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Symvanta
The code graph your AI coding agent is missing
Index your GitHub repos into a live code knowledge graph. Give AI agents exact symbols, call graphs, and blast radius over MCP, across every repo.
I started Symvanta after watching and dealing with AI agents try to guess their way through real codebases, grep wrong things with right intention, pick the wrong caller, break prod with full confidence. I spent 18 years in software engineering and most of it deep inside other people's large codebases. When I was first introduced to AI, the agents were failing exactly where humans do. They don't know who calls what and obviously we don't expect them to care. The missing layer is a real code graph (symbols, callers, blast radius, cross-repo links) served to the agent over MCP. So I built it.
Symvanta indexes your GitHub repositories into a symbol-level call graph and serves it over MCP. Your agent asks "who calls this", "what breaks if I change it", "where is the handler for POST /orders", and gets exact answers with file and line, across repositories and branches, in 11 languages.
The approach changed a lot along the way. The first version walked syntax trees only, and the answers looked right while being subtly wrong. Don't want to get too technical but after lots of trial and error, I managed to find the sweet spot. Now the graph is built with the same machinery compilers use to resolve symbols, which is the difference between plausible and exact.
It is hosted. Add https://mcp.symvanta.com/mcp to Claude Code, Cursor, or any MCP client, sign in with OAuth, and it works. There is no local index to build or keep fresh. You can also test the system with public repositories.
Three things to try in the first five minutes:
1. Ask your agent "what breaks if I change a specific function" and watch it call blast radius once where it used to grep five times. 2. Ask "how does a specific work" and check the answer's file:line citations. 3. Open https://symvanta.com/architectur... to see the graph render a codebase you already know.
I currently have limited server resources as this is a bootstrapped thing I do, meaning I totally expect a queue on the parser side but I don't expect the site or any of the functionality to go down. I'll be here all day, ask me anything about the graph, the parser, or MCP.
About Symvanta on Product Hunt
“The code graph your AI coding agent is missing”
Symvanta was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #127 on the daily leaderboard. Index your GitHub repos into a live code knowledge graph. Give AI agents exact symbols, call graphs, and blast radius over MCP, across every repo.
On the analytics side, Symvanta competes within Developer Tools and Artificial Intelligence — topics that collectively have 997.1k followers on Product Hunt. The dashboard above tracks how Symvanta performed against the three products that launched closest to it on the same day.
Who hunted Symvanta?
Symvanta was hunted by Ugur. 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.
For a complete overview of Symvanta including community comment highlights and product details, visit the product overview.
Ugur
Hi Product Hunt, I'm Ugur, the founder.
I started Symvanta after watching and dealing with AI agents try to guess their way through real codebases, grep wrong things with right intention, pick the wrong caller, break prod with full confidence. I spent 18 years in software engineering and most of it deep inside other people's large codebases. When I was first introduced to AI, the agents were failing exactly where humans do. They don't know who calls what and obviously we don't expect them to care. The missing layer is a real code graph (symbols, callers, blast radius, cross-repo links) served to the agent over MCP. So I built it.
Symvanta indexes your GitHub repositories into a symbol-level call graph and serves it over MCP. Your agent asks "who calls this", "what breaks if I change it", "where is the handler for POST /orders", and gets exact answers with file and line, across repositories and branches, in 11 languages.
The approach changed a lot along the way. The first version walked syntax trees only, and the answers looked right while being subtly wrong. Don't want to get too technical but after lots of trial and error, I managed to find the sweet spot. Now the graph is built with the same machinery compilers use to resolve symbols, which is the difference between plausible and exact.
It is hosted. Add https://mcp.symvanta.com/mcp to Claude Code, Cursor, or any MCP client, sign in with OAuth, and it works. There is no local index to build or keep fresh.
You can also test the system with public repositories.
Three things to try in the first five minutes:
1. Ask your agent "what breaks if I change a specific function" and watch it call blast radius once where it used to grep five times.
2. Ask "how does a specific work" and check the answer's file:line citations.
3. Open https://symvanta.com/architectur... to see the graph render a codebase you already know.
I currently have limited server resources as this is a bootstrapped thing I do, meaning I totally expect a queue on the parser side but I don't expect the site or any of the functionality to go down.
I'll be here all day, ask me anything about the graph, the parser, or MCP.