2026.6.2901 • Published 2h agoCLI

nano-brain

Licence

MIT

Version

2026.6.2901

Deps

Size

45 kB

Vulns

Weekly

11.9K

Install scriptsThis package runs scripts during installation (preinstall/install/postinstall)

Summary Dependency Versions

nano-brain

Built for agents. Not humans.

Agent-oriented memory and code intelligence. AI agents don't read docs — they need structured context, impact analysis, and call chains. nano-brain provides exactly that via MCP.

TL;DR

# Install
npm install -g @nano-step/nano-brain

# Start
nano-brain serve -d

# Your AI agent now has persistent memory, code intelligence, and impact analysis

Why Star This Project?

If you've ever wished your AI agent stopped flying blind in your codebase.

Most memory tools optimize for conversation recall. nano-brain optimizes for agent comprehension — the ability to understand codebases, trace dependencies, and predict the blast radius of changes.

nano-brain is:

Agent-oriented — Built around how agents actually work: impact analysis before edits, call chain tracing, symbol lookup. Not a document store with MCP slapped on top.
Self-hosted — Your data stays on your server. No cloud dependency.
Works everywhere — OpenCode, Claude Code, Cursor, any MCP client.
Actually useful — Not a toy demo. Production-ready with 16 MCP tools, hybrid search, code intelligence, and agent-oriented benchmarks.
Built for developers — Go binary, PostgreSQL, zero magic. You can read the code.
Beating competitors — P@5 of 80% vs LlamaIndex's 55% and Qdrant's 27% on real-world queries.

Star it if you want agents that understand your code, not just search it.

What It Does

nano-brain is an agent-oriented infrastructure layer that sits between your AI agent and your codebase.

It solves two problems agents have:

Session amnesia — Agents forget everything when the session ends. nano-brain persists context across sessions via harvesting, indexing, and retrieval.
Codebase blindness — Agents can't trace dependencies, measure blast radius, or understand control flow. nano-brain builds a live code graph and exposes it via 16 MCP tools.

Why MCP? Because agents don't read docs. They call tools. Every capability is a tool call — no REST API ceremony, no JSON parsing, no manual file reading.

How agents use it

Agent needs to...	Tool	What it returns
Understand a feature	`memory_query`	Hybrid search results with context
Check what breaks before editing	`memory_impact`	Blast radius — all dependent files
Trace an execution path	`memory_trace`	Call chain from entry point
Find a function definition	`memory_symbols`	Symbol location + kind
Recall a past decision	`memory_query`	Past session context
Save a discovery	`memory_write`	Persisted for future sessions

Architecture

graph LR
    A[Your AI Agent] -->|MCP Protocol| B[nano-brain]
    B --> C[PostgreSQL + pgvector]
    B --> D[Session Harvesting]
    B --> E[Code Intelligence]
    B --> F[Hybrid Search]
    
    D --> D1[OpenCode Sessions]
    D --> D2[Claude Code Sessions]
    
    E --> E1[Symbol Graph]
    E --> E2[Flow Diagrams]
    E --> E3[Impact Analysis]
    
    F --> F1[BM25 Full-Text]
    F --> F2[Vector Similarity]
    F --> F3[RRF Fusion]

Agent-Oriented Design

nano-brain isn't a memory tool with MCP bolted on. It's designed from the ground up around how agents actually behave.

The agent workflow loop

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│  Agent       │────▶│  memory_query │────▶│  Context     │
│  receives    │     │  /impact/trace│     │  window      │
│  task        │     │              │     │  filled      │
└─────────────┘     └──────────────┘     └──────┬──────┘
                                                 │
                                          ┌──────▼──────┐
                                          │  Agent       │
                                          │  implements  │
                                          │  change      │
                                          └──────┬──────┘
                                                 │
                                          ┌──────▼──────┐
                                          │  memory_write │
                                          │  (persist)    │
                                          └─────────────┘

Why agent behavior matters

Human workflow	Agent workflow	nano-brain response
Opens file, reads it	`memory_get` or `memory_search`	Returns structured content, not raw bytes
Traces call chain manually	`memory_trace`	Returns function-by-function chain with line numbers
Greps for callers	`memory_graph(direction="in")`	Returns all callers in one call
Thinks "what breaks?"	`memory_impact`	Returns full blast radius in <50ms
Remembers past decisions	`memory_query`	Returns cross-session context

The 50ms rule

At 50ms latency, agents run impact analysis on every edit. At 500ms, they skip it. nano-brain is designed for the 50ms world — every code intelligence tool call is sub-50ms, making it practical for agents to use them on every operation.

