Agent Memory
How AI agents store, recall, and share what they learned across sessions — persistence, A2A, and multi-agent memory.
18 articles
RAG vs Agent Memory: What the Source Code Actually Shows
At query time a memory layer runs the same hybrid retrieval a RAG stack does, and the shipping source proves it. The difference is on the write path, and it is narrower than the category claims.
Knowledge Graph vs Retrieval for AI Agent Memory
Knowledge graph vs retrieval for AI agent memory: the split that matters is learned vs asserted structure, connected by provenance. Evidence-first guide.
Agent memory: evidence versus policy
Agent memory splits into evidence and policy, not short versus long term: three scopes, the fields each entry needs, and why closing a fact beats replacing it.
Agent memory feedback: the missing signal
Agent memory learns most from explicit feedback, but production agents rarely send it. Measurements show what changes and how to prescribe feedback.
What Is an Agent OS? Six Things the Term Means
Agent OS means six things: a metaphor, academic kernels, a memory pager, a platform, an OS feature, and an inversion. One test tells them apart.
The Missing Layer: No Protocol Says What Agents Know
Agent memory interoperability needs rules for identity, schema, provenance, consistency, and permissions. A2A and MCP do not define them.
AI Introspection: Why a Voice Is Not an Audit
AI introspection is real but unreliable. Chain-of-thought and self-reports cannot replace durable audit records outside the model.
Claude's Global Workspace: Why AI Memory Lives Outside
Anthropic's global workspace research reveals Claude's transient working memory, and why AI agent memory must persist outside the model.
Knowledge-Graph Memory for AI Agents
Knowledge-graph memory reframes agent memory from transcript to navigable substrate — GraphRAG, temporal graphs, PPR, and why it shouldn't rewrite itself.
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: JSON at a well-known URL describing an agent's skills, endpoint, and auth — fetched to discover and call it.
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.