Initialize an AgentDB Cognitive Container (.rvf file) in the current project. Sets up storage, embedder config, and the agentdb MCP server. Use when the user is starting a new project that needs vector memory, or asks to "set up agentdb" / "init agentdb".
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Sets up a fresh AgentDB instance for the current project. Creates a single-file .rvf Cognitive Container that holds vectors, indexes, learning state, and the audit log.
./memory.rvf at the project root, or ~/.agentdb/<project-name>.rvf for global memory).Xenova/all-MiniLM-L6-v2 at 384d — fast, free, runs in-process).claude mcp add agentdb -- npx agentdb@latest mcp start
agentdb_pattern_store (the first store auto-creates the schema), or via CLI:
npx agentdb@latest init ./memory.rvf
*.rvf to .gitignore unless the user explicitly wants memory checked into source control..rvf is a single binary file. Back it up like a SQLite database..rvf per coordinated namespace; use separate files for trust-boundary isolation.agentdb_pattern_*, agentdb_reflexion_*, etc.) or the npm library (import { SelfLearningRvfBackend } from 'agentdb')..rvf file by default — it can hold session-specific data, including content from messages.init on an existing .rvf file without confirming — it will refuse rather than overwrite, but a confused user might delete the existing file thinking it's stale.还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
中文简介正在准备中。当前显示原文说明。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
中文简介正在准备中。当前显示原文说明。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
中文简介正在准备中。当前显示原文说明。
Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
中文简介正在准备中。当前显示原文说明。