Awesome Context Engineering

A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).

Context engineering is the art and science of filling the context window with just the right information at each step of an agent's trajectory.

中文版本 | English

What is Context Engineering?

Context Engineering is the systematic optimization of information payloads for Large Language Models (LLMs). It encompasses:

Context Retrieval & Generation: Selecting and creating relevant information
Context Processing: Organizing and structuring context for optimal consumption
Context Management: Handling context windows, memory, and state across interactions
Context Compression: Reducing token usage while preserving essential information
Context Isolation: Separating concerns across different context spaces

📖 Featured Articles

Context Rot: How Increasing Input Tokens Impacts LLM Performance

https://research.trychroma.com/context-rot

Manus Context Engineering

Context Engineering for AI Agents: Lessons from Building Manus

📄 Original: Manus Blog
🇨🇳 Chinese Translation: docs/manus/AI智能体的Context工程：构建Manus的经验教训.md

Key insights from building a production AI agent:

Design around KV-Cache for performance optimization
Mask, don't remove tools for better action selection
Use file system as external context memory
Manipulate attention through recitation techniques

Claude Code Best Practices

Claude Code Best Practices

📄 Original: Anthropic Engineering
🇨🇳 Chinese Translation: docs/claudecode/claude-code-best-practices-zh.md

Anthropic / Claude

LangChain Context Engineering

Context Engineering for Agents

📄 Original: LangChain Blog
🇨🇳 Chinese Translation: 智能体的Context工程-中文版

Comprehensive guide covering four key strategies:

Write Context: Saving information outside context window
Select Context: Pulling relevant information into context
Compress Context: Retaining only necessary tokens
Isolate Context: Splitting context across different spaces

dbreunig Context Engineering Series

How Long Contexts Fail and How to Fix Them

📄 Original Part 1: How Long Contexts Fail
🇨🇳 Chinese Translation: 长上下文的失效原理及解决方案
📄 Original Part 2: How to Fix Your Context
🇨🇳 Chinese Translation: 上下文修复的实用指南

Deep dive into context failure modes and management strategies:

Context poisoning, distraction, confusion, and clash patterns
RAG, tool loadout, context quarantine, pruning, summarization, and offloading techniques

Advanced Context Engineering for Coding Agents

Guide for using AI to solve hard problems in complex codebases.

Don't Build Multi-Agents (Cognition)

📄 Original: Cognition Blog

Principles for building reliable agents: share full context and avoid fragile parallel multi-agent architectures.

The New Skill in AI is Not Prompting, It's Context Engineering (Philipp Schmid)

📄 Original: philschmid.de

Argues most agent failures stem from poor context, not the model; breaks context into instructions, history, long-term memory, RAG, tools, and output format.

Context Engineering (Simon Willison)

📄 Original: simonwillison.net

Why "context engineering" is a better framing than "prompt engineering", curating the foundational discussion of the term.

Wide Research: Beyond the Context Window (Manus)

📄 Original: Manus Blog

Parallel sub-agent architecture that avoids fabrication when processing many items, by decomposing tasks across dedicated sub-agents instead of one overloaded context window.

SWE-grep: RL for Fast Context Retrieval (Cognition)

📄 Original: Cognition Blog

RL-trained models that run up to 8 parallel searches per turn to retrieve code context an order of magnitude faster while minimizing context pollution.

Compaction Is a Lossy Operation

https://loopandretry.github.io/posts/compaction-is-a-lossy-operation/

Why compacting an agent's context is lossy compression, not free summarization — what survives, what silently drops, and how it degrades long multi-turn runs.

📑 Research Papers

Survey Papers

A Survey of Context Engineering for Large Language Models

📄 arXiv: 2507.13334
📊 Comprehensive analysis of 1400+ research papers
🎯 Establishes formal taxonomy of context engineering components

The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple prompt design to encompass the systematic optimization of information payloads for LLMs.

Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime

📄 Preprints: 202603.1756
📊 Audits 63 harness-relevant works across academia and public engineering practice
🎯 Decomposes the agent harness layer into Control, Agency, and Runtime (CAR); proposes HarnessCard as a reporting artifact

Situates harness engineering in the arc from software engineering through prompt and context engineering, arguing that the harness — the runtime loop wrapping tool execution, control, and agency around an LLM — is a first-class layer whose effects are often mistaken for model-driven gains.

