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… and environment validation
- Implemented Celery tasks for document processing, content analysis, batch processing, and periodic cleanup in `ailf/workers/tasks.py`. - Created interactive build workflow script in `build_workflow.py` for cleaning, testing, building, and publishing the package. - Added development setup script in `dev_setup.sh` for setting up the development environment. - Introduced Redis messaging examples in `examples/ailf_redis_agents.py` and `examples/ailf_redis_example.py`. - Developed documentation helper client example in `examples/doc_helper_client.py`. - Added ZMQ device manager tests in `tests/unit/messaging/test_zmq_device_manager.py`. - Created utility schemas for documentation in `utils/schemas/documentation.py`. - Added various integration and unit test files for Redis and ZMQ implementations.
…iner configuration
- Removed multiple schema files and consolidated schemas into a single directory. - Deleted unused files related to web scraping, workers, and storage setup. - Updated pyproject.toml to reflect new package structure and dependencies. - Migrated existing code to a src layout for better organization and maintainability. - Removed legacy setup.py content, delegating to pyproject.toml for package configuration. - Added migration script to facilitate src layout transition.
…loudStorage implementations
- Added `setup_storage.py` for managing SQLite database and Google Cloud Storage (GCS) setup. - Included functions for database initialization, GCS bucket creation, environment setup, and validation. - Integrated logging and monitoring for tracking operations. - Created `web_scraper.py` for web scraping utilities. - Established worker utilities in `workers/__init__.py` and configured Celery in `celery_app.py`. - Defined distributed tasks in `tasks.py` for processing documents and analyzing content. - Added integration tests for AI engine functionality and Redis messaging utilities. - Implemented unit tests for AI engine, environment variable handling, and package imports.
- Implemented ZMQ utilities for messaging with context management, error handling, and common patterns. - Created a local file storage manager for development, including JSON file handling and directory management. - Added cloud storage integration with Google Cloud Storage for data storage and retrieval. - Developed a setup module for initializing storage infrastructure, including SQLite database and GCS bucket provisioning. - Enhanced logging and monitoring capabilities across storage modules.
…nd testing - Created the AILF-Kagent integration package with adapters for AIEngine, tools, agents, and memory systems. - Developed schema translation utilities to facilitate data interchange between AILF and Kagent. - Added unit tests for agent adapters, memory bridges, schema translations, and tool adapters to ensure functionality and correctness. - Updated project configuration files (pyproject.toml, setup.py) for dependency management and project structure. - Documented integration issues and mitigation strategies in issues_mitigation.md. - Provided a detailed integration plan in kagent-integration.md outlining phases, architecture, and testing strategies.
…TTP client, enhanced routing, and enhanced selector - Implemented comprehensive tests for the AdaptiveLearningManager, covering initialization, prompt identification, improvement suggestions, and adaptation cycles. - Created tests for the PerformanceAnalyzer, validating interaction loading, prompt success analysis, and correlation findings. - Developed tests for the HTTPRegistryClient, ensuring proper tool and agent discovery, registration, and error handling. - Added tests for enhanced routing components, including task delegation and message routing based on rules. - Established tests for the enhanced selector component, focusing on keyword-based and RAG-based tool selection strategies.
- Created integration tests for the AILF (AI Learning Framework) modules, including cognition and feedback components. - Implemented comprehensive tests for the PromptLibrary class, focusing on template management and versioning. - Developed unit tests for the AdaptiveLearningManager, ensuring proper handling of prompt optimizations and performance analysis. - Established fixtures for setting up test environments and mock objects to simulate behavior of various components. - Verified functionality of template updates, version history retrieval, and optimization processes through detailed test cases.
- Implemented an A2A-compatible Echo Agent using the AILF framework that echoes back messages. - Created a Jupyter notebook demonstrating multi-agent communication using ZeroMQ utilities, including task generation, processing, and result collection. - Added requirements for A2A integration in `a2a.txt`. - Introduced cloud utilities for Google Cloud Storage and secrets management, including `gcs_config_stash.py` and `secrets.py`. - Enhanced logging and error handling in cloud utilities.
- Implemented unit tests for PushNotificationManager and PushNotificationClient to validate push notification functionality. - Created integration tests for A2ARegistryClient and A2ARegistry to ensure proper agent registration and discovery. - Developed unit tests for A2A server components, including task management and message handling. - Added scripts to update Pydantic v1 methods to v2 and to replace datetime.utcnow() with datetime.now(datetime.UTC) across relevant files.
