# Hindsight

> Most accurate agent memory system achieving 91.4% on LongMemEval with four parallel retrieval strategies and four distinct memory networks for world knowledge, experience, and opinions.

- **URL:** https://hindsight.vectorize.io/
- **Category:** LLM Tools
- **Tags:** agent-memory, retrieval, mcp
- **Featured:** yes
- **Updated:** 2026-03-24 13:36
- **Canonical page:** https://vectordb.ever.works/items/hindsight

## Details

## Overview

Hindsight is the most accurate agent memory system ever tested according to benchmark performance. On LongMemEval, Hindsight hits 91.4% overall accuracy, with multi-session questions jumping from 21.1% to 79.7% and temporal reasoning from 31.6% to 79.7%.

## Key Features

Your AI agent stores information via retain(), searches with recall(), and reasons with reflect() — all interactions with its dedicated memory bank. Hindsight uses four parallel retrieval strategies (semantic, BM25, graph traversal, temporal) with cross-encoder reranking.

Hindsight maintains four distinct memory networks:
- World Network (objective external facts)
- Experience Network (the agent's own first-person action history)
- Opinion Network (subjective beliefs with confidence scores that update as evidence accumulates)

## Recent Updates (2026)

Several integration guides were published in March 2026:

- Running Hindsight with Ollama gives you a fully local AI memory system with no API keys, no cloud costs, no data leaving your machine
- With the hindsight-pydantic-ai integration, you can wire long-term memory into any Pydantic AI agent in five lines of Python
- Hindsight gives AI agents persistent, structured memory via MCP

## Development

Hindsight was built by Vectorize.io ($3.5M raised, April 2024) and battle-tested on Jerri, their internal AI project manager that compounds knowledge across weeks of meetings, decisions, and action items.

## Pricing

Open-source with commercial support available.

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