ProtoMap helps engineers and AI coding agents understand how services, APIs, and data fields depend on each other across large codebases. It builds a deterministic dependency map for gRPC and Protobuf systems, updated on every commit and accessible through a web app or MCP. Instead of wasting time and tokens searching code across repositories, agents query ProtoMap directly to understand change impact. One enterprise team reduced change-planning time and tokens by 8×.
AI coding agents are writing more of our code every day, and every line adds services, calls and fields that the next agent has to rediscover. In a large gRPC + Protobuf codebase, a simple question like "what breaks if I change this service, RPC or field?" sends an agent off to grep, find and read files across repos, trying to piece together relationships in code that often isn't even checked out on the developer's machine. That's slow and expensive, the answer is often incomplete, and it changes from one run to the next.
How ProtoMap is different 🚀
ProtoMap doesn't replace your coding agent. It gives your agent the context it is missing. A deterministic static-analysis engine maps every service, API and field across your monorepos and microservices, refreshes the map on every commit, and serves it to engineers in a web platform and to coding agents over MCP.
🔹 Multi-hop service dependencies: see the blast radius of a change up to five hops out.
🔹 Method dependencies: find who calls an RPC, plus every call site with its file location.
🔹 Field dependencies: see which services and repos send or receive a field like credit_card.
🔹 Code owners: see every inbound service, plus the maintainers and dependents to notify.
🔹 Unused RPCs: find dead RPCs, orphaned calls, and proto messages declared differently in two repos.
Because agents query the graph instead of re-reading code, the cost of an answer depends on the question, not on the size of your codebase. For one enterprise customer with a monorepo of millions of lines and a complex web of gRPC services, ProtoMap reduced the time and tokens required for change planning by 8×.
Who is it for?
Engineering teams running gRPC + Protobuf at scale, and the AI coding agents working alongside them.
Benchmark
In the gallery video, you’ll see two live Claude Code sessions (Opus 5.5, high effort) running side by side, given the same prompt at the same moment and recorded on one screen.
The prompt:
"We want to remove the RPC acme.documents.v1.DocumentQueryService/ResolveDocuments. Find every place in every repo that calls it, including indirect calls through helpers, method names built from strings, registries, or reflection, and give file:line and the calling service for each so we can migrate them."
On this prompt, ProtoMap was 7× faster and 36× cheaper, and used 93× fewer tokens:
■ Claude Code alone (no MCP): 6:01 · $14.44 · 24.5M tokens
Visit https://hounddog.ai/videos/protomap-demo/ to see our live demo at MozFest, which includes querying the service graph in our web platform to track dependencies for services, APIs and fields across multiple hops, including callers, callees and call sites. The demo also walks through our detailed benchmarks.
🔗 Get started
ProtoMap is free to run on your machine: the self-hosted web platform and CLI scanner. When you're ready to roll it out org-wide, the Enterprise plan keeps the graph fresh across every repo, with CI scans on every pull request and GitHub, Bitbucket and GitLab integrations. Get started at https://github.com/hounddogai/protomap
We'd love your feedback, especially from anyone managing a large gRPC codebase!
When it says a change is safe, can I see which callers the agent actually opened and which ones it skipped? That list is what I'd check before trusting the verdict.
Showing the code owners next to each dependency is the part I would use most. Does the field-level view, like where credit_card flows, work across repos or only inside one monorepo?
benchmark is kinda the best case for a graph tho. would be cool to see one where the agent has to actually ship the change after
i like the idea of asking questions directly from the data map. Can non-technical privacy teams use it without developer help?
