If DeepResearch isn't starting after submitting a query, follow these steps to diagnose and resolve the issue:
1. Verify Basic Functionality and Client-Side Errors First, confirm whether the issue is consistent across different queries or specific to one input. Check the browser console (using F12 or browser dev tools) for JavaScript errors, network request failures, or CORS issues. For example, a malformed API request or an expired authentication token might prevent the query from reaching the server. If the UI doesn’t respond, ensure the submission button isn’t disabled due to validation errors (e.g., empty input fields). Test with a simple query like "test" to rule out input-specific problems. If the network tab shows a failed HTTP request (e.g., 500 error), note the response details for further investigation.
2. Inspect Server Logs and Backend Health
If client-side checks pass, examine server logs for errors. For instance, a database connection timeout, missing environment variables (e.g., API keys), or unhandled exceptions in the research pipeline could halt processing. If DeepResearch relies on third-party services (e.g., LLM APIs or data sources), verify their status and rate limits. Use tools like curl or Postman to manually send a query to the backend API endpoint—this helps isolate whether the issue is in the frontend integration or backend logic. For example, if the manual request returns a "503 Service Unavailable" error, check if critical microservices (e.g., a task queue like Celery) are running.
3. Review Dependencies and Environment
Ensure all dependencies are up to date and compatible. A version mismatch in libraries (e.g., Python packages like requests or machine learning frameworks) might break the research initialization. For containerized setups, rebuild the Docker image to address missing files or misconfigured paths. Check disk space and memory usage—if the server is out of resources, it might fail to spawn new research tasks. Temporarily enable debug logging in the application to trace the exact step where the process stalls (e.g., logging Starting research phase... or Calling API X). If the issue persists, roll back recent code changes or deploy to a staging environment to test fixes safely.
By systematically isolating the problem (client, network, server, dependencies), you’ll identify the root cause efficiently. For example, a common fix might involve renewing an API key, adjusting timeout settings, or patching a dependency conflict.