- Updated `ailf_roadmap.md` to include advanced A2A protocol features such as push notification support, registry integration, and multi-agent orchestration. - Introduced a detailed testing and verification plan in `validation.md` covering automated and manual testing strategies for core components of the AILF framework. - Implemented examples for A2A orchestration and push notifications in `orchestration_example.py` and `push_notification_example.py`, demonstrating conditional routing, sequential chains, and parallel processing.
…end-to-end tests for AILF framework
…and datetime serialization
- Updated validation_tracker.md to reflect the implementation status of various test categories, including E2E and performance tests. - Created run_a2a_tests.sh script to automate the execution of A2A-related unit, integration, and performance tests. - Developed run_validation_tests.sh script to run all validation tests with detailed reporting. - Added README-a2a-testing.md to document the A2A testing structure and execution instructions. - Expanded README.md to provide a comprehensive overview of the AILF Framework testing architecture. - Implemented tests/docs/__init__.py and tests/docs/test_a2a_documentation.py for validating documentation examples. - Created tests/e2e/test_a2a_e2e.py for end-to-end testing of A2A protocol workflows, including multi-agent orchestration and error handling.
…mple usage - Added Pydantic schemas for Tree of Thoughts including ThoughtNode, ToTConfiguration, ToTState, and ToTResult. - Developed TreeOfThoughtsProcessor for managing thought generation, evaluation, and processing. - Created comprehensive documentation for Tree of Thoughts implementation. - Introduced advanced example demonstrating complex problem-solving using Tree of Thoughts. - Added basic example showcasing Tree of Thoughts processor usage. - Implemented integration tests for Tree of Thoughts processor functionality. - Developed unit tests for Tree of Thoughts schemas and processor methods.
- Added AG-UI protocol documentation outlining integration with AILF. - Created example demonstrating AG-UI server and client interaction. - Developed comprehensive integration tests for AG-UI protocol. - Introduced secret management providers for AWS, Azure, Google, and Vault. - Implemented DotEnv provider for local environment variable management. - Established base interface for secret management providers. - Enhanced logging for secret providers and error handling.
…red output - Added AIEngine class for AI and LLM interactions. - Introduced GeminiSettings, OpenAISettings, and AnthropicSettings for configuration. - Implemented error handling and monitoring features. - Provided examples for basic usage and tool registration. feat: Create Web Scraper module with rate limiting and caching - Developed WebScraper class for web scraping operations. - Included features like response caching, automatic retries, and user-agent rotation. - Added methods for extracting text and managing files. feat: Establish MCP Server base implementation - Created BaseMCP class for Model Context Protocol (MCP) servers. - Implemented tool, resource, and prompt registration with automatic schema generation. - Added context handling for tools and resources. feat: Introduce Local File Storage Manager - Developed LocalStorage class for managing local file storage. - Included utilities for JSON serialization, directory management, and file operations. - Added async and sync methods for file handling. feat: Set up Database Configuration and Session Management - Created database module with SQLAlchemy integration. - Implemented session management and context manager for database sessions. - Provided a base model for defining database schemas.
Properly handling diverse AI engine response formats and implementing fallback behaviors are key to building resilient agents. Real-world AI outputs can be unpredictable, and these changes directly address that. Improved parsing and clearer logging will greatly aid in debugging and understanding agent behavior during complex planning phases. Improved Agent Execution Core (Agent class): Support for both Pydantic v1 and v2 ensures broader compatibility and future-proofing. Standardizing the conversion of AI engine responses and enhancing structured output schema validation with better error handling are excellent for type safety and reliability. This aligns well with the project's goal of "Structured LLM interactions via Pydantic models." The ability to correctly extract content from various dictionary response structures adds to the agent's adaptability. More Usable and Demonstrative Examples: A comprehensive comparison of different agent types in an example file is invaluable for users to understand the trade-offs and choose the right approach. Improved error handling and API key checking in examples make them more practical and easier for new users to get started. Support for a "mock mode" is an excellent feature for testing and development, especially when wanting to avoid API costs or dependencies. Clearer demonstrations of agent capabilities will significantly improve the learning curve and adoption of your framework.
