clawsomeflow 0.1.11

Vertical agent workflow orchestration platform on top of ClawTeam and OpenClaw

⚡ ClawsomeFlow ⚡

🌐 clawsomeflow.com

English · 简体中文

Turn your goal into a task flow, and let an active scheduler drive a team of AI agents to execute it — parallel, isolated, observable, and convergent. You orchestrate the work; ClawsomeFlow keeps it under control.

Full compatibility with OpenClaw, Claude Code, Codex, Cursor, Hermes and other CLI Agents.

Quick Start · News · Core Features · How It Works · Why ClawsomeFlow · Contributor Local Deploy · Roadmap · WeChat Community

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📰 News

• 2026-06-02: ClawsomeFlow public release 🎉

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✨ Core Features

ClawsomeFlow turns scattered AI agents into a controllable engineering system — from the first instruction to the final, reviewable result.

🗣️ Get it done in natural language	🧠 Precise orchestration, not guesswork	🚀 Many agents, one graph
Define flows, create agents, orchestrate tasks, and step in at runtime — all by describing what you want. No glue code, no SDK wrangling.	Control flow lives in code, not in a prompt. The scheduler decides dispatch, retry, timeout and convergence — so behavior is predictable and tokens aren't wasted.	Lay out your work as a DAG and let multiple agents collaborate in parallel; a leader summarizes and converges the results into one deliverable.

🔐 Isolation & rollback by default	📊 Observability you can audit	🔄 A system that improves itself
Every agent runs in its own isolated workspace and branch — parallel work without cross-talk or accidental writes, with checkpoint / merge / cleanup built in.	Every dispatch, completion and failure is recorded as a RunEvent — each run is traceable, replayable and reviewable, with no black boxes.	Not happy with a result? File a complaint and the system reflects, reworks, and writes the lesson back — so the next run is better than the last.

ClawsomeFlow inherits the following capabilities from ClawTeam:

• Git Worktree workspace isolation: each Agent has an independent branch and directory, running in parallel without interference, with checkpoint / merge / cleanup support.
• Inter-Agent messaging: point-to-point inbox and broadcast, so team members share progress in real time.

On top of this, ClawsomeFlow adds AI combined with precise orchestration, deep OpenClaw adaptation, failure convergence, human guardrails and Web productization.

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🛠️ How It Works

From a sentence to a shipped result. You stay in charge of the goal; ClawsomeFlow handles the coordination, the parallelism, and the recovery when things go wrong.

1. Describe your goal — Tell ClawsomeFlow what you want in plain language, or compose a Flow visually as a graph of tasks and dependencies.
2. Agents run in parallel — The scheduler actively dispatches ready tasks to the right agents, each in its own isolated workspace, and drives them to completion.
3. Watch, steer, recover — Follow every step live. Retry, skip or abort with clear strategies, and approve results at human checkpoints before anything lands.
4. Converge & deliver — A leader merges the parallel work into one reviewed deliverable — and the run history stays fully auditable.

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🤖 Supported Agents

Agent	Kind	Runtime	Status
OpenClaw	openclaw	TUI	⭐ Deeply adapted
Claude Code	claude	TUI	✅ Full support
Codex	codex	TUI	✅ Full support
Gemini CLI	gemini	TUI	Testing
Cursor	cursor	TUI	✅ Full support
Hermes	hermes	TUI	✅ Full support
Kimi CLI	kimi	TUI	Testing
Qwen Code	qwen	TUI	Testing
OpenCode	opencode	TUI	Testing
nanobot	nanobot	TUI	Testing

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🤔 Why ClawsomeFlow?

The common pain point of multi-agent frameworks is not "insufficient model capability", but "unstable collaboration control flow": the process is written in the Prompt, the final behavior depends on the Agent's in-the-moment understanding and model quality, and the system's predictability, cost and recoverability are all too weak.

ClawsomeFlow's approach is direct: migrate coordination from natural language back into code, make concurrency isolation a default capability, and make failure handling a built-in part of the process.

🆚 Comparison with Other Agent Orchestration Platforms

Dimension	Other Multi-Agent Orchestration Platforms	✅ ClawsomeFlow
Task orchestration fit	Mostly framework-specific, bound to a single ecosystem	Task orchestration is deeply adapted to OpenClaw Agents, while also being compatible with any CLI Agent (Claude / Codex / Cursor, etc.) collaborating in the same graph
Concurrency & isolation	Easy contention in parallel, workspace conflicts, context cross-talk	Solves OpenClaw collaboration instability: workspace isolation and rollback under multi-task parallelism, and thoroughly resolves session conflicts
Control approach	Pure Prompt self-scheduling (black box) or pure code (heavy)	AI combined with precise orchestration: get everything done in natural language while the scheduler precisely controls behavior (dispatch / retry / timeout / abort)
Engineering harness	Generally missing; failures rely on Agent improvisation	Harness engineering: human checkpoints, rollbackable results, complaint-loop mechanism, periodic entropy management
Failure recovery	Relies on Agent self-healing, uncertain outcome	Clear retry / skip / abort strategies, recovery paths folded into a standard state machine
Observability	Context is mostly a black box	Full-chain RunEvent — traceable, auditable, replayable

✨ The Result?

