AI Code Research is an AI engineer that opens any public GitHub repository in real-time, reads the actual source code, and gives you an engineer's answer in plain English — about how a project works, how to compare tools, how to plan a migration, or how to onboard to inherited code. You ask, we read, you decide.
What is AI Code Research?
AI Code Research is HowWorks's specialist agent for reading code. Where general AI tools summarize what they read in training data, AI Code Research opens the GitHub repo at request time and reads what's there now. Engineers, PMs, and founders use it like they'd use a code reader, repo wiki, or code analyzer — but tuned for research, not autocompletion.
The verb is the point. Every other "AI for code" product treats reading the source as a side effect. AI Code Research treats it as the whole product. You point it at a repo (or describe the project you're trying to find), it opens the code, and it returns an answer that an engineer would write — not an answer summarized from blog posts about that repo.
That distinction shows up in three places. It's fresh — the agent reads HEAD on demand, not a cache from months ago. It's honest — when the source is closed, we tell you we're researching docs and SDK code instead of pretending to read what we can't. And it's conversational — you can keep asking follow-ups against the same investigation, drilling from "how does this work" into "how do I migrate to it" without restarting.
AI Code Research is one of HowWorks's specialist agents — alongside AI Research and AI Marketing. See /solutions for the others.
How is it different from ChatGPT, DeepWiki, and Cursor?
ChatGPT summarizes blog posts. DeepWiki shows a pre-generated wiki. Cursor edits code in your IDE. AI Code Research investigates the actual source on every question — fresh, not cached, and on any repo, not a fixed list.
vs ChatGPT and Claude (general LLMs)
General chatbots answer from training data. That data has a cutoff, and even within the cutoff it's mostly summaries — blog posts, README excerpts, Stack Overflow threads — not the real source files. When you ask "how does X actually work in 2026," ChatGPT can give you a confident answer that's quietly wrong because the project has moved on, or because the blog posts it learned from were imprecise to begin with.
A real engineer, on Hacker News, on a maintained AI-generated wiki: "hallucinating pretty convincingly... a struct/package/function was named for something it wasn't doing anymore". That's the shape of the failure mode — confident output, drifting truth.
AI Code Research opens the repo every time you ask. The answer is grounded in what the code does now, not what it did in some training corpus.
vs DeepWiki
DeepWiki generates a static wiki for a repo, then refreshes on a fixed cadence. Even when fresh, it's static — one snapshot, no follow-up. Looking at DeepWiki's own page for anthropics/claude-code: last indexed 2026-04-23, with a notice that says "wait 2 days to refresh." Useful for a quick scan; not great when you actually have a question that doesn't fit the wiki's chapter structure.
Another HN voice, an LLVM maintainer reviewing a DeepWiki page on his own project: "results ranged from incomplete to just plain incorrect... omits some of the most important passes in LLVM". Static wikis hit a ceiling.
AI Code Research runs on demand. You ask the question; we read what's actually there; you can keep going.
vs Cursor and GitHub Copilot
Cursor, GitHub Copilot, and Windsurf are great inside an IDE — they autocomplete, refactor, and ship features while you write. AI Code Research lives one step earlier and one space over. It's for the questions that come before your fingers hit the keyboard: which tool should I pick, how does this project actually work, what already exists I can copy, how should I plan this migration, how does this open-source library handle X.
It runs on the web, on any repo, without you opening an editor.
What can you do with it?
AI Code Research is built for five jobs you can't easily get done with a chatbot or a static wiki: comparing tools, decoding hot projects, planning a build, planning a migration, and onboarding to inherited code.
Comparing AI tools or libraries
Decisions like "Cursor vs Claude Code" or "LangChain vs LlamaIndex" don't get answered by another listicle. They get answered by reading both sides. Claude Code is open (anthropics/claude-code — 119K stars), so we read the actual source. Cursor is closed, so we research the public surface — docs, GitHub issues, SDK code — and tell you upfront which side has full source visibility and which doesn't. The output is a code-level comparison: architecture, capabilities, edge cases, and where each option wins.
Decoding a hot AI project
When something blows up — MCP, Manus, OpenClaw, the next thing — the README is too short, the blog posts are too breathless, and the docs lag. We open the repo and produce a plain-English explanation of how it actually works. (See the OpenClaw worked example below.)