What agents actually need

Research from 15+ production code intelligence tools shows:

Impact analysis is #1 — "What breaks if I change this?" is the most common agent query
Call chains > control flow — Agents trace across files (inter-procedural), not within functions (intra-procedural)
Component composition > internal logic — For frontend frameworks, "who uses this component?" matters more than "what does the template do?"

nano-brain optimizes for exactly these three patterns.

Key Features

Hybrid Search

graph LR
    Q[Query] --> BM25[BM25 Full-Text]
    Q --> Vector[Vector Similarity]
    BM25 --> RRF[RRF Fusion]
    Vector --> RRF
    RRF --> Results[Ranked Results]

BM25 full-text + pgvector HNSW cosine similarity + Reciprocal Rank Fusion + recency decay.

Code Intelligence

graph TD
    A[Entry Point] --> B[Function Call]
    B --> C[Method Call]
    B --> D[Database Query]
    C --> E[External Service]
    D --> F[Redis Cache]

Symbol extraction — Functions, types, interfaces, constants
Call chain tracing — Follow execution paths across files
Impact analysis — "What breaks if I change this?"
Flow diagrams — Mermaid flowcharts and sequence diagrams

Session Harvesting

graph LR
    S1[OpenCode DB] --> H[Harvester]
    S2[Claude Code JSONL] --> H
    H --> L[LLM Summarizer]
    L --> I[Indexer]
    I --> DB[PostgreSQL]

Auto-ingest from OpenCode and Claude Code sessions. Map-reduce LLM summarization. Incremental harvest with dedup.

16 MCP Tools

Tool	Description
`memory_query`	Hybrid search — default first tool for broad questions
`memory_search`	BM25 keyword search for exact text/errors
`memory_vsearch`	Vector similarity for fuzzy concepts
`memory_get`	Get document by path or ID
`memory_write`	Write/update document
`memory_graph`	One-hop callers/callees/imports
`memory_trace`	Downstream call chain trace
`memory_impact`	Pre-change blast radius analysis
`memory_symbols`	Symbol search (functions, types, interfaces)
`memory_flow`	HTTP route execution flow
`memory_flowchart`	Function-level control-flow graph
`memory_workspaces_resolve`	Resolve path to workspace hash
`memory_tags`	List tags with counts
`memory_status`	Server and queue health
`memory_update`	Trigger re-embedding
`memory_wake_up`	Session-start workspace briefing

Quick Start

Prerequisites

Go 1.23+ OR pre-built binary
PostgreSQL 17 with pgvector 0.8.2
Ollama (for embeddings) or any OpenAI-compatible provider

Install

# Via npm (recommended)
npm install -g @nano-step/nano-brain

# Or build from source
CGO_ENABLED=0 go build -o nano-brain ./cmd/nano-brain

Start

# Start PostgreSQL
docker run -d --name nanobrain-pg -p 5432:5432 \
  -e POSTGRES_USER=nanobrain -e POSTGRES_PASSWORD=nanobrain -e POSTGRES_DB=nanobrain_dev \
  pgvector/pgvector:pg17

# Start nano-brain
nano-brain serve -d

# Register your project
nano-brain init --root=/path/to/your/project

Configure Your AI Agent

Add to your MCP client config (Claude Code, OpenCode, Cursor, etc.):

{
  "mcp": {
    "nano-brain": {
      "type": "http",
      "url": "http://localhost:3100/mcp"
    }
  }
}

Demo

Query Your Codebase

# Search for authentication patterns
curl -X POST http://localhost:3100/api/v1/query \
  -H "Content-Type: application/json" \
  -d '{"workspace": "abc123", "query": "how does authentication work"}'

Trace Call Chains

# Trace from entry point
curl -X POST http://localhost:3100/api/v1/graph/trace \
  -H "Content-Type: application/json" \
  -d '{"workspace": "abc123", "node": "main.go::main", "max_depth": 5}'

