MCP server providing vector memory, drift detection, model routing, skills discovery, session management, and codebase indexing for AI-assisted development pipelines.
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# ensemble-mcp A **harness infrastructure layer** delivered as a Python MCP server — providing **vector memory**, **drift detection**, **model routing**, **skills discovery**, **session management**, **codebase indexing**, and **context compression** for AI agent pipelines. All intelligence is local — zero LLM/API calls. Uses ONNX Runtime embeddings (~5ms), numpy cosine similarity, and SQLite storage. --- ## What is an Agent Harness? **Agent = Model + Harness.** A harness is every piece of code, configuration, and execution logic that wraps a model to turn it into a useful agent. Without a harness, a model can only take in text and output text — it can't maintain state, execute code, access tools, or learn from past work. A complete agent harness typically includes: - **System prompts & configuration** — instructions that shape agent behavior - **Tools, Skills & MCPs** — capabilities the agent can invoke - **Execution environment** — filesystem, bash, sandbox, browser - **Orchestration logic** — subagent spawning, handoffs, model routing - **Memory & context management** — compaction, skills, session persistence - **Hooks & middleware** — deterministic checks like linting, formatting, drift detection > *For a deeper dive, see [The Anatomy of an Agent Harness](https://www.langchain.com/blog/the-anatomy-of-an-agent-harness) by LangChain.* ## Where ensemble-mcp Fits Agent harnesses like Claude Code, Codex, and Cursor provide the **execution layer** — filesystem, bash, sandbox. ensemble-mcp provides the **intelligence infrastructure layer** that plugs into any harness via MCP: ``` ┌─────────────────────────────────────────────────────────┐ │ AGENT HARNESS │ │ │ │ ┌───────────────────────────────────────────────────┐ │ │ │ Execution Layer │ │ │ │ (provided by Claude Code / Codex / Cursor) │ │ │ │ …
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