Hybrid vector + reasoning retrieval, agent memory, multi-agent orchestration, MCP server, and RAG.
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io.github.atul-fusionpact/fusionpact-vectordb is an MCP server that hybrid vector + reasoning retrieval, agent memory, multi-agent orchestration, MCP server, and RAG. Its tool list has not been published yet over stdio and sse, requires no API key, and scores 79/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"fusionpact": {
"env": {
"EMBEDDING_PROVIDER": "ollama"
},
"args": [
"fusionpact",
"mcp"
],
"command": "npx"
}
}
}Are you the author?
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Hybrid Vector + Reasoning + Memory for AI Agents
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npx -y 'fusionpact' 2>&1 | head -1 && echo "✓ Server started successfully"
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Hybrid Vector + Reasoning + Memory for AI Agents
Similarity ≠ Relevance. FusionPact is the first retrieval engine that combines HNSW vector search, reasoning-based tree retrieval, and agent memory in a single platform — purpose-built for AI agents and multi-agent systems.
Quickstart · Hybrid Retrieval · Agent Memory · Multi-Agent · MCP Server · Tree Index · RAG Pipeline · API Reference · Benchmarks · Contributing
Traditional vector databases retrieve what's similar. But similar ≠ relevant. Ask a vector DB for "Q3 2024 revenue" and you might get Q2 or Q4 data — semantically similar, but the wrong answer.
FusionPact solves this by combining three retrieval paradigms:
| Strategy | How It Works | Best For |
|---|---|---|
| Vector Search (HNSW) | Embedding similarity, O(log N) | Broad search across large collections |
| Tree Reasoning | LLM navigates document structure | Precise retrieval in structured documents |
| Keyword Search (BM25) | Term frequency matching | Exact match requirements |
Plus purpose-built agent memory, multi-agent orchestration, and MCP server — all zero-dependency, local-first, and free.
┌──────────────────────────────────────────────────────────┐
│ FusionPact Retrieval Engine │
│ │
│ ┌────────────┐ ┌─────────────┐ ┌────────────────┐ │
│ │ Vector │ │ Tree │ │ Keyword │ │
│ │ (HNSW) │ │ (Reasoning) │ │ (BM25) │ │
│ └─────┬──────┘ └──────┬──────┘ └───────┬────────┘ │
│ └────────────┬────┴─────────────────┘ │
│ ▼ │
│ Reciprocal Rank Fusion │
│ ▼ │
│ ┌──────────────────────────────────────────────────┐ │
│ │ Agent Memory (Multi-Agent) │ │
│ │ Episodic │ Semantic │ Procedural │ Shared │ │
│ └──────────────────────────────────────────────────┘ │
│ ┌──────────────────────────────────────────────────┐ │
│ │ MCP Server (Claude, Cursor, etc.) │ │
│ └──────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────┘
# Install
npm install fusionpact
# Run the demo
npx fusionpact demo
# Start HTTP + MCP server
npx fusionpact serve --port 8080
# Start MCP server for Claude Desktop
npx fusionpact mcp
const { create } = require('fusionpact');
const fp = create({ embedder: 'ollama' }); // or 'mock' for zero-config
// Ingest a document — auto-chunks, embeds, indexes
await fp.rag.ingest('Your document text here...', { source: 'doc.pdf' });
// Hybrid search — vector + reasoning + keyword, fused automatically
const results = await fp.retriever.retrieve('What safety protocols exist?', {
collection: 'default',
strategy: 'hybrid'
});
// Or build LLM-ready context directly
const context = await fp.rag.buildContext('What safety protocols exist?');
console.log(context.prompt); // Ready to paste into any LLM
The core differentiator: a single API that intelligently routes queries through multiple retrieval strategies and fuses results using Reciprocal Rank Fusion.
const { create } = require('fusionpact');
c
... [View full README on GitHub](https://github.com/FusionpactTech/fusionpact-vectordb#readme)