GraphRAG + LangGraph + LLM

Tired of re-explaining
your codebase to AI?

Without real context, AI breaks production.

Cerebro gives your AI the relevant context on every iteration.

Get early access See how it works

Early adopters get preferential pricing.

Your code, structured

Every function, class, and decision: connected and queryable.

Cerebro's knowledge graph visualizes your codebase as interconnected nodes: Modules connect to Functions, Classes, and ADRs (Architecture Decision Records). Each Function links to its Dependencies and Imports. Each Class links to its Methods and Dependencies. Cross-references between nodes reveal hidden architectural relationships that LLMs need to generate coherent code.

Module Function Class ADR Dep Import Method Dep

This is your day with AI

If any of this sounds familiar, you already know the problem.

The real bottleneck

The problem with AI and code isn't generation. It's context.

×

You explain context. Again.

You spend more time giving the AI context about your project than actually building. You paste files, describe patterns, explain constraints, and it still gets it wrong.

×

You review PRs you can't trust

The AI generated code that looks right but violates architectural decisions nobody documented. You're reviewing for coherence that the AI never had access to.

×

Every session starts from zero

Yesterday you spent 40 minutes teaching the AI about your auth flow. Today it doesn't remember. The bugs you fixed, the patterns you agreed on, the decisions your team made. All gone.

×

No relevant context

LLM context windows are limited. You can't fit your entire codebase into a prompt, but that's not even the real problem. The real problem is that dumping everything in is wasteful and noisy. The AI needs the right context for each task, not all of it at once.

×

No memory

Architectural decisions, resolved bugs, team conventions: nothing persists between sessions. The AI has no history of your project's evolution.

×

No determinism

The AI skips steps, ignores tools, takes shortcuts. You can't trust it to follow the correct process every time. There's no enforced pipeline, just probabilistic output.

×

The context window is finite.

Every LLM has a hard limit on what it can see at once. On a real project, that's never the full codebase. What's not in context doesn't exist.

×

So the code loses coherence.

The model improvises from partial information. Architectural decisions get reversed. The same pattern gets written three ways. Technical debt compounds with every session.

×

Every session starts from zero.

Decisions made yesterday don't persist today. The model has no memory of your project's evolution. It improvises when it forgets — and it always forgets.

Cerebro changes the equation

Instead of feeding context to the AI, let the AI query it from a structured representation of your codebase.

✓

Relevant context, not everything

Cerebro builds a knowledge graph of your project. The AI queries only what it needs: functions, dependencies, relationships, constraints; extracted from the actual structure of your code.

✓

Persistent, searchable memory

Conversations become artifacts: ADRs, problem tracking, ideas, all linked to the exact code in the graph. Nothing gets lost. Everything is searchable.

✓

Enforced pipelines, not suggestions

The AI follows explicit state machine pipelines. Every step is mandatory, auditable, and retryable. It can't generate without querying the graph first.

The human thinks. The AI structures.

Context before generation.

How it works

Every interaction follows an enforced pipeline. The LLM cannot skip steps.

Context Acquisition

Query the knowledge graph for structure, files, and relationships.

Content Retrieval

Fetch file contents, docstrings, and linked documentation.

Dependency Mapping

Map the full dependency chain: what breaks if you change this.

Constrained Generation

The LLM generates with only the relevant context for this task. Data, not assumptions.

Validation

Verify consistency. If it fails, retry from the exact failure point.

Every step is logged. If something fails, intelligent retry from the exact point of failure. No repeated work.

What powers it

Neo4j Neo4j

Understands your architecture

Cerebro maps your entire codebase as a navigable graph. Every function, class, dependency, and relationship: connected and queryable.

  • Knows what calls what, and what breaks if you change it
  • Tracks inheritance, imports, and ownership across modules
  • The AI sees the full picture, not just the open file
Qdrant Qdrant

Ask in plain language, find by meaning

You don't need to remember file names or grep for keywords. Ask what you need, and Cerebro finds the relevant code by meaning.

  • "How do we handle authentication?" finds the right code
  • Works across languages and naming conventions
  • Combines semantic search with graph context for precision
MongoDB MongoDB

Every decision stays connected

Decisions, discussions, and documentation don't disappear after a meeting. They stay linked to the exact code they affect.

  • Architectural decisions recorded and linked to code
  • Bug history and problem tracking that persists
  • Full-text search across all project documentation
LangGraph LangGraph

The AI follows rules, not guesses

The AI can't skip steps or take shortcuts. Every action follows an enforced pipeline: query context first, then generate, then validate. No exceptions.

  • Every step is logged, auditable, and retryable
  • If something fails, it retries from the exact failure point
  • Built for production, not a prototype

You talk with intention. Cerebro resolves the context.

// You say:
"I need to refactor the payments module"

// Cerebro queries the knowledge graph before the AI writes anything
MATCH (m:Module {name: "payments"})-[:CONTAINS]->(e)
MATCH (e)<-[:CALLS|DEPENDS_ON*1..3]-(affected)
MATCH (e)-[:DOCUMENTED_IN]->(doc)
RETURN e, affected, doc

// Result: 23 entities, 8 external callers, 2 ADRs, 1 known constraint
// The AI generates with full structural context — not guesses

It's not a wrapper. It's a different approach.

Copilot / Cursor Raw LLM Cerebro
Context Current file + neighbors Whatever you paste Relevant context per task
Memory None Session only Persistent + searchable
Dependencies Doesn't understand Doesn't understand Full dependency graph
Impact analysis None Superficial Transitive analysis
Determinism Probabilistic Probabilistic Enforced pipelines
Architecture Doesn't know Doesn't know Graph-aware

Honest answers

Is this another LLM wrapper?

No. Cerebro builds a knowledge graph of your code (Neo4j) and forces the LLM to query it before responding. It's structured context extracted from your codebase, not text pasted into a prompt.

Is my code safe?

Multi-tenant architecture with isolated databases per user. Your code lives in your instance: your Neo4j, your MongoDB, your Qdrant. Not shared with anyone.

Which LLM does it use?

BYOK (Bring Your Own Key). Use your preferred AI provider's API key. The value isn't in the LLM; it's in the knowledge graph and the deterministic orchestration that wraps it.

What languages are supported?

Python, JavaScript, and TypeScript today. Rust and Go are next. The plugin architecture allows adding languages without changing core.

How much will it cost?

Pricing TBD. Early adopters get preferential pricing.

What stage is the product in?

Core parsing, knowledge graph construction, and orchestration pipeline are built. We're working on the editor plugin, web dashboard, and expanding language support. Follow the Engineering section for real-time progress.

Become an early adopter

Leave your email and we'll reach out when Cerebro is ready for you. Early adopters get early access and preferential pricing.

No spam. We only reach out when there's something real to share.