🧠 BrainAPI

Turn raw text into a living knowledge graph — automatically.

You send text. A swarm of agents reads it, takes notes, and draws the graph for you.
Then you ask questions and get answers backed by a traceable path — not a similarity guess.

BrainAPI Cloud • Quickstart • What it is • How it works • Use cases • Docs ↗

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📖 What is BrainAPI?

BrainAPI reads your text and builds a knowledge graph out of it — by itself.

You feed it documents, notes, messages or events. Behind the scenes a group of AI agents reads everything, understands what happened, writes down the facts, and connects them into a queryable graph. You then ask questions in plain language and get answers grounded in the actual connections it found.

The trick is that BrainAPI is event-centric. Instead of just storing "A is related to B," it captures who did what, to whom, when, and in what context. That's what lets it answer multi-step questions and always show you the path it used to get there.

Feed it one sentence:

"Emily organized the AI Ethics Meetup in London on March 8, 2024."

Ask something it was never explicitly told:

result = client.retrieveContext("Who organized AI events in London in Q1 2024?")
# → "Emily organized the AI Ethics Meetup in London on 2024-03-08."
# → result.triples shows the full graph path used to derive this

That trace is the difference. Not a nearest-neighbour guess — a reasoned, walkable path through a graph it built for you.

▶ Watch the full demo →

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🏃 Quickstart (fastest way to run it)

The quickest way to get BrainAPI running is the brainapi TUI — an interactive CLI that clones the project, sets up a Python environment, walks you through configuration, starts the backing services, and launches everything for you. Pick the defaults and you'll be up in a few minutes.

npm install -g brainapi-tui
brainapi init     # clone, install deps, interactive setup → choose "Use default settings"
brainapi start    # backing services + API + MCP + worker + console

When it's up:

• API → http://localhost:8000
• Console (web UI) → http://localhost:8000/console
• MCP server → http://localhost:8001/mcp

Log in to the console with the BRAINPAT_TOKEN generated during setup. That's it — you have a working brain.

TUI commands

Command	What it does
brainapi init	Clone the repo into ~/.brainapi/source/, create a venv, run the setup wizard
brainapi start	Bring up backing services and run the API, MCP server, and Celery worker (Ctrl-C stops all)
brainapi config	Re-open the setup wizard to change one area (databases, models, pipeline, plugins, …)
brainapi doctor	Check Python, Docker, Ollama, cloud credentials, and configured services
brainapi update	git pull the install and reinstall Python dependencies

Running any command (except help) before init will run init first automatically.

Useful brainapi start flags: --pipeline accurate|lightweight, --no-services, --no-api / --no-mcp / --no-worker, --only api,mcp,worker. Full reference → tui/README.md.

The TUI installs into ~/.brainapi/ (source, venv, .env, and state.json) — separate from any git clone. Override with BRAINAPI_HOME, BRAINAPI_REPO_URL, or BRAINAPI_BRANCH.

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🛠️ Run it locally (dev mode)

If you're contributing or hacking on BrainAPI, run it straight from the repo with make:

git clone https://github.com/lumen-labs/brainapi2.git && cd brainapi2
poetry install
make start-all      # backing services + API (:8000) + MCP (:8001) + Celery worker

make start-all runs everything from source with hot reload enabled, so code changes are picked up live.

Console in dev mode:

make build-console && make start-api   # serve console at http://localhost:8000/console
# — or hot-reload the console UI separately —
make start-api          # terminal 1
make start-console-dev  # terminal 2 → http://localhost:5173/console/

Other handy targets: make start-api, make start-mcp, make stop-all, and make install-extras (auto-installs heavy ML extras like local OCR based on your .env).

Docker only:

docker compose -f example-docker-compose.yaml up -d

Then ingest your first data point:

curl -X POST http://localhost:8000/ingest/ \
  -H "Authorization: Bearer $BRAINPAT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"data": {"data_type": "text", "text_data": "Emily organized the AI Ethics Meetup in London on 2024-03-08."}, "brain_id": "default"}'

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🖥️ The Console

The Console is a web UI for exploring everything inside your brain: the graph, text chunks, vectors, observations, tasks, and an ingest page to push new data. It's served on the same port as the API at http://localhost:8000/console and you log in with your BRAINPAT_TOKEN.

Set CONSOLE_ENABLED=false to disable static serving in environments where you don't want the UI exposed.

