Power your agent
คำสั่งของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
npx -y [email protected]Claude Code ไม่มีลิงก์ติดตั้งสำหรับคำสั่งแบบโลคัล ให้คัดลอกคำสั่งด้านล่างแล้วรันในเทอร์มินัล
claude mcp add agentdb -- npx -y agentdb@3.10.1รันในเทอร์มินัล
{ "mcpServers": { "agentdb": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
เปิดVector memory that gets smarter every time your agent uses it.
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.
Building agents that remember? Explore the wider ecosystem.
Explore Cognitum One · Try ruOS, an agentic desktop in your browser.
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
rUvon theruvectorRust engine.
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
There are three ways to use AgentDB depending on what you're building. Pick whichever matches your stack:
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
# 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
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.
Most retrieval systems are read-only. AgentDB closes the loop:
search ──► top-k ──► agent picks the useful ones ──► recordFeedback()
│
▼
Thompson Sampling bandit
│
▼
re-weights ranking · re-picks RL algorithm · re-tunes compression
│
▼
next search is sharper
The bandit isn't a gimmick — it's used at four decision points:
Each decision logs reward over time. Bad arms decay fast. Good arms stick.
Run them yourself: npm run bench from this repo.
41 tools across six families, all callable from Claude Code, Cursor, Cline, or any MCP-compatible client:
AgentDB powers the memory + learning layer in:
If you ship something on top of AgentDB, open an issue and we'll add you.
AgentDB compiles to four targets from one source:
# Browser
import { createBrowserDb } from 'agentdb/browser';
# Docker
docker compose -f docker/docker-compose.yml up
Agent memory and vector database for AI agents: single-file HNSW search, episodic memory, causal graph, reinforcement learning and MCP tools.
{ "mcpServers": { "agentdb": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Agentdb ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-14 Agent memory and vector database for AI agents: single-file HNSW search, episodic memory, causal graph, reinforcement learning and MCP tools.