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How to use Hindsight: what you can do with it

What is Hindsight? Hindsight is an open-source agent memory system focused on agents that learn, not just recall chat history. You store facts with retain, fetch them with recall, and get a reasoned answer over memory with reflect, organized in memory banks. It runs as a Docker container, with Docker Compose, from pip or on Kubernetes with Helm, has Python, Node.js and Go clients, and an LLM wrapper that adds memory to an existing OpenAI or Anthropic client in two lines.

How do I use it? 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). Full steps below.

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What do you want to do?

3 use cases

Quick start with Hindsight

  1. Check you have what it needs: Docker, pip or Kubernetes; An LLM API key (OpenAI in the quick start); Python, Node.js or Go for the client.
  2. Open vectorize-io/hindsight on GitHub and read the README and license.
  3. Start a Hindsight server, for example with the Docker command from the README on ports 8888 and 9999
  4. Install the wrapper: pip install hindsight-litellm
  5. 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)
  6. Call client.chat.completions.create as before; use wrap_anthropic() for the Anthropic SDK

These steps come from our top use case, Add long-term memory to an existing chatbot in two lines, which also has a copy-paste agent prompt.

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Hindsight alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forDevelopers building assistants or agents that should remember users and past work across sessions.
  • Why it is hot53k stars, talked about in 2 AI sources we track
  • LanguagePython
  • Sourcegithub.com/vectorize-io/hindsight · Back to Hot repos

Requirements

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

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Questions about Hindsight

What is Hindsight?

Hindsight is an open-source agent memory system focused on agents that learn, not just recall chat history. You store facts with retain, fetch them with recall, and get a reasoned answer over memory with reflect, organized in memory banks. It runs as a Docker container, with Docker Compose, from pip or on Kubernetes with Helm, has Python, Node.js and Go clients, and an LLM wrapper that adds memory to an existing OpenAI or Anthropic client in two lines. It is an open-source project on GitHub: https://github.com/vectorize-io/hindsight.

How do I use Hindsight?

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). The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Hindsight for?

Developers building assistants or agents that should remember users and past work across sessions.

What can I do with Hindsight?

3 use cases so far, for example: Add long-term memory to an existing chatbot in two lines; Store, search and reason over agent memory with retain, recall and reflect; Self-host the Hindsight memory server. Each has steps and a copy-paste agent prompt.

What do I need to run Hindsight?

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

What are alternatives to Hindsight?

Other hot AI repos in Software and DevOps: Deep Infer, Harness2, Orca, Opencode, Graphify.

Is Hindsight safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.