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⚙️ How It Works: The Agent Swarm

Every piece of text you send is read and discussed by a swarm of specialized agents. After they understand it, they write down notes and draw the graph. Each agent has one narrow job, which keeps the pipeline reliable and auditable end to end.

flowchart TD
    Text["Raw text"] --> Scout["🔍 Scout<br/>extract ALL entities"]
    Text --> Observer["📝 Observations Agent<br/>write notes about facts<br/>using prior context"]
    Scout -->|entities| Architect["🏛️ Architect<br/>build event-centric<br/>relationships"]
    Architect -->|relationships + entities| Janitor["🧹 Janitor<br/>verify correctness<br/>+ check for duplicates"]
    Janitor -->|"errors + instructions"| Architect
    Janitor -->|"OK"| Graph["📊 Knowledge Graph<br/>(append-only)"]
    Observer --> Graph

Loading

1. 🔍 Scout reads the text and extracts all the entities it contains — people, things, events, quantities.
2. 📝 Observations Agent runs alongside and writes down notes about the facts in the text, taking the previously known context into account.
3. 🏛️ Architect takes the Scout's entities, re-reads the text, and creates the connections between them as event-centric relationships — subject —rel→ event —rel→ object. Actions become first-class event hubs rather than flat edges.
4. 🧹 Janitor checks the Architect's work: are the relationships correct, and do similar nodes (the same entity) or triplets (the same fact) already exist in the graph? If something is wrong, the Janitor tells the Architect exactly what to fix and expects a corrected output, then checks again — looping until it's clean.

The Triangle of Attribution

Every action is modeled as a central Event Hub connecting three points, which is what makes multi-hop, traceable answers possible:

                    ┌─────────────────┐
                    │   EVENT HUB     │
                    │  (Action Node)  │
                    └────────┬────────┘
                             │
           ┌─────────────────┼─────────────────┐
           ▼                 ▼                 ▼
    ┌──────────────┐  ┌──────────────┐  ┌──────────────┐
    │    ACTOR     │  │    TARGET    │  │   CONTEXT    │
    │   (Source)   │  │ (Recipient)  │  │  (Anchor)    │
    └──────────────┘  └──────────────┘  └──────────────┘
         :MADE            :TARGETED       :OCCURRED_WITHIN

So "Emily organized the AI Ethics Meetup in London" becomes:

(Emily)-[:MADE]->(Organizing Event)-[:TARGETED]->(AI Ethics Meetup)
                                     \-[:OCCURRED_WITHIN]->(London)

Append-only by design

BrainAPI follows an append-only philosophy: nothing is deleted from the graph. Every new action is appended as its own event node rather than overwriting what was already there. This keeps the full history intact and lets you reconstruct how facts evolved over time — events are never merged away, so the provenance trail always survives.

Two pipeline modes

• accurate (default) — the full swarm runs: Scout extracts every entity, the Observations agent writes notes, and the Janitor validates and deduplicates the Architect's relationships.
• lightweight — a faster, cheaper path that extracts only the most important entities and skips the heavier validation steps. Switch with --pipeline lightweight or PIPELINE_MODE in .env.

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🔎 Retrieving Knowledge

Once your data is in, BrainAPI exposes purpose-built retrieval endpoints (REST, on port 8000):

Endpoint	Method	What you get
/retrieve/context	POST	Relevant information for a piece of text — the matching graph triples plus historical context
/retrieve/entity/status	GET	Existence check for a specific entity — returns whether it exists, its node, relationships, and observations
/retrieve/entity/synergies	GET	Similar things related to a given one — effectively a recommendation algorithm over the graph

Get context for a question:

curl -X POST http://localhost:8000/retrieve/context \
  -H "Authorization: Bearer $BRAINPAT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"text": "Who organized AI events in London in March 2024?", "brain_id": "default"}'
# → text_context + triples (the graph path) + historical_context

Check if an entity exists:

curl "http://localhost:8000/retrieve/entity/status?target=Emily" \
  -H "Authorization: Bearer $BRAINPAT_TOKEN"
# → { "exists": true, "node": {...}, "relationships": [...], "observations": [...] }

Find synergies (recommendations):

curl "http://localhost:8000/retrieve/entity/synergies?target=Neural%20Networks%20101" \
  -H "Authorization: Bearer $BRAINPAT_TOKEN"
# → similar entities ranked by how strongly they connect to the target

These three cover the most common needs, but there are more (/retrieve/hops, /retrieve/entities/neighbors, /retrieve/text-chunks, …). Full list → REST API Reference.

