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Agent-Memory

4 picks

All picks tagged “Agent-Memory”.

Acquisition · 1Addiction · 1Aesthetics · 1Agent Architecture · 53Agent Engineering · 78Agent Environment · 1Agent Evaluation · 1Agent Harness · 1Agent Infrastructure · 13Agent Loop · 1
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08-23

Self-evolving context database for AI agents

OpenViking is an open-source context database for AI agents. It unifies agent memory, knowledge resources, and skills into a single viking:// virtual file system, letting agents locate context with familiar file commands instead of querying an opaque vector store. Content is indexed into three tiers — L0 abstract, L1 overview, L2 details — at write time and loaded on demand, which cuts token usage and latency. Retrieval is directory-recursive: vector search first lands on the best-matching directory, then drills down layer by layer so results keep their surrounding context. Every query leaves an observable browsing trajectory that can be traced when answers go wrong. The project ships a CLI, server, benchmark harness, and integrations for Claude Code, Codex, Cursor, MCP clients, and more. It is well suited for engineers building agent memory, agentic RAG, or context engineering pipelines.

github.com · 19 min · Agent-Memory · Agentic RAG · Context Database
06-23

Loop Engineering

Loop Engineering proposes a shift from hand-prompting coding agents to designing autonomous loops: a system with five components (scheduled automations for discovery, worktrees for parallel isolation, skills to codify project context, plugins/connectors via MCP, and verifier sub-agents) that lets agents iterate without manual intervention. The post maps these primitives across Codex and Claude Code, noting that memory persisted outside the conversation (via AGENTS.md or Linear) is the critical sixth piece. The core insight is that loop design is harder than prompt engineering—the engineer's role moves from operator to system architect, while verification burden, comprehension debt, and cognitive surrender remain unresolved challenges that the loop itself cannot eliminate.

addyosmani.com · 14 min · Agent Architecture · Agent-Memory · Ai Tooling
06-17

Persistent Memory Engine for AI: Auto-Extract, Update, and Forget Intelligently

Supermemory is a memory and context layer for AI. It automatically extracts facts from conversations, builds and maintains user profiles, resolves contradictions, and intelligently forgets expired information. Combining hybrid search (RAG + memory), document processing, and live connectors (Google Drive, GitHub, etc.) into one API, it gives AI agents instant, personalized context. With plugins for Claude Code, Cursor, and more, it targets both developers integrating memory into apps and users wanting persistent AI memory across tools.

github.com · 14 min · Agent-Memory · Ai-Memory · Cloudflare
06-14

Hermes Agent: A Self-Improving, Multi-Platform AI Agent Runtime

Hermes Agent is a self-improving AI agent framework with a closed learning loop. It creates skills from experience, manages persistent memory across sessions, and operates over Telegram, Discord, Slack, and CLI via a single gateway. Any LLM backend can be used without code changes, and it runs on a $5 VPS or serverless infrastructure with near-zero idle cost. Built‑in cron scheduling, subagent delegation, and batch trajectory generation make it suitable for engineers and researchers who need an autonomous agent that evolves with use.

github.com · 11 min · Agent-Memory · Agents · CLI