If DeepResearch isn’t starting after submitting a query, begin by verifying basic setup and connectivity. First, confirm that all required dependencies, such as libraries or external services, are installed and running. For example, if DeepResearch relies on a database or API, ensure those services are accessible. Check configuration files for errors, such as incorrect API keys or invalid endpoints. A common issue could be a missing environment variable (e.g., API_KEY not set) or a typo in a configuration parameter. Use tools like curl or Postman to test API endpoints directly, which helps isolate whether the problem is with DeepResearch itself or an external service. If the service is local, verify it’s running on the correct port and hasn’t crashed due to resource constraints.
Next, review logs and error messages for actionable clues. Most applications log startup errors or failed requests, often including stack traces or HTTP status codes. For instance, if DeepResearch uses a Python framework like Flask or FastAPI, check the application logs for exceptions related to missing modules, authentication failures, or timeout errors. If the error mentions a dependency issue (e.g., ModuleNotFoundError), reinstall the correct package versions using a tool like pip freeze or requirements.txt. For network-related errors (e.g., ConnectionRefusedError), confirm firewalls or security groups aren’t blocking traffic. If the application uses asynchronous tasks (e.g., Celery), ensure workers are running and queues are properly configured. Logs might also reveal resource limits, such as memory exhaustion or database connection pools being maxed out.
Finally, test the system in a minimal environment to rule out environmental factors. Create a simplified test case—for example, a script that initializes DeepResearch with hardcoded input—to see if the issue persists. If it works, the problem likely lies in how the application integrates with your codebase, such as conflicting dependencies or incorrect initialization. For instance, if your project uses a virtual environment with outdated packages, update them. If the issue remains, debug step-by-step: add print statements or breakpoints to trace where the process halts. For web-based tools, use browser developer tools to inspect network requests and responses for errors like 500 Internal Server Error or 403 Forbidden. If all else fails, consult documentation or community forums for known issues, and consider reinstalling or updating DeepResearch to the latest stable version.