HoundDog.ai Dataflow Context Engine builds a deterministic service catalog of every API, every field, and every downstream consumer across your repos, auto-generates gRPC documentation from your protobuf files and service code, and continuously feeds that context to AI coding agents. Built for the largest monorepos and most complex microservices architectures.
On large monorepos and microservice estates, the cross-repo context agents need lives in code no developer has checked out locally.
Without centralized dataflow context, agents burn tokens grepping repos and writing throwaway bash scripts to parse code relationships.
OpenAPI, protobuf, and GraphQL schemas define contracts. They do not list the services, fields, and resolvers actually consuming them.
Anthropic puts the onus on teams to maintain context via CLAUDE.md and MCP. For cross-repo relationships, hand-written docs fall behind on day one.
The result: slower prompts, higher token spend, and avoidable context churn on every API or service change.
HoundDog.ai Dataflow Context Engine builds a full service catalog of every API, every field, and every downstream consumer across your repos, then feeds that context to your existing AI coding agents through a local MCP server, CLI, and Skills.
â•â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â•® │ >_ OpenAI Codex (v0.137.0) │ │ │ │ model: gpt-5.5 xhigh fast /model to change │ │ directory: ~/hounddog-workspace │ ╰──────────────────────────────────────────────────────╯ â–Œ Rename the email field to contact_email in UserService.GetUser • Calling HoundDog.ai Dataflow Context Engine (MCP + Skills)...> 28 downstream services consume GetUser()> order-service reads email at line 142> notification-service reads email at line 89> billing-service reads email at line 215> ...and 25 more, across 9 repos• Editing 28 call sites with full dependency awareness. No grep, no guessing.
Do not waste cycles fixing generated code that breaks API functionality or brings down services.
Do not wait for your AI coding agent to grep files and assemble context on its own. HoundDog.ai Dataflow Context Engine provides API context automatically.
Do not waste tokens on code that can be statically analyzed more efficiently and deterministically. HoundDog.ai Dataflow Context Engine gives agents the context needed to reason about API dependencies.
HoundDog.ai Dataflow Context Engine integrates with your existing AI agents using mainstream methods including a local MCP server, CLI, and Skills.
Connect via MCP, CLI, or Skills. Works locally with your AI coding agents.
Free tier runs on the developer's machine. Enterprise runs in your tenanted cloud or fully on-premises. Your code never leaves your infrastructure either way.
The engine is extremely lightweight and fast. Built in Rust, it can analyze millions of lines of code in less than a minute.
The larger and more complex the codebase, the more valuable HoundDog.ai Dataflow Context Engine becomes. It thrives where other tools struggle.
HoundDog.ai Dataflow Context Engine supports any AI coding agent that implements the MCP protocol.
Two deployment models for two scales. Local for individuals and small repos. Centralized for the codebases no developer can keep checked out on a laptop.
The MCP server runs as a local process and indexes whatever code is checked out locally. No infrastructure to stand up. Free.
Cloud or on-premises. Connects directly to GitHub, Bitbucket, or GitLab. Auto-scans every selected repo from your SCM, with no local checkout needed. Developers and AI agents query a centralized catalog that already holds the answer.
gRPC and Thrift are supported today, with GraphQL and REST on the near-term roadmap. See the full list of supported languages and protocols.
Full protobuf and service code analysis: gRPC documentation, dependency graphs, and field-level API context for AI coding agents.
Thrift IDL and service code analysis: method and field-level API context, implementations, and cross-service call sites.
Schema documentation and cross-service GraphQL dependency mapping, next on the roadmap.
REST API documentation and cross-service dependency mapping, following GraphQL.
Your code never leaves your machine. Our engine is battle-tested by the world's most demanding organizations.
We publish our SBOM and penetration test reports in our Trust Center.
HoundDog.ai Dataflow Context Engine is built using the same minimal dependency Rust engine that powers the HoundDog.ai Privacy Code Scanner.
Trusted by Replit to detect privacy risks for 50M creators, running 100,000 scans per day.
AI agents are great at reasoning. They are an expensive and inefficient way to repeatedly rediscover deterministic facts about your codebase.
Without precomputed context, every agent has to search files, follow references, inspect protobuf definitions, and reconstruct dependencies before it can start solving the actual problem. Multiply that across developers, prompts, and repositories, and you repeatedly pay for the same discovery in tokens, latency, and compute.
HoundDog.ai separates discovery from reasoning.
The Dataflow Context Engine computes API, field, consumer, and service relationships deterministically, keeps them current as code changes, and makes that evidence available to your existing AI agents through MCP.
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. HoundDog.ai integrates directly into your CI workflows to continuously regenerate this context from source code.
Our fast, deterministic analysis engine runs on standard CPU infrastructure with minimal impact on CI latency. Instead of asking AI agents to repeatedly search repositories and reconstruct the same relationships, HoundDog.ai computes those relationships once and refreshes them as your code changes.
The resulting evidence can power your service catalog, API and gRPC and Thrift 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.
Stop making AI agents rediscover your codebase with every prompt. Give them continuously updated context about your APIs, fields, downstream consumers, and service dependencies.
Runs locally or across your organization. Your code never leaves your infrastructure.