agi-farm

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Interactive setup wizard that creates a fully working multi-agent AI team on OpenClaw. One command bootstraps agents, SOUL.md personas, comms infrastructure (inboxes/outboxes/broadcast), cron jobs, auto-dispatcher (HITL + rate-limit backoff + dependency checking), and a portable GitHub bundle — all customized to team name, size (3/5/11 agents), domain, and frameworks (autogen/crewai/langgraph). Includes a React + SSE live ops dashboard with file-watcher (~350ms push latency) and persistent macOS LaunchAgent. Model-selection guidance built in. Commands: setup | status | rebuild | export | dashboard | dispatch

modbender By modbender schedule Updated 3/6/2026
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name: agi-farm

description: >

Interactive setup wizard that creates a fully working multi-agent AI team on OpenClaw.

One command bootstraps agents, SOUL.md personas, comms infrastructure (inboxes/outboxes/broadcast),

cron jobs, auto-dispatcher (HITL + rate-limit backoff + dependency checking), and a portable

GitHub bundle — all customized to team name, size (3/5/11 agents), domain, and frameworks

(autogen/crewai/langgraph). Includes a React + SSE live ops dashboard with file-watcher

(~350ms push latency) and persistent macOS LaunchAgent. Model-selection guidance built in.

Commands: setup | status | rebuild | export | dashboard | dispatch

agi-farm

Builds a complete multi-agent AI team on OpenClaw. One wizard, full team.

Commands

| Command | What it does |

|---------|-------------|

| /agi-farm setup | Full wizard — agents, workspace, crons, bundle, GitHub |

| /agi-farm status | Team health: agents, tasks, cron status |

| /agi-farm rebuild | Regenerate workspace from existing bundle (preserves edits) |

| /agi-farm export | Push bundle to GitHub |

| /agi-farm dashboard | Launch live ops room — see references/dashboard.md |

| /agi-farm dispatch | Run auto-dispatcher — see scripts/auto-dispatch.py |

/agi-farm setup

Ask one question at a time. Do not proceed until confirmed.

Step 1 — Team name

"What should we call your team? (e.g. NovaCorp, TradingDesk — default: MyTeam)"

Store as TEAM_NAME.

Step 2 — Orchestrator name

"What's your orchestrator's name? (default: Cooper)"

Store as ORCHESTRATOR_NAME.

Step 3 — Team size

"How many agents?

3 — Minimal: Orchestrator + Researcher + Builder

5 — Standard: adds QA + Content

11 — Full stack: complete AGI system (recommended)"

Store as PRESET.

Step 3.5 — Domain

"What domain? software / trading / research / general (default) / custom"

If custom: ask for one-phrase description. Store as DOMAIN.

Step 3.6 — Custom agents (PRESET 3 or 5 only)

"Add a custom agent? (yes/no, default: no)"

If yes, collect per agent: id, name, emoji, role, goal. Max 3 custom agents.

Append to roster in Step 7 with "template": "generic".

Step 4 — Frameworks

"Collaboration frameworks? autogen / crewai / langgraph / all / none"

Store as FRAMEWORKS list. all → ["autogen", "crewai", "langgraph"].

Step 5 — GitHub

"Create a GitHub repo for the bundle? yes / no"

Store as CREATE_GITHUB.

Step 6 — Confirm

Show summary, ask "Shall I proceed? (yes/no)". If no → restart Step 1.

Step 7 — Write team.json


mkdir -p ~/.openclaw/workspace/agi-farm-bundle/

openclaw agents list --json   # use output to assign appropriate models per role

Use the openclaw agents list output to assign each agent a model appropriate for

its role. Write resolved model strings directly into the "model" fields.

Model selection cheat sheet (based on openclaw agents list --json output):

| Role | Recommended tier | Why |

|------|-----------------|-----|

| Orchestrator | High-capability (e.g. sonnet, opus) | Needs broad reasoning, delegation judgment |

| Solution Architect / Researcher | High-capability | Deep analysis + design |

| Implementation Engineer | Mid-tier (e.g. glm-5, sonnet) | Fast code gen; cost-efficiency matters |

| Debugger | High-capability (e.g. opus) | Root-cause analysis benefits from deep reasoning |

| Business Analyst / Knowledge | Mid-high (e.g. gemini-2.0-pro-exp) | Long-context research tasks |

| QA Engineer | Fast/cheap (e.g. glm-4.7-flash) | High volume, pattern-matching checks |

| Content / Multimodal | Multimodal-capable (e.g. gemini-2.0-pro-exp) | Vision + rich generation |

| R&D / Process Improvement | High-capability | Creative + structured experimentation |

Tip: assign opus or sonnet to roles that make decisions; use flash/glm-4.7-flash for high-frequency reviewers to manage cost.

