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How to store, search and reason over agent memory with retain, recall and reflect with Hindsight

Use the client SDK to control exactly what goes into memory and how it is used.

Short answer: Install a client: pip install hindsight-client -U (or the Node.js or Go client); then Store a fact: client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer"); Search it: client.recall(bank_id="my-bank", query="What does Alice do?"). All steps.

Who it is forA developer who wants explicit control over memory instead of automatic wrapping.

The problemAutomatic memory can store noise or miss the facts that matter.

The outcomeA memory bank you write to on purpose and query for facts or for a reasoned summary.

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. Install a client: pip install hindsight-client -U (or the Node.js or Go client)
  2. Store a fact: client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer")
  3. Search it: client.recall(bank_id="my-bank", query="What does Alice do?")
  4. Ask for a reasoned answer: client.reflect(bank_id="my-bank", query="Tell me about Alice")

Prompt for your agent

Using vectorize-io/hindsight's Python client, write a script that retains <facts> into a bank, then compares recall and reflect answers for <question>, and prints both.

Caveat.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for vectorize-io/hindsight and "Store, search and reason over agent memory with retain, recall and reflect"

Add Deduped to Claude Code once

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

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