Introduction

MarkDB is a memory database for AI agents. It sits between your agent and the language model as an OpenAI-compatible proxy, capturing every prompt, tool call, and response into a structured store you can search and summarize.

Point any coding agent -- Cursor, Codex, or Claude Code -- at the MarkDB proxy and you get durable memory, hybrid search, and automatic summaries without changing how your agent works.

What you get

• Durable agent memory. Every exchange is mirrored into a structured store, organized into chats, sessions, and turns.
• Hybrid search. Retrieve past work with combined vector (pgvector) and full-text (Meilisearch) search.
• Automatic enrichment. A background worker summarizes turns, sessions, and whole chats so long histories stay compact and retrievable.
• One OpenAI-compatible endpoint. /v1/chat/completions, /v1/responses, /v1/messages, and /v1/embeddings, dispatched natively to Anthropic, OpenAI, and Gemini.
• MCP server. Expose memory to any MCP-aware client for recall and search.

How it fits together

   your agent ──▶  MarkDB proxy  ──▶  model provider (Anthropic / OpenAI / Gemini)
                       │
                       ├─▶ mirror every turn ─▶ memory store (Postgres)
                       │                              │
                       └────────────────────  enrichment worker ─▶ summaries + search index

The proxy forwards your request to the model and, in the same pass, records the exchange. A worker then enriches and indexes it in the background. Your agent sees a normal model response; MarkDB quietly builds the memory.

Next steps

• Quickstart - mint a key and connect a client in minutes.
• Connect a client - Cursor, Codex, Claude Code, or any OpenAI-compatible tool.
• Concepts - how MarkDB models memory.