SAME (Stateless Agent Memory Engine) VS Agent Client Protocol

• Persistent Context for Stateless Systems
  SAME allows inherently stateless agents (like typical LLM API calls) to maintain continuity across sessions, enabling more coherent long-term interactions without requiring the underlying model to natively support memory.
• Scalability
  By decoupling memory storage from the agent's core processing, SAME can potentially scale independently, allowing multiple agent instances to share or access consistent memory stores without bottlenecking the agent's compute resources.
• Flexibility Across Models
  Since the memory engine operates externally to the AI model itself, it can theoretically be used with various LLMs or agent frameworks, making it adaptable rather than locked into a single vendor's ecosystem.
• Simplified Agent Architecture
  Developers can offload memory management complexity to SAME, allowing them to focus on core agent logic rather than building custom memory persistence solutions from scratch.
• Improved Personalization
  With persistent memory, agents can better tailor responses based on historical user interactions, preferences, and past context, leading to more relevant and personalized outputs over time.

Possible disadvantages

• Limited Public Information
  As a relatively niche or newer product, there may be limited documentation, case studies, or third-party reviews available, making it harder to fully evaluate its reliability, performance, and real-world effectiveness before adoption.
• Potential Latency Overhead
  Introducing an external memory retrieval step for every agent interaction could add latency compared to fully stateless calls, especially if the memory store is large or the retrieval mechanism isn't optimized.
• Data Privacy and Security Concerns
  Storing persistent memory about user interactions raises questions about data privacy, security, and compliance with regulations like GDPR, especially if sensitive information is retained without clear user consent mechanisms.
• Integration Complexity
  Depending on the existing agent architecture, integrating an external memory engine like SAME may require non-trivial engineering work, including handling synchronization, consistency, and error states between the agent and memory store.
• Dependency Risk
  Relying on a third-party service for core memory functionality introduces a dependency risk—if the service experiences downtime, pricing changes, or discontinuation, it could significantly impact the reliability of agents built on top of it.

• Standardized Interoperability
  ACP defines a common JSON-RPC based protocol between coding agents and code editors, allowing any compliant agent to work with any compliant editor without needing custom, one-off integrations for each pairing.
• Decoupled Development
  Editors and agents can be developed independently by different teams or organizations. Editor authors don't need to know the internals of every agent, and agent authors don't need to build UI for every editor.
• Reduced Duplication of Effort
  Without a shared protocol, each editor would need bespoke plugins for each agent (and vice versa), leading to an N×M integration problem. ACP reduces this to an N+M problem, saving significant engineering effort across the ecosystem.
• Rich, Structured Communication
  The protocol supports structured message types for things like file edits, terminal commands, permissions requests, and streaming updates, enabling more sophisticated and interactive agent-editor workflows than simple text-based interfaces.
• Open and Extensible
  Being an open specification (backed by Zed and other contributors) means the community can propose extensions, implementations can be built in multiple languages, and the protocol can evolve to support new agent capabilities over time.

Possible disadvantages

• Early-Stage Adoption
  As a relatively new protocol, only a limited number of editors and agents currently support ACP, which reduces its practical usefulness until more of the ecosystem adopts it.
• Implementation Overhead
  Both editor and agent developers must invest time to implement the protocol correctly, including handling JSON-RPC messaging, permission flows, and streaming updates, which adds complexity compared to simpler, ad-hoc integrations.
• Feature Lag Behind Native Integrations
  Because ACP is a generalized protocol, it may not immediately expose every specialized feature of a specific agent or editor that a deep, custom-built native integration could provide.
• Governance and Evolution Risk
  As with any open protocol still maturing, there's uncertainty around governance, versioning stability, and how backward compatibility will be handled as the spec evolves, which could create friction for early adopters.
• Limited Ecosystem Tooling
  Debugging tools, comprehensive documentation, and community resources for troubleshooting ACP-based integrations are still developing, making it harder to diagnose issues compared to more established protocols.

Tracking SAME (Stateless Agent Memory Engine) since Aug 2026.

• Pi 1.0

  I'd be very interested especially if ACP (https://agentclientprotocol.com/get-started/introduction) was supported over stdio so when I'm not using it directly in the terminal, I could also use it in my other ACP frontends. - Source: Hacker News / 4 days ago

• Pi.dev: You Said No MCP

  Agreed. I personally don't use any MCP but I think it was a good move. I hope they'll do the same and eventually add native support for ACP (https://agentclientprotocol.com/get-started/introduction) which, on the contrary, I use quite. - Source: Hacker News / 5 days ago