Analyze Impact

# What breaks if I change this file?
curl -X POST http://localhost:3100/api/v1/graph/impact \
  -H "Content-Type: application/json" \
  -d '{"workspace": "abc123", "node": "src/auth/login.ts", "max_depth": 2}'

Generate Flow Diagrams

# Get flow diagram for a controller
curl -X POST http://localhost:3100/api/v1/graph/flow \
  -H "Content-Type: application/json" \
  -d '{"workspace": "abc123", "entry": "POST /users"}'

Returns Mermaid flowchart:

flowchart LR
  POST_/users["POST /users"]
  POST_/users --> UsersController#create
  UsersController#create --> User.create
  UsersController#create --> Mailer.welcome

Use Cases

Agent-assisted refactoring

Before refactoring, your agent calls memory_impact on the target function. Gets the full blast radius. Decides whether to split the change. After refactoring, runs affected tests only — not the full suite.

Multi-session feature development

Session 1: Agent explores the codebase, discovers patterns. memory_write saves findings. Session 2: Agent recalls session 1's discoveries via memory_query. No context lost between sessions.

Legacy codebase onboarding

Index a 5-year-old codebase. Your agent can now answer "what does this function do?", "why does this class exist?", "if I change this file, what else breaks?" — without reading every file.

Cross-service debugging

Agent traces a bug from frontend to backend. memory_trace follows the call chain across services. memory_graph shows which microservices depend on the failing endpoint.

One server, whole team. Every developer's AI agent connects to the same PostgreSQL. Decisions, architecture notes, code intelligence — instantly shared. New hires get full context from day one.

Performance

Search Quality vs Competitors

Metric	nano-brain	LlamaIndex	Qdrant/Mem0	Cognee	GraphRAG	Zep
P@5	80%	55%	27%	—	—	—
MRR	95%	—	—	—	—	—
Latency	42ms	—	—	—	—	—
Code Intelligence
Symbol Graph
Impact Analysis
Flow Diagrams

Tested on 60 domain-specific queries across 3 workspaces. nano-brain is the only solution with code intelligence — competitors focus on conversation memory and document retrieval.

Competitive Landscape

What competitors offer:

Mem0 / Zep — Conversation memory, temporal ranking, chat history recall
Cognee / GraphRAG — Document-level knowledge graphs, multi-hop reasoning
LlamaIndex — Flexible RAG pipelines, document retrieval

What nano-brain adds (agent-oriented):

Impact analysis — "What breaks if I change this?" — the #1 question agents ask. Pre-computed blast radius in <50ms.
Call chain tracing — Follow execution paths across files. Agent gets a structured trace, not raw source.
Symbol graph — Find definitions, callers, callees. memory_symbols + memory_graph.
Agent-oriented benchmarks — Measures how well agents find context for domain tasks — not just search precision in isolation.

The difference: Competitors optimize for "did the agent find the right document?" nano-brain optimizes for "did the agent understand the codebase well enough to make the right change?"

At 50ms latency, agents run impact analysis on every edit. At 500ms, they skip it. nano-brain is designed for the 50ms world.

Agent-Oriented Capability Benchmarks

nano-brain is built for agents. These benchmarks measure how well agents can find relevant context for real-world domain tasks using nano-brain's MCP tools — not just search quality in isolation.

Each benchmark runs a deterministic agent workflow:

query_question — natural-language domain question
query_input — optimized search query
symbols_identifiers — symbol lookup for known identifiers

This mimics how a real agent explores a codebase: broad understanding first, then targeted retrieval.

Scores

Workspace	Domain	Overall	Multi-tool	Search-QA	Symbol-Lookup
nano-brain	Go daemon	1.000	1.000	1.000	1.000
TypeScript	CS2 item trading	0.885	1.000	0.817	1.000
Rails	CS2 item trading	0.795	1.000	0.726	0.667

What this means:

Multi-tool 1.000 — When agents combine search + symbols, they find every expected context item
Overall 0.885 — TypeScript workspace: agent finds 88.5% of expected domain artifacts
Fixed vs Agent — Agent workflow improves recall by 15-40% over single-tool queries
Unique capability — No competitor offers agent-oriented benchmarks or code intelligence

How to Run

# TypeScript workspace (CS2 item trading domain)
NANO_BRAIN_URL=http://localhost:3100 \
NANO_BRAIN_WORKSPACE=<your-workspace-hash> \
go test -v -tags=capbench -run TestCapabilityBenchmark \
  ./benchmarks/typescript/capability/

