Show HN: BrainAPI's event-centric graph from any data with four-stage pipeline

Lumen Labs launched BrainAPI, an open-source tool that automatically builds event-centric knowledge graphs from raw text using a swarm of AI agents. The system extracts facts and relationships to answer multi-step questions with traceable reasoning paths, distinguishing it from similarity-based retrieval. Available via a CLI or Docker, it targets developers needing structured knowledge extraction from unstructured data.

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 https://brainapi.lumen-labs.ai/ • Quickstart -quickstart-fastest-way-to-run-it • What it is -what-is-brainapi • How it works ️-how-it-works-the-agent-swarm • Use cases -where-you-can-use-it • Docs ↗ https://brainapi.lumen-labs.ai/docs/v2 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. 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. | 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 → . /Lumen-Labs/brainapi2/blob/main/tui/README.md 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 . 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"}' 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. 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. php 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 🔍 Scout reads the text and extracts all the entities it contains — people, things, events, quantities. 📝 Observations Agent runs alongside and writes down notes about the facts in the text, taking the previously known context into account. 🏛️ 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. 🧹 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. 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: php Emily - :MADE - Organizing Event - :TARGETED - AI Ethics Meetup \- :OCCURRED WITHIN - London 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. default — the full swarm runs: Scout extracts every entity, the Observations agent writes notes, and the Janitor validates and deduplicates the Architect's relationships. accurate — a faster, cheaper path that extracts only the most important entities and skips the heavier validation steps. Switch with lightweight --pipeline lightweight or PIPELINE MODE in .env . 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 https://brainapi.lumen-labs.ai/docs/rest . 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 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 → https://registry.brain-api.dev/app 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. The MCP server runs as a separate process on port 8001 http://localhost:8001/mcp . - Start it: make start-mcp or it's already running via brainapi start . - 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" } } - Make sure URL-based MCP servers are enabled in Claude Desktop settings. | Platform | Package | Status | |---|---|---| Python | lumen brain | Node.js lumen-brain python 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. ▶ Watch: BrainAPI non-technical Overview Video https://www.youtube.com/watch?v=wWwTFU5-qeA | Resource | Link | |---|---| | 🖥️ Local CLI TUI | tui/README.md | npm install -g brainapi-tui brainapi.lumen-labs.ai/docs/v2 https://brainapi.lumen-labs.ai/docs/v2 brainapi.lumen-labs.ai/docs/quickstart https://brainapi.lumen-labs.ai/docs/quickstart registry.brain-api.dev/app https://registry.brain-api.dev/app brainapi.lumen-labs.ai/docs/rest https://brainapi.lumen-labs.ai/docs/rest pypi.org/project/lumen brain https://pypi.org/project/lumen brain/ npmjs.com/package/@lumenlabs/lumen-brain https://www.npmjs.com/package/@lumenlabs/lumen-brain Discord https://discord.gg/VTngQTaeDf - Fork the repository - Create a feature branch git checkout -b feature/amazing-feature - Commit your changes git commit -m 'Add amazing feature' - Push to the branch git push origin feature/amazing-feature - Open a Pull Request 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 mailto:hello@lumen-labs.ai from Lumen Platforms Inc. See the LICENSE /Lumen-Labs/brainapi2/blob/main/LICENSE file for full details. Built with ❤️ by Lumen Labs