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PatchBay

Coordinate local Codex CLI workers from ChatGPT.

Quick start · ChatGPT usage · Architecture · Security · Testing

Status: pre-release verified MCP: Streamable HTTP and stdio Runtime: Python and FastAPI Codex CLI baseline: 0.142.2 License: MIT

PatchBay routes ChatGPT context through MCP into local Codex workers, then returns reports, diffs, tests, and explicit integration.

PatchBay is an independent local MCP bridge that lets ChatGPT coordinate user-authorized Codex CLI work in local repositories. It routes conversation context, generated files, worker briefs, reports, diffs, and follow-up instructions through a reviewable local workflow instead of making the human copy-paste between ChatGPT and terminal Codex.

Use it when the best task context already lives in ChatGPT — a long conversation, Project, uploaded file, generated artifact, planning thread, or prior decision trail — but the real work needs your local repository, git state, dependencies, tools, and configured Codex CLI.

Why PatchBay?

Without PatchBay, the human becomes the transport layer.

You copy a brief from ChatGPT into Codex, copy files or snippets back, paste diffs into the chat, ask for a revision, copy the next instruction back into the terminal, then repeat. That workflow loses context, wastes attention, and makes multi-step agent work feel smaller than it should.

PatchBay removes that manual bridge.

Without PatchBay With PatchBay
Copy prompts from ChatGPT into terminal Codex ChatGPT briefs local Codex workers through MCP
Paste reports, files, test output, and diffs back manually Worker reports, artifacts, changed files, and diffs flow back into the chat
Restart context every time the task changes Named workers can continue across turns and PatchBay restarts
Coordinate one-off agent calls by hand ChatGPT can manage investigator, implementer, reviewer, and verifier workers
Treat local execution and high-context reasoning as separate loops ChatGPT plans and coordinates while Codex works in the local repo

The 30-second workflow

flowchart LR
    A["ChatGPT<br/>context + plan"] --> B["PatchBay<br/>local MCP bridge"]
    B --> C["Local Codex worker<br/>Investigator"]
    B --> D["Local Codex worker<br/>Implementer"]
    B --> E["Local Codex worker<br/>Reviewer"]
    C --> F["Reports"]
    D --> G["Diffs + tests"]
    E --> H["Review notes"]
    F --> I["ChatGPT synthesis"]
    G --> I
    H --> I
    I --> J["Human-approved<br/>integration"]
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  1. Start PatchBay on an approved local repository.
  2. Connect ChatGPT to PatchBay through the MCP connector or another MCP client.
  3. Ask ChatGPT to appoint named Codex workers.
  4. Workers investigate, edit, test, review, or verify in local context.
  5. ChatGPT compares reports, changed files, and diffs.
  6. You explicitly approve integration into the base checkout.

What PatchBay gives you

Benefit What it means
No copy-paste bridge Move briefs, generated files, worker reports, diffs, and follow-up instructions through MCP instead of manually shuttling text between apps.
ChatGPT as project lead Use ChatGPT for high-context planning, decomposition, worker assignment, report comparison, and final synthesis.
Named durable workers Start workers such as Architecture Investigator, Backend Implementer, Adversarial Reviewer, or Verification Worker, then continue them later by name.
Local execution stays local Codex still runs through your local Codex CLI against your repository, git state, dependencies, shell, and configured account.
Reviewable integration Inspect reports, changed files, one-file diffs, and integration previews before applying accepted worker output.
Artifact transfer Import ChatGPT-generated files or zip packages into local worker context without manual file handling.
Advanced escalation loop Store a local blocked-problem report for ChatGPT, write the answer back into PatchBay, then explicitly dispatch it to a worker when useful.
Optional machine fleet Experimental hub/edge mode lets one ChatGPT connector create durable task groups and route grouped worker lanes to enrolled PatchBay machines.

Example: ChatGPT as manager

Use PatchBay. Act as the manager of local Codex workers, not as the primary file reader.

Open this repository and appoint:
- one architecture investigator,
- one implementation worker,
- one adversarial reviewer,
- one verification worker.

Use isolated worktrees for writable work. Compare their reports, inspect diffs,
run focused checks, and integrate only explicitly accepted changes.