Harness-Zero: Harness Distillation via Agent-as-Harness

📄 arXiv: 2609.24974
🎯 An agent-as-harness training loop distills optimized harness behaviors into model weights, retaining harness-level gains at deployment without the harness at inference time (code)

Context Engineering 2.0: The Context of Context Engineering

📄 arXiv: 2510.26493
🎯 Traces context engineering back two decades and proposes a systematic definition and design principles across the human-computer to human-agent evolution

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

📄 arXiv: 2510.04618
🎯 Treats context as an evolving playbook (Generator/Reflector/Curator) to prevent context collapse; reports +10.6% on agents and +8.6% on finance tasks (ICLR 2026)

Meta Context Engineering via Agentic Skill Evolution

📄 arXiv: 2601.21557
🎯 A bi-level framework that co-evolves context-engineering skills and context artifacts, replacing static CE heuristics (ICML 2026, code)

Memory in the Age of AI Agents: A Survey

📄 arXiv: 2512.13564
🎯 A "forms-functions-dynamics" taxonomy of agent memory, distinguishing it from RAG and context engineering

PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting

📄 arXiv: 2606.08878
📊 110 scenarios across 10 topologies, testing how LLMs allocate context fragments to sub-agent roles
🎯 The first benchmark for multi-agent orchestration prompt writing; 33 commercial models average 17.2% pass rate on role-fragment assignment

Evaluates LLMs' ability to assign information fragments to sub-agent roles in multi-agent orchestration scenarios. Scoring uses strict pass (zero omissions and zero leaks) plus partial-credit metrics. Data covers 10 loop-centered topologies across 100 professional domains.

The Agent Loop: A Survey of Control Strategies, Skills, and Harnesses for LLM Agents

📄 SSRN: 7186738
🎯 Takes the agent loop as the unit of analysis; covers in-loop context management and the harness as context manager, alongside termination, verification, recovery and skills

Core Research Areas

Memory Systems: Reflexion, Generative Agents, StructMem (code)
Retrieval-Augmented Generation: RAG Survey
Tool Integration: Tool Selection, BigTool
Context Compression: Recursive Summarization, Context Pruning, TokenPilot (code)
Long-Context Limitations: Lost in the Middle
Harness Adaptation: Training Agents to Evolve with Their Harness: TaoLive Digital Avatar Agent Technical Report — Studies training for changes in skills, tool schemas, prompts, and runtime hooks.

🛠️ Tools & Projects

Comprehensive Resources

Prompt Engineering Guide
- Comprehensive guide to prompt engineering, context engineering, RAG, and AI agents
- Covers techniques, papers, tools, and best practices
- Widely-used educational resource
Awesome Context Engineering Survey
- Comprehensive survey of context engineering techniques
- Methodologies and applications overview
- Academic research focus
Context Engineering Intro
- Practical guide for AI coding assistants
- Claude Code centered approach
- Hands-on implementation strategies
Context-Engineering (davidkimai)
- First-principles handbook for context engineering
- From foundations to advanced techniques
Context-Engineering (jasontang-ai)
- Karpathy-inspired first-principles handbook
- From prompt engineering toward context design, orchestration, and optimization
The Context Course (HuggingFace)
- HuggingFace course on context engineering for code agents
- Covers skills, MCP, plugins, subagents, hooks, and building your own agent
- Reference agents: Claude Code, Codex, OpenCode
Agent Systems Handbook
- Practical AI agents handbook covering agentic workflows and multi-agent architecture
- Spans LangGraph, MCP/A2A, context engineering, agent memory, evaluation, and observability

Context Engineering in 2026: Harnesses, Skills & Protocols

deepseek-harness (dsh): DeepSeek's official open-source agent harness (Aug 2026). Its append-only session log is the only source of model context ("model-visible means logged"), with prompt sections, tool schemas, skills and sub-agents all composed as swappable plugins — a reference design for context assembly
Agent Skills open standard: SKILL.md folders loaded via progressive disclosure (name/description → full instructions → bundled files), adopted by 40+ agents (Claude Code, Codex, Gemini CLI, Cursor, OpenCode, dsh...). Skills are the portable unit of procedural context
MCP 2026-07-28 spec: stateless core, cacheable tools/list results with ttlMs, and header-based routing — all aimed at keeping tool context cheap and fresh for agents
Claude Code context docs: CLAUDE.md memory hierarchy, /compact, subagents and hooks — the most widely copied practical context-management model
Raven: open-source multi-agent harness whose agent loop is split into four decoupled strategy modules (Memory, Planning, Capability, Action) that an experimental Curator rewrites per agent and installs only after verification; its host agent plans a complex task as a DAG and delegates the parts to built-in and third-party agents

