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#multi-agent
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#ai-agents#developer-tools#open-source#multi-agent#automation#free#tool
An open-source super-agent harness for research, coding, sub-agents, memory, sandboxes, and skills. What it is DeerFlow is ByteDance's open-source super-agent harness for longer jobs that need more than a single chat turn. It combines sub-agents, memory, sandboxes, skills, and a message gateway so an agent can research, code, create artifacts, and continue work across multi-step sessions. Who should use it Use it if you are evaluating agent infrastructure for deep research, coding workflows, report generation, or multi-agent task execution. It is most relevant for builders who already understand the cost and risk of letting agents use tools, files, shells, or browser/search providers. How to evaluate it Start with the official repository and installation guide. Run it locally or in Docker before exposing it to shared users. Use make setup and make doctor to generate config and catch setup problems. Test one contained workflow first: research summary, codebase inspection, or document generation. Keep sandbox mode and provider limits tight until you understand the execution path. Limits and risks DeerFlow is powerful because it can coordinate tools, models, files, and sub-agents. That also means misconfiguration can create security risk. Review the .env, model-provider config, shell/file-write permissions, sandbox settings, and any skill code before running it on sensitive projects. The repo also notes that DeerFlow 2.0 is a ground-up rewrite, so teams using older DeerFlow material should check whether guidance applies to the current branch. Source links Primary source: https://github.com/bytedance/deer-flow Discovery/context: https://github.com/topics/ai-agents
#openclaw#skill#agent#multi-agent#workflow#free
A community OpenClaw skill for defining agent roles, task states, handoffs, and review gates before multi-agent work gets messy. Agent Team Orchestration is a community OpenClaw skill for teams that use more than one agent on the same stream of work. It gives the orchestrator a concrete operating model: define roles, move tasks through clear states, require handoff notes, and add review gates before agent-produced work ships. What it helps with Builder and reviewer agent loops for code, docs, research, or operations work. Clear task states such as inbox, assigned, in progress, review, done, or failed. Handoff messages that include what changed, where artifacts live, how to verify them, known gaps, and the next action. Quality checks when several agents are passing work across sessions. Who should evaluate it Use this as a candidate when an OpenClaw setup already has repeated multi-agent delegation and the weak point is coordination rather than raw model capability. It is most useful for long-running workflows, parallel research, build-review loops, and agent teams that need predictable artifact paths. Skip it for simple one-off delegation or a solo assistant. The process overhead only pays off when multiple agents are producing, reviewing, or routing work across more than one task. Setup surface The ClawHub page lists the install command as openclaw skills install @arminnaimi/agent-team-orchestration. Do not install it blindly on a production Pi. Review the skill file, reference files, permissions, and any tool assumptions first, then test it in an isolated OpenClaw workspace. Risk notes This is editorial discovery, not a runner-verified recommendation. Community skills can change after publication, and orchestration skills may influence how agents spawn work, communicate, and mark tasks complete. Treat the ClawHub and index pages as source material, then perform your own review before using it with sensitive repos, credentials, or external actions. Sources Awesome OpenClaw Skills: https://github.com/VoltAgent/awesome-openclaw-skills ClawHub listing: https://clawhub.ai/arminnaimi/skills/agent-team-orchestration Skill mirror: https://clawskills.sh/skills/arminnaimi-agent-team-orchestration
#AI Agents#Multi-Agent#Claude Code#Codex#MCP#Developer Workflow
A premium field guide for evaluating and planning a multi-agent orchestration layer for Claude Code and Codex without blindly installing it. This premium Loot gives you a cautious, high-leverage way to evaluate Ruflo: a multi-agent AI harness for Claude Code and Codex. The public sources describe a system for coordinated swarms, persistent memory, MCP tools, plugins, hooks, federation, and security controls. What the sources confirm The GitHub repository positions Ruflo as a multi-agent AI harness for Claude Code and Codex. The npm package ruflo is published under MIT license and exposes a ruflo CLI. The package metadata currently requires Node.js 20+. The status documentation describes MCP tools, CLI commands, plugins, hooks, memory, agent coordination, and verification workflows. The README presents two different adoption paths: a lighter Claude Code plugin path and a fuller CLI/MCP install path. Evaluation Prompt: Should I Add This Agent Layer? Use this before installation or rollout. Disposable Repo Test Plan Use this to avoid letting a new agent harness touch a production repo first. Team Rollout Prompt Use this when the question becomes operational, not just technical. Security Review Prompt Use this before trusting any autonomous or federated agent layer. Practical adoption ladder Why this is worth watching The interesting shift is not just “more agents.” It is the move from single-session assistance toward coordinated agent teams with persistent memory, task routing, plugins, and verifiable runtime behavior. That is useful, but it also raises the operational bar. Source links GitHub: ruvnet/ruflo npm: ruflo Status doc: Ruflo STATUS.md
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