Agent Fusion

Agent Fusion MCP tool gives multiple AI coding assistants instant access to your files—code, documents, PDFs, and more—through intelligent indexing. Self-contained deployment: runs as a single JAR with no external APIs, Docker, or cloud dependencies—just Java and a TOML config
It has two independent components (each can be used alone or together):

- MCP Context Engine: RAG for Code & Documents – Local semantic search and graph traversal for AI agents. Indexes code, Word docs, PDFs, and Markdown into chunks with embeddings. Query via query_context using three parallel providers: semantic (sentence-transformers), symbol (identifier matching), full-text (keyword search). Returns ranked results with hierarchical relationships encoded as chunk IDs (parent, children, siblings, prev/next). Agents can search semantically, then traverse the knowledge graph to gather surrounding context—useful for understanding class structures, call chains, or document organization. File watcher monitors configured directories and automatically re-indexes changes, so your search index stays current as you code—no manual refresh needed. Two-file deployment (JAR + TOML config), no external dependencies, runs entirely local. Compatible with MCP protocol (works with Claudeany and any MCP-compatible AI agent). Embedding model is configurable. Includes lightweight embedding model; swap for a more powerful model if needed (see docs/other_model.md).
- Task Manager – Optionally coordinates work between multiple AIs. Routes tasks, enables voting on decisions, and tracks everything in a web dashboard.

🎥 Watch the demo to see AI assistants collaborating in action.

Why Semantic Search Matters

Most AI coding agents rely on grep and text matching. Ask them "find authentication logic" and they grep for…