Context Engineering Systems & Kits

Prompt Engineering Guide ⭐76.1k: Comprehensive guide and resources for prompt engineering, context engineering, RAG, and AI agents
get-shit-done (GSD) ⭐64.5k: Meta-prompting, context engineering and spec-driven development system for Claude Code
screenpipe ⭐19.9k: Source-available, local-first work-context layer that captures screen, audio, input, browser, and meeting activity for agent retrieval through MCP, REST, and CLI
Agent Skills for Context Engineering ⭐16.7k: Collection of Agent Skills for context engineering and multi-agent architectures
Context Engineering Intro ⭐13.5k: Context engineering introduction and methodology for AI coding assistants
Context-Engineering (jasontang-ai) ⭐9.1k: Karpathy-inspired first-principles handbook for context engineering
GSD-2 ⭐7.7k: Meta-prompting / context engineering system enabling long-running autonomous agent work
MineContext ⭐5.4k: Volcengine's proactive, context-aware AI companion
Awesome-Context-Engineering (Meirtz) ⭐3.2k: Comprehensive survey of context engineering
how-claude-code-works ⭐2.7k: Deep dive into Claude Code source: architecture, agent loop, and context engineering
pro-workflow ⭐2.3k: Self-correcting memory Claude Code workflow, including context engineering
Advanced Context Engineering for Coding Agents ⭐1.7k: Advanced context engineering techniques for coding agents
Context Engineering Kit ⭐1.2k: A context engineering skills toolkit
ACE (Agentic Context Engineering) ⭐1.2k: Evolving language agents through agentic context engineering (ACE)
context-space ⭐812: Context engineering infrastructure built from MCPs and integrations
Agent QA ⭐772: Source-available QA agent that retains scoped test memory and failure evidence across natural-language web and mobile runs
Practical Guide to Context Engineering ⭐708: A practical, hands-on guide (in Chinese) to context engineering for LLM applications
AI Badger ⭐21: Local-first tool that extracts focused, token-efficient repository context for AI chats, with Agent Skills for code review and coding-agent handoff
get-shit-done (GSD): Meta-prompting and spec-driven development / context engineering system for Claude Code
GSD-2: Meta-prompting / context engineering system enabling long-running autonomous agent work
how-claude-code-works: Deep dive into Claude Code source: architecture, agent loop, and context engineering
Agent Skills for Context Engineering: Collection of Agent Skills for context engineering and multi-agent architectures
Advanced Context Engineering for Coding Agents: Advanced context engineering techniques for coding agents
ax: Local telemetry and recall graph for AI coding-agent sessions, costs, skills, routing, and hooks
ACE (Agentic Context Engineering): Evolving language agents through agentic context engineering
Context Engineering Kit: A toolkit for context engineering
MineContext: Volcengine's proactive, context-aware AI companion
pro-workflow: Self-correcting memory system that lets Claude Code learn from its mistakes, covering context engineering, parallel worktrees, agent teams, and 17 skills
kit: Context engineering toolkit for AI dev tools, providing codebase mapping, symbol extraction, and code search
RepoPrompt CE: Community edition of RepoPrompt, a native macOS context engineering app for AI coding agents, with an MCP CLI
DevProjex: Builds clean, token-aware project context with GUI, TUI, CLI, and a read-only MCP server, plus live preview, secret redaction enforced in MCP mode, Git scopes, and syntax-aware compression.
context-engineering (outcomeops): Working reference implementation of context engineering with five components (corpus, retrieval, injection, output, enforcement), running end-to-end on Amazon Bedrock
Project Context Records (PCR): A context-engineering methodology that keeps a durable, repo-versioned archive of a project's meta-context (the why, architecture, and maintainer decisions) so AI collaborators inherit its judgment instead of re-deriving it
interview-prep-template: A three-layer context-engineering template (immutable sources → agent-maintained wiki → operating-manual file) where the agent synthesizes raw material into reusable answers, frameworks, and scored debriefs that compound over time
Practical Guide to Context Engineering: A practical, hands-on guide (in Chinese) to context engineering for LLM applications
MFS: A context harness that unifies code, docs, chat, databases, and object stores into one file-like, searchable namespace (ls/cat/grep + hybrid semantic search), so agents pull context incrementally instead of injecting it all up front; self-hosted and local-first (local ONNX embeddings + Milvus, no API key)
wiki: Indexed Markdown knowledge bases that let agents retrieve project context incrementally, with deterministic indexes, cross-links, linting, and merge handling for parallel edits
Hexis: Git-backed platform for skills, tools, and context for AI agents, with Git review workflows, role-based access, encrypted secrets, and remote MCP access
PickySteve: A skill router and context picker for coding agents — hybrid BM25 + embedding retrieval (RRF-fused), cross-encoder rerank with a calibrated floor, and an LLM judge select the right skill, then assemble a minimal nonce-boundaried context bundle for the execution model; a fail-closed ONNX prompt-injection gate scans both the request and every retrieved doc. Local-first (runs offline on Ollama qwen3:8b or any OpenAI-compatible endpoint), with an MCP server and OpenAI-compatible proxy wired into 18 coding agents (Claude Code, Codex, Cursor, Windsurf, etc.)
YYLO: Command-line orchestrator for coding agents that isolates each task in a dedicated branch/worktree for scoped implementation and focused tests, with typed task, validation, merge, and release-readiness boundaries
ctx-optimize: Deterministic code knowledge graph for coding agents — one static Go binary indexes a repo, and optionally its databases, buckets, queues and APIs, into a local graph, so the agent answers "who calls this" / "what breaks if I change this" from the store in one call instead of a grep-and-read chain; ships an agent skill for Claude Code and Codex, with no LLM, embeddings, database, MCP, or credentials at rest
CueParcel: Local-first browser extension for selecting sections of web pages, combining multiple sources, and packaging source-grounded context for AI agents, with structured TaskSpec output and GitHub Issue, GitHub Pull Request, and technical-documentation adapters
Archcore: Git-native context engineering and spec-driven development for AI coding agents
YYLO Benchmark: Coding-agent evaluation runner for trusted hosts: runs each attempt in a fresh repository and retains outputs for independent re-evaluation; workspace separation provides context hygiene, not a security sandbox
open-bridge: Plain-text git workspace that coding agents read at session start, with a CI-enforced byte budget for always-on context and on-demand indexes for everything else
Curtain: Step-by-step skill runner that reveals instructions only when their stage is reached, with explicit human approval gates between stages.
Bandpass: A context compiler for AI agents that turns memory, state, files, and evidence into task-scoped, provenance-aware Context Capsules with token budgeting, conflict resolution, explicit unknowns, and MCP tools

