Agent Memory Protocol (AMP): Open Spec for Interoperable AI Agent Memory on Top of MCP

The Agent Memory Protocol (AMP) is an open specification that standardizes persistent memory for MCP-compatible AI agents, addressing memory fragmentation across backends like Mem0, smriti-memcore, and custom vector DBs.
Six Core Verbs
amp.encode— Store new memoriesamp.recall— Retrieve relevant memoriesamp.forget— Permanently delete memoriesamp.consolidate— Trigger backend reorganization or summarizationamp.pin— Mark important memories as permanentamp.stats— Get backend health and usage stats
Conformance Levels
AMP defines Core and Full conformance levels, ships with a JSON schema, compliance test suite, and a minimal example that runs with zero dependencies.
Reference Implementation
Install via pip: pip install amp-server — wraps smriti-memcore as a compliant backend. The spec is MIT-licensed and independent (not affiliated with Anthropic/MCP).
Community Feedback Wanted
The author wants input on: which backends to wrap first (Chroma, Pinecone, pgvector, Zep, etc.), missing verbs, and real-world pain points. PRs welcome.
📖 Read the full source: r/openclaw
👀 See Also

Cowork Chrome Extension Automates Personal Data Removal from Data Brokers
A Reddit user reports that using the Cowork Chrome extension with a Gmail connection automated filling forms, writing emails, and verifying removal requests to delete personal data from major data providers in just a few hours.

Developer shares 10+ MCP servers for AI agent settlement, reputation, and micropayments
A developer built BlindOracle on Claude Code with 100+ agents and created 10+ MCP servers for settlement, reputation, and micropayments. The architecture includes private commit-reveal forecasts, on-chain scoring, per-request micropayments, and verifiable agent attestation.

Lore: MCP Server That Shares AI Agent Session History Across Tools
Lore is an MCP server that indexes AI agent sessions into a local SQLite store, letting any agent—regardless of tool—access another's session history. Fresh client sessions, no shared context, yet agents can pull up past conversations on demand.

Steerling-8B: An Interpretable Language Model with Token-Level Attribution
Guide Labs released Steerling-8B, an 8-billion-parameter language model trained on 1.35 trillion tokens that can trace any generated token to input context, human-understandable concepts, and training data sources. The model achieves competitive performance with models trained on 2-7× more data.