To troubleshoot DeepResearch errors or failures, start by examining the error logs and validating inputs, then verify dependencies and system resources. Each step isolates potential causes and provides actionable fixes.
First, review error logs and messages. DeepResearch typically provides logs detailing where the failure occurred. Look for specific error codes (e.g., HTTP 404 for missing resources) or messages indicating invalid parameters, permission issues, or data unavailability. For example, an authentication error might suggest expired API keys or incorrect credentials. If logs are vague, enable debug mode to capture granular details about the request lifecycle. For instance, a timeout error during data fetching could indicate network issues or an overloaded external API. Structured logging tools like Splunk or Grafana can help filter and pinpoint issues faster.
Next, validate input parameters and data sources. Ensure required fields (e.g., date ranges, query filters) are correctly formatted and within expected ranges. For example, a date formatted as YYYY-MM-DD might fail if submitted as DD/MM/YYYY. Test with minimal inputs to isolate the problem: if a report fails when including a specific dataset, that dataset might contain malformed or unsupported data types. Verify external data sources (e.g., APIs, databases) are accessible and returning valid responses. Use tools like curl or Postman to test API endpoints directly. If DeepResearch relies on user-provided code (e.g., custom data processors), lint or test that code separately to rule out syntax errors.
Finally, check dependencies and system resources. Ensure all libraries, APIs, or services DeepResearch depends on are up-to-date and compatible. For example, a Python-based tool might fail if a required package version changed. Confirm environment variables (e.g., API URLs, secrets) are correctly configured. Resource constraints like insufficient memory or CPU can also cause failures, especially with large datasets. Monitor resource usage during execution using tools like htop or docker stats. If the failure occurs in a cloud environment, check service quotas (e.g., AWS Lambda timeout limits). Replicating the issue in a isolated environment (e.g., a Docker container) can help rule out conflicts with other services.