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How to add long-term memory to an existing chatbot in two lines with Hindsight

Wrap your OpenAI client so memories are recalled before each call and stored after it.

Short answer: Start a Hindsight server, for example with the Docker command from the README on ports 8888 and 9999; then install the wrapper: pip install hindsight-litellm; import it with from hindsight_litellm import wrap_openai and wrap your OpenAI client, passing a bank_id per user and hindsight_api_url for a self-hosted server (http://localhost:8888). All steps.

Credentials. The server holds your LLM key in its environment; do not expose it publicly without auth.

Who it is forA developer whose assistant forgets the user between sessions.

The problemHand-rolling memory means deciding what to store, when to search, and how to inject it.

The outcomeThe same chat code, now recalling relevant memories per user bank and saving new ones automatically.

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. Start a Hindsight server, for example with the Docker command from the README on ports 8888 and 9999
  2. Install the wrapper: pip install hindsight-litellm
  3. Import it with from hindsight_litellm import wrap_openai and wrap your OpenAI client, passing a bank_id per user and hindsight_api_url for a self-hosted server (http://localhost:8888)
  4. Call client.chat.completions.create as before; use wrap_anthropic() for the Anthropic SDK

Prompt for your agent

Add vectorize-io/hindsight memory to my chatbot in <file> using the LLM wrapper against a local server, one bank per user id, and show me a two-session test where the second session remembers a fact from the first.

Caveat.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for vectorize-io/hindsight and "Add long-term memory to an existing chatbot in two lines"

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