How to Build an Automated Research Assistant with Paradime and OpenClaw

Running a daily research workflow shouldn't require you to babysit a script. It should read a topic from a spreadsheet, search the web, compile findings, and drop a structured document in your Google Drive — all while you sleep. This guide walks you through building exactly that: a Paradime OpenClaw research assistant that uses OpenClaw's agent capabilities, Google Workspace APIs, and Paradime Bolt's scheduling to automate topic research on a cron schedule.

The approach here is incident-friendly: structured steps, a decision-tree mindset, and a focus on reproducibility. If something breaks at 3 AM, you should be able to find the first clue in under 60 seconds and apply a minimal fix.

What Is Paradime?

Paradime is an all-in-one AI platform that replaces dbt Cloud™. It lets data teams code, ship, fix, and scale data pipelines for analytics and AI from a single workspace. The platform is built around three core products:

• Code IDE — An AI-native IDE for dbt™ and Python development, powered by DinoAI. It cuts rote analytics engineering work by 83%+.

• Bolt — A production orchestration engine for scheduling dbt™ jobs, Python scripts, and CI/CD pipelines. It supports cron-based scheduling, SLA alerts, and AI-powered debugging of failed runs.

• Radar — A FinOps module for monitoring and reducing Snowflake and BigQuery warehouse costs.

For this guide, the key product is Bolt. It lets you schedule arbitrary Python scripts alongside dbt™ commands, manage environment variables (including secrets like API keys), and get notified via Slack or email when something fails.

Paradime is SOC 2 Type II certified, GDPR and CCPA compliant, and offers 99.9% uptime SLA — important when your research pipeline needs to run reliably every day.

What Is OpenClaw?

OpenClaw is an open-source AI agent framework that runs locally on your hardware and orchestrates tasks across chat apps, files, the web, and your operating system. It is not an LLM itself — instead, it connects to models like Claude or GPT via API and uses skills and tools to act on your behalf.

Key capabilities relevant to this project:

OpenClaw's Python SDK provides a clean interface for creating agents and running tasks:

Architecture Overview

Before diving into setup, here's how the pieces fit together:

Figure 1: End-to-end flow of the Paradime OpenClaw research assistant. Each run processes one topic.

Setup: OpenClaw SDK + Web Search + Google Sheets API + Google Docs API

Step 1: Install the OpenClaw Python SDK

The OpenClaw Python SDK requires Python 3.9+. Install it with pip:

Verify the installation:

Step 2: Configure Web Search

OpenClaw's web_search tool auto-detects your search provider in this order: Brave → Gemini → Perplexity → Grok. You need at least one API key:

For deeper content extraction from search results, configure Firecrawl as a fallback for web_fetch:

Step 3: Set Up Google Workspace Access (gog Skill)

OpenClaw uses the gog CLI skill for Google Sheets and Google Docs access. Set it up once:

Tip: Set GOG_ACCOUNT=you@gmail.com as an environment variable to avoid passing --account on every command.

Google Sheets Commands You'll Use

Google Docs Commands You'll Use

Step 4: Set Up Environment Variables

Create a .env file (add it to .gitignore):

The Script: Read → Research → Compile

Here's the core Python script that ties everything together. It reads a topic from Google Sheets, runs an OpenClaw research agent, and writes findings to a Google Doc.

Project Structure

pyproject.toml

research_assistant.py

Decision Tree: What Happens on Each Run

Figure 2: Decision tree for each daily run. The script processes exactly one topic per execution, making failures isolated and easy to diagnose.

Environment Variables Reference

These are the environment variables your script needs, configured in Paradime Bolt:

Setting Env Vars in Paradime Bolt

1. Navigate to Settings → Workspaces → Environment Variables

2. In the Bolt Schedules section, click Add New

3. Enter the Key (e.g., OPENCLAW_API_KEY) and Value

4. Click the Save icon

For bulk configuration, use CSV upload with headers Key,Value:

Access these in your Python script:

Bolt Schedule: Cron Daily (1 Topic per Run)

Option A: Schedules as Code (Recommended)

Create a paradime_schedules.yml file in your repository root:

Key detail: The first command must be poetry install. This installs dependencies and creates the virtual environment before your script runs. Paradime Bolt uses Poetry for Python dependency management.

Option B: UI-Based Schedule

1. Navigate to Bolt in the Paradime app

2. Click Create Schedule

3. Configure:

4. Add notification destinations (email and/or Slack)

5. Click Deploy

Why 1 Topic per Run?

This is deliberate:

• Failure isolation — If OpenClaw's web search fails on one topic, it doesn't block the rest. Tomorrow's run picks up the next topic.

• Reproducibility — Each run has a single input (one topic) and a single output (one doc section). Easy to re-run on failure.