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🧩 Plugins: Make BrainAPI Fit Your Use Case

Out of the box BrainAPI is a general-purpose understanding engine. Plugins let you reshape it into something far more specific — without forking the core. Because plugins can add routes, swap agent prompts, and register MCP tools, the same engine can become:

• A cheap semantic search engine — extract entities once, then serve fast graph-backed search instead of paying for vector queries every time.
• A recommendation system — build on /retrieve/entity/synergies and custom routes to surface related products, content or collaborators.
• A domain-specific extractor — override the Scout/Architect prompts so the swarm understands your ontology (CRM, legal, medical, …).
• An agent toolset — register custom MCP tools so any LLM runtime can read and write your brain.

A plugin can:

• Add custom REST routes to the BrainAPI server
• Override or extend the agents' prompts for domain-specific extraction
• Register new MCP tools for agent runtimes
• Ship its own background (Celery) tasks

Install & publish

poetry run brainapi plugins install @community/crm-entities
poetry run brainapi plugins list --remote

poetry run brainapi plugins register         # become a publisher
poetry run brainapi plugins publish ./my-plugin.tar.gz

Browse and publish at the Plugin Registry →

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🚀 Where You Can Use It

BrainAPI shines wherever messy, ever-changing text needs to become structured, queryable understanding:

• AI memory for agents — persistent, structured memory across sessions and documents, with provenance.
• Recommendation systems — grounded in real behavioural paths, not click co-occurrence.
• Semantic search — retrieve by meaning and multi-hop reasoning across docs, tickets and chat.
• Expert & community mapping — "Who in our org has worked on Kubernetes and ML pipelines together?"
• Business intelligence from qualitative data — turn feedback and support conversations into queryable signals.
• Investigation & compliance — connect people, events, documents and timestamps into a coherent, traceable graph.

✅ Reach for BrainAPI when you need structured knowledge from messy sources, long-term explainable memory, or multi-hop queries with a traceable answer.

⚠️ Skip it when your data already fits a fixed schema you control, or a single SQL query solves the problem.

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🔌 Connecting Claude Desktop via MCP

The MCP server runs as a separate process on port 8001 (http://localhost:8001/mcp).

1. Start it: make start-mcp (or it's already running via brainapi start).
2. In Claude Desktop → Settings → Developer → Edit Config, add:

"brainapi-local": {
  "command": "/path/to/node/bin/npx",
  "args": ["-y", "@pyroprompts/mcp-stdio-to-streamable-http-adapter"],
  "env": {
    "URI": "http://localhost:8001/mcp",
    "MCP_NAME": "brainapi-local",
    "PATH": "/path/to/node/bin:/usr/local/bin:/usr/bin:/bin",
    "BEARER_TOKEN": "your-pat-here"
  }
}

3. Make sure URL-based MCP servers are enabled in Claude Desktop settings.

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📦 SDKs

Platform	Package	Status
Python	lumen_brain	⚠️ Pre-release
Node.js	lumen-brain	⚠️ Pre-release

from lumen_brain import BrainAPI

client = BrainAPI(api_key="your-key")
client.ingest.text("Emily organized the AI Ethics Meetup in London on 2024-03-08.")

result = client.retrieveContext("Who organized AI events in London in March 2024?")
print(result.text_context)   # the answer
print(result.triples)        # the graph path used to derive it

Both SDKs are pre-1.0 and under active development. For production, use the REST API directly until v1.0 ships. You can mix modes freely — ingest over REST, retrieve via MCP inside an agent runtime, or use the SDKs for everything.

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▶ Watch: BrainAPI (non-technical) Overview Video

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📚 Resources

Resource	Link
🖥️ Local CLI (TUI)	tui/README.md — npm install -g brainapi-tui
📖 Documentation	brainapi.lumen-labs.ai/docs/v2
⚡ Quick Start Guide	brainapi.lumen-labs.ai/docs/quickstart
🔌 Plugin Registry	registry.brain-api.dev/app
🛠️ REST API Reference	brainapi.lumen-labs.ai/docs/rest
🐍 Python SDK (PyPI)	pypi.org/project/lumen_brain
📦 Node.js SDK (npm)	npmjs.com/package/@lumenlabs/lumen-brain
💬 Community & Support	Discord

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under AGPLv3 + Commons Clause — free for personal, research and non-commercial use. Commercial usage (SaaS, embedding, redistribution) requires an Enterprise License from Lumen Platforms Inc.

See the LICENSE file for full details.

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