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AgentDB

Vector 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.

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

3 lines to self-learning search:

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

Path B — CLI in 10 seconds

Path C — MCP server (Claude Code, Cursor, etc.)

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

| Capability | Description |

| 🧠 Self-Learning Search | Up to +36% search quality from feedback alone — Thompson Sampling bandit re-tunes ranking, RL choice, and compression tier with zero manual config | | 🐝 Cognitive Memory | 6 human-inspired patterns — episodic replay (Reflexion), skill library, causal reasoning, hierarchical context, semantic clustering, working memory | | 🤖 9 RL Algorithms | Q-Learning, SARSA, DQN, PPO, Actor-Critic, Policy Gradient, Decision Transformer, MCTS, Model-Based RL — bandit picks the right one per task | | ⚡ 150× faster than SQLite | RuVector Rust engine via NAPI bindings; HNSW vector search; sub-millisecond reads on 100k+ vectors | | 🔍 Hybrid Search | BM25 keyword + dense vector fused with Reciprocal Rank Fusion — exact terms *and* semantic intent in one query | | 🕸️ Graph Intelligence | Cypher queries, causal edges, GNN 8-head attention (+12.4% recall), hyperedges for n-ary relationships | | 💾 Single-file Storage | Everything (vectors, indexes, learning state, audit log) in one .rvf "Cognitive Container" — no servers, no API keys, no monthly bills | | 🎯 Quantization | 4-32× memory reduction with PQ8 / PQ4 / binary quantization; minimal recall loss; runs on commodity hardware | | 🔌 41 MCP Tools | First-class Claude Code / Cursor / Cline integration — pattern store, search, skill library, reflexion, causal edges, hierarchical recall, delete | | 🌐 Runs Anywhere | Node, browser (WASM), edge runtimes, fully offline — same API, same .rvf file | | 🛡️ Enterprise Security | JWT auth, API key rotation, Argon2id hashing, SOC2 / GDPR audit trails, cryptographic witness chain | | 🔗 agentic-flow Integration | Drop-in for agentic-flow — backs ReasoningBank, MemoryController, NightlyLearner, and 30+ agents |

🧠 Self-Learning Loop

Most retrieval systems are read-only. AgentDB closes the loop:

The bandit isn't a gimmick — it's used at four decision points:

1. Pattern ranking — which historical pattern matches this new query best? 2. RL algorithm selection — Q-Learning for tabular tasks, PPO for continuous control, MCTS for planning, etc. 3. Compression tier — full precision when accuracy matters, PQ4 when memory does. 4. Skill composition — chain skill A→B→C or skill A→D→E?

Each decision logs reward over time. Bad arms decay fast. Good arms stick.

⚡ Performance

| Metric | AgentDB | Baseline | Source |

| Vector search (100k vectors) | Pattern Store / Search (8 tools)

| Tool | Purpose |

| agentdb_pattern_store | Store a successful pattern with embedding + metadata | | agentdb_pattern_search | Semantic search for similar past patterns | | agentdb_pattern_stats | Retrieval stats, cache hits, hit-rate trends | | agentdb_pattern_delete | Remove a pattern by id | | agentdb_batch_insert | Bulk insert many patterns at once | | agentdb_batch_search | Parallel multi-query search | | agentdb_export | Dump patterns to JSON / CSV | | agentdb_import | Load patterns from JSON / CSV |

Hierarchical / Causal Memory (10 tools)

| Tool | Purpose |

| agentdb_hierarchical_store | Tier-aware memory store (working / short / long) | | agentdb_hierarchical_recall | Tier-filtered retrieval | | agentdb_hierarchical_delete | Remove hierarchical entry by key | | agentdb_causal_edge | Add a causal relationship between memories | | agentdb_causal_edge_delete | Remove a causal edge by id | | agentdb_causal_node_delete | Cascade-delete a node + incident edges | | agentdb_edges_by_endpoints | Bulk-delete edges by (from, to, label) | | agentdb_causal_query | Cypher-like graph queries | | agentdb_causal_explain | Explain why two memories are connected | | agentdb_attestation_log | Cryptographic audit trail |

Reflexion + Skill Library (12 tools)

| Tool | Purpose |…

Description from the npm listing.