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Hindsight is State-of-the-Art on Memory for AI Agents | Read the paper β†’
This is documentation for Hindsight 0.7, which is no longer actively maintained.
For up-to-date documentation, see the latest version (0.8).
πŸ€–
Using a coding agent? Run this to install the Hindsight docs skill:
npx skills add https://github.com/vectorize-io/hindsight --skill hindsight-docs

Quick Start

Get up and running with Hindsight in 60 seconds.

Clients​

Start the API Server​

  • pip (API only)
  • Docker (Full Experience)
pip install hindsight-api
exportOPENAI_API_KEY=sk-xxx
exportHINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY

hindsight-api

API available at http://localhost:8888

LLM Provider

Hindsight requires an LLM with structured output support. Recommended: Groq with gpt-oss-20b for fast, cost-effective inference. See LLM Providers for more details.


Use the Client​

  • Python
  • Node.js
  • CLI
  • Go
pip install hindsight-client
from hindsight_client import Hindsight

client = Hindsight(base_url="http://localhost:8888")

# Retain: Store information
client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer")

# Recall: Search memories
client.recall(bank_id="my-bank", query="What does Alice do?")

# Reflect: Generate disposition-aware response
client.reflect(bank_id="my-bank", query="Tell me about Alice")

What's Happening​

OperationWhat it does
RetainContent is processed, facts are extracted, entities are identified and linked in a knowledge graph
RecallFour search strategies (semantic, keyword, graph, temporal) run in parallel to find relevant memories
ReflectRetrieved memories are used to generate a disposition-aware response

Integrations​

Browse all supported integrations in the Integrations Hub.

Next Steps​

  • Retain β€” Advanced options for storing memories
  • Recall β€” Search and retrieval strategies
  • Reflect β€” Disposition-aware reasoning
  • Memory Banks β€” Configure disposition and mission
  • Server Deployment β€” Docker Compose, Helm, and production setup

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