Development Frameworks

LangGraph: Low-level orchestration framework for context management
LangSmith: Agent tracing and evaluation platform
LangMem: Memory management abstractions

Memory & Compression

claude-mem: Persistent cross-session memory for Claude Code that automatically captures, compresses, and re-injects context across sessions
Data Olympus: Governed project memory for coding agents. Agents can propose context, humans promote accepted rules, and retrieval serves only in-force knowledge after validity-window and supersession checks
GoodMemory: Local-first, auditable memory layer for AI apps and coding agents, with Codex/Claude Code host integration, a standalone MCP server, TypeScript/Python clients, and SQLite or Postgres storage
LeanCTX (lean-ctx): Context intelligence layer for AI agents — a single local Rust binary that decides what agents read, remember, and save; 60–90% fewer tokens, with 76 MCP tools and local-first design
headroom: Compresses tool outputs/logs before they reach the LLM, saving 60-95% of tokens
Accordion: A pi extension that renders an agent's entire context window as a live "map" and folds (reversibly compacts) less-relevant blocks in the background via pluggable "conductors" that rank block relevance, instead of lossy all-or-nothing compaction
Mnemoverse: Hosted persistent memory reachable over MCP from Claude Code, Cursor, VS Code and ChatGPT with one key or OAuth; recall is re-ranked by whether a recalled memory helped or misled
Letta (MemGPT): Framework for building stateful agents with long-term memory
Mem0: Memory layer for AI agents and assistants
Vestige: "Local-first MCP memory server (single ~23MB Rust binary, data stays on-device) with entity/causal retrieval — links memories by shared file/env-var/service/symbol to recall the earlier change behind a later failure that similarity search can miss"
Tree Ring Memory: Lifecycle-aware local-first memory framework for AI agents with recall, audit, forgetting, consolidation, and project-scoped .tree-ring storage
LLMLingua: Prompt compression for accelerated and cost-efficient LLM inference
LLMSlim: Open-source (MIT) Python context compression for RAG and agents. It selects query-relevant units under a hard token budget, lexically or with an optional 22M cross-encoder, and publishes a reproducible head-to-head against LLMLingua-2 and LongLLMLingua
Cognee: Open-source AI memory platform giving agents persistent long-term memory via a self-hosted knowledge-graph engine
Graphiti: Framework for building real-time, temporal knowledge graphs for AI agents (the engine behind Zep's memory)
Supermemory: Fast, scalable memory + context engine with a single Memory API; supports fully local runs
skillreaper: Measures what share of an agent's loaded context ever actually fires — skills, MCP servers, subagents, hooks — from session transcripts, then prunes the unused parts reversibly
LWC: Proactive, source-grounded project memory for coding agents, preserving immutable sources, citations, provenance, and atomic changesets with SQLite/FTS5 retrieval, optional document/code graphs, lifecycle hooks, and a bounded one-tool MCP interface
billion-context-dsh: Model-driven context compression (Active Context Pruning) for the DeepSeek Harness — the model decides when and what to compress
TekMyra: Governed context compression for LLM pipelines that verifies protected spans are present exactly once before emitting and counts refusals against its published numbers
trace-mcp: Local MCP server that serves a precomputed cross-language code graph instead of file reads — framework-aware edges link a PHP controller to the Vue page it renders, a DI decorator to its provider, an ORM call to the migration that defines the table (81 languages, 87 framework integrations), plus code-linked decision memory across sessions; on 60 merged PRs from six third-party repositories, review context drops from a median 13,595 to 1,326 input tokens
langstate: Compresses long LLM conversations into an inspectable [SCAFFOLD STATE] message instead of an opaque summary — keeps system messages and recent turns verbatim, and validate() returns a deterministic receipt for named facts
