Hands‑On Guide: Creating and Running OpenClaw Agent Evaluation Jobs

OpenClaw is a cutting‑edge benchmark suite that lets you evaluate AI agents end‑to‑end, and the UBOS platform provides a turnkey environment to define, configure, run, and interpret those evaluation jobs with just a few clicks.

1. Introduction

AI agents have exploded onto the scene in 2024, powering everything from autonomous assistants to real‑time decision engines. With the hype around ChatGPT‑style agents, Claude, and emerging multimodal models, developers need a reliable way to measure performance, cost, and safety before shipping to production.

OpenClaw, the open‑source evaluation framework, offers a standardized set of tasks—ranging from navigation to reasoning—that simulate real‑world workloads. When paired with UBOS, you gain:

• Scalable compute on demand.
• Built‑in data pipelines and environment orchestration.
• Rich dashboards for metric visualization.

Ready to turn hype into hard data? Let’s dive into the hands‑on workflow.

2. Defining Evaluation Jobs

What is an evaluation job?

An evaluation job is a self‑contained specification that tells OpenClaw which agent, dataset, and metrics to run. Think of it as a recipe: ingredients (model, data), steps (pipeline), and the final tasting notes (metrics).

Required parameters and configuration options

{
  "job_name": "gpt4o‑benchmark",
  "agent": {
    "type": "openai",
    "model": "gpt-4o"
  },
  "tasks": ["navigation", "code‑generation", "reasoning"],
  "environment": "docker‑ubuntu‑22.04",
  "resources": {
    "cpu": "8",
    "gpu": "A100",
    "memory_gb": "32"
  },
  "metrics": ["latency", "accuracy", "cost_usd"]
}

Key sections:

• job_name: Unique identifier for tracking.
• agent: Provider and model details.
• tasks: OpenClaw task IDs you want to run.
• environment: Docker image or VM snapshot.
• resources: Compute allocation.
• metrics: Desired output measurements.

Example job definition (YAML)

job_name: claude‑3‑benchmark
agent:
  type: anthropic
  model: claude-3-opus
tasks:
  - multi‑turn‑dialogue
  - tool‑use
environment: docker‑python‑3.11
resources:
  cpu: 4
  gpu: none
  memory_gb: 16
metrics:
  - success_rate
  - token_usage
  - latency_ms

Save the JSON or YAML file locally, then upload it through the UBOS UI or via the CLI.

3. Configuring Pipelines

OpenClaw pipelines stitch together data sources, runtime environments, and post‑processing steps. UBOS offers a visual Workflow automation studio that lets you drag‑and‑drop components, or you can define them as code.

Setting up data sources and environments

Typical data sources include:

• Public datasets hosted on Chroma DB integration.
• Private S3 buckets linked via UBOS Telegram integration on UBOS for secure token exchange.
• Real‑time streams from OpenAI ChatGPT integration.

For the runtime, you can pick a pre‑built Docker image from the Web app editor on UBOS or supply a custom Dockerfile. The environment must expose the OpenClaw CLI (installed by default on UBOS images).

Integrating with UBOS services

UBOS provides native hooks for logging, alerting, and cost tracking. Add the following snippet to your pipeline YAML to enable automatic log shipping to the UBOS Enterprise AI platform by UBOS:

hooks:
  - type: ubos-logging
    destination: /var/log/openclaw
  - type: cost-monitor
    alert_threshold_usd: 10

Sample pipeline configuration

pipeline:
  name: openclaw‑full‑suite
  steps:
    - name: fetch‑data
      action: chroma-db-fetch
      params:
        collection: openclaw‑tasks
    - name: run‑benchmark
      action: openclaw-run
      job_file: ./gpt4o‑benchmark.json
    - name: post‑process
      action: ubos‑analytics
      params:
        metrics: ["latency","accuracy","cost_usd"]

Commit this file to your repo, then point UBOS to the repository URL in the UBOS partner program dashboard.

4. Executing Benchmarks

With the job and pipeline ready, you can launch the benchmark from the UBOS CLI, the web console, or via the REST API.

Running the job on UBOS

CLI example (assuming you have ubos-cli installed):

# Authenticate
ubos login --api-key $UBOS_API_KEY

# Upload job definition
ubos job upload --file ./gpt4o-benchmark.json

# Trigger pipeline
ubos pipeline run --name openclaw-full-suite

Monitoring progress and logs

UBOS streams live logs to the console, but you can also watch the web UI. The UBOS pricing plans include a real‑time dashboard that shows:

• CPU/GPU utilization.
• Task‑level progress bars.
• Cost accrual per minute.