This is the intended PatchBay posture: ChatGPT coordinates the work, local Codex workers execute it, and the human remains the authority for repository integration.

Who PatchBay is for

PatchBay is built for developers and AI-assisted builders who already use ChatGPT and Codex CLI, and who want the two systems to operate as one controlled local workflow.

It is especially useful when you:

  • do serious repository work from long ChatGPT conversations;
  • use ChatGPT Projects, memory, uploaded files, generated artifacts, or planning threads as source context;
  • want ChatGPT to coordinate several local coding workers instead of giving one-off advice;
  • need local execution against real dependencies, git state, and project tools;
  • want inspectable reports, diffs, and explicit integration instead of hidden automation;
  • experiment with MCP, local-agent workflows, or multi-worker software engineering loops.

What PatchBay can coordinate

Area PatchBay support
MCP transport Streamable HTTP /mcp and stdio for local MCP hosts
ChatGPT connector use ChatGPT-ready descriptors, setup output, tokenized tunnel URLs, and worker-first tool mode
Workspace context Repository tree, file reads, search, git status/diff, AGENTS, skills, context packs, and .ai-bridge handoffs
Codex workers Named workers, continuation, status, reports, peer context, changed-file inspection, diffs, and stop/integrate controls
Isolation Isolated writing worktrees by default, with explicit integration back to the base checkout
Artifacts Import ChatGPT-generated files or zip packages into worker context
Repository boundary Allowed roots, path guard, tokenized public access, tool modes, and mutation locks
Advanced loops Pro Escalation requests, local handoff scripts, Codex job control, review jobs, resume/interactive flows

The full public tool surface is documented in docs/reference/public-tool-surface.md. Additional operational details moved out of the root README are in docs/reference/tool-surface-and-worker-details.md.

Quick start

Important

Start with a disposable repository. PatchBay gives ChatGPT a controlled route into local Codex and local files.

Requirements:

  • Python 3.10+
  • Git
  • codex CLI on PATH
  • Codex CLI login or API key configured for the local Codex CLI

From the PatchBay repository:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e ".[test]"
codex login
patchbay doctor

Start PatchBay against a local repository with the worker-first tool surface:

patchbay start --root /path/to/repo --tool-mode worker

For ChatGPT web, start PatchBay with an HTTPS tunnel and a private tokenized Server URL:

export PATCHBAY_HTTP_TOKEN='<long-random-token>'
patchbay start \
  --root /path/to/repo \
  --tunnel-mode cloudflare \
  --tool-mode worker \
  --save-profile \
  --reveal-token

Then create a ChatGPT connector using the printed HTTPS /mcp Server URL. Use No Authentication / None in ChatGPT because the copied Server URL already carries the PatchBay token.

See QUICKSTART.md for the complete disposable-repo flow and docs/user/chatgpt-connector-setup.md for the connector-specific setup notes.

Safety and usage boundaries

PatchBay is powerful by design. It gives ChatGPT a route into local development workflows, so the project is intentionally explicit about where authority begins and ends.

  • PatchBay is an independent open-source project. It is not affiliated with, endorsed by, sponsored by, or maintained by OpenAI.
  • PatchBay is a local workflow bridge, not a quota bypass layer.
  • It does not bypass OpenAI rate limits, usage limits, billing, safety systems, account controls, or Codex usage accounting.
  • It does not scrape ChatGPT, automate hidden ChatGPT UI extraction, reverse engineer OpenAI services, modify OpenAI clients, pool accounts, or resell access.
  • ChatGPT interactions remain under the user's ChatGPT account and connector permissions.
  • Codex execution remains under the user's local Codex CLI configuration, subscription, API key, or billing arrangement.
  • Repositories must be owned by the user or explicitly authorized for use.
  • The recommended ChatGPT-facing default is --tool-mode worker, which exposes worker orchestration and read-only context before broader write/bash surfaces.
  • Writing workers use isolated worktrees by default, and integration is explicit: inspect reports, changed files, and diffs before applying accepted output.

Note

PatchBay uses OpenAI product names only to describe compatibility with user-configured OpenAI services. It should not be presented as an official OpenAI product or partnership.