3-agent roster:


{"team_name":"<TEAM_NAME>","orchestrator_name":"<ORCHESTRATOR_NAME>","preset":"3",

 "domain":"<DOMAIN>","frameworks":<FRAMEWORKS_JSON>,"created_at":"<ISO_TIMESTAMP>",

 "agents":[

   {"id":"main",       "name":"<ORCHESTRATOR_NAME>","emoji":"🦅","role":"Orchestrator",           "goal":"Orchestrate the team, delegate tasks, synthesize results",             "model":"<MODEL>","workspace":"."},

   {"id":"researcher", "name":"Sage",               "emoji":"🔮","role":"Researcher",             "goal":"Research deeply and surface the insights that matter most",            "model":"<MODEL>","workspace":"researcher"},

   {"id":"builder",    "name":"Forge",              "emoji":"⚒️","role":"Builder",                "goal":"Implement solutions cleanly and efficiently",                          "model":"<MODEL>","workspace":"builder"}

 ]}

5-agent: add to 3-agent roster:


{"id":"qa",     "name":"Vigil", "emoji":"🛡️","role":"QA Engineer",       "goal":"Ensure every output meets quality standards","model":"<MODEL>","workspace":"qa"},

{"id":"content","name":"Anchor","emoji":"⚓", "role":"Content Specialist","goal":"Craft clear content that communicates complex ideas simply","model":"<MODEL>","workspace":"content"}

11-agent roster:


[

  {"id":"main",  "name":"<ORCHESTRATOR_NAME>","emoji":"🦅","role":"Orchestrator",            "goal":"Orchestrate specialists, delegate tasks, synthesize results",                          "model":"<MODEL>","workspace":"."},

  {"id":"sage",  "name":"Sage",              "emoji":"🔮","role":"Solution Architect",       "goal":"Design robust, scalable architectures",                                               "model":"<MODEL>","workspace":"solution-architect"},

  {"id":"forge", "name":"Forge",             "emoji":"⚒️","role":"Implementation Engineer", "goal":"Implement clean, well-tested code efficiently",                                        "model":"<MODEL>","workspace":"implementation-engineer"},

  {"id":"pixel", "name":"Pixel",             "emoji":"🐛","role":"Debugger",                "goal":"Find the true root cause of any bug or failure",                                       "model":"<MODEL>","workspace":"debugger"},

  {"id":"vista", "name":"Vista",             "emoji":"🔭","role":"Business Analyst",        "goal":"Research deeply and surface the insights that matter most",                            "model":"<MODEL>","workspace":"business-analyst"},

  {"id":"cipher","name":"Cipher",            "emoji":"🔊","role":"Knowledge Curator",       "goal":"Curate and surface knowledge so the team never forgets",                              "model":"<MODEL>","workspace":"knowledge-curator"},

  {"id":"vigil", "name":"Vigil",             "emoji":"🛡️","role":"QA Engineer",             "goal":"Ensure every output meets quality standards",                                         "model":"<MODEL>","workspace":"quality-assurance"},

  {"id":"anchor","name":"Anchor",            "emoji":"⚓", "role":"Content Specialist",     "goal":"Craft clear content that communicates complex ideas simply",                           "model":"<MODEL>","workspace":"content-specialist"},

  {"id":"lens",  "name":"Lens",              "emoji":"📡","role":"Multimodal Specialist",   "goal":"Extract meaning from images, documents, and multimodal inputs",                       "model":"<MODEL>","workspace":"multimodal-specialist"},

  {"id":"evolve","name":"Evolve",            "emoji":"🔄","role":"Process Improvement Lead","goal":"Make the team better systematically through continuous improvement",                   "model":"<MODEL>","workspace":"process-improvement"},

  {"id":"nova",  "name":"Nova",              "emoji":"🧪","role":"R&D Lead",                "goal":"Turn hypotheses into proven capabilities through structured experimentation",          "model":"<MODEL>","workspace":"r-and-d"}

]

Step 8 — Generate workspace files


python3 ~/.openclaw/skills/agi-farm/generate.py \

  --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \

  --output ~/.openclaw/workspace/ \

  --all-agents --shared --bundle

Step 9 — Create OpenClaw agents

For each agent except main (skip if already exists):


openclaw agents add \

  --agent <id> --name "<name>" --emoji "<emoji>" \

  --model "<model>" \

  --workspace "~/.openclaw/workspace/agents-workspaces/<workspace>"

Use agent["model"] from team.json directly.

Step 10 — Register cron jobs


python3 ~/.openclaw/skills/agi-farm/scripts/register-crons.py \

  --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json

Timezone is read automatically from OpenClaw config. Skips any cron that already exists.

