awesome-context-engineering

Table of contents

• ✍️ Write Context
• 🔎 Select Context
• ✂️ Compress Context
• 📦 Isolate Context

What is Context Engineering?

Tobias Lütke (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 (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 (mem0ai) Memory for AI Agents; Announcing OpenMemory MCP - local and secure memory management.
• letta (letta-ai) Letta (formerly MemGPT) is the stateful agents framework with memory, reasoning, and context management.
• graphiti (getzep) Build Real-Time Knowledge Graphs for AI Agents
• cognee (topoteretes) Memory for AI Agents in 5 lines of code
• Memary The Open Source Memory Layer For Autonomous Agents
• memobase (memodb-io) Profile-Based Long-Term Memory for AI Applications. Memobase handles user profiles, memory events, and evolving context
• A-mem (agiresearch) A-MEM: Agentic Memory for LLM Agents
• MemoryOS (BAI-LAB) A memory operation system for personalized AI
• core (RedPlanetHQ) Your personal plug and play memory layer for LLMs

🔎 Select Context

MCP Servers

• awesome-mcp-servers A collection of MCP servers.
• mcp-servers (modelcontextprotocol) Model Context Protocol Servers

MCP Frameworks

• mcp-python-sdk (modelcontextprotocol) The official Python SDK for Model Context Protocol servers and clients
• fastmcp (CEO at PrefectHQ) The fast, Pythonic way to build MCP servers and clients
• fastapi_mcp (tadata-org) Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth!
• mcp-agent (lastmile-ai) Build effective agents using Model Context Protocol and simple workflow patterns
• mcp-use (mcp-use) mcp-use is the easiest way to interact with mcp servers with custom agents
• 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 (featureform) EnrichMCP is a python framework for building data driven MCP servers

✂️ Compress Context

Prompt compression

• 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 (microsoft) A library for prompt engineering and optimization (SAMMO = Structure-aware Multi-Objective Metaprompt Optimization)
• 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 (3DAgentWorld) Toolkit for Prompt Compression
• 500xCompressor 500xCompressor: Generalized Prompt Compression for Large Language Models

RAG compression

• xRAG xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
• recomp RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation.
• CompAct (dmis-lab) CompAct: Compressing Retrieved Documents Actively for Question Answering
• QGC (XMUDeepLIT) Retaining Key Information under High Compression Rates: Query-Guided Compressor for LLMs

📦 Isolate Context

Multi-Agent Frameworks

• MetaGPT (FoundationAgents) The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
• agno (agno-agi) Full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning.
• camel (camel-ai) CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents.
• agent-squad (awslabs) Flexible and powerful framework for managing multiple AI agents and handling complex conversations
• 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 (langroid) Harness LLMs with Multi-Agent Programming
• LazyLLM (LazyAGI) Easiest and laziest way for building multi-agent LLMs applications.