Hindsight

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.