Local-First Documentation: What It Is and Why Your AI Agent Needs It

You’re mid-session with your AI coding assistant. It’s been writing solid code for the last twenty minutes — referencing the right framework APIs, using current patterns. Then it starts hallucinating. The cloud documentation service hit its rate limit, and your assistant fell back to its training data. Now it’s confidently suggesting APIs that were deprecated two versions ago.

This is the fundamental reliability problem with cloud-based documentation for AI agents. Local-first documentation solves it.

What is local-first documentation?

Local-first documentation means indexing library docs into a local database and serving them to your AI agent without any network calls. Instead of your assistant querying a cloud API every time it needs to reference a framework, it reads from a file on your machine.

The concept borrows from the broader local-first software movement: your data lives on your device, works offline, and doesn’t depend on someone else’s server being up. Applied to AI documentation, it means:

• Docs are stored locally — typically as a SQLite database or similar portable format
• Queries never leave your machine — sub-10ms lookups instead of 100–500ms cloud round-trips
• No network dependency — works on a plane, in an air-gapped environment, or when your Wi-Fi drops
• You control the version — index docs for the exact library version you’re using

This isn’t a new idea for developer tools. DevDocs, Zeal, and Dash have offered offline documentation browsing for years. What’s new is applying this architecture to AI agents — giving your coding assistant the same offline, instant, version-accurate access to docs that you’d want for yourself.

The problem with cloud documentation services

Cloud documentation services solve a real problem: AI coding assistants need access to current docs that aren’t in their training data. Services like Context7 provide this by hosting documentation and serving it through an API.

But cloud-first architecture introduces its own failure modes:

• Rate limits cut you off mid-session. Most services cap requests at 60 per hour. A single complex coding session can burn through that in minutes, especially with agentic workflows where the AI makes dozens of tool calls. Once you hit the limit, your assistant loses access to docs entirely.
• Latency adds up. Each cloud lookup takes 100–500ms. In a session with 30+ doc queries, that’s 3–15 seconds of accumulated waiting — enough to noticeably slow down an interactive coding session.
• Version mismatch. Most cloud services index only the latest version of a library. If your project is pinned to Next.js 15 but the service indexed Next.js 16, every answer references the wrong API. The version lag cuts both ways — if you’re on the latest and the service is behind, you still get wrong answers.
• Privacy exposure. Every query goes to a third-party server. For teams working with proprietary codebases, internal APIs, or sensitive project structures, that’s a non-trivial concern. The queries themselves reveal what you’re building and what you’re struggling with.
• Cost scales with usage. Free tiers have tight limits. Paid plans charge per query or per month. For teams with multiple developers using AI assistants heavily, costs compound.

None of these are deal-breakers for casual use. If you’re prototyping something quick and always-latest docs are fine, cloud services work. The problems surface when reliability and accuracy matter — production codebases, version-pinned dependencies, teams that can’t afford their AI assistant going dark mid-session.

Why local-first is a better fit for AI agents

AI agents have different access patterns than human developers browsing docs. A developer might look up a few API references per hour. An AI agent in an agentic coding session might query docs 50+ times in a single task — checking types, verifying method signatures, reading examples for each file it touches.

This high-frequency access pattern is exactly where local-first shines:

• No rate limits, ever. Your agent can query docs hundreds of times per session. The database is a file on disk — there’s no server to throttle you.
• Sub-10ms latency. SQLite queries against a local FTS5 index return in under 10 milliseconds. That’s fast enough that doc lookups add zero perceptible delay to your coding session.
• Version pinning. Index docs for the exact Git tag your project uses. When you’re on ai@6.0.86, you get v6 docs — not a blend of every version that existed at training time, and not whatever “latest” the cloud service indexed.
• Works everywhere. Airplane mode, air-gapped networks, coffee shop Wi-Fi that drops every five minutes. Once the docs are indexed locally, your AI never loses access.
• Free and unlimited. No per-query pricing, no monthly subscriptions, no tier limits. Index as many libraries as you need, query as often as you want.
• Private by default. Your queries stay on your machine. No third party sees what APIs you’re looking up, what frameworks you’re using, or what internal docs you’ve indexed.

How local-first documentation works

The architecture is straightforward:

1. Point at a source. Give the tool a Git repository URL (or a local directory). It clones the repo’s docs — typically Markdown files in a /docs folder.
2. Pick a version. Select the exact Git tag or branch you want. This is what makes version pinning possible.
3. Index into a local database. The tool parses documentation into semantically chunked sections and indexes them with full-text search (FTS5 + BM25 ranking) into a portable SQLite .db file.
4. Serve via MCP. The tool starts a local Model Context Protocol server. Your AI coding assistant — Claude Code, Cursor, VS Code Copilot, Windsurf — connects to it and queries docs through the standard MCP protocol.

The result: your AI assistant asks “How do I create middleware in Next.js?” and gets an answer from the exact version of Next.js docs you indexed, in under 10ms, without touching the internet.

@neuledge/context implements this architecture. Three commands to set up:

npm install -g @neuledge/context
context add https://github.com/vercel/next.js --tag v16.0.0
context mcp

The .db files are portable — check them into your repo or share them on a drive. Every developer on your team gets the same indexed docs with zero setup.

Local-first vs. cloud documentation: when to use each

	Local-First	Cloud
Rate limits	None	60 req/hour typical
Latency	<10ms	100–500ms
Offline	Yes	No
Version pinning	Exact tags	Latest only
Privacy	100% local	Cloud-processed
Cost	Free	$10+/month
Setup	3 commands	API key + config
Internal docs	Yes, free	Paid or unsupported

Use local-first when:

• You’re working on a production codebase pinned to specific dependency versions
• You’re in an offline or air-gapped environment
• Privacy matters — proprietary code, internal APIs, sensitive projects
• Your AI workflow is agentic (high-frequency doc queries that would hit rate limits)
• You want to index internal documentation alongside open-source libraries

Use cloud when:

• You’re prototyping and always-latest docs are acceptable
• You want zero-setup, zero-install convenience
• Your AI usage is light enough that rate limits don’t matter

Both approaches have their place. Cloud services offer convenience for light use. Local-first offers reliability and accuracy when it counts.

Get started

If your AI coding assistant keeps hitting rate limits, suggesting deprecated APIs, or losing access to docs mid-session, local-first documentation fixes all three:

npm install -g @neuledge/context
context add https://github.com/vercel/next.js
claude mcp add context -- npx @neuledge/context mcp

• Getting started tutorial — full setup walkthrough with editor integration
• Product page — features, architecture, and comparison table
• Compare alternatives — cloud services vs. local-first documentation
• Documentation — quick start and CLI reference
• GitHub repo — source, issues, and contributions