More detail: SECURITY.md, docs/security/product-boundary.md, and docs/security/usage-boundaries-openai.md.

Technical highlights

Technical area Detail
Protocol MCP server with Streamable HTTP and stdio transports
Runtime Python + FastAPI
Execution engine Local Codex CLI subprocesses
Worker model Durable named workers with continuation, status, reports, partial notes, and evidence
Coordination Multi-worker reports, peer context, review context, current/recent/history scopes, and compact team status
Isolation Isolated writing worktrees by default; shared-write and read-only modes exist for deliberate cases
Review model Changed-file inventory, paged worker-side file reads, one-file diffs, and integration preview
Repository controls Allowed roots, path guard, token-gated public tunnels, per-repo mutation locks, and dirty-base checks
Artifacts ChatGPT-generated files/zips can be imported into worker context without editing the repo
Power modes worker, standard, full, and minimal tool surfaces with runtime-aware tool advertisement
Optional hub/edge mode patchbay hub start plus patchbay edge start can expose several enrolled machines behind one ChatGPT connector, with durable work groups pinning each task to one machine

Current status

PatchBay is pre-release verified, not public-release complete. It is already used internally for ChatGPT Pro to a private PatchBay VM managing local Codex workers. Occasional small bugs are still expected, and broader public/browser multi-session validation remains a release target.

Core local validation currently covers:

  • Python compile checks;
  • the test suite;
  • live local MCP probing against a disposable repo;
  • named worker continuity;
  • isolated worker write/restart/continue/diff/cleanup;
  • multi-worker peer report/diff relay;
  • worker integration preview/apply;
  • direct multi-client MCP ownership and takeover behavior.

See TESTING.md, docs/testing/evals.md, and docs/testing/current-readiness.md for the full validation matrix.

Current validation snapshot
Area Status
Codex CLI baseline Current local verification recorded codex-cli 0.142.2
Python checks compileall passes
Test suite 281 tests pass
Live local MCP probe scripts/live_mcp_eval.py --json passes against a disposable repo
Pro Escalation request loop Unit tests and live MCP probe cover create/list/read/claim/respond/dispatch paths
Named worker continuity eval scripts/worker_phase1_eval.py --timeout 600 passes real Codex start/restart/continue
Isolated writing worker eval scripts/worker_phase2_eval.py --timeout 900 passes real Codex isolated write/restart/continue/diff/cleanup
Multi-worker coordination eval scripts/worker_phase3_eval.py --timeout 900 passes real Codex peer diff/report relay
Worker integration eval scripts/worker_phase4_eval.py --timeout 900 passes real Codex integration preview/apply
Real MCP worker negative-case trial scripts/real_mcp_worker_trial.py --include-safety-cases passes lifecycle and negative cases
Direct multi-client MCP trial scripts/real_mcp_worker_trial.py --multi-client --tool-mode worker passes two-session ownership/takeover/integration checks
Public tunnel MCP probe Earlier tokenized ngrok simulation passed core connector behavior; current run requires a validation hostname
Active ChatGPT Pro VM worker use Working reliably in current internal use for ChatGPT Pro to private PatchBay VM workflows
Parallel ChatGPT browser conversations Pending
Real apply-job diff eval from ChatGPT Pending
Real resume/continuation eval from ChatGPT Pending

Documentation map

Need Read
Full documentation index docs/README.md
First disposable run QUICKSTART.md
ChatGPT tool-use pattern docs/user/chatgpt-instructions.md
ChatGPT connector setup docs/user/chatgpt-connector-setup.md
Worker bridge docs/worker-bridge/README.md
Architecture docs/architecture/overview.md
Tool reference docs/reference/public-tool-surface.md
Migrated tool and worker details docs/reference/tool-surface-and-worker-details.md
Configuration reference docs/reference/configuration.md
Security model SECURITY.md
OpenAI usage boundaries docs/security/usage-boundaries-openai.md
Testing TESTING.md
Current validation status docs/testing/current-readiness.md
Product rationale docs/project/why-patchbay.md
CodexPro attribution NOTICE

Credits

PatchBay includes behavior, documentation, tests, and implementation patterns derived from or inspired by open-source CodexPro work. See NOTICE for attribution and license details.

License

MIT