Returning actual caller locations alongside the dependency graph is a useful detail. It gives an engineer something concrete to inspect before deciding who needs to be involved in a change.
the same proto message declared diffrently in two repos is such a real one, nobody notices until something breaks in prod
since the map refreshes on every commit, what does the agent see for a field that only changed on an open branch??? or is it main only for now
About ProtoMap on Product Hunt
“Stop wasting AI tokens rediscovering dependencies”
ProtoMap launched on Product Hunt on November 9th, 2026 and earned 144 upvotes and 14 comments, placing #5 on the daily leaderboard. ProtoMap helps engineers and AI coding agents understand how services, APIs, and data fields depend on each other across large codebases. It builds a deterministic dependency map for gRPC and Protobuf systems, updated on every commit and accessible through a web app or MCP. Instead of wasting time and tokens searching code across repositories, agents query ProtoMap directly to understand change impact. One enterprise team reduced change-planning time and tokens by 8×.
ProtoMap was featured in Developer Tools (522.7k followers), Artificial Intelligence (483.2k followers) and GitHub (41.5k followers) on Product Hunt. Together, these topics include over 262.5k products, making this a competitive space to launch in.
Who hunted ProtoMap?
ProtoMap was hunted by Ben Lang. 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 ProtoMap stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ben Lang
Hey Product Hunt! I'm Amjad, co-founder of HoundDog.ai. Today we're launching ProtoMap.
The problem
AI coding agents are writing more of our code every day, and every line adds services, calls and fields that the next agent has to rediscover. In a large gRPC + Protobuf codebase, a simple question like "what breaks if I change this service, RPC or field?" sends an agent off to grep, find and read files across repos, trying to piece together relationships in code that often isn't even checked out on the developer's machine. That's slow and expensive, the answer is often incomplete, and it changes from one run to the next.
How ProtoMap is different 🚀
ProtoMap doesn't replace your coding agent. It gives your agent the context it is missing. A deterministic static-analysis engine maps every service, API and field across your monorepos and microservices, refreshes the map on every commit, and serves it to engineers in a web platform and to coding agents over MCP.
🔹 Multi-hop service dependencies: see the blast radius of a change up to five hops out.
🔹 Method dependencies: find who calls an RPC, plus every call site with its file location.
🔹 Field dependencies: see which services and repos send or receive a field like credit_card.
🔹 Code owners: see every inbound service, plus the maintainers and dependents to notify.
🔹 Unused RPCs: find dead RPCs, orphaned calls, and proto messages declared differently in two repos.
Because agents query the graph instead of re-reading code, the cost of an answer depends on the question, not on the size of your codebase. For one enterprise customer with a monorepo of millions of lines and a complex web of gRPC services, ProtoMap reduced the time and tokens required for change planning by 8×.
Who is it for?
Engineering teams running gRPC + Protobuf at scale, and the AI coding agents working alongside them.
Benchmark
In the gallery video, you’ll see two live Claude Code sessions (Opus 5.5, high effort) running side by side, given the same prompt at the same moment and recorded on one screen.
The prompt:
"We want to remove the RPC acme.documents.v1.DocumentQueryService/ResolveDocuments. Find every place in every repo that calls it, including indirect calls through helpers, method names built from strings, registries, or reflection, and give file:line and the calling service for each so we can migrate them."
On this prompt, ProtoMap was 7× faster and 36× cheaper, and used 93× fewer tokens:
■ Claude Code alone (no MCP): 6:01 · $14.44 · 24.5M tokens
■ Claude Code + ProtoMap MCP: 0:50 · $0.40 · 262K tokens
For more detailed benchmarks across 20 prompts on 9 gRPC repositories, see our benchmark page: https://hounddog.ai/protomap-benchmark/
Live Demo
Visit https://hounddog.ai/videos/protomap-demo/ to see our live demo at MozFest, which includes querying the service graph in our web platform to track dependencies for services, APIs and fields across multiple hops, including callers, callees and call sites. The demo also walks through our detailed benchmarks.
🔗 Get started
ProtoMap is free to run on your machine: the self-hosted web platform and CLI scanner. When you're ready to roll it out org-wide, the Enterprise plan keeps the graph fresh across every repo, with CI scans on every pull request and GitHub, Bitbucket and GitLab integrations. Get started at https://github.com/hounddogai/protomap
We'd love your feedback, especially from anyone managing a large gRPC codebase!