Summary

Hindsight is an agent memory system designed to overcome limitations of traditional RAG and knowledge graph approaches, enabling AI agents to learn and improve over time. It addresses critical challenges like inconsistency and hallucinations in AI agents, making them more reliable for task automation and conversational interfaces. The system provides a robust long-term memory foundation, enhancing agent performance and user experience.

How It Works

Hindsight employs biomimetic data structures, organizing agent memories in a manner analogous to human cognition. It categorizes memories into 'World' (facts), 'Experiences' (agent actions/outcomes), 'Opinion' (confidence-scored beliefs), and 'Observation' (derived insights). Memories are stored using a combination of entities, relationships, time series, and sparse/dense vector representations. The core operations are Retain, which ingests information by extracting and normalizing key data via an LLM; Recall, which retrieves memories through parallel semantic, keyword, graph, and temporal search strategies, followed by re-ranking; and Reflect, which analyzes existing memories to generate new insights, facilitating agent learning.

Quick Start & Requirements

The recommended installation is via Docker:

export OPENAI_API_KEY=your-key
docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
  -e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
  -e HINDSIGHT_API_LLM_MODEL=o3-mini \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
  ghcr.io/vectorize-io/hindsight:latest

This exposes the API at http://localhost:8888 and UI at http://localhost:9999. Client libraries are available via pip install hindsight-client -U or npm install @vectorize-io/hindsight-client. An OpenAI API key is required for LLM integration. Embedded Python installation is available with pip install hindsight-all -U. Further details can be found in the Documentation and Cookbook.

Highlighted Details

• Achieves state-of-the-art performance on the LongMemEval benchmark for conversational AI memory systems.
• Utilizes biomimetic data structures for memory organization, mimicking human memory pathways.
• The Reflect operation enables agents to learn from experiences, generating new observations and opinions.
• Employs a multi-strategy retrieval approach (semantic, keyword, graph, temporal) with advanced re-ranking for accurate recall.

Maintenance & Community

Hindsight is developed by Vectorize.io. The project maintains a Slack Community for user interaction and support. Continuous Integration status is monitored via GitHub Actions.

Licensing & Compatibility

The project is released under the permissive MIT license, allowing for broad compatibility with commercial and closed-source applications.

Limitations & Caveats

The project's research is currently being prepared for conference submission, suggesting it is an active area of development. Specific limitations, known bugs, or unsupported platforms are not detailed in the provided README. Integration requires an LLM API key.