The authors propose Portable Agent Memory, an open protocol for transferring an AI agent's accumulated context (episodic events, knowledge, skills, working state, identity preferences) across different vendor platforms and model architectures. The protocol uses a five-part structured memory model with content-addressable entries linked by a Merkle-DAG for tamper-evidence, capability-based access control for selective disclosure, and an injection-resistant rehydration protocol that adapts recalled memory to heterogeneous target models. They provide a Python SDK with 54 tests and demonstrate cross-model memory transfer between GPT-4, Claude, Gemini, and Llama.
Main takeaways:
- AI agents accumulate rich context, but it's locked in vendor-specific runtimes; this protocol makes memory portable across heterogeneous models and platforms.
- Memory is structured as five components (episodic, semantic, procedural, working, identity) with content-addressable entries linked by a Merkle-DAG for tamper-evidence and provenance.
- Capability-based access control lets agents selectively share memory segments; the rehydration protocol adapts recalled content to target models while mitigating indirect prompt injection.
- Demonstrated working cross-model transfers between GPT-4, Claude, Gemini, and Llama with a Python SDK and agent skills for multiple platforms.
- Open-source under Apache 2.0 with a JSON-first serialization format and optional CBOR compaction.