Caura: Governed shared memory for AI agent fleets — scoped recall across agents and tenants, trust tiers, keystone policies, audit trails, and a knowledge graph, exposed over MCP
Statewave: Memory runtime that compiles agent events into structured memories and assembles reproducible, provenance-tagged context bundles per subject instead of query-time retrieval, self-hosted on Postgres + pgvector with Python/TypeScript SDKs and an MCP server
Ctxlens: Context-window profiler for AI agents: shows what is eating your tokens
tokencut: Measures and cuts the token cost of LLM and agent message payloads
contextpress: Deterministic context compression for LLM chat, RAG, and agent pipelines (pip install contextpress) — Tier-1 NLP stages, no API key; optional Tier-2 LLM backends
toldya: Turns the corrections you keep repeating to Claude Code (found in local session history) into CLAUDE.md / AGENTS.md rules after per-rule approval, then counts whether each rule stopped the repeats
Hyperconsciousness: Developer-alpha Rust knowledge store with encrypted, append-only records, device sync, and MCP retrieval constrained by scoped, expiring grants
elephant: Claude Code plugin for persistent per-repo memory — keeps an ELEPHANT.md in the repo root that is auto-loaded at session start and committed so teammates share the same context, plus a global cross-repo memory file, cold-start seeding from git history, and compaction of old entries
context-bank: Git-committed, size-capped project memory for coding agents under .ai/, with retrieval-first history, a doctor check and Cline Memory Bank migration
Gobstopper: Local proxy for Claude Code, Codex, and other coding agents that replaces older turns with a mechanical summary once a request passes a token threshold and sends the newest turns verbatim; can also prepare smaller copies of saved sessions
Agent-Body: Plugin layer for DeepSeek Harness with intent-based tool-schema gating, deterministic reflexes, and idle-time memory consolidation; includes an offline demo and a reproducible tool-schema token benchmark
Context Guru: Provider-agnostic context engine combining prompt-cache management, tool-output filtering, and context compaction, with Claude Code and Codex integrations
Dory: Project memory in a linked Markdown wiki, with decisions, findings and a taskboard; Claude Code hooks load the index at session start and check wiki structure after changes
Sunstone: Source-available (BSL 1.1) VS Code extension that indexes the folders you open, on your machine, and gives any model in the picker, Copilot's included, a tool to search them; for your own model servers, the turns and tool results a window cannot hold are folded into that local store and come back when they are the closest match to the question, with a recall tool to search them
ctxprune: Small token-classifier model (LLMLingua-2 style, CPU/ONNX) that deletes low-value words from tool outputs, logs and documents while keeping IDs, paths and numbers intact (pip install ctxprune)
mu: Coding agent built on pi that hands context decisions to a small judge model: long tool output is judged chunk by chunk and only what matters now enters the context (the rest is archived behind a pointer), stale tool results become one-line tombstones above a context threshold, compaction keeps or prunes passages without writing a summary, and exact repeats in failing test logs can be folded losslessly (51% fewer characters on the authors' seven largest real logs)
akc-cycle: Claude Code plugin that keeps the rules and skills in a setup from piling up and going stale; it audits skills, rules and agent definitions, proposes keep, merge or retire for each file with a reason, changes nothing until you confirm, and promotes advice that keeps coming back into always-loaded rules
contexttax: Measures the fixed context a coding agent loads on every request (CLAUDE.md/AGENTS.md and their @ imports, always-on rules, skill descriptions, MCP tool schemas) per agent, largest first, and points out what can load on demand instead

