Show HN: Emem – External memory of the physical world for AI agents (github.com)

The launch of emem introduces a revolutionary external memory layer for multi-agent AI systems, enabling disparate agents with no shared infrastructure or trust to reference and verify the same signed facts autonomously. Initially focused on Earth observation, emem allows agents to store verified information about physical locations in a permanent, accessible format, enhancing the reliability of data integrity among varied operators and models. Key functionalities include a REST API that anyone can use without an account, providing signed records that are anchored to real observations—ensuring that data doesn’t lose precision even when context changes or sessions end. Significantly, emem addresses critical issues within AI by ensuring facts outlive their context and can be audited independently. In scenarios where agents complete complex tasks or switch models, emem prevents information degradation by allowing agents to maintain verifiable tokens rather than paraphrases. This capability minimizes redundancy—so agents do not need to re-establish information—and enhances accountability, as any fact can be independently verified. With emem, the AI community can achieve greater accuracy and trust in cross-agent interactions, paving the way for more robust AI applications across numerous domains.