• Cost control — OpenClaw API calls cost money. Processing one topic at a time gives you a predictable daily cost.

• SLA clarity — A 30-minute SLA on "research one topic" is meaningful. A 30-minute SLA on "research 20 topics" is not.

Monitoring and Debugging

The "Time to First Clue" Mindset

When a research run fails at 3 AM, you need to find the root cause in under 60 seconds. Paradime Bolt gives you three layers of logs:

Figure 3: The three-layer debugging approach. Most issues are resolved at the Summary Log level.

Setting Up Notifications

Configure alerts so you learn about failures from Slack, not from a stakeholder:

Notification events you should enable:

• Failed — The run exited with a non-zero code (script error, API failure)

• SLA — The run exceeded your SLA threshold (e.g., 30 minutes). This catches hangs and slow API responses.

YAML configuration:

Monitoring the Spreadsheet

Beyond Bolt's built-in monitoring, add a simple health check to your script:

Troubleshooting Common Issues

Decision Tree: Diagnosing Failures

Figure 4: Troubleshooting decision tree. Start at Summary Logs and work your way down to Debug Logs only if needed.

Issue 1: KeyError: 'OPENCLAW_API_KEY'

Cause: Environment variable not set in Bolt.

Fix (minimal):

1. Go to Settings → Workspaces → Environment Variables

2. Add OPENCLAW_API_KEY with your key value

3. Re-trigger the schedule from Bolt UI

Verify: Check Console Logs for the os.environ call that failed.

Issue 2: gog Command Not Found

Cause: The gog CLI isn't installed in the Bolt runtime environment.

Fix: Add the gog installation to your pyproject.toml dependencies or use the Google API Python client directly instead of the CLI:

Issue 3: Web Search Returns Empty Results

Cause: Brave API key exhausted or rate-limited.

Fix:

1. Check your Brave API usage dashboard

2. If rate-limited, add a fallback provider:

3. OpenClaw auto-detects providers in order: Brave → Gemini → Perplexity

Issue 4: Schedule Runs but Produces No Output

Cause: All topics in the spreadsheet are already marked as "completed."

Fix: Add new topics to the spreadsheet, or check the Status column for unexpected values.

Prevention: Add the health check function to your script so the Console Logs always show remaining topic count.

Issue 5: SLA Breach (Run Takes Too Long)

Cause: OpenClaw agent is fetching too many pages or hitting slow websites.

Fix (minimal):

1. Limit the number of URLs fetched per topic:

2. Add a timeout to the agent run:

3. Increase the SLA threshold in your schedule if the research genuinely needs more time.

Issue 6: PARA-1000 — Missing Production Warehouse Connection

Cause: Bolt requires a production warehouse connection even for Python-only scripts.

Fix: Go to Account Settings → Connections and add/activate a production warehouse connection.

Issue 7: PARA-1003 — Could Not Read from Remote Repository

Cause: GitHub API outage or rate limiting.

Fix:

1. Check GitHub Status for incidents

2. If intermittent, manually trigger the schedule from the Bolt UI once the issue clears

Paradime Error Reference

Putting It All Together: Before and After

Figure 5: Manual vs. automated research workflow. The automated version runs unattended and processes one topic per day.

Wrapping Up

You've now built a Paradime OpenClaw research assistant that:

1. Reads research topics from a Google Spreadsheet using the gog skill

2. Researches each topic via OpenClaw's web search and content extraction tools

3. Compiles findings into a structured Google Doc

4. Runs daily on a Paradime Bolt cron schedule (0 8 * * *)

5. Alerts you via Slack or email if anything fails or breaches SLA

The design principles that make this maintainable:

• One topic per run — Failures are isolated. Re-running is trivial.

• Three-layer logging — Summary → Console → Debug. Most issues resolved at layer one.

• Minimal fix philosophy — Each troubleshooting entry tells you the smallest change that resolves the issue.

• Reproducibility — Same input (topic from spreadsheet) always produces the same workflow. No hidden state.

Next Steps

• Add more search providers — Configure Perplexity or Gemini as fallbacks for deeper research

• Expand the output — Use the Google Docs API to create formatted documents with headings, tables, and citations

• Scale to multiple topics — Adjust the cron to run multiple times daily or process a batch

• Add Radar monitoring — Use Paradime Radar to track warehouse costs if your pipeline includes dbt™ transformations downstream

Useful Links

• Paradime Documentation

• Paradime Bolt Schedules

• Paradime Bolt Schedules as Code

• Paradime Environment Variables

• Paradime Error List

• OpenClaw Documentation

• OpenClaw Python SDK

• OpenClaw Web Search Tools

• OpenClaw gog Skill (Google Workspace)

• Cron Expression Builder