Fusionpact Vectordb
Combines vector similarity, tree-based reasoning, and keyword search in a single retrieval pipeline. Exposes hybrid search operations where HNSW handles broad queries, LLM-guided tree navigation handles structured documents (think 10-Ks or technical specs), and BM25 catches exact matches. Results get fused with reciprocal rank scoring. Also includes agent memory primitives (episodic, semantic, procedural, shared) and multi-agent orchestration. The MCP server surfaces these as tools Claude can call. Runs locally with Ollama or mock embeddings. Reach for this when vector search alone keeps returning similar-but-wrong results and you need reasoning about document structure. Particularly relevant for financial docs, compliance materials, or any corpus where precision beats semantic fuzziness.
- tools
- 11
- transport
- STDIO
- env var
- 1
- last updated
- Mar 10, 2026
Tools
11 toolsPublic tool metadata for what this MCP can expose to an agent.
fusionpact_create_collectionCreate HNSW-indexed vector collection
Create HNSW-indexed vector collection
No parameter schema in public metadata yet.
fusionpact_searchSemantic vector search
Semantic vector search
No parameter schema in public metadata yet.
fusionpact_hybrid_searchHybrid retrieval (vector + tree + keyword)
Hybrid retrieval (vector + tree + keyword)
No parameter schema in public metadata yet.
fusionpact_rag_ingestOne-click RAG ingestion
One-click RAG ingestion
No parameter schema in public metadata yet.
fusionpact_rag_queryBuild LLM-ready context
Build LLM-ready context
No parameter schema in public metadata yet.
fusionpact_memory_rememberStore episodic memory
Store episodic memory
No parameter schema in public metadata yet.
fusionpact_memory_recallRecall relevant memories
Recall relevant memories
No parameter schema in public metadata yet.
fusionpact_memory_learnAdd semantic knowledge
Add semantic knowledge
No parameter schema in public metadata yet.
fusionpact_memory_shareShare cross-agent knowledge
Share cross-agent knowledge
No parameter schema in public metadata yet.
fusionpact_memory_forgetGDPR-style memory erasure
GDPR-style memory erasure
No parameter schema in public metadata yet.
fusionpact_memory_conversationManage conversation threads
Manage conversation threads
No parameter schema in public metadata yet.
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EMBEDDING_PROVIDEREmbedding provider: ollama (free), openai, or mock
Readme
⚡ FusionPact
The Agent-Native Retrieval Engine
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
Why FusionPact?
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.
Details
- Repository
- github.com/fusionpacttech/fusionpact-vectordb
- Category
- AI & LLM Tools
- Package
fusionpact- Transport
- STDIO
- Registry
- MCP registry, active
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