All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Agent System: Goal-driven execution with multi-turn conversation support
- Tool Orchestration: Execute tools in parallel with retry and fallback
- Streaming Support: Token-by-token streaming with progress updates
- Memory Management: Short-term memory with token window management and multiple strategies (FIFO, sliding)
- OpenAI Provider: Full support for GPT-4, GPT-4o, GPT-3.5-turbo with tiktoken-based token counting
- Anthropic Provider: Claude 3 (Opus, Sonnet, Haiku) and Claude 3.5 Sonnet support
- Google Gemini Provider: Gemini Pro and Gemini 1.5 Pro support
- Unified Interface: Switch providers with a single line change
- Native MCP Client: Connect to any MCP server
- Tool Discovery: Automatic discovery of MCP tools
- Type-Safe Calls: Convert MCP tools to FlowLLM tool definitions
- Server Management: Connection lifecycle management
- Error Handling: Comprehensive error handling with detailed logging
- Retry Logic: Exponential backoff with configurable retries
- Cost Tracking: Real-time cost tracking for all providers
- Middleware System: Custom middleware for logging, caching, etc.
- Logging: Structured logging with Pino
- Token Counting: Accurate token counting per provider
- TypeScript-First: Full type safety and IntelliSense support
- Simple API: Clean
defineAgent()anddefineTool()APIs - Monorepo Structure: Well-organized packages (@flowllm/core, @flowllm/providers, @flowllm/mcp)
- Comprehensive Examples: 6+ working examples covering all features
- Full Documentation: Getting started guide, API reference, and best practices
flowllm/
├── packages/
│ ├── core/ # Core agent framework
│ ├── providers/ # OpenAI, Anthropic, Gemini providers
│ ├── mcp/ # MCP integration
│ └── flowllm/ # Main SDK (re-exports all packages)
├── examples/ # Working examples
├── docs/ # Comprehensive documentation
└── tests/ # Unit and integration tests
- 4 packages with clear separation of concerns
- 15+ core classes implementing the framework
- 3 LLM providers (OpenAI, Anthropic, Gemini)
- 6 working examples demonstrating all features
- Comprehensive test coverage for core functionality
- 2500+ lines of production-ready TypeScript code
- Long-term memory with vector databases (Pinecone, Chroma)
- Agent collaboration patterns (multi-agent systems)
- Built-in observability (OpenTelemetry tracing, Prometheus metrics)
- Web UI for agent testing and debugging
- Additional providers (Cohere, local models via Ollama)
- Persistent conversation storage (Redis, PostgreSQL)
- Rate limiting and quota management
- Advanced memory strategies (summarization)
- Multi-agent orchestration framework
- Advanced planning (ReAct, Tree of Thoughts, Chain of Thought)
- RAG (Retrieval-Augmented Generation) with vector search
- Deployment platform (Targetly integration)
- Enterprise features (SSO, RBAC, audit logs)
- Performance optimizations (caching, batching)
- Additional MCP features (resources, prompts)
- Plugin system for extensibility
- Core agent framework
- Multi-provider support (OpenAI, Anthropic, Gemini)
- Native MCP integration
- Conversation memory management
- Tool/function calling
- Streaming responses
- Production features (retries, cost tracking, logging)
- TypeScript support
- Comprehensive documentation
- Working examples
- Additional test coverage
- Performance benchmarks
- CI/CD pipeline
- Phase 2 features (see roadmap above)
- Community feedback integration
- Additional provider support
- Enhanced MCP capabilities
This is the first official release. No migration needed.
This project follows semantic versioning. For more information, see semver.org.
For a detailed list of changes, see the commit history.