Agent Memory
How AI agents store, recall, and share what they learned across sessions — persistence, A2A, and multi-agent memory.
9 articles
AI Agent Memory: What It Is
AI agent memory explained: what it is, how it works, the approach families, how it is evaluated, and how to choose a real memory layer.
The A2A Agent Card: How Agents Discover Each Other
What an A2A Agent Card is: the JSON at a well-known URL describing an agent's skills, endpoint, and auth — the primitive agents fetch to discover and call each other.
A2A Integration How-To (Python)
A2A tutorial for Python: install the SDK, publish an Agent Card, run a server, delegate a task, stream artifacts, and add domain-scoped memory.
A2A Protocol (Agent2Agent), Explained
A2A protocol explained: Agent2Agent primitives, transports, how it complements MCP, and why shared agent memory stays a separate layer.
A2A vs MCP: How They Differ (and Compose)
A2A vs MCP compared: MCP connects agents to tools; A2A connects agents to agents. They compose as complementary layers, not rivals — and leave memory open.
Hebbian memory for AI agents
Hebbian memory for AI agents: how learned associations, automatic query expansion, and outcome feedback differ from static vector database similarity retrieval.
Is Mnemoverse a vector database?
Is Mnemoverse a vector database? No. A vector DB does static similarity retrieval; Mnemoverse adds learning, recency, and consolidation on top.
Rescorla-Wagner for agent memory
Rescorla-Wagner for agent memory: how prediction-error outcome feedback updates valence and improves recall in Mnemoverse.
Shared Memory for Multi-Agent Systems
Shared memory for AI agents needs explicit domains, isolation, and hierarchical reads. Protocols coordinate work; memory preserves context.