AI agents are only as capable as their context—but managing stateful, long-term memory across complex enterprise deployments has remained a fragmentation bottleneck.
Today, we are excited to share the launch of Agent Memory Protocol (AMP) v1.1, representing a major architectural evolution in how persistent cognitive memory is structured, isolated, and scaled.
The Evolution: From MCP Tools to Service-First
In v1.0, AMP was modeled purely as a Model Context Protocol (MCP) toolset. While perfect for local prototyping, this created system-level bottlenecks: low-level DB operations (like consolidation or stats) were exposed directly to the LLM's prompt, bloating context and increasing cognitive load.
AMP v1.1 solves this by transitioning to a Service-First architecture (HTTP REST / gRPC first) with an optional MCP Tool Adapter.
This separates concerns cleanly:
Agent-Facing Tools: Clean cognitive hooks (encode, recall, forget) mapped directly to the LLM context.
Harness-Facing APIs: Background operations (consolidate, pin, stats) handled out-of-band by the application orchestration framework (LangChain, LlamaIndex, Letta).
Key Enhancements in v1.1
-
Dual-Delivery Channel Paradigm: Run the exact same memory contract in two ways. Use the lightweight MCP Adapter Channel (STDIO/SSE) for rapid local development, and scale instantly to the Standalone REST/gRPC API Channel for production-grade microservices without rewriting a single schema.
-
Multi-Dimensional Scoping: Moving beyond single
agent_idisolation. v1.1 standardizes intersection-based scoping acrossorg_id,app_id,user_id,session_id,agent_id,group_id, andworkspace_idto natively power collaborative multi-agent workspaces. -
Reserved Metadata Vocabulary Registry: Eliminating database-specific fragmentation. Standardizing properties like TTL, confidence scores, extracted entities, and Subject-Predicate-Object graph relationships (
amp.relations) directly in the schema. -
Memory Exchange Format (MXF): Frictionless NDJSON-based migrations. Back up memory states from platforms like Supermemory or Zep and restore them into local implementations (like
smriti-memcore) with absolute structural fidelity.
🤝 Built by the Community, For the Community
AMP is an open standard designed to ensure complete backend interoperability. Whether you are building single-user productivity loops or high-throughput enterprise agent platforms, AMP v1.1 provides the robust database-agnostic interface required to manage persistent cognitive state.
Special thanks to Shivam Tyagi, Brad Jones, and the incredible open-source contributors driving this draft forward.
🔗 Explore the full specification and reference implementations on GitHub:
AI agents are only as capable as their context—but managing stateful, long-term memory across complex enterprise deployments has remained a fragmentation bottleneck.
Today, we are excited to share the launch of Agent Memory Protocol (AMP) v1.1, representing a major architectural evolution in how persistent cognitive memory is structured, isolated, and scaled.
The Evolution: From MCP Tools to Service-First
In v1.0, AMP was modeled purely as a Model Context Protocol (MCP) toolset. While perfect for local prototyping, this created system-level bottlenecks: low-level DB operations (like consolidation or stats) were exposed directly to the LLM's prompt, bloating context and increasing cognitive load.
AMP v1.1 solves this by transitioning to a Service-First architecture (HTTP REST / gRPC first) with an optional MCP Tool Adapter.
This separates concerns cleanly:
Agent-Facing Tools: Clean cognitive hooks (encode, recall, forget) mapped directly to the LLM context.
Harness-Facing APIs: Background operations (consolidate, pin, stats) handled out-of-band by the application orchestration framework (LangChain, LlamaIndex, Letta).
Key Enhancements in v1.1
-
Dual-Delivery Channel Paradigm: Run the exact same memory contract in two ways. Use the lightweight MCP Adapter Channel (STDIO/SSE) for rapid local development, and scale instantly to the Standalone REST/gRPC API Channel for production-grade microservices without rewriting a single schema.
-
Multi-Dimensional Scoping: Moving beyond single
agent_idisolation. v1.1 standardizes intersection-based scoping acrossorg_id,app_id,user_id,session_id,agent_id,group_id, andworkspace_idto natively power collaborative multi-agent workspaces. -
Reserved Metadata Vocabulary Registry: Eliminating database-specific fragmentation. Standardizing properties like TTL, confidence scores, extracted entities, and Subject-Predicate-Object graph relationships (
amp.relations) directly in the schema. -
Memory Exchange Format (MXF): Frictionless NDJSON-based migrations. Back up memory states from platforms like Supermemory or Zep and restore them into local implementations (like
smriti-memcore) with absolute structural fidelity.
🤝 Built by the Community, For the Community
AMP is an open standard designed to ensure complete backend interoperability. Whether you are building single-user productivity loops or high-throughput enterprise agent platforms, AMP v1.1 provides the robust database-agnostic interface required to manage persistent cognitive state.
Special thanks to Shivam Tyagi, Brad Jones, and the incredible open-source contributors driving this draft forward.
🔗 Explore the full specification and reference implementations on GitHub:
