The official Python SDK for Model Context Protocol servers and clients. It allows building MCP clients, servers, and provides tools for interacting with LLMs in a standardized way.
The Universal Tool Calling Protocol (UTCP) is an open standard that describes how to call existing tools directly, eliminating the need for wrappers. It focuses on direct communication with tool endpoints (HTTP, gRPC, WebSocket, CLI, etc.) to reduce latency and maintain existing security and billing systems.
Guidance on choosing the best AI model for GitHub Copilot projects, considering speed, depth, cost, and task complexity. Models discussed include GPT-4.1, GPT-4o, Claude 3.5 Sonnet, o4-mini, o3, Gemini 2.0 Flash, and GPT-4.5.
- Composio: Streamline agent development with tool integrations.
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
- Julep: Build stateful AI agents with efficient context management.
- E2B: Secure sandbox for AI execution with code interpreter capabilities.
- Camel-ai: Framework for building and studying multi-agent systems.
- CopilotKit: Integrate AI copilot features into React applications.
- Aider: AI-powered pair-programmer for code assistance and repo management.
- Haystack: Composable pipeline framework for RAG applications.
- Pgvectorscale: High-performance vector database extension for PostgreSQL.
- GPTCache: Semantic caching solution for reducing LLM costs.
- Mem0 (EmbedChain): Add persistent memory to LLMs for personalized interactions.
- FastEmbed: Fast and lightweight library for embedding generation.
- Instructor: Streamline LLM output validation and extraction of structured data.
- LiteLLM: Drop-in replacement for OpenAI models, supporting various providers