# Rails workspace (CS2 item trading domain)
NANO_BRAIN_URL=http://localhost:3100 \
NANO_BRAIN_WORKSPACE=<your-workspace-hash> \
go test -v -tags=capbench -run TestCapabilityBenchmark \
  ./benchmarks/rails/capability/

# nano-brain itself (Go daemon)
NANO_BRAIN_URL=http://localhost:3100 \
NANO_BRAIN_WORKSPACE=nano-brain \
go test -v -tags=capbench -run TestCapabilityBenchmark \
  ./benchmarks/capability/

Task Categories

Category	What It Tests	Tools Used
search-qa	Domain concept retrieval via search	`query_question`, `query_input`
symbol-lookup	Known identifier resolution	`query_input`, `symbols_identifiers`
multi-tool	Cross-tool workflow (search → symbols)	All three tools in sequence

See individual benchmark READMEs for full task breakdowns:

Ruby / Rails Support

nano-brain supports Ruby and Ruby on Rails code intelligence:

Rails routes — resources, get/post/patch/put/delete, namespace
Control-flow graphs — if/else, loops, begin/rescue, method defs
Cross-file resolution — Class→file index, resolver, reconcile edges
Flow diagrams — Controller→service→model chains (20-34 nodes)

Example flow for a Rails controller action:

flowchart LR
  POST_/users["POST /users"]
  POST_/users --> UsersController#create
  UsersController#create --> User.create
  UsersController#create --> Mailer.welcome

Tech Stack

Go 1.23 — Single static binary (CGO_ENABLED=0)
PostgreSQL 17 — Full-text search (tsvector/tsquery)
pgvector 0.8.2 — HNSW vector indexing
Echo v4 — HTTP framework
sqlc — Type-safe SQL code generation
goose v3 — Database migrations
zerolog — Structured JSON logging
koanf — YAML + env configuration
fsnotify — File system watching

Configuration

Config file: ~/.nano-brain/config.yml

server:
  host: localhost
  port: 3100

database:
  url: postgres://nanobrain:nanobrain@localhost:5432/nanobrain_dev

embedding:
  provider: ollama
  url: http://localhost:11434
  model: nomic-embed-text

search:
  rrf_k: 60
  recency_weight: 0.3
  limit: 20

See Configuration for full options.

Documentation

Getting Started — Step-by-step setup guide
Configuration — All config options
REST API — HTTP endpoints
CLI Commands — Command reference
MCP Tools — Tool documentation
Architecture — System design
Changelog — What's new
Roadmap — What's planned
Feature Showcase — Visual examples

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Development Setup

# Clone the repo
git clone https://github.com/nano-step/nano-brain.git
cd nano-brain

# Build
CGO_ENABLED=0 go build -o nano-brain ./cmd/nano-brain

# Run tests
go test -race -short ./...

# Run integration tests (requires PostgreSQL)
go test -race -tags=integration ./...

Project Structure

nano-brain/
├── cmd/nano-brain/       # CLI dispatcher + server startup
├── internal/
│   ├── config/           # Configuration management
│   ├── server/           # HTTP server + handlers
│   ├── storage/          # PostgreSQL + sqlc
│   ├── search/           # Hybrid search pipeline
│   ├── embed/            # Embedding queue
│   ├── watcher/          # File system watcher
│   ├── harvest/          # Session harvesting
│   ├── mcp/              # MCP protocol tools
│   ├── graph/            # Code intelligence
│   └── ...
├── migrations/           # Database migrations
└── benchmarks/           # Performance benchmarks

Community

GitHub Discussions — Ask questions, share ideas
Discord — Real-time chat
Twitter — Updates and announcements

License

MIT — see LICENSE for details.

Acknowledgments

Built with:

Go — Fast, statically typed language
PostgreSQL — The world's most advanced open source database
pgvector — Open-source vector similarity search
Echo — High performance, extensible, minimalist Go web framework
sqlc — Generate type-safe code from SQL
goose — Database migration tool
zerolog — Zero allocation JSON logger
koanf — Configuration manager
fsnotify — Cross-platform file system notifications

Keywords

ai memory mcp coding-agent search pgvector