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  "path": "/t/loom-a-repo-native-state-graph-for-local-coding-agents/1379978#post_1",
  "publishedAt": "2026-04-28T17:44:11.000Z",
  "site": "https://community.openai.com",
  "textContent": "I have been working on Loom, a Markdown-native project-state protocol for AI agents which has greatly enhanced the experience with Codex (both the App and CLI).\n\nThe motivation is pretty simple: I wanted fewer agent surfaces.\n\nA plan (like the lovely ExecPlan) file is useful until it starts carrying too many jobs. A spec tool, issue tool, memory file, review prompt, and scratchpad can each help, but they also give the agent more places to scatter state.\n\nLoom puts the agent-facing work record into one project graph inside the repo.\n\nResearch holds discovery and rejected paths.\nSpecs hold intended behavior.\nPlans hold sequencing.\nTickets track live execution.\nEvidence records observations.\nCritique holds review findings.\nWiki holds accepted explanation.\nPackets hold bounded worker contracts.\nMemory helps recall without becoming the ledger.\n\nThis is useful for local agents because there is no hosted service or runtime database. The graph is Markdown. Agents can search it with rg, follow links, edit records, and use Git for implementation history.\n\nThe important part is packet compilation. A packet is a bounded snapshot of the upstream graph: project constraints, initiative, research, spec, plan, ticket, evidence, critique, source context, write scope, stop conditions, and output contract.\n\nThe worker gets less context by volume, but better context by shape.\n\nThen retrospective pushes accepted learning back up into the graph.\n\nRepo: z3z1ma/agent-loom\n\nHow are people here handling project state across agent sessions today? Memory files, plans, local issue trackers, worktrees, scratch docs, custom prompts?",
  "title": "Loom: A repo-native state graph for local coding agents"
}