Step 11 — Install frameworks

For each framework in FRAMEWORKS:


if [ ! -d ~/.openclaw/skills/<fw>-collab ]; then

  TMP=$(mktemp -d)

  git clone --depth 1 --filter=blob:none --sparse \

    https://github.com/oabdelmaksoud/openclaw-skills.git "$TMP"

  cd "$TMP" && git sparse-checkout set <fw>-collab

  cp -r <fw>-collab ~/.openclaw/skills/ && rm -rf "$TMP"

fi

python3 ~/.openclaw/skills/<fw>-collab/build_agents.py --force 2>/dev/null || true

Step 12 — GitHub (if chosen)


cd ~/.openclaw/workspace/agi-farm-bundle

git init -b main && git add . && git commit -m "feat: <TEAM_NAME> AGI farm"

gh repo create agi-farm-<TEAM_NAME_LOWER> --public --source . --remote origin --push

Step 13 — Commit workspace


cd ~/.openclaw/workspace

git add -A && git commit -m "feat: <TEAM_NAME> AGI team — agi-farm setup complete"

Step 14 — Initialize registries + health check


# Write TASKS.json and AGENT_STATUS.json

python3 - << 'EOF'

import json

from pathlib import Path

ws   = Path.home() / ".openclaw/workspace"

team = json.loads((ws / "agi-farm-bundle/team.json").read_text())

(ws / "TASKS.json").write_text("[]")

(ws / "AGENT_STATUS.json").write_text(json.dumps(

    {a["id"]: {"status": "available", "name": a["name"]} for a in team["agents"]}, indent=2))

print("✅ registries written")

EOF



# Health check

AGENTS=$(openclaw agents list --json 2>/dev/null | python3 -c "import json,sys; print(len(json.load(sys.stdin)))" || echo 0)

CRONS=$(openclaw cron list 2>/dev/null | grep -c "<TEAM_NAME_LOWER>" || echo 0)

[ -d ~/.openclaw/workspace/comms/inboxes ] && echo "✅ comms OK" || echo "❌ comms missing"

[ -f ~/.openclaw/workspace/TASKS.json ]    && echo "✅ TASKS.json OK" || echo "❌ TASKS.json missing"

echo "✅ Agents: $AGENTS | Crons: $CRONS"

Step 15 — Done


✅ <TEAM_NAME> AGI team is live!

Agents   : <PRESET> (<AGENT_NAMES_LIST>)

Workspace: ~/.openclaw/workspace/

Bundle   : ~/.openclaw/workspace/agi-farm-bundle/

GitHub   : <URL if created>



Next: talk to <ORCHESTRATOR_NAME> · /agi-farm status · /agi-farm dashboard

/agi-farm status


openclaw agents list --json | python3 -c "

import json,sys

for a in json.load(sys.stdin):

    print(f'  {a.get(\"identityEmoji\",\"🤖\")} {a.get(\"identityName\",a[\"id\"])}: {a.get(\"model\",\"?\")}')

"

python3 -c "

import json

from pathlib import Path

ws = Path.home() / '.openclaw/workspace'

tasks = json.loads((ws/'TASKS.json').read_text()) if (ws/'TASKS.json').exists() else []

t = [t for t in tasks if isinstance(t,dict)]

print(f'  Tasks: {len(t)} total · {sum(1 for x in t if x.get(\"status\")==\"pending\")} pending · {sum(1 for x in t if x.get(\"status\")==\"needs_human_decision\")} HITL')

"

openclaw cron list 2>/dev/null | head -15

/agi-farm rebuild


python3 ~/.openclaw/skills/agi-farm/generate.py \

  --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \

  --output ~/.openclaw/workspace/ \

  --all-agents --shared --no-overwrite

--no-overwrite skips files that already exist, preserving manual edits.

Add --force (remove --no-overwrite) to overwrite everything.

/agi-farm export


cd ~/.openclaw/workspace/agi-farm-bundle

git add -A

git commit -m "export: $(date +%Y-%m-%d)" 2>/dev/null || echo "Nothing to commit"

git push 2>/dev/null || echo "No remote — run /agi-farm setup first"

/agi-farm dashboard

React + SSE ops room. File-watcher pushes live data to the browser in ~350ms on any workspace .json or .md change. Runs as a persistent macOS LaunchAgent — always on, auto-restarts on crash.

