Dev & Engineering multi-runtime-orchestrationmcpappend-only-ledgercoding-workflowsremote-operationsim-bridgeschatgpt-web

CCCC Coordination Console

Run coding agents as a durable, trackable team across runtimes, machines, and remote operations.

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What does this agent do, and when should you use it?

CCCC is a local-first coordination system for multi-agent software development, centered on a daemon that manages groups, actors, and message state. Its append-only JSONL ledger records messages and events, while a Web UI, CLI, MCP surface, and IM bridges use the same control plane. A group can combine 16 first-class runtimes, including Claude Code, Codex CLI, and ChatGPT Web, with a custom runtime path for other commands. It fits teams that need long-running coding work, auditable handoffs, and delivery/read/reply tracking, with runtime state stored in CCCC_HOME rather than the repository.

Run cccc attach . to bind a directory as the work scope, cccc setup to configure runtimes, and cccc actor add <id> --runtime <runtime> to create a foreman and peers. cccc group start launches actors; the daemon delivers cccc send and cccc tracked-send messages and appends messages and events to ledger.jsonl. Actors use the MCP collaboration core for inboxes, messaging, files, context, tasks, and state, while on-demand capabilities include cccc_repo, cccc_terminal, cccc_actor, cccc_automation, and remote-group access tools. The Web UI exposes chat, delivery/read state, embedded terminals, automation rules, and settings; bridges for Telegram, Slack, Discord, Feishu/Lark, DingTalk, WeCom, and Weixin expose coordination and status operations through IM.

  1. An engineering lead coordinating Claude Code and Codex CLI on one repository, with explicit read receipts and validation handoffs.
  2. A developer or team supervising long-running coding work from a phone through Telegram, Slack, or another supported IM bridge.
  3. A distributed team running CCCC groups on Windows, WSL, Macs, servers, or trusted teammate machines and using Group Bridge for explicit cross-group collaboration.
  4. An engineering organization that needs replayable message and event records to audit whether delegated work was delivered, read, acknowledged, and answered.
  5. A user who wants to bind a ChatGPT Web conversation as a development actor with browser delivery and a remote MCP connector.

What are this agent's strengths and limitations?

Pros
  • A single-writer daemon and append-only ledger.jsonl provide replayable, auditable message and event state.
  • Its coordination model includes routing, read cursors, attention acknowledgments, and reply-required obligations rather than only terminal prompting.
  • Web UI, CLI, MCP, and IM bridges share daemon-owned state, supporting both local terminal control and phone-based operations.
  • It supports 16 first-class runtimes plus custom, allowing combinations such as Claude Code, Codex CLI, and ChatGPT Web in one group.
Limitations
  • The default Python distribution requires Python 3.11+; the native distribution requires Rust 1.88+, and both cccc executables must not coexist on PATH.
  • The ChatGPT Web runtime depends on browser delivery, remote MCP, and a public HTTPS exposure; GPT-5.x Pro sessions cannot use this integration.
  • Daemon IPC has no authentication and binds to localhost by default; non-local Web exposure requires an Admin Access Token and a network boundary.
  • Group Bridge read and full access can let trusted remote groups inspect context or operate on a workspace, so permissions require careful administration.

How do you install or deploy this agent?

The default distribution requires Python 3.11+: pip install -U cccc-pair. The native Rust distribution requires Rust 1.88+: cargo install cccc --locked. Do not keep both cccc executables on PATH. Then run cccc; it starts the daemon and local Web UI by default. Open http://127.0.0.1:8848.

How do you use this agent?

From the target repository, run: cccc attach .; cccc setup; cccc actor add foreman --runtime claude; cccc actor add implementer --runtime codex; cccc group start. Send ordinary coordination with cccc send "Please inspect the repo and propose the first safe task." --to foreman. For a durable delegation, run cccc tracked-send "Please take the first concrete task and reply with validation evidence." --to implementer --title "First concrete task" --outcome "The change and validation evidence are reported". Run cccc doctor if an actor stays stopped. The ChatGPT Web runtime additionally requires exposing CCCC through a public HTTPS URL and configuring a remote MCP connector in one bound ChatGPT conversation.

How does this agent compare with similar options?

Compared with native agent teams such as Claude Code subagents/teams, CCCC adds cross-runtime groups, state that survives restarts, phone/IM operations, and an audit ledger. Compared with isolated parallel task runners, it emphasizes messaging, handoffs, acknowledgments, and daemon-owned coordination; the README positions pure DAG orchestration as a case for a dedicated orchestrator.

FAQ

Does it require a database, message broker, or Docker?
The README describes a one-install, zero-external-dependency setup with no database, message broker, or Docker required. Docker is offered as a separate deployment option.
How can I tell whether a delegated task was actually handled?
Ordinary messages have delivery state and read cursors. For work requiring a result and evidence, use cccc tracked-send, which tracks reply-required obligations.
What should I do before accessing it remotely?
The Web UI is high privilege. Create an Admin Access Token in Settings > Web Access before non-local exposure. Group Bridge starts message-only; grant read or full access only to trusted groups.
What if an actor does not start?
Run cccc doctor to check the environment and runtime availability. The README also directs first-run troubleshooting to its FAQ.
Can it replace a DAG workflow orchestrator?
No. The project describes itself as a collaboration kernel that can compose with CI/CD, orchestrators, and deployment tools; it recommends a dedicated orchestrator for pure DAG workflows.

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