How do I install Awesome Context Engineering?

Run npx awesome-context-engineering, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Awesome Context Engineering safe to use with an AI agent?

Its trust score is 67 out of 100 (good). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Awesome Context Engineering still maintained?

The last commit was a year ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.

About Awesome Context Engineering

Awesome Context Engineering is an MCP server published by jihoo-kim in the AI category: 🔥 Comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems. hundreds of papers, frameworks, and implementation guides for LLMs and AI agents. It has been installed 0 times through Conduid.

The repository has 105 stars and 13 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

npx awesome-context-engineering

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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!Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

README

awesome-context-engineering

✍️ 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.