Hindsight

Documentation • Integrations • Cookbook • Benchmarks • Paper • Hindsight Cloud

What is Hindsight?

Hindsight™ is an agent memory system built to create smarter agents that learn over time. Most agent memory systems focus on recalling conversation history. Hindsight is focused on making agents that learn, not just remember.

It eliminates the shortcomings of alternative techniques such as RAG and knowledge graph and delivers state-of-the-art performance on long term memory tasks.
🆕 Introducing Hindsight Models: decision, rerank and embedding models built for agent memory. Open weights on Hugging Face, or hosted on Hindsight Inference. Read the post →

Contents

- Memory Performance & Accuracy
- Quick Start — server · clients · platforms · embedded
- Adding Hindsight to Your Agent — LLM Wrapper · integrations · coding agents · MCP
- Core Concepts — memory types · retain / recall / reflect · observations · mental models & knowledge pages · decision models · banks
- Use Cases
- Running in Production
- Resources

Memory Performance & Accuracy

Hindsight is the most accurate agent memory system ever tested according to benchmark performance. It has achieved state-of-the-art performance on the LongMemEval benchmark, widely used to assess memory system performance across a variety of conversational AI scenarios. The current reported performance of Hindsight and other agent memory solutions as of January 2026 is shown here:
Live, continuously updated results — including per-model accuracy, latency and cost — are published at benchmarks.hindsight.vectorize.io.

The benchmark performance data for Hindsight has been independently reproduced by research collaborators at the Virginia Tech Sanghani Center for Artificial Intelligence and Data Analytics and The Washington Post. Other…