You own the goal, and ClawsomeFlow turns multi-agent collaborative execution into a stable, controllable, convergent engineering system.

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🧩 Relationship with ClawTeam

ClawsomeFlow is built on top of ClawTeam.

🔍 ClawTeam vs ClawsomeFlow at a Glance

Dimension	ClawTeam	ClawsomeFlow
Positioning	Swarm-intelligence protocol foundation (Agent self-organization)	Agent workflow orchestration platform
Collaboration driver	Agents self-poll and self-schedule in the Prompt	Server-side scheduler actively dispatches, deterministic execution
Task model	Kanban + dependency chain	DAG Flow compilation, Leader summarizes and converges
OpenClaw adaptation	Supported as an optional CLI Agent	Deeply adapted, resolving session and workspace concurrency conflicts
Failure & guardrails	Basic lifecycle protocol	Human checkpoints / rollback / complaint-loop / entropy management
Skill configuration	Requires extra skill setup on the Agent platform	No extra skill configuration needed, works out of the box
Usage form	CLI + MCP + monitoring dashboard	Web UI + CLI, full-flow governance in natural language

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🚀 Quick Start

Install

Linux/macOS

curl -fsSL https://clawsomeflow.com/install.sh | bash

Common Commands

# Lifecycle
csflow start
csflow stop
csflow status
csflow doctor

# Flow / Run
csflow flows list
csflow runs list
csflow runs start <flow-id> --input k=v
csflow runs abort <run-id>

# Agent governance
csflow agents list
csflow agents create "Describe the Agent you want in natural language"
csflow agents chat <agent-id> "Keep improving this Agent's capabilities"

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👩‍💻 Contributor Local Deploy and Test

For contributors iterating on source code, use the isolated developer entrypoint:

bash scripts/deploy-contributor.sh

Default behavior of deploy-contributor.sh:

• Uses isolated data/runtime under ~/.clawsomeflow-dev (does not reuse ~/.clawsomeflow).
• Starts backend on 17117 and Vite on 5174.
• Keeps ClawTeam runtime isolated via ~/.clawsomeflow-dev/.clawteam-data.

bash scripts/deploy-contributor.sh is recommended for day-to-day source testing because it keeps regular user service state isolated.

Example with custom profile/ports:

CSFLOW_DEV_HOME=~/.clawsomeflow-dev-alice \
CSFLOW_DEV_BACKEND_PORT=18117 \
CSFLOW_DEV_FRONTEND_PORT=5184 \
bash scripts/deploy-contributor.sh

Stop the contributor service

To stop the contributor profile started by deploy-contributor.sh, use the dedicated stop script:

bash scripts/stop-contributor.sh

Do not use csflow stop for the contributor profile — that targets the end-user service. If you used a custom profile, pass the same env overrides:

CSFLOW_DEV_BACKEND_PORT=18117 CSFLOW_DEV_FRONTEND_PORT=5184 \
bash scripts/stop-contributor.sh

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🗺️ Roadmap

Phase	Content	Status
P0	Agent Store — a shareable marketplace for ready-made Agents, Teams and Flow templates: install, reuse, and contribute domain experts in one click.	🚧 In progress
P1	Broader Agent platform support — onboard more CLI Agent runtimes and keep pace with emerging ecosystems, so any Agent can join the same graph.	📅 Planned
P2	Mobile & server mode — a mobile-friendly console plus multi-user server deployment, to monitor and intervene in Runs anywhere.	💡 Exploring
P3	Cloud & SSH Agents — drive Agents on remote / cloud hosts over SSH, scaling collaboration beyond a single machine.	💡 Exploring

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🙏 Acknowledgements

• [ClawTeam] — the spark that inspired this project. Thank you for showing what Agent self-organization can be.
• Our Agent platform teammates — the real "team members" that do the actual work inside every Flow: Claude, OpenClaw, Codex, Gemini, and the growing roster of CLI Agents. ClawsomeFlow is only as clawsome as the Agents it coordinates.

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💬 WeChat Community

If ClawsomeFlow helps you coordinate your Agent team, please give us a ⭐ Star — it genuinely keeps us going.

Got questions about using ClawsomeFlow, or curious about building an OPC (One-Person Company)? Come hang out with us — scan the QR code below to join our WeChat discussion group:

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📄 License

MIT