Example API call

For CI/CD integration, POST the job definition to the UBOS endpoint:

curl -X POST https://api.ubos.tech/v1/jobs \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d @gpt4o-benchmark.json

The response includes a job_id you can poll:

curl -X GET https://api.ubos.tech/v1/jobs/$JOB_ID/status \
  -H "Authorization: Bearer $TOKEN"

5. Interpreting Results

When the pipeline finishes, UBOS stores the raw JSON output in the UBOS portfolio examples bucket. The most useful sections are:

Understanding the output metrics

Visualizing performance dashboards

UBOS automatically generates a Grafana‑style dashboard. Embed it in your internal wiki or share a read‑only link. Key widgets include:

• Heatmap of latency across tasks.
• Box‑plot of accuracy per model version.
• Cost trend over multiple runs.

Tips for analysis and reporting

When presenting results to stakeholders, follow these best‑practice bullets:

• Normalize latency by input length to avoid skew.
• Correlate cost spikes with GPU utilization peaks.
• Use the UBOS templates for quick start to generate a PDF executive summary.

6. Real‑World Example: Evaluating Claude‑3‑Opus

Below is a complete end‑to‑end walkthrough that evaluates Anthropic’s Claude‑3‑Opus on a mixed‑task suite.

Step 1 – Create the job file (YAML)

job_name: claude‑3‑opus‑full
agent:
  type: anthropic
  model: claude-3-opus
tasks:
  - reasoning
  - tool‑use
  - multi‑turn‑dialogue
environment: docker‑python‑3.11‑anthropic
resources:
  cpu: 8
  gpu: none
  memory_gb: 24
metrics:
  - success_rate
  - token_usage
  - latency_ms

Step 2 – Define the pipeline (JSON)

{
  "pipeline": {
    "name": "claude‑full‑suite",
    "steps": [
      {"name":"fetch-data","action":"chroma-db-fetch","params":{"collection":"claude‑tasks"}},
      {"name":"run-benchmark","action":"openclaw-run","job_file":"./claude-3-opus-full.yaml"},
      {"name":"store-results","action":"ubos-storage","params":{"bucket":"benchmark‑results"}}
    ]
  }
}

Step 3 – Launch from the CLI

ubos job upload --file claude-3-opus-full.yaml
ubos pipeline run --name claude-full-suite

Step 4 – Review the dashboard

After ~12 minutes, the dashboard shows a success_rate of 0.88, average latency of 112 ms, and a total cost of $0.45. The AI marketing agents team used these numbers to decide whether to promote Claude‑3‑Opus for their next campaign.

Screenshots (illustrative)

Below are placeholder images that would normally be captured from the UBOS UI.

7. Best Practices & Tips

• Optimize resource usage: Match GPU type to task complexity. For pure‑language tasks, CPU‑only runs save money.
• Handle failures gracefully: Enable the retry flag in your pipeline YAML. UBOS will automatically re‑queue failed steps up to three times.
• Version‑control job files: Store JSON/YAML in Git; tag releases so you can reproduce historic benchmarks.
• Stay current with AI agent hype: Subscribe to the About UBOS newsletter for quarterly updates on new model releases and benchmark suites.
• Leverage pre‑built templates: The AI SEO Analyzer template shows how to embed OpenClaw metrics into a marketing dashboard.
• Use voice feedback: Pair the ElevenLabs AI voice integration with your pipeline to get spoken alerts when a run exceeds cost thresholds.

8. Conclusion

OpenClaw combined with the UBOS platform gives developers a reproducible, scalable, and cost‑transparent way to benchmark AI agents—from the latest GPT‑4o to Claude‑3‑Opus. By defining a job, wiring a pipeline, executing the run, and interpreting the metrics, you turn hype into actionable insight.

Ready to start your own evaluation? Host your OpenClaw jobs on UBOS today and join the community of data‑driven AI engineers.

9. References

• OpenClaw official documentation – GitHub repository
• UBOS platform overview – UBOS platform overview
• Latest AI agent trend article – AI Agent Hype 2024
• Chroma DB integration guide – Chroma DB integration
• Telegram + ChatGPT workflow – ChatGPT and Telegram integration