Context7

Active

GitHub TypeScript MIT

Description

Context7 is Upstash's context-engineering toolkit for agents, helping applications manage long context windows, retrieval injection, and history compression.

Key Features

Real-time documentation retrieval — pulls latest version-specific library docs and code examples via MCP or CLI, injected directly into LLM context
Dual-mode integration — supports CLI + Skills (ctx7 commands) and MCP Server, compatible with Cursor/Claude Code/OpenCode and other major agents
Library version matching — automatically resolves library names and versions to fetch precise, version-appropriate docs
Context injection — embeds retrieved documentation directly into prompts without tab-switching, reducing hallucination
CLI toolchain — ctx7 library search and ctx7 docs query commands, scriptable for pipeline integration
TypeScript SDK — provides @upstash/context7-sdk and Vercel AI SDK tool extensions

Use Cases

💡 When developing in Claude Code or Cursor, automatically fetch latest API docs to avoid generating non-existent interfaces

💡 Getting precise version-specific documentation when working with libraries like Next.js 14 or Supabase

💡 Integrating context retrieval into prompt engineering workflows for AI Agent development

💡 Quickly verifying latest library usage patterns without manually browsing official documentation

💡 Batch-querying documentation for multiple libraries via CLI in automation scripts

Strengths & Limitations

✅ Strengths

• Actively maintained, recent updates
• High community interest (60.8k stars)
• Permissive open-source license (MIT)
• Responsive to issues, low backlog

Quick Start

npx ctx7 setup

Visit GitHub Visit Website View Docs

Related Projects

Contextal

2.5k · Python

Stale B

Contextal is a context management and retrieval-enhancement tool for multi-turn agents, long conversations, and complex knowledge injection workflows.

contextretrievalmemory +1

· Context management and organization for multi-turn agent conversations
· Retrieval-augmented generation (RAG) toolkit for knowledge injection workflows
· Long conversation context tracking and quality optimization

View on GitHub

Graphiti

30.0k · Python

Active A+

Graphiti is a temporal knowledge-graph engine for agent memory, helping systems continuously accumulate long-term context.

memoryknowledge-graphrag +1

· Temporal fact management — Each fact has a validity window; old facts are invalidated, not deleted. Query what's true now or at any point in time
· Provenance & lineage tracking — Every entity and relationship traces back to raw data episodes, full lineage from derived fact to source
· Hybrid retrieval — Combines semantic embeddings, keyword BM25, and graph traversal for low-latency high-precision queries without LLM summarization

View full details → View on GitHub

Mem0

63.4k · Python

Active A+

Mem0 is a long-term memory layer for AI agents, supporting cross-session memory management and personalized context retrieval.

memoryragpersonalization +1

· Multi-level memory — seamlessly manages user, session, and agent memory states with adaptive personalization
· Single-pass ADD extraction — one LLM call extracts memories with no UPDATE/DELETE, memories accumulate only
· Entity linking — entities extracted, embedded, and linked across memories for retrieval boosting

View full details → View on GitHub

LangMem

1.6k · Python

Active A

LangMem is LangChain's memory layer for agents, helping developers add long-term memory, replay summaries, and context management to improve multi-turn performance.

memorylangchaincontext +1

· Core memory API that works with any storage system for flexible agent memory
· Hot-path memory tools agents use to record and search during active conversations
· Background memory manager that auto-extracts, consolidates, and updates knowledge

View on GitHub

上下文工程长上下文RAG

Context Engineering: Context Decay and Recovery in Long-Conversation Agents

Long-conversation agents fail at context management, not model capability. A systematic comparison of sliding window, retrieval injection, and layered compression strategies with practical decay diagnosis and recovery patterns.