AgentDB Vector Search
AgentDB is a standalone RuvNet package, not this deployment's memory path; durable agent/project memory here is RuVector-postgres via mcp__claude-flow__memory_* (bge-small-en-v1.5, 384-dim).
Semantic vector search, RAG pipelines, similarity matching, quantization and HNSW tuning, caching, batch operations, and MCP server integration with local AgentDB. (Includes all content previously in the deprecated agentdb-optimisation skill.)
For AgentDB architecture and performance overview, see AgentDB Overview.
When Not To Use
- Distributed multi-database or QUIC sync features — use agentdb-advanced instead
- Reinforcement learning algorithms and training plugins (Decision Transformer, Q-Learning, SARSA, Actor-Critic, etc.) — use agentdb-advanced (RL Plugins section)
- Agent conversation memory and session patterns — use agentdb-memory-patterns
- Full-text search without vector embeddings — a standard database with FTS is simpler
- Cloud-managed vector search (Pinecone, Weaviate) — this skill is specific to local AgentDB
References
Load the relevant reference on demand:
- references/cli-cookbook.md — CLI recipes:
init (dimensions, presets, in-memory), query (top-k, thresholds, distance metrics, output formats), import/export, and stats.
- references/api-and-rag.md — TypeScript API: adapter setup, vector storage, similarity and hybrid search, RAG pipeline, batch operations, HNSW/caching/MMR feature notes, and MCP server integration.
- references/performance-and-troubleshooting.md — performance tuning tips and fixes for slow search, high memory, poor relevance, and wrong dimensions.
Learn More
- CLI Help:
npx agentdb@latest --help
- Command Help:
npx agentdb@latest help <command>
- See AgentDB Overview for general links.
1---2name: agentdb-vector-search3description: Use when building RAG pipelines, running semantic/similarity vector search, optimising search speed, tuning HNSW indexing or quantization, or scaling to millions of vectors with local AgentDB. Covers CLI, TypeScript API, caching, batch operations, and MCP integration. NOT for distributed multi-database/QUIC sync or RL training plugins (use agentdb-advanced), conversation-memory patterns (use agentdb-memory-patterns), plain full-text search, or cloud vector services (Pinecone/Weaviate).4---56# AgentDB Vector Search78AgentDB is a standalone RuvNet package, not this deployment's memory path; durable agent/project memory here is RuVector-postgres via `mcp__claude-flow__memory_*` (bge-small-en-v1.5, 384-dim).910Semantic vector search, RAG pipelines, similarity matching, quantization and HNSW tuning, caching, batch operations, and MCP server integration with local AgentDB. (Includes all content previously in the deprecated agentdb-optimisation skill.)1112For AgentDB architecture and performance overview, see [AgentDB Overview](../agentdb-advanced/docs/agentdb-overview.md).1314<!-- lint-ok: third-party/meta fact, not a RuVector claim --> **Additional prerequisites**: an OpenAI API key (for embeddings) or a custom embedding model — this is AgentDB's own default requirement, not something this deployment's memory path needs; `mcp__claude-flow__memory_*` here uses bge-small-en-v1.5 via Xinference instead.1516## When Not To Use1718- Distributed multi-database or QUIC sync features — use **agentdb-advanced** instead19- Reinforcement learning algorithms and training plugins (Decision Transformer, Q-Learning, SARSA, Actor-Critic, etc.) — use **agentdb-advanced** (RL Plugins section)20- Agent conversation memory and session patterns — use **agentdb-memory-patterns**21- Full-text search without vector embeddings — a standard database with FTS is simpler22- Cloud-managed vector search (Pinecone, Weaviate) — this skill is specific to local AgentDB2324## References2526Load the relevant reference on demand:2728- **[references/cli-cookbook.md](references/cli-cookbook.md)** — CLI recipes: `init` (dimensions, presets, in-memory), `query` (top-k, thresholds, distance metrics, output formats), import/export, and `stats`.29- **[references/api-and-rag.md](references/api-and-rag.md)** — TypeScript API: adapter setup, vector storage, similarity and hybrid search, RAG pipeline, batch operations, HNSW/caching/MMR feature notes, and MCP server integration.30- **[references/performance-and-troubleshooting.md](references/performance-and-troubleshooting.md)** — performance tuning tips and fixes for slow search, high memory, poor relevance, and wrong dimensions.3132## Learn More3334- CLI Help: `npx agentdb@latest --help`35- Command Help: `npx agentdb@latest help <command>`36- See [AgentDB Overview](../agentdb-advanced/docs/agentdb-overview.md#links) for general links.
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