Memory and state
Persistent memory layers that give agents recall across turns and sessions: knowledge graphs, vector stores, and session-capture tools that survive a restart. The state a harness needs but rarely ships with.
| # | Project | Stars | Tier | OSS | Description |
|---|---|---|---|---|---|
| 1 | claude-mem memory | 94.3k | slightly complex | ✅ | Session-memory plugin for Claude Code, Codex, OpenClaw, Gemini, Copilot, and more: captures everything an agent does during a session, AI-compresses it, and injects the relevant context into future sessions. Session-to-session memory as a drop-in. |
| 2 | Mem0 memorypython | 65.7k | slightly complex | ✅ | Universal memory layer for AI agents: stores user/org/session memory, retrieves on demand. Apache-2.0; the de-facto memory primitive paired with most harnesses in 2026. |
| 3 | Graphiti (Zep) memoryragworkflowpython | 31k | slightly complex | ✅ | Zep's open-source memory engine: real-time temporal knowledge graphs that track how facts about users and entities change over time, so agents can answer "what was true when." The layer behind Zep's hosted memory platform. |
| 4 | cognee memoryragworkflowpython | 30.9k | slightly complex | ✅ | Open-source memory layer for agents: an extract–cognify–load pipeline that turns your data into a queryable knowledge graph plus vector store, so agents recall facts and relationships across sessions instead of re-reading context. |
| 5 | beads memory | 27.3k | mostly simple | ❓ | Portable persistent-memory layer for coding agents: tracks decisions and task state outside the harness's own context window so it survives session resets and model swaps. |