Complete RAG agents (chatbot, MCP) with little or no code
Introducing Hindsight™, a new approach to agent memory. Best in the world on benchmarks. Best in production for your agents.
Hindsight stores per-user memory that survives session boundaries with cross-session persistence. Users' preferences, history, and decisions remain separate and instantly recalled in under 100ms, eliminating cold starts and enabling agents to pick up context weeks later.
Hindsight offers both free self-hosted deployment via Docker (MIT licensed, no restrictions) and managed Hindsight Cloud. The model-agnostic architecture works with any LLM, and MCP integration allows one-command setup with Claude or Cursor, enabling rapid deployment without boilerplate.
Hindsight provides a four-network memory architecture that automatically detects patterns, synthesizes experiences into judgment, and enables agents to avoid repeating errors. The reflection layer consolidates raw observations into reusable knowledge, allowing agents to build curated mental models that guide decision-making in similar situations.
Hindsight achieves 94.6% on the peer-reviewed LongMemEval benchmark, independently verified and reproducible. This state-of-the-art score provides confidence in memory system reliability and learning capability compared to competing solutions (Supermemory 85.2%, Zep 71.2%, GPT-4o 60.2%).
Hindsight's compound memory architecture allows Agent A to learn a user preference that Agent B applies automatically. Shared context across agents and per-user memory across every session creates a unified knowledge base that improves as more agents interact with the same users.