Agent-Fusion

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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`](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.

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