100::method5StreamAPI),
            new TestMethod("方法6: Stream Reduce", Sum1To100::method6StreamReduce),
            new TestMethod("方法7: 并行Stream", Sum1To100::method7ParallelStream)
        };

aworld-cli/data/agent_registry/search_reasoning_agent/search_reasoning_agent.md (150)

Using a bare except: is a risky practice because it catches all exceptions, including system-exiting ones like SystemExit or KeyboardInterrupt. This can make the program difficult to terminate and can hide unexpected errors. It's better to catch a more specific exception, such as json.JSONDecodeError in this context.

            except json.JSONDecodeError:

aworld-cli/src/aworld_cli/console.py (440-442)

Using a bare except: pass silently swallows all exceptions, which can make debugging very difficult. Even if the intention is for the feature to fail silently for the user, it's good practice to at least log the exception for development and maintenance purposes.

        except Exception as e:
            # If prompt_toolkit is not available or error occurs, fail silently but log for debugging.
            logger.debug(f"Failed to initialize Esc key listener: {e}")

aworld-cli/src/aworld_cli/console.py (856-858)

Printing the full traceback to the console is not ideal for user experience and can expose internal implementation details. It's better to log the exception with its traceback for debugging and show a more user-friendly error message in the console.

                    except Exception as e:
                        logger.error(f"Error during test: {e}", exc_info=True)
                        self.console.print(f"[red]An error occurred during the test. Please check the logs for details.[/red]")

aworld-cli/src/aworld_cli/console.py (977-978)

Printing the full traceback directly to the console can expose sensitive information and create a poor user experience. It's recommended to log the detailed exception for debugging purposes and display a generic, user-friendly error message instead.

            except Exception as e:
                logger.error(f"An unexpected error occurred in chat session: {e}", exc_info=True)
                self.console.print(f"[red]An unexpected error occurred. Please check the logs for details.[/red]")

aworld/experimental/loaders/agent_version_control_registry.py (401-402)

Using a bare except: pass can hide all exceptions, including unexpected ones, making debugging difficult. It's better to catch specific exceptions or, at a minimum, log the exception that occurred to aid in future maintenance.

        except Exception as e:
            logger.warning(f"Failed to get agent descriptions from LocalAgentRegistry: {e}")

aworld/experimental/metalearning/optimizer_hook.py (29)

The URL for traj_validation_dataset is hardcoded. This reduces the flexibility of the learning strategy and makes it difficult to switch between different environments (e.g., development, staging, production). This should be made configurable, for instance, through an environment variable or a configuration file.