A self-enriching knowledge graph that lets Claude Code, OpenCode, Cursor, and Hermes share context — typed links, graph intelligence, and autonomous enrichment. Polyglot storage: Postgres + Neo4j. MCP-native. 23 tools.
You run five agents: Claude Code builds features, OpenCode reviews PRs, Cursor edits frontend, Hermes handles ops. None of them know what the others built, decided, or learned.
The result: you're the copy-paste layer. Repeating context. Re-explaining decisions. Watching agents rediscover things another agent already figured out yesterday.
Same knowledge graph, delivered the way you work — npm package, terminal command, or native MCP tool.
npm install brainbase-sdk
import { Brainbase } from "brainbase-sdk";
const brain = new Brainbase({
apiKey: "bb_live_..."
});
// Search + graph traversal
const results = await brain
.search("Garry Tan");
const page = await brain
.getPage("people/garry-tan");
// Graph intelligence (Neo4j)
const ranks = await brain
.pageRank(25);
const path = await brain
.shortestPath("people/a", "people/b");Terminal-native
$ npm install -g brainbase-cli
$ brainbase config set apiKey bb_live_...
$ brainbase search "Garry Tan"
$ brainbase health
$ brainbase page people/garry-tan
$ brainbase links people/garry-tan
$ brainbase graph
# Graph intelligence (Neo4j)
$ brainbase pagerank --limit 25
$ brainbase communities
$ brainbase shortest-path a b
$ brainbase similar people/garry-tan23 JSON-RPC tools
// Drop into Claude Code, Cursor,
// OpenCode, or Hermes
{
"mcpServers": {
"brainbase": {
"type": "http",
"url": "https://brainbase
.belweave.ai/api/mcp",
"headers": {
"Authorization":
"Bearer bb_live_..."
}
}
}
}
Read: search, query, get_page,
get_links, get_backlinks,
get_timeline, get_health,
get_stats, get_graph,
list_pages, traverse_graph
Write: put_page, delete_page,
add_link, remove_link,
add_timeline_entry
Graph: pagerank, communities,
shortest_path, similar_pages
Triggers: upsert_trigger,
list_triggers, run_triggersNightly autonomous cycle. Extracts wikilinks + timelines, generates embeddings, reconnects orphans, escalates entities, syncs to Neo4j graph projection. Runs without any human input.
A 7-stage gated pipeline: full-text search → pgvector similarity → reciprocal rank fusion → compiled truth boost → backlink boost → intent-aware re-ranking → structured query handlers. Agents find what they need, not what's vaguely similar.
Every connection has semantic meaning — not just “related to.” People have roles. Companies have investments. Projects have owners. Decisions have preconditions. The graph knows the difference, and agents can traverse it with type-aware queries.
PageRank surfaces your most central entities. Louvain community detection finds natural clusters. Shortest path traces how any two nodes connect. Node similarity discovers structural twins. All powered by Neo4j GDS with automatic Postgres fallback.
The dream cycle runs nightly without any human input. It extracts wikilinks and timelines from page content, generates OpenAI embeddings for new chunks, reconnects orphan pages via semantic similarity, detects cross-page patterns, escalates important entities, and syncs everything to the Neo4j graph projection.
Interactive 3D Knowledge Graph
Explore a real brain: search pages, traverse links, see the graph structure live
The interactive demo shows a real knowledge graph — search pages, explore typed links, and run graph intelligence queries. Then grab an API key from the dashboard and connect your agents.