vectorize-io/hindsight

What it solves

Hindsight is an agent memory system designed to move beyond simple conversation history or basic RAG, enabling AI agents to actually learn and build a persistent, evolving understanding of their world over time. It addresses the accuracy and recall limitations of traditional vector search and knowledge graphs in long-term memory tasks.

How it works

Hindsight uses biomimetic data structures to organize information into four distinct memory types: world facts, personal experiences, consolidated observations, and synthesized mental models. It operates through three primary functions:

• Retain: Extracts key facts, entities, and temporal data from input using an LLM and normalizes them into canonical representations.
• Recall: Employs four parallel retrieval strategies—semantic (vector), keyword (BM25), graph (entity/causal), and temporal—which are then merged and reranked for high accuracy.
• Reflect: Performs deep analysis of existing memories to form new connections and answer complex questions that require reasoning rather than simple lookup.

Additionally, it consolidates related facts into evidence-backed "observations" and maintains "mental models" (standing answers to specific questions) that can be accessed instantly without new LLM calls.

Who it’s for

Developers building AI agents, coding assistants, and enterprise AI applications that require long-term, high-accuracy memory across different users, projects, or agents (organized via isolated "memory banks").

Highlights

• High Accuracy: Achieves state-of-the-art performance on the LongMemEval benchmark.
• Broad Integration: Offers 60+ integrations including LangChain, LlamaIndex, CrewAI, and various coding agents like Claude Code and Cursor.
• Flexible Deployment: Available as a managed cloud service, a Docker container, a Kubernetes Helm chart, or an embedded Python library (no server required).
• LLM Agnostic: Supports 25+ providers including OpenAI, Anthropic, Gemini, and local options via Ollama or LM Studio.
• MCP Support: Includes a built-in Model Context Protocol endpoint to expose memory tools to MCP clients.