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How to self-host the Hindsight memory server with Hindsight

Run the server and UI in one Docker container with a persistent volume.

Short answer: Set export OPENAI_API_KEY=sk-xxx; then run the README's docker run command for ghcr.io/vectorize-io/hindsight:latest with ports 8888 and 9999 and the hindsight-data volume; Or use Docker Compose from docker/docker-compose with a database password, or pip install hindsight-api and run hindsight-api. All steps.

Credentials. The LLM key is passed as an environment variable to the container; keep it out of shell history and scripts you share.

Who it is forA team that wants agent memory on its own infrastructure.

The problemHosted memory services keep user data on someone else's servers.

The outcomeA Hindsight API on port 8888 and UI on 9999, with data kept in a Docker volume.

What you need first

  • Docker, pip or Kubernetes
  • An LLM API key (OpenAI in the quick start)
  • Python, Node.js or Go for the client

Steps

  1. Set export OPENAI_API_KEY=sk-xxx
  2. Run the README's docker run command for ghcr.io/vectorize-io/hindsight:latest with ports 8888 and 9999 and the hindsight-data volume
  3. Or use Docker Compose from docker/docker-compose with a database password, or pip install hindsight-api and run hindsight-api
  4. Point your clients at http://localhost:8888

Prompt for your agent

Self-host vectorize-io/hindsight with Docker on this server with a named volume, confirm the API answers on 8888, and tell me how to back up and restore the data.

Caveat.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for vectorize-io/hindsight and "Self-host the Hindsight memory server"

Add Deduped to Claude Code once

claude mcp add --transport http deduped https://dedupedai.com/mcp

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