Memory MCP Server — Orchestrator

The Memory MCP Server – Orchestrator is a purpose‑built backend that gives AI agents the ability to act like a persistent, context‑aware collaborator. It solves the common problem of stateless conversational agents that lose track of prior interactions and cannot reason about a project’s codebase or history. By storing rich, multi‑turn memory and providing semantic search over a project’s files, the server lets an assistant maintain continuity across sessions, remember user preferences, and pull in relevant code snippets or documentation automatically.

At its core, Orchestrator offers an advanced memory layer that threads conversations across multiple users and sessions. It couples this with AI‑powered task planning, allowing the assistant to decompose a high‑level goal into concrete sub‑tasks and schedule them intelligently. The system is code‑literate, supporting TypeScript, JavaScript, Python and PHP; it extracts entities from source files and represents them as 3072‑dimensional vectors. This semantic representation powers a hybrid Retrieval Augmented Generation (RAG) engine that blends vector similarity, keyword matching and knowledge‑graph traversal to surface the most relevant context for a query.

For developers building AI workflows, Orchestrator’s MCP tools expose a rich set of capabilities: multi‑model orchestration (Gemini, Codestral, Mistral), intelligent routing that directs code queries to the most suitable model, and batch processing with dynamic sizing and rate limiting. The server also offers incremental updates via file‑hash detection, robust database management (SQLite for both memory and vector data), and built‑in error resilience. Its web integration layer can pull in external search results with source tracking, making it a versatile component for everything from code review assistants to project management bots.

Real‑world use cases include autonomous code review, where the assistant remembers past review comments and can reference them in new PRs; project management, where it tracks task progress across multiple developers; and knowledge‑base construction, where it continuously ingests new documentation and updates its semantic index. Because the server exposes all functionality through MCP, any AI assistant that understands the protocol can tap into persistent memory, advanced search, and intelligent routing without needing custom integration code.