Agent Memory is a Cloudflare private beta service for letting agents remember users, teams, projects, and task context across conversations. It fits facts, events, instructions, and tasks; RAG documents, product data, files, and audit logs should still live in AI Search, Vectorize, D1, or R2.
Looplane resolves user and root-to-leaf project instructions before rendering named prompt sections for runtime, skills, workspace state, and the latest 20 explicit memories. The pipeline is traceable and reloadable, but it is not semantic memory and repository text does not become system authority.
Cognee is a data-to-memory pipeline: a relational store preserves sources and provenance, a vector store finds semantically similar content, and a graph store represents entity relationships, exposed through remember, recall, improve, and forget.
Letta extends MemGPT's operating-system analogy but is not a standalone memory API. The runtime persists agent state, editable in-context blocks, conversation history, and external archival memory, while the model can actively curate memory through tools.
Mastra is a TypeScript agent framework that combines agents, typed workflows, memory, MCP, tracing, and scorers in one Node.js development environment.
Mem0 sits between an agent and storage: it extracts durable facts from interactions, scopes them by user, agent, or run, and searches them before a later generation. Its appeal is a small API; its risks are extraction errors, stale memories, and authorization boundaries.
Zep does not merely vectorize chat history. It turns episodes into entities and facts with validity time, allowing new information to invalidate an old relationship without erasing history. Graphiti is the open-source framework; Zep adds managed scale and governance.
Hermes memory has hard caps: 2,200 characters for MEMORY.md and 1,375 for USER.md, and an over-limit write returns an error instead of auto-compacting, forcing the agent to make room itself. Skills are maintained by a curator that runs every 7 days after 2 hours of idle, marks skills stale at 30 days and archives at 90 — but never deletes. The switches actually worth flipping are `memory.write_approval` and `skills.write_approval`, which stage the background self-improvement writes for review.
Chroma tested 18 frontier models and all of them degrade as input grows — as a cliff, not a slope. Memory failures are usually retrieval failures in disguise. And the real cost of KV cache is bandwidth, not storage: every generated token reads the whole cache.
MUSE-Autoskill (2026) introduces a five-stage skill lifecycle framework. Self-created skills achieve 60.35% (+7.16%) on SkillsBench overall, and an impressive 87.94% on tasks where skill generation succeeds — surpassing the human-authored skill ceiling. This post synthesizes six arXiv papers to map the full landscape of skill evolution research.
Agent memory isn't a plugin — it's part of the harness itself. Pick the right memory type, estimate data volume, then decide on the technology. And finally, figure out whether you actually own that memory.
By default every DM lands in one main session, and group activity and background work report back into it. Memory is entirely Markdown on disk — the model only remembers what gets saved, with no hidden state. But if more than one person can DM your agent, DM isolation is something you have to turn on.
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
RAG is read-only. Agent Memory lets AI not only read but also write and persist information. Three memory types: Procedural (behavior patterns), Episodic (temporal events), and Semantic (factual knowledge) form a complete cognitive memory system.
An AI agent is not a black box — it is built from three layers: what it knows (Context), how it thinks (Cognition), and what it can do (Action). Understanding these three layers is the key to grasping why agents are sometimes brilliant and sometimes go off the rails, and how to design a truly effective agent system.
Building a chatbot is more than just calling an API. Conversation state management, memory mechanisms, streaming, guardrails, observability, and tech stack selection — every layer affects the user experience.
After each conversation, asynchronously extract likely user preferences and skill level, then automatically personalize search parameters on the next query — no manual setup required.