A single-file cognitive container — vectors, indexes, learning state, and a cryptographic audit trail in one .rvf. Self-learning search improves up to 36% from feedback alone, with no manual tuning. Runs in Node, the browser, edge runtimes, and offline.
Why AgentDB?
Most vector databases store embeddings and call it done. AgentDB watches which results your agent actually used, learns from that signal, and ranks the next query better. The bandit underneath also picks the right RL algorithm, the right compression tier, and the right pattern weighting on its own — so the database itself gets sharper while you focus on the agent.
The name: a database that thinks like an agent — episodic memory, skill library, causal reasoning, and a learning loop, all in one file. Built by rUv on the ruvector Rust engine.
What AgentDB Does
Self-Learning Vector Memory
Agent ──► AgentDB (.rvf) ──► HNSW search ──► top-k results
│ │
▼ ▼
recordFeedback(id, reward) ◄── agent uses some, ignores rest
│
▼
Bandit re-tunes ranking / RL choice / compression ──► next query is smarter
3 lines to self-learning search:
const backend = await SelfLearningRvfBackend.create({ learning: true, storagePath: "./my.rvf" });
const results = await backend.searchAsync(query, 10); // search
backend.recordFeedback(results[0].id, 0.9); // learn — next search is smarter
Quick Start
There are three ways to use AgentDB depending on what you're building. Pick whichever matches your stack:
| npm library | CLI | MCP server |
|---|
| What you get | TypeScript / JS API for any Node app | agentdb binary, scriptable from any shell | 41 tools callable from Claude Code, Cursor, Cline, etc. |
| Install | npm i agentdb | npx agentdb … (no install) | claude mcp add agentdb -- npx agentdb mcp start |
| Best for | Embedding the engine in your own code | Quick experiments, CI scripts, ad-hoc memory | Plugging memory + learning into an LLM agent |
Path A — npm library
npm install agentdb
import { SelfLearningRvfBackend } from 'agentdb';
const db = await SelfLearningRvfBackend.create({
learning: true,
storagePath: './memory.rvf',
});
await db.insertAsync('doc1', new Float32Array(384), { text: 'Hello world' });
const hits = await db.searchAsync(queryEmbedding, 5);
db.recordFeedback(hits[0].id, 1.0); // it was useful — db gets smarter
Path B — CLI in 10 seconds
# Try it without installing
npx agentdb init my-memory.rvf
npx agentdb add my-memory.rvf "vector memory that learns"
npx agentdb search my-memory.rvf "self-improving search" --top-k 5
Path C — MCP server (Claude Code, Cursor, etc.)
claude mcp add agentdb -- npx agentdb@latest mcp start
That registers 41 MCP tools — agentdb_pattern_store, agentdb_pattern_search, agentdb_hierarchical_store, agentdb_causal_edge, agentdb_skill_library, agentdb_reflexion, etc. They call the same engine the npm library does, just over Claude's tool-calling surface.
What You Get
