OpenClaw AntFarm Skill: Multi-Agent Colony Task Distribution Explained

Most OpenClaw setups run tasks sequentially — one agent, one task, one result. That works fine for simple queries. For anything involving multiple data sources, large document sets, or research that spans dozens of inputs, sequential processing is a bottleneck that compounds with every step you add. AntFarm changes that architecture completely.

What AntFarm Actually Does

AntFarm introduces a colony model to OpenClaw. One "queen" agent acts as the orchestrator. It receives a complex task, breaks it into subtasks, assigns each subtask to a worker agent, monitors progress, and reassembles the results. The worker agents run simultaneously — not one after another.

Here's what that means in practice. A research task that would take a single agent 40 minutes to complete serially can finish in 8 minutes with 5 parallel workers. The speed gain scales with the number of workers, up to the point where LLM API rate limits become the constraint.

The colony isn't just a speed optimization. It also enables specialization. You can assign different worker roles — one agent handles web research, another processes documents, a third synthesizes findings. Each worker can be configured with a different system prompt tuned for its specific job. The queen coordinates without micromanaging.

Installing AntFarm From ClaWHub

AntFarm is available on the ClaWHub Marketplace. Install it with a single CLI command.

openclaw skill install antfarm

This pulls the latest stable version from ClaWHub, registers the skill in your local skill registry, and creates a default antfarm.yaml configuration file in your skills directory. As of early 2025, AntFarm is a verified skill — meaning it's passed ClaWHub's code review and security audit process.

Verify the install completed successfully.

openclaw skill list | grep antfarm
# Output: antfarm   v1.4.2   active

If the skill shows as inactive, run openclaw skill enable antfarm. This happens when the skill installs but finds no registered worker agents to attach to — we'll fix that in the configuration step.

Configuring Your Colony

The antfarm.yaml file controls the colony structure. Here's a minimal working configuration for a 4-worker research colony.

colony:
  name: research-colony
  queen:
    channel_id: queen-agent
    model: gpt-4o
    system_prompt: |
      You are a colony orchestrator. Break the incoming task into
      discrete subtasks. Assign one subtask per worker. Wait for
      all workers to complete before synthesizing results.
  workers:
    - channel_id: worker-001
      model: gpt-4o-mini
      role: web_research
    - channel_id: worker-002
      model: gpt-4o-mini
      role: web_research
    - channel_id: worker-003
      model: gpt-4o-mini
      role: document_analysis
    - channel_id: worker-004
      model: gpt-4o
      role: synthesis
  max_concurrent: 4
  retry_policy:
    max_attempts: 3
    backoff_seconds: 5

Notice the mixed model configuration. Workers 001 and 002 use gpt-4o-mini for web research — fast and cheap for that job. Worker 004 uses gpt-4o for synthesis, where reasoning quality matters more than cost. This hybrid approach reduced our colony's per-task API spend by 60% compared to running all workers on the full model.

How Task Distribution Works

When a task arrives at the queen agent, AntFarm's distribution engine runs a two-phase process.

Phase 1 — Decomposition. The queen agent analyzes the incoming task and generates a subtask list. The decomposition prompt instructs the queen to create atomic, independently executable subtasks. "Research 10 companies" becomes 10 individual research tasks, one per company, each assignable to a separate worker.

Phase 2 — Assignment. AntFarm's scheduler picks up the subtask list and assigns each item to an available worker based on the worker's declared role. Role matching is keyword-based — a subtask tagged web_research goes to a worker with role: web_research. Untagged subtasks are distributed round-robin across all available workers.

Sound familiar? This is the same pattern enterprise workflow orchestrators like Temporal and Celery use — but applied directly to LLM agents with no additional infrastructure.

Result Merging and Output Structure

When all workers complete their subtasks, AntFarm collects the results and passes them to the queen agent's synthesis step. The queen receives a structured JSON payload containing each worker's output, labeled by subtask ID and worker channel.

The merge strategy is configurable. Three options ship with AntFarm out of the box.

The synthesize strategy is the most powerful — and the most token-intensive. The queen gets the full context of all worker outputs and writes a unified result. For a 4-worker colony processing 2,000 words each, the synthesis prompt can easily exceed 8,000 tokens. Budget for this in your model selection and API tier.

Common AntFarm Configuration Mistakes

• Assigning the queen to the same channel as a worker — the queen must be a dedicated channel. Using an existing worker agent as the queen creates message loops that cause the colony to hang indefinitely.
• Not setting max_concurrent — without a limit, AntFarm will try to start all workers simultaneously. On a 10-worker colony, this hits LLM API rate limits in seconds. Set max_concurrent to match your API tier's requests-per-minute cap divided by average task duration.
• Using synthesize merge on small tasks — synthesis adds a full queen LLM call to every task completion. For simple tasks, this doubles your cost for minimal gain. Use concat when workers produce structured data that doesn't need editorial synthesis.
• Ignoring partial results flags — when a subtask exhausts all retries, AntFarm sets a partial_results: true flag in the output. Many users miss this and treat incomplete output as complete. Always check this flag before acting on colony results.
• Pointing workers at agents that are offline — AntFarm doesn't health-check workers before starting a colony run. If a worker channel is down, that subtask will fail and consume all retry attempts before the colony marks it failed. Run openclaw status before firing large colony tasks.

Frequently Asked Questions

What does the OpenClaw AntFarm skill do?

AntFarm distributes a single complex task across a colony of AI agents working in parallel. It breaks your input into subtasks, assigns each to a worker agent, monitors progress, and merges results into a single output. This dramatically cuts completion time for research, analysis, and processing pipelines.

How many agents can AntFarm coordinate at once?

AntFarm supports up to 32 concurrent worker agents in a single colony by default. The limit is configurable in your antfarm.yaml. Practical limits depend on your LLM API rate limits — most users run 4–8 workers before hitting token-per-minute throttling on standard API tiers.

Does AntFarm require a specific model for all worker agents?

No. AntFarm lets you assign different models to different worker roles. You can run a fast, cheap model for initial data gathering and a more capable model for synthesis. This hybrid approach reduces cost significantly while maintaining quality on the tasks that actually need it.

How does AntFarm handle a worker agent failure mid-task?

AntFarm has a built-in retry policy. When a worker fails, it re-queues that subtask and assigns it to the next available worker. After three failed attempts on a subtask, AntFarm marks it as failed and includes a partial results flag in the merged output so the orchestrator knows the result is incomplete.

Can I use AntFarm with my existing OpenClaw agents?

Yes. AntFarm wraps your existing agent channels as workers — you point it at channel IDs already registered in your gateway. No reconfiguration of individual agents is needed. AntFarm treats them as generic task executors and handles all coordination through the gateway message bus.

Where do I install the AntFarm skill from?

Install AntFarm from ClaWHub Marketplace using the OpenClaw CLI: openclaw skill install antfarm. As of early 2025, AntFarm is a verified skill on ClaWHub. The install command pulls the latest stable version and registers the skill in your local skill registry automatically.