What does Hindsight Mempalace add over vanilla Hindsight?
It adds a taxonomy layer (rooms, halls, layers), auto-classification, closets, tunnels, cross-bank bridges, and a standalone MCP server with 5 tools.
Hindsight Mempalace MCP Server
@holetron
基本信息
关于 Hindsight Mempalace MCP Server
Self-hosted long-term memory for AI agents. An MCP server that gives agents persistent, hierarchical recall (L0–L3) over pgvector — retain preferences, decisions and observations, then recall them by topic across sessions. MIT, clone-and-run, requires Postgres + pgvector.
Hindsight Mempalace MCP Server 配置
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"hindsight-mempalace": {
"command": "npx",
"args": [
"-y",
"hindsight-mempalace-mcp"
],
"env": {
"HINDSIGHT_URL": "http://127.0.0.1:5100",
"MEMPALACE_BANK": "mempalace-main"
}
}
}
}Hindsight Mempalace MCP Server 工具
5Save a fact, observation, or document to long-term memory with automatic room/hall classification. Rooms: auth, pipeline, schema, infrastructure, ui, api, deployment, monitoring, agent, general. Halls: fact, event, decision, preference, discovery, procedure, warning. Layers: L0=Identity (always loaded), L1=Critical, L2=Session (default), L3=Deep.
Search long-term memory for relevant facts. Uses semantic search with optional room/hall scoping for significantly improved retrieval accuracy. Supports layer cascade (L0 results always prioritized).
Deep reasoning over memory — synthesizes facts, finds patterns, answers complex questions with citations. Use for analysis: "What patterns emerge from recent events?" or "Summarize everything about X."
Create compressed memory summaries (closets) from stored facts. Groups memories by room+hall and creates AI-generated summaries with source pointers. Use when a topic has accumulated many facts.
Create a cross-bank memory bridge (tunnel) between two related memories in different banks. Relations: same_concept, depends_on, contradicts, extends. Use when concepts in separate banks are related.
概览
What is Hindsight Mempalace?
Hindsight Mempalace is a hierarchical memory system for AI agents, merging Hindsight's long-term vector store with MemPalace's taxonomy architecture. It provides structured memory with semantic search, rooms, halls, layers, closets, and tunnels, plus an MCP server for client integration.
How to use Hindsight Mempalace?
Clone the repository, copy .env.example to .env, edit configuration, then run docker compose -f docker-compose.mempalace.yml up -d. API available at http://localhost:5100. For the MCP server, install npm dependencies in mcp-server/ and run with environment variables.
Key features of Hindsight Mempalace
- Vector store with hierarchical taxonomy (rooms, halls, layers)
- Keyword-based auto-classification (<1ms, zero LLM cost)
- L0–L3 priority tiers (always loaded to archive)
- Cross-bank tunnels for multi-agent memory sharing
- Closet compression: summaries with source pointers
- 5 MCP tools: retain, recall, reflect, compress, bridge
Use cases of Hindsight Mempalace
- AI agents needing structured long-term memory with topic isolation
- Multi-agent systems sharing insights across banks via tunnels
- Development teams storing deployment procedures, warnings, and decisions
- Knowledge management with automatic classification and compression
常见问题
What are the runtime requirements?
Docker for the API server; Node.js for the MCP server. Ships with `BAAI/bge-small-en-v1.5` embeddings (English-optimized). Optional multilingual model `BAAI/bge-m3` for other languages.
How does data classification work?
A keyword-based classifier (`room_hall_classifier.py`) assigns room and hall automatically in under 1ms without LLM calls. Users can also specify these manually.
Can I use this as a drop-in replacement for Hindsight?
Yes. The base `/retain` and `/recall` endpoints are fully backward-compatible. All new parameters are optional.
How do tunnels and bridges work?
Tunnels are cross-bank bridges between agents. Agent A discovers a memory; Agent B accesses it through a tunnel without duplicating data. Created via the `/bridge` endpoint or `memory_bridge` MCP tool.
What models are supported for embeddings?
Default English model `BAAI/bge-small-en-v1.5` (384-dim). For multilingual use, set `HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-m3` (1024-dim) on an empty store.
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