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Satori
Semantic and Symbol-aware repo context for AI coding agent
Satori helps MCP coding agents move from plain-English intent to precise repo evidence without grep chains or giant file dumps. It indexes code with AST-aware chunks, semantic search, symbol ownership, exact reads, caller/callee context when supported, freshness checks, and recovery guidance, so agents spend fewer tokens getting the right context before edits.
Real codebases are not flat text dumps. They have symbols, ownership boundaries, wrappers, callers, callees, stale files, generated output, and implementation details spread across folders.
Without structured repo context, agents fall into grep chains.
They search one file. Then another. Then another. Then they edit from fragments.
I built Satori to give MCP-compatible coding agents a more efficient investigation path through real codebases.
Satori indexes a repo with AST-aware chunks, semantic retrieval, symbol ownership, exact symbol and line-range reads, caller/callee context when supported, freshness checks, and recovery guidance when context is stale or incomplete.
The agent can move from plain-English intent to precise code evidence without dumping huge files into context.
The goal is simple:
Less grep chaining. Less context waste. Fewer blind edits. More visible evidence before the first diff.
Satori does not edit your source code. It gives agents better repo context so developers can steer and approve changes with more confidence.
I’d love feedback from developers using Codex, Claude Code, OpenCode, Cursor-style workflows, or other MCP clients:
Where do your agents usually lose context?
This is currently in pre-alpha stage. And planned offline support
Finally something that stops my agent from blindly greping the whole repo. The AST-aware chunks actually return the right symbol and its callers in one call, which shaved a ton of tokens off my last refactor.
About Satori on Product Hunt
“Semantic and Symbol-aware repo context for AI coding agent”
Satori was submitted on Product Hunt and earned 0 upvotes and 2 comments, placing #50 on the daily leaderboard. Satori helps MCP coding agents move from plain-English intent to precise repo evidence without grep chains or giant file dumps. It indexes code with AST-aware chunks, semantic search, symbol ownership, exact reads, caller/callee context when supported, freshness checks, and recovery guidance, so agents spend fewer tokens getting the right context before edits.
Satori was featured in Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers), GitHub (41.3k followers) and Development (6k followers) on Product Hunt. Together, these topics include over 208.5k products, making this a competitive space to launch in.
Who hunted Satori?
Satori was hunted by Hamza Ahmed. 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.
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Hamza Ahmed
Hi, I’m Hamza, the maker of Satori.
Most AI coding agents can search files.
That is not enough.
Real codebases are not flat text dumps. They have symbols, ownership boundaries, wrappers, callers, callees, stale files, generated output, and implementation details spread across folders.
Without structured repo context, agents fall into grep chains.
They search one file.
Then another.
Then another.
Then they edit from fragments.
I built Satori to give MCP-compatible coding agents a more efficient investigation path through real codebases.
Satori indexes a repo with AST-aware chunks, semantic retrieval, symbol ownership, exact symbol and line-range reads, caller/callee context when supported, freshness checks, and recovery guidance when context is stale or incomplete.
The agent can move from plain-English intent to precise code evidence without dumping huge files into context.
The goal is simple:
Less grep chaining.
Less context waste.
Fewer blind edits.
More visible evidence before the first diff.
Satori does not edit your source code. It gives agents better repo context so developers can steer and approve changes with more confidence.
I’d love feedback from developers using Codex, Claude Code, OpenCode, Cursor-style workflows, or other MCP clients:
Where do your agents usually lose context?
This is currently in pre-alpha stage.
And planned offline support