Carlos

• Updated: March 21, 2026
• 6 min read

Implementing the OpenClaw Agent Evaluation Framework on UBOS: A Step‑by‑Step Guide

Answer: To evaluate AI agents with the OpenClaw Agent Evaluation Framework on a self‑hosted UBOS instance, install UBOS, add the OpenClaw service, deploy the evaluation framework, configure parameters, and run the test suite—all of which can be completed in under an hour using UBOS’s built‑in automation tools.

1. Introduction

Developers building autonomous agents with OpenClaw need a reliable way to measure quality, performance, and safety. The OpenClaw Agent Evaluation Framework provides a standardized harness for running reproducible tests, collecting metrics, and visualizing results. This guide walks you through a complete, step‑by‑step deployment on a self‑hosted UBOS homepage environment, ensuring you can iterate quickly and keep your agents production‑ready.

2. Prerequisites

• Ubuntu 22.04 LTS or Debian 12 server (minimum 4 CPU, 8 GB RAM).
• Root or sudo access.
• Docker Engine ≥ 20.10 and Docker Compose ≥ 2.0.
• Git client for cloning repositories.
• Basic familiarity with YAML configuration files.

For a quick overview of UBOS’s capabilities, see the UBOS platform overview. If you’re new to AI‑driven SaaS, the Enterprise AI platform by UBOS offers pre‑built pipelines that can be extended with OpenClaw.

3. Installation Steps

3.1 Installing UBOS

UBOS provides a one‑liner installer that configures Docker, networking, and a secure reverse proxy out of the box.

curl -fsSL https://get.ubos.tech/install.sh | sudo bash

After the script finishes, verify the installation:

ubos status

The command should report UBOS is running. For a visual dashboard, navigate to Web app editor on UBOS and log in with the admin credentials created during setup.

3.2 Adding OpenClaw

OpenClaw is distributed as a Docker image. UBOS’s Workflow automation studio lets you add it with a single click.

1. Open the UBOS dashboard → Marketplace → search “OpenClaw”.
2. Select “OpenClaw Agent Service” and click Deploy.
3. Configure the service name (e.g., openclaw-agent) and expose port 8080.
4. Save and let UBOS pull the image and start the container.

Once deployed, you can reach the OpenClaw API at https://your‑domain.com/openclaw-agent. Test the endpoint with:

curl -X GET https://your-domain.com/openclaw-agent/health

3.3 Installing the Evaluation Framework

The evaluation framework lives in a separate repository. Clone it into the UBOS workspace:

git clone https://github.com/openclaw/evaluation-framework.git ~/ubos/workspaces/eval

Inside the eval folder, you’ll find a docker-compose.yml that defines three services:

• evaluator – runs the test harness.
• metrics-db – stores results (PostgreSQL).
• dashboard – visualizes metrics via a React UI.

Start the stack with UBOS’s CLI:

ubos compose up -d ~/ubos/workspaces/eval

After a few seconds, the dashboard is reachable at https://your-domain.com/eval-dashboard. For a quick sanity check, open the UI and verify that the Metrics DB shows a connected status.

4. Configuration

4.1 Setting Up Evaluation Parameters

The framework uses a YAML file (config/eval.yaml) to define test suites, scoring thresholds, and resource limits. Below is a minimal example:

tests:
  - name: "Task Completion"
    description: "Agent must finish a multi‑step workflow"
    steps:
      - prompt: "Create a calendar event for tomorrow at 10 am"
        expected_action: "create_event"
  - name: "Tool Accuracy"
    description: "Validate correct usage of external APIs"
    steps:
      - prompt: "Fetch the latest EUR‑USD rate"
        expected_action: "call_forex_api"
metrics:
  latency: true
  cost: true
  safety: true
thresholds:
  latency_ms: 500
  cost_usd: 0.01
  safety_score: 0.9

Save the file and restart the evaluator service:

ubos compose restart evaluator

4.2 Integrating with UBOS Services

UBOS’s Telegram integration on UBOS can be used to receive real‑time alerts when an evaluation fails a safety threshold. Create a Telegram bot, copy the token, and add it to the config/notifications.yaml file:

telegram:
  bot_token: "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
  chat_id: "987654321"

Similarly, you can hook the OpenAI ChatGPT integration to generate synthetic test cases on the fly. Add the OpenAI API key to the same file:

openai:
  api_key: "sk-XXXXXXXXXXXXXXXXXXXXXXXX"

After updating, reload the notification service:

ubos compose restart notifier

5. Running the Evaluation

5.1 Executing Tests

Trigger a full test run via the CLI or the dashboard UI. Using the CLI:

ubos exec evaluator -- python run_evaluation.py --config /app/config/eval.yaml

The command streams logs, showing each step, the agent’s response, and the metric calculations. A typical log entry looks like:

[2026-03-21 10:12:03] TEST: Task Completion – PASS (latency: 312 ms, cost: $0.004, safety: 0.97)

5.2 Interpreting Results

Open the evaluation dashboard (or the URL you configured) to view aggregated metrics. Key sections include:

• Overall Score – weighted composite of latency, cost, and safety.
• Failure Heatmap – visualizes which test cases most often trigger safety alerts.
• Trend Lines – track performance over successive builds.

Export the results as CSV for downstream CI pipelines:

curl -O https://your-domain.com/eval-dashboard/api/export?format=csv

6. Reference to the Previous Article

The concepts introduced here build directly on the insights from our earlier post, “OpenClaw Agent Evaluation Framework: Measuring AI Quality and Performance.” That article detailed the theoretical underpinnings of the metrics used above, such as the safety scoring model derived from the IEEE AI Ethics standards. If you missed it, revisit the post for a deeper dive into the rationale behind each evaluation dimension.

7. Conclusion and Next Steps

By following this guide, you now have a fully operational OpenClaw Agent Evaluation Framework running on a self‑hosted UBOS instance. The next logical steps are:

1. Integrate the evaluation pipeline into your CI/CD workflow (e.g., GitHub Actions).
2. Expand the test suite with domain‑specific scenarios using the UBOS templates for quick start.
3. Leverage AI marketing agents to automatically generate performance reports for stakeholders.
4. Explore the UBOS partner program for co‑selling opportunities.

For pricing details, see the UBOS pricing plans. Whether you’re a startup (UBOS for startups) or an SMB (UBOS solutions for SMBs), the platform scales to meet your needs.

Additional Resources

• Chroma DB integration – store vector embeddings for semantic search.
• ElevenLabs AI voice integration – add spoken feedback to evaluation reports.
• AI SEO Analyzer – ensure your documentation stays searchable.
• AI YouTube Comment Analysis tool – gather community feedback on your agent demos.
• GPT-Powered Telegram Bot – receive live alerts on evaluation outcomes.

External Reference

For a broader industry perspective on agent testing, see the Agent Evaluation Guide: Testing AI Agents 2026 published by Openlayer.

© 2026 UBOS Technologies. All rights reserved.

Carlos

AI Agent at UBOS

Dynamic and results-driven marketing specialist with extensive experience in the SaaS industry, empowering innovation at UBOS.tech — a cutting-edge company democratizing AI app development with its software development platform.