Dataflow Context Engine for AI Coding Agents

The Problem

Fragmented context slows AI agents down

On large monorepos and microservice estates, the cross-repo context agents need lives in code no developer has checked out locally.

Agents rediscover context by brute force

Without centralized dataflow context, agents burn tokens grepping repos and writing throwaway bash scripts to parse code relationships.

API specs only tell part of the story

OpenAPI, protobuf, and GraphQL schemas define contracts. They do not list the services, fields, and resolvers actually consuming them.

Manual documentation does not scale

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.

Even as the context windows of foundational models keep growing, it is not practical or cost-effective to expect AI coding agents to infer API dependencies across millions of lines of code. For protocols like gRPC and GraphQL, context is fragmented across definition files and application code. Documenting gRPC APIs manually is especially painful: protobuf definitions live in .proto files, but service consumers and field usage live inside application code, with no single source of truth.

The Solution

Automated gRPC documentation and API context

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.

╭──────────────────────────────────────────────────────╮
│ >_ OpenAI Codex (v0.137.0)                           │
│                                                      │
│ model:       gpt-5.5 xhigh   fast   /model to change │
│ directory:   ~/hounddog-workspace                    │
╰──────────────────────────────────────────────────────╯

▌ 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.

Identify which downstream services are affected by API changes
Understand where specific fields are ingested by each service
Map cross-service dependencies before generating code
Reduce token burn and eliminate repetitive file scanning
Export API graphs locally for documentation and collaboration

Watch Claude Code complete the same task 7x cheaper and 6x faster with the Dataflow Context Engine. Watch the demo

Key Outcomes

Better AI-generated code, faster development

Less buggy code

Do not waste cycles fixing generated code that breaks API functionality or brings down services.

Faster development

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.

Lower cost

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.

How It Works

MCP server, CLI, and Skills integration

HoundDog.ai Dataflow Context Engine integrates with your existing AI agents using mainstream methods including a local MCP server, CLI, and Skills.

Easy integration

Connect via MCP, CLI, or Skills. Works locally with your AI coding agents.

Code stays inside your environment

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.

Built in Rust

The engine is extremely lightweight and fast. Built in Rust, it can analyze millions of lines of code in less than a minute.

Scales with complexity

The larger and more complex the codebase, the more valuable HoundDog.ai Dataflow Context Engine becomes. It thrives where other tools struggle.

Where It Runs

Local on your machine. Or centralized across your org.

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.

Local · Free

Runs on the developer's machine

The MCP server runs as a local process and indexes whatever code is checked out locally. No infrastructure to stand up. Free.

Local MCP server, CLI, and Skills
Sees code that is checked out on the developer's machine
Best for individual repos, pilots, and evaluation
Deterministic by construction, runs entirely on your hardware

Start Free on GitHub

Centralized · Enterprise

Runs across your entire organization

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.

Direct integrations with GitHub, Bitbucket, and GitLab
Auto-scans selected repos, no developer checkout required
Centralized context across every repo and cross-service relationship
Runs in CI on every pull request, kept fresh org-wide
SOC-2, SSO, RBAC, audit logs; on-premises available

Why centralized? On a massive monorepo that no developer can keep checked out completely, or an estate of hundreds (or thousands) of microservices updated at development speed, context has to be gathered centrally. Developers don't check anything out. The MCP server fetches the relevant slice from the central catalog and serves it to the AI coding agent on demand.

Supported Protocols

Protocol support: gRPC today, GraphQL and REST coming soon

Starting with gRPC, with GraphQL and REST on the near-term roadmap.

Available today

gRPC

Full protobuf and service code analysis: gRPC documentation, dependency graphs, and field-level API context for AI coding agents.

Coming soon

GraphQL

Schema documentation and cross-service GraphQL dependency mapping, next on the roadmap.

Coming soon

REST

REST API documentation and cross-service dependency mapping, following GraphQL.

Why Trust Us

Enterprise-grade security

Your code never leaves your machine. Our engine is battle-tested by the world's most demanding organizations.

SOC 2 compliant

We publish our SBOM and penetration test reports in our Trust Center.

Minimal dependency Rust engine

HoundDog.ai Dataflow Context Engine is built using the same minimal dependency Rust engine that powers the HoundDog.ai Privacy Code Scanner.

Battle-tested at scale

Trusted by Replit to detect privacy risks for 45M creators, running 10,000 scans per day.

Visit our Trust Center

Early Access

Get early access

Are you working on large code repositories and struggling to give your AI coding agents the right context every time? Join our waitlist and get early access to HoundDog.ai Dataflow Context Engine when it becomes available.

Runs locally. Your code never leaves your machine.

Join the waitlist

FAQ

Frequently asked questions

What is the HoundDog.ai Dataflow Context Engine?+

HoundDog.ai Dataflow Context Engine is a deterministic static analysis engine that builds a service catalog of every API, every field, and every downstream consumer across your repos, auto-generates gRPC documentation from protobuf files and service code, and feeds that context to AI coding agents through a local MCP server, CLI, and Skills.

Is the Dataflow Context Engine the same as the API Context Engine?+

Yes. The product previously previewed as the HoundDog.ai API Context Engine is launching as the HoundDog.ai Dataflow Context Engine. Same engine, same roadmap, new name.

Does my code leave my machine?+

On the free local tier, scans run on the developer's machine and your code never leaves it. On Enterprise, scans run inside your tenanted environment (cloud or on-premises), and only the resulting catalog and findings are centrally accessible. Your code never leaves your infrastructure in either tier.

Which AI coding agents are supported?+

Any AI coding agent that implements the MCP protocol, including Cursor, Codex, Claude Code, GitHub Copilot, JetBrains AI, OpenCode, and Windsurf.

Which API protocols are supported?+

gRPC is supported today, with GraphQL and REST coming soon.

The Dataflow Context Engine for gRPC documentation and AI coding.