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agsuperbrain

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Super-Brain: Your codebase's working memory. Local graph + vector intelligence for AI coding assistants.

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Direct dependencies26From release 0.0.1
Last observed21 Oct, 09:30 UTC

README

Version 0.0.1 documentation

Super-Brain

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MEDHIRA

Your codebase's working memory — local, fast, permanent.


Super-Brain gives your AI coding assistant a persistent, structured understanding of your code. Instead of re-reading files every turn, your assistant queries a local knowledge graph and gets back the exact functions, call paths, and semantics it needs.

100% local. 14 IDEs supported. Zero code uploaded.


The 30-second pitch

AI coding assistants have a context problem. Every session starts from zero. Every question re-reads the same files. Every long task burns tokens on exploration instead of answers.

Super-Brain fixes that. It ingests your code once, builds a call graph and vector index, and exposes it to your assistant via MCP. From then on:

  • "What calls processPayment?" → one graph query, millisecond response
  • "Explain the auth flow" → retrieve the actual path from HTTP handler to session store
  • "Where's this class used?" → transitive closure, not grep
  • "Summarize this module" → pre-clustered subsystems, already grouped

No cloud. No re-ingestion per session. No fabricated call relationships.


Why Super-Brain

Pain pointClaude Code / Codex / Cursor / Copilot aloneWith Super-Brain
Session amnesiaStarts from zero every sessionGraph persists on disk
Re-reading filesEvery turnOne ingest, then queries
Exploration costScales with codebaseFlat, query-shaped
Call graphGrep-and-hopeDeterministic, AST-derived
Path reasoningChains of readspath_between one-hop
PrivacyCode uploads to vendorNothing leaves your machine
Cross-repoOne project at a timeUnlimited repos, one graph
Docs & audioInvisibleFirst-class via ingest-doc / ingest-audio
Hallucinated dependenciesPossible under loadZero — AST is ground truth
Language coverageTop ~20306 via tree-sitter

See the full comparison for 12 detailed pain points.


Install

pip install agsuperbrain

Or with uv:

uv add agsuperbrain

Before you install

  • Python 3.11, 3.12, or 3.13 — Python 3.14 is not yet supported (native-extension deps don't ship wheels for 3.14).
  • Windows only — install Visual Studio Build Tools 2022 with the "Desktop development with C++" workload. Several deps compile native code and need a C/C++ toolchain. WSL2 users can skip this.
  • FFmpeg — required only if you plan to ingest audio/video. Skip it for code-only use.

Full prerequisites and troubleshooting: Installation guide.


Quick start

agsuperbrain init                     # configures + auto-ingests + indexes
agsuperbrain claude-install           # or: cursor-install, aider-install, etc.

The first command sets up config, auto-detects your source directory, builds the graph, embeds it for semantic search, and starts the background watcher — all in one pass. The second wires Super-Brain into your AI coding tool.

That's it. Your assistant now has permanent, structured access to your codebase.


Works with 14 AI coding tools

One install command per platform:

ToolCommand
Claude Codeagsuperbrain claude-install
Cursoragsuperbrain cursor-install
Aideragsuperbrain aider-install
Codexagsuperbrain codex-install
OpenCodeagsuperbrain opencode-install
GitHub Copilot CLIagsuperbrain copilot-install
VS Code Copilot Chatagsuperbrain vscode-install
Gemini CLIagsuperbrain gemini-install
Hermesagsuperbrain hermes-install
Kiroagsuperbrain kiro-install
Google Antigravityagsuperbrain antigravity-install
OpenClawagsuperbrain openclaw-install
Factory Droidagsuperbrain droid-install
Trae / Trae CNagsuperbrain trae-install

All at once: agsuperbrain install --platform all.

Also works with any MCP-speaking agent framework (LangChain, LangGraph, AutoGen, CrewAI, SmolAgents).


What it gives you

  • 306 language support — Python, JS/TS, Go, Rust, Java, C/C++, Ruby, PHP, Kotlin, Swift, Scala, and 296 more via tree-sitter
  • Deterministic extraction — call graph comes from AST, not LLM inference
  • Hybrid retrieval — vector search for semantics + graph expansion for structure
  • Local LLM (optional) — bundled Llama-3.2-1B for free, offline answers
  • Interactive visualization — Cytoscape.js graph, click to navigate
  • File watcher — code changes re-indexed automatically
  • Community detection — Leiden clustering surfaces subsystems
  • Multimodal — ingest PDFs, DOCX, MD, audio, and video into the same graph

Privacy by default

Every byte Super-Brain touches stays on your machine:

  • Graph database: local KùzuDB file
  • Vector store: local Qdrant directory
  • Embeddings: local sentence-transformers
  • LLM (optional): local llama.cpp
  • Transcription: local faster-whisper

No accounts. No telemetry. No network required.


Configuration

Create or edit .agsuperbrain/config.yaml:

exclude:
  - .venv
  - node_modules

watcher:
  debounce_ms: 500

graph:
  db_path: ./.agsuperbrain/graph

vector:
  db_path: ./.agsuperbrain/qdrant

Super-Brain also honors .gitignore and the Super-Brain-specific .agsuperbrainignore.


Requirements

  • Python 3.11, 3.12, or 3.13 (3.14 not yet supported — native-extension wheels unavailable)
  • Windows only: Visual Studio Build Tools 2022 with "Desktop development with C++" workload
  • ~500 MB disk (5 GB with embedding-model cache, 8 GB with local LLM)
  • FFmpeg (only if you ingest audio/video)

Documentation


License

Apache License 2.0 — see LICENSE.


Contributing

Contributions welcome. Open an issue first for anything non-trivial so we can align on approach.


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