- Added a new script to generate API documentation markdown files for Sphinx based on the project structure. - Supports both src-based package structure and legacy modules. - Ensures proper directory creation and handles module imports for documentation. - Includes templates for module and index files. docs: Create documentation transition plan - Documented the current state of the AILF library's transition from a flat structure to a src-based layout. - Outlined how to write documentation for new and existing code. - Provided instructions for building documentation and known issues during the transition. docs: Outline remaining tasks for documentation transition - Created a detailed plan for completing the transition of documentation to support the src-based layout. - Listed completed and remaining tasks with priorities for fixing build issues, addressing content, and improving quality. docs: Add contributing documentation guide - Provided guidelines for contributing to the AILF project's documentation. - Included instructions for writing docstrings, creating user guides, and building/testing documentation. docs: Document the structure of the AILF documentation - Explained the organization of the documentation into user guides, API reference, examples, and project reference. - Provided guidelines for fixing common documentation issues. docs: Define import patterns for AILF project - Explained recommended import patterns for both src-based and legacy modules. - Included best practices for handling imports during the transition period. feat: Introduce src-based layout for AILF project - Migrated the AILF library to a standardized src-based layout. - Organized the project structure to separate package code from supporting files. feat: Add Pydantic schemas for cognitive processing - Implemented Pydantic schemas for cognitive processing and reasoning in the AILF library. - Defined models for ReAct steps, states, plans, and intent refinement requests. chore: Create workspace configuration for development - Added a VS Code workspace configuration file for easier development setup. - Configured Python interpreter and linting settings for the project.
…lanning feat: Add OpenAI Embeddings API entity schemas for embedding management feat: Implement feedback schemas for interaction logging and performance analysis feat: Create OpenAI Assistants API entity schemas for assistant management feat: Develop tooling schemas for enhanced tool integration and metadata feat: Establish vector store API entity schemas for semantic search capabilities feat: Implement AILF Tooling subsystem with tool selection and execution management feat: Add ToolManager for registering and executing tools with validation feat: Create ToolSelector for selecting appropriate tools based on queries test: Add migration check script to verify existence and readability of migrated files test: Implement migration test script to validate functionality of migrated modules test: Create simple migration test script for testing tooling and schema imports
…tion - Added TaskTrackingStore for tracking delegated tasks and their statuses. - Implemented TaskDelegator class to manage task delegation to agents. - Introduced async methods for delegating tasks and handling results. - Added support for callbacks on task completion. - Created schemas for memory, prompt engineering, tree of thought, and WebSocket messaging. - Established storage setup and initialization for SQLite and Google Cloud Storage. - Added tests to verify successful migration to src-based layout.
…ctionality - Created markdown documentation for the following modules: - ailf.cognition.base - ailf.cognition.intent_refiner - ailf.cognition.processors - ailf.cognition.prompt_library - ailf.cognition.prompts - ailf.cognition.react_processor - ailf.cognition.task_planner - ailf.cognition.tree_of_thoughts - ailf.communication.a2a_client - ailf.communication.a2a_orchestration - ailf.communication.a2a_push - ailf.communication.a2a_registry - ailf.communication.a2a_server - ailf.communication.acp_handler - ailf.communication.ag_ui_advanced - ailf.communication.ag_ui_client - ailf.communication.ag_ui_executor - ailf.communication.ag_ui_server - ailf.communication.ag_ui_state - ailf.communication.ag_ui_tools - ailf.communication.handler - ailf.feedback.adaptive_learning_manager - ailf.feedback.interaction_logger - ailf.feedback.performance_analyzer - ailf.interaction.adapters - ailf.interaction.manager - ailf.memory.base - ailf.memory.file_memory - ailf.memory.in_memory - ailf.memory.long_term - ailf.memory.memory - ailf.memory.redis - ailf.memory.redis_cache - ailf.memory.redis_memory - ailf.memory.reflection - ailf.memory.reflection_engine - ailf.memory.short_term - ailf.registry_client.base - ailf.registry_client.http_client - ailf.routing.agent_router - ailf.routing.execution - ailf.routing.flow - ailf.routing.task_delegator - ailf.schemas.documentation - ailf.schemas.embedding - ailf.schemas.feedback - ailf.schemas.memory - ailf.schemas.openai_entities - ailf.schemas.prompt_engineering - ailf.schemas.tooling - ailf.schemas.tree_of_thought - ailf.schemas.vector_store - ailf.schemas.websockets - ailf.tooling.manager - ailf.tooling.selector - Implemented cloud-specific logging functionality in ailf.cloud.logging - Enhanced memory system interfaces in ailf.memory.interfaces - Defined agent memory schemas in ailf.schemas.agent_memory
- Deleted messaging schemas including agent_protocol, devices, websockets, and zmq. - Removed the storage schema and test schemas. - Cleaned up the messaging __init__.py file. - Updated imports in gcs_config_stash and tasks.py to reflect new module structure. - Added a test script for the documentation module. - Removed outdated scripts for updating imports and datetime methods. - Consolidated test scripts for installation and Redis implementation.
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