awesome-context-engineering

# awesome-context-engineering ### Table of contents - ✍️ [Write Context](#%EF%B8%8F-write-context) - 🔎 [Select Context](#-select-context) - ✂️ [Compress Context](#%EF%B8%8F-compress-context) - 📦 [Isolate Context](#-isolate-context) ### What is Context Engineering? [Tobias Lütke](https://x.com/tobi/status/1935533422589399127) (2025.06.19) > I really like the term “**context engineering**” over prompt engineering. > > It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. [Andrej Karpathy](https://x.com/karpathy/status/1937902205765607626) (2025.06.26) > +1 for "context engineering" over "prompt engineering". > > ... **context engineering** is the delicate art and science of filling the context window with just the right information for the next step ...  Image source: https://blog.langchain.com/context-engineering-for-agents/ ## ✍️ Write Context ### Long-term memory - [mem0](https://github.com/mem0ai/mem0) (`mem0ai`)  Memory for AI Agents; Announcing OpenMemory MCP - local and secure memory management. - [letta](https://github.com/letta-ai/letta) (`letta-ai`)  Letta (formerly MemGPT) is the stateful agents framework with memory, reasoning, and context management. - [graphiti](https://github.com/getzep/graphiti) (`getzep`)  Build Real-Time Knowledge Graphs for AI Agents - [cognee](https://github.com/topoteretes/cognee) (`topoteretes`)  Memory for AI Agents in 5 lines of code - [Memary](https://github.com/kingjulio8238/Memary)  The Open Source Memory Layer For Autonomous Agents - [memobase](https://github.com/memodb-io/memobase) (`memodb-io`)  Profile-Based Long-Term Memory for AI Applications. Memobase handles user profiles, memory events, and evolving context - [A-mem](https://github.com/agiresearch/A-mem) (`agiresearch`)  A-MEM: Agentic Memory for LLM Agents - [MemoryOS](https://github.com/BAI-LAB/MemoryOS) (`BAI-LAB`)  A memory operation system for personalized AI - [core](https://github.com/RedPlanetHQ/core) (`RedPlanetHQ`)  Your personal plug and play memory layer for LLMs ## 🔎 Select Context ### MCP Servers - [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers)  A collection of MCP servers. - [mcp-servers](https://github.com/modelcontextprotocol/servers) (`modelcontextprotocol`)  Model Context Protocol Servers ### MCP Frameworks - [mcp-python-sdk](https://github.com/modelcontextprotocol/python-sdk) (`modelcontextprotocol`)  The official Python SDK for Model Context Protocol servers and clients - [fastmcp](https://github.com/jlowin/fastmcp) (`CEO at PrefectHQ`)  The fast, Pythonic way to build MCP servers and clients - [fastapi_mcp](https://github.com/tadata-org/fastapi_mcp) (`tadata-org`)  Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth! - [mcp-agent](https://github.com/lastmile-ai/mcp-agent) (`lastmile-ai`)  Build effective agents using Model Context Protocol and simple workflow patterns - [mcp-use](https://github.com/mcp-use/mcp-use) (`mcp-use`)  mcp-use is the easiest way to interact with mcp servers with custom agents - [golf](https://github.com/golf-mcp/golf) (`golf-mcp`)  Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents - [enrichmcp](https://github.com/featureform/enrichmcp) (`featureform`)  EnrichMCP is a python framework for building data driven MCP servers ## ✂️ Compress Context ### Prompt compression - [LLMLingua](https://github.com/microsoft/LLMLingua) (`microsoft`)  To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss. - [sammo](https://github.com/microsoft/sammo) (`microsoft`)  A library for prompt engineering and optimization (SAMMO = Structure-aware Multi-Objective Metaprompt Optimization) - [Selective_Context](https://github.com/liyucheng09/Selective_Context)  Compress your input to ChatGPT or other LLMs, to let them process 2x more content and save 40% memory and GPU time. - [Toolkit-for-Prompt-Compression](https://github.com/3DAgentWorld/Toolkit-for-Prompt-Compression) (`3DAgentWorld`)  Toolkit for Prompt Compression - [500xCompressor](https://github.com/ZongqianLi/500xCompressor)  500xCompressor: Generalized Prompt Compression for Large Language Models ### RAG compression - [xRAG](https://github.com/Hannibal046/xRAG)  xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token - [recomp](https://github.com/carriex/recomp)  RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation. - [CompAct](https://github.com/dmis-lab/CompAct) (`dmis-lab`)  CompAct: Compressing Retrieved Documents Actively for Question Answering - [QGC](https://github.com/XMUDeepLIT/QGC) (`XMUDeepLIT`)  Retaining Key Information under High Compression Rates: Query-Guided Compressor for LLMs ## 📦 Isolate Context ### Multi-Agent Frameworks - [MetaGPT](https://github.com/FoundationAgents/MetaGPT) (`FoundationAgents`)  The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming - [agno](https://github.com/agno-agi/agno) (`agno-agi`)  Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning. - [camel](https://github.com/camel-ai/camel) (`camel-ai`)  CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. - [agent-squad](https://github.com/awslabs/agent-squad) (`awslabs`)  Flexible and powerful framework for managing multiple AI agents and handling complex conversations - [PraisonAI](https://github.com/MervinPraison/PraisonAI) (`MervinPraison`)  PraisonAI is a production-ready Multi AI Agents framework, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. - [langroid](https://github.com/langroid/langroid) (`langroid`)  Harness LLMs with Multi-Agent Programming - [LazyLLM](https://github.com/LazyAGI/LazyLLM) (`LazyAGI`)  Easiest and laziest way for building multi-agent LLMs applications.