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Madar is a local context compiler for AI coding agents. It builds a structural graph of TypeScript/Node.js codebases and compiles compact, verifiable context packs for Claude Code, Codex, Cursor, Copilot, Gemini, Aider, and OpenCode. The goal is to reduce repeated repo exploration, token waste, and noisy context.
Hi Product Hunt 👋
I built Madar because AI coding agents often spend too much time rediscovering the same codebase.
They grep, read files, summarize, lose context, then repeat the same exploration in the next task.
Madar tries to make that workflow more disciplined.
It builds a local structural graph of your TypeScript/Node.js workspace and compiles compact, task-specific context packs for AI coding agents.
It works with Claude Code, Codex, Cursor, Copilot, Gemini, Aider, and OpenCode.
The project was previously published as graphify-ts, but it has now moved to @lubab/madar with the new `madar` CLI.
The latest release includes:
* local graph generation
* MCP integrations
* context-pack-first workflows
* graph summary
* execution slices
* benchmark commands
* first-pass routing-controllers support
* share-safe benchmark reports
In real backend benchmarks, Madar has reduced provider-reported input tokens significantly for some prompts. This is not a universal guarantee, but it shows that better context preparation can reduce token waste and improve agent focus.
I would love feedback from developers using AI coding agents, especially around MCP workflows, token waste, codebase context, and benchmark design.
Thanks for checking it out.
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About Madar on Product Hunt
“Compact codebase context for AI coding agents”
Madar was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #138 on the daily leaderboard. Madar is a local context compiler for AI coding agents. It builds a structural graph of TypeScript/Node.js codebases and compiles compact, verifiable context packs for Claude Code, Codex, Cursor, Copilot, Gemini, Aider, and OpenCode. The goal is to reduce repeated repo exploration, token waste, and noisy context.
Madar was featured in Open Source (68.5k followers), Developer Tools (514.1k followers), Artificial Intelligence (471.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 207.9k products, making this a competitive space to launch in.
Who hunted Madar?
Madar was hunted by Mohammed Naji. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Madar stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Mohammed Naji