Up-to-date documentation for LLMs and AI code editors
By Tanmay Verma, Founder · Last verified 07 Jun 2026
In short
Context7 — Up-to-date documentation for LLMs and AI code editors. Best for Developers building LLM-powered tools that need current API docs, Teams using AI code editors like Cursor or Copilot to reduce hallucinations, Projects reliant on fast-moving frameworks (e.g., Python, JavaScript, cloud SDKs). Free to use.
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Context7 solves a real pain point for LLM developers: stale documentation. If your AI agent needs to answer questions about fast-moving frameworks, this is a must-try. The tight integration with Upstash means latency is low, but heavy reliance on that ecosystem may limit flexibility.
Compare with: Context7 vs Marvin, Context7 vs Cognition AI, Context7 vs Snyk DeepCode AI
Last verified: June 2026
Context7 addresses the critical issue of outdated documentation in AI-assisted coding. When an LLM references deprecated APIs, it breaks developer trust. Context7 constantly refreshes its indexes, so your AI stays current. It's especially valuable for teams using AI code editors like Cursor or Copilot who need real-time docs for Python, JavaScript, or cloud services. The tool offers pre-built 'Document Packs' that cover popular frameworks, plus live rankings to see which docs are most requested or updated. Being an Upstash project, it leverages serverless Redis for low-latency retrieval, making it suitable for production use. However, if your stack doesn't include Upstash, you might face integration overhead. Also, Context7 currently focuses on tech documentation; non-software domains (e.g., legal, medical) aren't supported. Compared to alternatives like DocQuery or Pinecone-based solutions, Context7 is more turnkey but less customizable. For AI teams prioritizing up-to-date context with minimal setup, Context7 is a smart choice. Caveat: the website mentions 'Enterprise' pricing but shows no details, so evaluate scalability for large deployments.
Skip Context7 if Skip Context7 if your AI coding assistant does not support the Model Context Protocol (MCP) or you primarily work with private/internal libraries not publicly documented.
How likely is Context7 to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Context7 delivers up-to-date documentation for LLMs and AI code editors, ensuring that AI assistants always reference the latest APIs, libraries, and best practices. By indexing and updating documentation in real time, Context7 prevents AI hallucinations and outdated answers. The platform offers pre-built document packs, live rankings, and enterprise-grade hosting via Upstash. Context7 is ideal for developers, AI engineers, and teams building LLM-powered tools that require accurate, current context. Unlike generic retrieval pipelines, Context7 is optimized specifically for AI code editors and LLM workflows.
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Concrete scenarios for the personas Context7 actually fits — and what changes day-one when you adopt it.
You need to write a Next.js middleware for JWT authentication but aren't sure of the latest API for the App Router.
Outcome: You append 'use context7' to your Cursor prompt. Context7 pulls the exact Next.js 15 docs, and you generate correct middleware code in under 30 seconds.
Your team's Claude Code assistant keeps generating deprecated Prisma Client APIs because its training data is stale.
Outcome: You set up Context7 MCP with version-pinned docs for your Prisma version. Future prompts automatically get current API examples, cutting hallucinated code by 90%.
You want to standardize how your AI coding assistants access internal library documentation across the team.
Outcome: After adding your private docs as a source in Context7 and sharing a teamspace, every developer's AI assistant uses the same approved patterns.
Only indexes public documentation — internal/private libraries need a separate MCP solution. Relies on your AI client supporting MCP; older ChatGPT/Gemini wrappers without MCP cannot use it. Index coverage is strong for top libraries but thinner for niche ones. Pricing is opaque beyond the free tier (contact sales for enterprise).
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published Context7 tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developer or small team wanting to evaluate Context7 with public libraries and a hosted MCP endpoint
What this tier adds
Starting tier — includes all indexed public libraries and a hosted MCP endpoint at no cost
The company stage and team size where Context7's pricing actually pencils out — and where peers do it cheaper.
Context7 offers a free tier that includes a hosted MCP endpoint and all public libraries — good for individual developers or small teams evaluating the tool. For teams needing private source support, usage analytics, or team management, you'll need to contact sales for enterprise pricing. Compared to DIY solutions like building your own MCP server for documentation, Context7 saves setup time but locks you into their hosting and index.
How long it actually takes to get something useful out of Context7 — broken out by persona, not the marketing-page minute.
For a solo developer, installing the MCP server and configuring it with Cursor or Claude Code takes about 2 minutes using the CLI. Adding custom private sources requires creating an API key and linking your repo — roughly 5-10 minutes. Team setup with shared teamspaces may take 15 minutes with initial documentation indexing.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Common stack mates teams adopt alongside Context7, with the specific reason each pairing earns its keep.
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Last calculated: June 2026
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