Midas: Local-first memory for long-horizon AI agents without LLM at ingest

Midas: Local-First Memory for Long-Horizon AI Agents

Midas provides a robust solution for building AI agents that require persistent, long-horizon memory without the computational overhead of constant LLM interaction during data ingestion. This tool focuses on an evaluation-first approach to memory management, ensuring that only relevant information is stored and retrieved, optimizing agent performance and resource utilization. Designed for developers, Midas integrates seamlessly into existing workflows, offering a powerful foundation for complex AI applications.

What Midas Does

Midas enables AI agents to maintain a continuous memory state across extended operational periods. Unlike traditional memory systems that rely heavily on LLM processing for every piece of incoming data, Midas processes and stores information locally first. This local-first strategy significantly reduces latency and cost associated with LLM calls during the ingestion phase. The system prioritizes evaluation, meaning data is assessed for relevance and importance before being committed to memory, leading to more efficient and targeted memory retrieval.

Key Features

• Local-First Memory Ingest: Processes and stores data locally, minimizing LLM dependency at ingest.
• Evaluation-First Approach: Prioritizes relevance and importance for efficient memory management.
• Long-Horizon Memory: Designed to support AI agents operating over extended periods.
• Python SDK: Provides a developer-friendly interface for integration.
• MCP Server: Offers a dedicated server for Midas functionality.
• Open Source: Available on GitHub for transparency and community contribution.

Who Midas is For

Midas is specifically built for AI developers working on advanced agent architectures. This includes:

• Developers building autonomous agents that require long-term memory for decision-making.
• Researchers exploring efficient memory management techniques for AI systems.
• Teams looking to reduce LLM inference costs by optimizing data ingestion and storage.
• Engineers integrating AI capabilities into applications requiring persistent state and context.