The context layer your agents are missing
A context engine for large codebases, with graph-based retrieval and an MCP endpoint. Plug it into Codex, Claude Code, or Cursor so they answer system-level questions instead of grep-ing your repo.
How the Context Engine Works
From a raw repo to a graph your agents can traverse, in four steps.
Deep code analysis and indexing
Parse every file across languages and frameworks, extract symbols, types, and call sites.
Relationship extraction and mapping
Map how classes, functions, services, and APIs reference and depend on each other across your code.
Multi-repository correlation
Stitch services, libraries, and infra repos into one index you can query across in a single call.
Continuous synchronization
Every commit incrementally updates the index, so the context is always fresh. No nightly re-indexing.
Under the Hood: Hybrid Retrieval Architecture
Graph traversal, vector search, and lexical search fused together, so a query can follow a call chain across services instead of returning text matches.
Knowledge Graph Construction
Embeddings alone lose structure. We extract classes, functions, services, APIs, and the edges between them into a graph you can traverse.
Graph-Aware Retrieval
Queries traverse the knowledge graph alongside vector similarity and lexical search to find contextually relevant code, not just textually similar snippets.
Multi-Hop Reasoning
Follow connections across services and repositories to answer complex questions that span multiple components.
Hierarchical Summarization
Each artifact gets an LLM-written summary, so an agent can pull the function body or the one-line description, depending on what it needs.
Real-Time Incremental Indexing
Keeps the index fresh as you commit. Every change incrementally updates the knowledge graph. No full re-indexing required.
Hybrid Search
Graph traversal + vector similarity + lexical search, fused with reranking for precise, context-aware results.
That's the difference between answering "how does our payment flow work" and "where does the word payment appear".
Retrieval covers PRs, ADRs, and ownership data
Code without intent is half the picture. Retrieval also pulls in the PRs, ADRs, and ownership data that explain why the code looks the way it does.
Golden Repositories
Tag the repos that show how things should be done, so agents weight their patterns higher than the rest.
Past Decisions
Surface why code was written this way through linked PRs, commits, and discussions.
Team Conventions
Capture and enforce your organization's patterns, standards, and naming rules.
Documentation Links
Connect code to specs, ADRs, and design docs so retrieval pulls in the rationale, not just the snippet.
Ownership Mapping
Know who owns what and who to ask, wired into every answer the engine returns.
Built for monorepos and multi-repo estates
Indexing that doesn't fall over at a million lines of code or twenty languages in one estate.
20+
Programming Languages
TypeScript, Python, Go, Rust, Java, C#, and the rest of what's in your stack.
1M+
Lines of Code
Tested on monorepos past a million lines and across 50+ repo estates.
Minutes
Always-Fresh Information
Each push reindexes only what changed, so the graph trails commits by minutes.
Cross-Service
Dependency Tracking
Follow calls, contracts, and data flows across service boundaries, not just within one repo.
What Teams Use the Context Engine For
Whether someone's looking up a function or planning a migration, the answers come from the same graph.
System-Wide Code Search
Find behaviors, contracts, and patterns across every repo, not just text matches in one project.
Impact Analysis
Understand downstream effects before you change a function, API, or shared library.
Architecture Documentation
Generate and keep docs aligned with the real, current state of the system.
Onboarding Acceleration
New hires can ask the codebase questions in week one instead of waiting on a senior for every answer.
Call the Context Engine from your existing tools
One engine, four ways to call it.
Chat Interface
A web chat that answers from the graph, with file and line references on every claim.
MCP Protocol
Drop-in context for Cursor, Claude Code, Continue, Cline, and other MCP-aware agents.
REST API
Search, query, and streaming endpoints for embedding context into your own tools.
IDE Extensions
Code-grounded answers right inside the editors your team already uses.
What the graph looks like for a real repo.
How the Context Engine maps the relationships in your code: calls, imports, implementations, and test coverage.
What the graph looks like for a real repo.
Connect a repo and try it.
Connect your repositories and put the Context Engine to work in minutes.