Building something new
Most "how to build an AI agent" tutorials show you one toy example. We read 5+ working open-source implementations of what you're building (AI agents, MCP servers, RAG systems, chatbots) and extract the patterns that actually ship to production — with reference code you can copy.
Migrating or modernizing code
Migrations break on the parts the textbook doesn't mention. JS to TS, Express to Fastify, MongoDB to Postgres, monolith to microservices — point AI Code Research at the codebase, describe the target, and you get a step-by-step migration plan including the first PRs ready to merge. Currently best for public repos; private repository support is on the roadmap.
Onboarding to inherited code
Just joined a team with a 200K-LOC repo no one explains? We turn an unfamiliar codebase into a navigable wiki — module map, hot paths, architecture diagram. Currently strong for explaining inherited code; the full onboarding wiki form is being expanded.
How does it work?
The 3-step process
- You describe what you need to know. Compare two tools, explain a project, plan a migration, onboard to a codebase — tell us the situation in your own words.
- We investigate the actual code in real-time. The agent opens the GitHub repos, reads the source, digs into docs and issues — the way a senior engineer would research, but compressed.
- You get an accurate answer. Continue the chat to drill in further — or request a comprehensive Deep Dive Report. Quick chat answers typically return in roughly 60 seconds; comprehensive Deep Dive Reports take a few minutes.
Worked example: How does OpenClaw actually work?
OpenClaw is a 2026 viral AI project — 365,782 stars on GitHub as of 2026-04-28, 74,969 forks, MIT licensed, openclaw/openclaw. Its description is "Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞" — the kind of project where the README skips most of what matters.
We typed: "How does OpenClaw actually work?"
The agent opened openclaw/openclaw, read the source — TypeScript core, plus Python skills, Swift macOS layer, Shell scripts — and produced a research report covering:
- Architecture: a "local-first AI operator platform" with one gateway governing identity, sessions, tools, and plugins across surfaces
- End-to-end flows: how messages from Discord, iMessage, and Google Chat get normalized at the transport edge before reaching the agent runtime
- Tech stack: Playwright + Chrome DevTools Protocol for browser automation; SQLite + vector storage for durable memory; launchd / systemd / Windows scheduled tasks for cross-platform daemons
- The session model in plain English: "When a message or task starts an agent run, the runtime resolves a session-specific execution lane from the session key" — queue policies prevent concurrent conflicts
- 7 strengths and 12 risks identified by reading the code, not the README
→ Read the actual OpenClaw deep dive report (8,500+ words, real production output)
Want the same on a different repo? Try a sample question — no signup required →
Who is it for?
AI Code Research serves five reader types — anyone who needs an engineer's answer about code, regardless of whether they write code themselves.
| ICP | "I'm trying to..." | Example query |
|---|---|---|
| Vibe Coder | ship an MVP, not learn syntax | "How do production lovable-style apps handle auth?" |
| AI Tool Decider | pick a tool from 50 options | "Cursor vs Claude Code at the code level" |
| Engineer / Library Evaluator | choose a library after evaluation | "Is LangChain LCEL production-ready or just hype?" |
| PM / Founder / Investor | evaluate tech without engineering | "What is OpenClaw actually built on?" |
| Tech Curious / Content Creator | understand and explain | "How does MCP work, in plain English?" |
The common thread: each of these people needs an engineer's answer about code, but doesn't have an engineer on call to read the repo for them. AI Code Research is that engineer.
When (and when not) to use it?
AI Code Research is built for code research — not code writing, real-time PR review, or enterprise compliance audits. Use the right tool for the job.
✅ Use it for:
- Understanding how a project works (any public repo, no setup)
- Picking between tools at the code level
- Planning a migration from spec to first PR
- Onboarding to an inherited codebase
- Writing technical content backed by real implementation
❌ Not for:
- Writing code in your editor → use Cursor or Claude Code
- PR review inside your CI/CD → use Greptile or CodeRabbit
- Enterprise monorepo audit / compliance → use Sourcegraph
Where can you start?
Free, no credit card. Open any public GitHub repo. Get an engineer's answer in roughly 60 seconds — or request a full Deep Dive Report when you need depth.