Production Tools

Claude Code: Auto-compact context management
ChatGPT: Long-term cross-session memory
Puppyone: File-system-based context storage for agents
Cursor: Rules-based context engineering
Windsurf: Advanced code context retrieval
OrcaReplay: Records the context an agent actually sent, turn by turn, and replays it offline
Praxix: Commercial macOS app for Claude Code that curates per-session context: pick exactly which skills, rules, and references each session loads (presets, per-project defaults, a lean CLAUDE.md), and see that context before a token is spent

💡 Expert Insights

Industry Leaders

Andrej Karpathy (OpenAI)

+1 for "context engineering" over "prompt engineering".

People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting... Too little or of the wrong form and the LLM doesn't have the right context for optimal performance. Too much or too irrelevant and the LLM costs might go up and performance might come down. Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits.

On top of context engineering itself, an LLM app has to:
- break up problems just right into control flows
- pack the context windows just right
- dispatch calls to LLMs of the right kind and capability
- handle generation-verification UIUX flows
- a lot more - guardrails, security, evals, parallelism, prefetching, ...

So context engineering is just one small piece of an emerging thick layer of non-trivial software that coordinates individual LLM calls (and a lot more) into full LLM apps. The term "ChatGPT wrapper" is tired and really, really wrong.

Key Principles

KV-Cache Optimization: Design for cache hit rates to reduce latency and cost
Append-Only Context: Avoid modifying previous context to maintain cache validity
External Memory: Use file systems and databases as extended context storage
Error Preservation: Keep failure traces for model learning and adaptation
Diversity Over Uniformity: Avoid repetitive patterns that lead to model drift

🔗 Model Context Protocol (MCP)

Context7 MCP Server

Up-to-date code documentation for LLMs and AI code editors

🔗 Repository: upstash/context7
🎯 Real-time code context for development workflows
🚀 Integration with popular AI coding assistants

MCP Ecosystem

Model Context Protocol: Standardized context sharing
Filesystem Server: File-based context management
Tool Integration: Seamless context flow between tools

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

How to Contribute

Add Resources: Submit papers, tools, or articles related to context engineering
Improve Translations: Help translate content to different languages
Share Insights: Contribute expert opinions and best practices
Report Issues: Help us maintain accuracy and relevance

Contribution Categories

📄 Research Papers
🛠️ Tools & Libraries
📖 Articles & Tutorials
💡 Expert Insights
🔧 Implementation Examples
🌐 Translations

⭐ Star History

📄 License

This project is licensed under the CC0 1.0.

🙏 Acknowledgments

Special thanks to all contributors and the research community advancing the field of context engineering.

Maintained by: yzfly | 云中江树（微信公众号: 云中江树）

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