To troubleshoot when DeepResearch fails or returns an error, start by analyzing the error message and input parameters. Most errors originate from invalid inputs, misconfigured settings, or resource limitations. For example, if the tool expects a specific data format (e.g., JSON with required fields like query or date_range) and receives malformed input, it may throw a validation error. Check the API documentation or configuration files to ensure all parameters align with requirements. Additionally, review logs or error details provided by DeepResearch—these often include codes (e.g., HTTP 400 for bad requests) or descriptions like “missing required field” to pinpoint the issue. If logs aren’t accessible, test inputs incrementally to isolate the problem. For instance, if a report fails when using a complex query, simplify the query to see if the issue persists.
Next, verify system dependencies and resource availability. DeepResearch might rely on external services, databases, or libraries. A common issue is connectivity failure—for example, if the tool cannot reach a required API endpoint due to network restrictions or authentication issues. Test network access by pinging the service or using tools like curl to confirm connectivity. Resource constraints, such as insufficient memory or processing power, can also cause failures. If DeepResearch processes large datasets, check system metrics (e.g., CPU usage, memory consumption) during execution. For example, a timeout error might occur if the system runs out of memory while handling a 10GB dataset. Scaling down the input size or optimizing queries (e.g., filtering data before processing) can resolve this. If dependencies are outdated, ensure libraries or APIs are updated to compatible versions—a version mismatch in a data parsing library could break report generation.
Finally, if the issue remains unresolved, replicate the problem in a controlled environment. Create a minimal reproducible example (e.g., a script with the exact input and configuration) to rule out environmental factors. For instance, if the report works locally but fails on a server, compare software versions, OS differences, or file permissions. Enable debug mode in DeepResearch, if available, to capture granular logs. If the tool is open-source, review the codebase for known issues related to your use case. For example, a GitHub repository might have open tickets for specific query types causing crashes. If no solution is found, provide the development team with detailed context: error logs, input samples, environment details (OS, runtime versions), and steps to reproduce. This accelerates their ability to diagnose and fix the problem. For instance, sharing a code snippet that triggers a “database connection pool exhausted” error helps them identify resource leaks or scaling limitations.