Architecture


dashboard.py          ← Python HTTP server (SSE + static)

  ├── WorkspaceWatcher  watchdog file-watcher, 250ms debounce

  ├── SlowDataCache     background thread — caches `openclaw agents list`

  │                     and `openclaw cron list` every 30s (each takes ~1-2s)

  ├── Broadcaster       thread-safe SSE fan-out to all connected clients

  └── /api/stream       SSE endpoint — pushes full snapshot on every file change



dashboard-react/      ← Vite + React 18 + Recharts frontend

  dist/               ← production build (served by dashboard.py)

  src/

    hooks/useDashboard.js   SSE hook — auto-reconnects on disconnect

    components/

      Header.jsx            live badge, stats, clock

      Nav.jsx               tab switcher

      tabs/

        Overview.jsx        stats, budget bar, SLA alerts, agent grid, broadcast preview

        Agents.jsx          full agent cards — model, inbox, quality, credibility, cache age

        Tasks.jsx           filterable table, expandable rows, ticking deadlines, pagination

        Velocity.jsx        7-day charts (Recharts), quality trend, task-type donut

        Budget.jsx          period bars, threshold markers, per-agent/model breakdown

        OKRs.jsx            objectives + KRs with progress bars

        RD.jsx              experiments, backlog, benchmarks

        Broadcast.jsx       terminal log, color-coded CRITICAL/BLOCKED/HITL

Data sources (all real-time from workspace files)

| Field | Source file | Refresh |

|-------|-------------|---------|

| tasks, task_counts, sla_at_risk | TASKS.json | instant |

| agents (inbox, perf, status) | AGENT_STATUS.json, AGENT_PERFORMANCE.json, comms/inboxes/ | instant |

| agent model, cron error/busy | openclaw agents/cron list | 30s cache |

| budget | BUDGET.json | instant |

| velocity | VELOCITY.json | instant |

| okrs | OKRs.json | instant |

| broadcast | comms/broadcast.md | instant |

| experiments / backlog | EXPERIMENTS.json, IMPROVEMENT_BACKLOG.json | instant |

| knowledge_count | SHARED_KNOWLEDGE.json | instant |

| memory_lines | MEMORY.md | instant |

LaunchAgent (always-on)

The dashboard is registered as ai.coopercorp.dashboard and starts automatically at login.


# Status

launchctl list | grep coopercorp

curl -s http://localhost:8080/api/data | python3 -m json.tool | head -5



# Restart

launchctl stop  ai.coopercorp.dashboard

launchctl start ai.coopercorp.dashboard



# Logs

tail -f /tmp/coopercorp-dashboard.log

tail -f /tmp/coopercorp-dashboard.err



# Disable / re-enable

launchctl unload ~/Library/LaunchAgents/ai.coopercorp.dashboard.plist

launchctl load   ~/Library/LaunchAgents/ai.coopercorp.dashboard.plist

URL: http://localhost:8080

Rebuild React frontend


cd ~/.openclaw/skills/agi-farm/dashboard-react

npm install        # first time only

npm run build      # outputs to dist/ — dashboard.py serves automatically

Full reference: references/dashboard.md

/agi-farm dispatch


# Dry-run (preview only)

python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py



# Execute

python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py --execute

Fires agent sessions for pending tasks, handles HITL notifications, stale task

resets, rate-limit backoff, and dependency checking. Cron (every 1 min):


* * * * * python3 ~/.openclaw/skills/agi-farm/scripts/auto-dispatch.py --execute \

            >> ~/.openclaw/workspace/logs/auto-dispatch.log 2>&1

Troubleshooting

Setup issues

| Symptom | Fix |

|---------|-----|

| generate.py fails with ModuleNotFoundError | Run pip3 install jinja2 |

| openclaw agents add says agent already exists | Safe to ignore — skip that agent |

| gh repo create fails | Run gh auth login first |

| Cron registration shows 0 crons added | Run openclaw cron list to check for duplicates; use --force flag on re-register |

| git commit fails in Step 13 | Run git config --global user.email and set name/email first |

Runtime issues

| Symptom | Fix |

|---------|-----|

| Auto-dispatcher fires but agents don't respond | Check logs/auto-dispatch.log; verify openclaw agents list shows agents |

| Dashboard shows stale data | Restart LaunchAgent: launchctl stop ai.coopercorp.dashboard && launchctl start ai.coopercorp.dashboard |

| TASKS.json parse error | Validate JSON: python3 -m json.tool ~/.openclaw/workspace/TASKS.json |

| Agent stuck >30 min | Check broadcast.md for [BLOCKED] tags; reassign task manually |

| Rate-limit backoff too aggressive | Edit RATE_LIMIT_BACKOFF_MIN in scripts/auto-dispatch.py (default: 10 min) |

| openclaw not found in cron | Set OPENCLAW_BIN=/path/to/openclaw in the cron environment, or add PATH=/opt/homebrew/bin:$PATH |

Recovery


# Re-run setup without overwriting existing files

python3 ~/.openclaw/skills/agi-farm/generate.py \

  --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \

  --output ~/.openclaw/workspace/ \

  --all-agents --shared --no-overwrite



# Force full regeneration (overwrites everything)

python3 ~/.openclaw/skills/agi-farm/generate.py \

  --team-json ~/.openclaw/workspace/agi-farm-bundle/team.json \

  --output ~/.openclaw/workspace/ \

  --all-agents --shared --bundle --force
Install via CLI
npx skills add https://github.com/modbender/skill-library-mcp --skill agi-farm
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