ProtoMap · CI Integrations

Keep AI context fresh with every build

AI coding agents are only as useful as the context they can access. In large codebases, that context changes every time services, APIs, fields, consumers, or dependencies change. ProtoMap integrates directly into your CI workflows to continuously regenerate this context from source code.

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Fully automated

Direct source code integrations

ProtoMap Enterprise pushes tailored CI configurations to every selected repo.

Cloud and enterprise editions

ProtoMap Enterprise

Pushes CI configs

as direct commits or pull requests requiring approval

ProtoMap scan added
  • checkout
  • cart
  • payments
  • shipping
  • email
  • currency
  • auth
  • catalog
  • frontend

… across thousands of repos, in minutes

Supported

Jenkins, Azure Pipelines, CircleCI and other CI platforms

ProtoMap runs on any CI platform. These aren’t configured automatically, but you can point your AI coding agent at your CI configuration and ask it to add a ProtoMap scan job, without much effort.

Your AI coding agent

Add a job to our Jenkinsfile that runs the ProtoMap scan on every pull request.

What CI regenerates

One deterministic chain, recomputed as your code changes

Code→service→API→method→field→consumer→callsite

Our fast, deterministic analysis engine runs on standard CPU infrastructure with minimal impact on CI latency. ProtoMap computes these relationships once and refreshes them as your code changes.

The resulting evidence can power your service catalog, API and gRPC discovery, microservices documentation, change blast radius analysis, and organization wide context for AI coding agents. Through MCP, authorized agents such as Claude Code can query this continuously updated context whenever they need it.

Division of labor

Use AI where reasoning adds value. Let CI handle the deterministic discovery.

Deterministic discovery runs in CI. The MCP server delivers structured traces, not raw source code. AI agents reason over facts they no longer have to rediscover.

  1. 1Static analysis discovers.
  2. 2AI reasons.
  3. 3MCP delivers.

Rollout

Start free on one machine. Roll out to your organization.

Step 1 · Free

Install it on your machine

Install the self-hosted platform and CLI with Docker, scan your repositories, and query ProtoMap in plain English or over MCP. Free for code scanned on your machine.

  • Self-hosted platform and CLI scanner
  • Bring your own AI provider: Anthropic, OpenAI, Google and others
  • MCP server for your coding agent, including uncommitted changes
Terminal 
git clone https://github.com/hounddogai/protomap.git
cd protomap
./self-hosted/scripts/install.sh
Start Free
Step 2 · Enterprise

Organization-wide context

Enterprise keeps context current across every repository in your organization, so agents no longer clone repositories they don’t own.

  • Cloud hosted or deployed on-premises in your org
  • Direct integrations with GitHub, Bitbucket, and GitLab
  • Runs scans in CI on every pull request
  • Every agent gets context for every repository over MCP
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What ships to your agents

What ProtoMap delivers

A deterministic, always-fresh view of your repositories, built for AI coding agents and engineering teams working in large monorepos and microservice estates.

Service Catalog & API Context

An always-fresh catalog of every API, field, and downstream consumer across your repos, traced from source code.

Learn more

AI Coding Agent Context

Real-time codebase context for coding agents, cutting token costs on large repos while improving change safety.

Learn more

Cross-Repo Dataflow

Trace dataflows across monorepos and microservice estates without asking each team to instrument or annotate their code.

See how it works

Try it on your code.

Install the self-hosted version for free. It runs on your machine with Docker, and your source code stays in your environment.

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Paste into your coding agent

Set up ProtoMap and show me what it can do. ProtoMap maps gRPC services and protobuf messages across repositories, and gives coding agents that graph over MCP. Docs: https://docs.protomap.ai/quickstart/

Tell me what you are about to do before each step that installs something.

1. Check that this computer has git, Docker with Compose 2.23.1 or newer, at least 4 GB of RAM, and 20 GB of disk space. If anything is missing, tell me and stop.

2. Install ProtoMap Server, in a folder outside this repository:

   git clone https://github.com/hounddogai/protomap.git
   cd protomap
   ./self-hosted/scripts/install.sh

   The installer asks questions. Choose Trial, which runs PostgreSQL locally and logs the CLI in, and keep the default port.

3. Show me the address and the one-time setup key the installer prints. Ask me to open the address, enter the key, and create my organization and owner account. Wait until I say that's done.

4. Scan this repository, from a new shell so the CLI is on the PATH:

   protomap scan .

   If it has no gRPC services in a supported language, tell me, and offer to scan a public example instead:

   git clone https://github.com/GoogleCloudPlatform/microservices-demo.git
   protomap scan microservices-demo

5. From the scanned repository's folder, add ProtoMap to yourself as an MCP server:

   Claude Code: claude mcp add protomap -- protomap mcp serve
   Codex: codex mcp add protomap -- protomap mcp serve
   Antigravity: add "protomap": { "command": "protomap", "args": ["mcp", "serve"] } under mcpServers in ~/.gemini/config/mcp_config.json
   Others: https://docs.protomap.ai/coding-agents/

   Tell me if I need to restart you for it to take effect.

6. Tell me what the scan found, then use ProtoMap's tools to answer one question about my services, such as which services call a method and where. Suggest two more questions I could ask.