AgentLoop

AgentLoop

Self-improving fresh-context loops for coding work you can watch.

Watch the demo | Try the replay - a real recorded run in the live dashboard

What it is

Plan a goal in ChatGPT, then let it rip. AgentLoop is a local orchestration daemon for coding agents: each cycle starts a fresh worker, work carries forward in project files, a fresh critic enforces your rubric, and the whole run is watchable on a local dashboard.

AgentLoop is for solo developers who run long Codex tasks across multiple projects and cannot supervise every session.

It exists because running coding agents by hand means shuttling plans between a chat and a terminal all day, and quality slips the moment you stop watching.

Your standards live in GUIDELINES.md and the critic enforces them every cycle, so you supervise the work without babysitting it.

Why sequential fresh-context

Long-running chats collect stale assumptions and irrelevant context. That is context rot. AgentLoop starts a new codex exec process for every worker and critic session, so no prior chat history follows them. The durable context is the project itself plus concrete critic fixes. That keeps a loop bounded, easier to leave unattended, and less likely to spend tokens re-reading an ever-growing conversation.

A loop is intentionally sequential. Independent queued tasks can run up to maxConcurrent, but one loop does not create a parallel swarm that races across the same project.

ChatGPT -> MCP bridge -> daemon -> worker/critic cycles -> dashboard

How it works

• Dispatch sends one one-shot task to POST /api/dispatch. Its file moves from pending to running to done, with a transcript and dashboard cancellation.
• Loop runs a project cycle by cycle. A worker reads PLAN.md and STATE.md, makes one increment, updates state, and exits. A fresh critic then reads PLAN.md, GUIDELINES.md, the worker output, and the project files. The next worker is a new process.
• Polish mode is an optional loop flag: after the first PASS, remaining cycles become polish cycles where the critic re-verifies the guidelines and proposes one improvement per cycle until it verdicts SHIP.
• Critic contract requires the final line to be exactly VERDICT: PASS or VERDICT: FAIL - . FAIL becomes injected fix notes for the next worker; PASS ends the loop unless polish mode is on. Polish cycles end with VERDICT: IMPROVE - or VERDICT: SHIP. maxCycles is capped at 1 to 10 and defaults to 3.
• Files are memory. PLAN.md, STATE.md, and GUIDELINES.md carry the goal, progress, and rubric. A loop project needs PLAN.md; missing STATE.md and GUIDELINES.md files are seeded automatically.
• Messages narrate a run. A connected chat client can post info, question, or results messages through the bridge. They appear in the dashboard Messages panel.
• Workers are sandboxed. Every Codex session uses workspace-write sandboxing, disables network access inside the sandbox, and routes boundary requests through automatic approval review.

The daemon is plain Node with no package dependencies. Task state, results, transcripts, events, and messages are stored as JSON or NDJSON files. The dashboard is one local HTML file at http://127.0.0.1:5757.

Built with Codex

AgentLoop started as my own bottleneck. I was the relay between ChatGPT planning the architecture and Codex executing the tasks, shuttling plans and results back and forth across multiple projects, and quality slipped whenever I stepped away. A bare retry loop was not the answer: loops without standards rot their context and never improve. The fix was to move the human judgment into the system itself, so I designed the sequential fresh-context loop, the files-as-memory model, the strict critic verdict contract, and the rubric-as-GUIDELINES pattern to mimic a demanding human in the loop. Codex CLI with GPT-5.6 turned that design into working code: the daemon, filesystem store, loop engine, critic, bridge, and dashboard wiring, roughly one focused session per slice. The workflow was plan in ChatGPT, execute in Codex sessions, review rounds with automated reviewers with Codex among them, then forward-fix commits.

AgentLoop then runs Codex CLI as both its worker and critic engine. Codex built a tool that drives Codex.

Independent evaluation

The reproducible [query parser evaluation](examples/query-parser) asked for the full repair in one pass. Cycle 1 produced nine passing tests, but a fresh critic found a mixed percent-decoding defect and returned FAIL. Cycle 2 fixed it, added regression coverage, passed 11 tests, and received PASS from a new critic.

[Read the evaluation record](docs/evaluation.md).

Quickstart

Requirements:

• Node.js 18 or newer
• Git
• Codex CLI installed and authenticated, with codex available on PATH

Windows:

git clone https://github.com/aiedwardyi/AgentLoop.git
cd AgentLoop
node src\daemon.js

Open http://127.0.0.1:5757. Select + New, enter a title and prompt, then select Dispatch task. No dependency install is required.

Connect ChatGPT

This is optional: once connected, you can plan and launch real work on your machine from a chat, without opening a terminal.

1. In the dashboard, open Connector and select Start.
2. Expose the local bridge on port 5758. For example:

cloudflared tunnel --url http://127.0.0.1:5758

1. Copy the authenticated connector URL from the Connector popover. Replace the local host with your tunnel host while preserving /mcp?key=....

https:///mcp?key=

Treat this URL as a secret; the token persists in state/mcp-token - delete that file and restart the bridge to rotate it.

1. Add that URL as a ChatGPT custom connector, then describe tasks in plain English.

The bridge listens only on 127.0.0.1:5758, uses a token, and exposes agentloop_status, dispatch_task, start_loop, and send_message.

Try the demo loop

With the daemon running, select + New and use the Loop form:

• Project: examples/starter
• Max cycles: 3
• Select Start loop

Cycle 1 deliberately implements only the normal input path. The critic reads GUIDELINES.md, rejects the missing hardening and command-line requirements, and emits a FAIL verdict. Cycle 2 receives those fixes, completes the utility, and should PASS.

Watch cycle progress in Running now and critic verdicts in Events. Open the completed task to see its transcript. Messages posted through the bridge appear in Messages.

Configuration

config.json contains the local runtime settings:

| Key | Default | Purpose | | --- | --- | --- | | dashboardPort | 5757 | Local dashboard port. | | maxConcurrent | 2 | Maximum concurrent queued tasks or loops. | | taskTimeoutMin | 45 | Timeout in minutes for each worker or critic session. | | defaultEngine | codex | Default loop engine. This release accepts codex only. | | mcpBridge.port | 5758 | Local MCP bridge port. | | model | gpt-5.6-terra when unset | Optional default model for workers and critics. |

The shipped config.json includes every key above except the optional model key.

Supported platforms

Windows is the primary path:

node src\daemon.js

macOS and Linux use the equivalent command:

node src/daemon.js

On every platform, install and authenticate Codex CLI first. The daemon and bridge bind to loopback addresses, so a tunnel is required for a hosted connector.

Roadmap

• Research loops. Cycles that gather sources first, then write against an explicit rubric - reports, docs, briefs.

• Two-way messages. The dashboard already receives questions from the chat client; answering from the panel closes the loop.

• More engines. The engine layer is pluggable by design. Codex ships first.

License

MIT. See [LICENSE](LICENSE).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: aiedwardyi
• Source: aiedwardyi/AgentLoop
• License: MIT
• Homepage: https://agentloop-replay.vercel.app

Install and usage instructions live in the source repository linked above.