AI product Open source · MIT
Hindsight is an agent memory system developed by Vectorize.io for storing, retrieving, and synthesizing long-term memories for AI agents. It organizes memories into world facts, experiences, observations, and mental models within isolated memory banks, with support for multilingual data and optional per-bank scanning for secrets and personally identifiable information.
Its retain operation uses an LLM to extract facts, entities, relationships, and temporal data, then normalizes them into searchable representations. Recall combines semantic vector search, BM25 keyword matching, entity/temporal/causal graph links, and time-range filtering, merging results with reciprocal-rank fusion and cross-encoder reranking. Reflect performs deeper analysis of stored memories to form connections and answer questions requiring more than retrieval. Mental models and knowledge pages provide continuously updated answers or documents derived from a bank's memories.
Hindsight can run as a Docker service, Python package, embedded database, Kubernetes deployment, or hosted Hindsight Cloud service. It provides Python, Node.js, Go, CLI, and REST clients, an OpenAI-compatible LLM wrapper, integrations for agent frameworks and coding agents, and a built-in Model Context Protocol server. Self-hosted storage uses PostgreSQL with pgvector or Oracle AI Database 23ai; the project is MIT-licensed.
2 uses taken from transcripts — each links to the moment in the video.
An agent memory system that helps agents learn from what they have done, beyond recalling old chats. It organizes memories into facts, experiences, observations, and evolving mental models, and searches them using multiple methods.
Open-source long-term memory for AI agents that learns from git history and past sessions. It provides memory banks, knowledge pages, SDKs, and a REST API for retaining, recalling, and reflecting on information.
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