MetaGPT

FileNotFoundError usually arises when the program attempts to access a file or directory that doesn't exist at the specified path. To fix it, verify that the file path is correct and the file actually exists at that location, paying attention to case sensitivity and relative vs. absolute paths. If the file should be created dynamically, ensure that the creation logic is executed before attempting to access it, and handle potential creation failures gracefully using try-except blocks.

The "ModuleNotFoundError" typically arises when a required Python package hasn't been installed or the Python environment isn't activated. To fix it, install the missing package using `pip install <package_name>` (e.g., `pip install semantic-kernel sparkai`) and ensure you're running your script within the correct Python environment where the package is installed (e.g., activate your venv). If using a virtual environment, activate it before running the script.

The "NotFoundError" in MetaGPT, often stemming from the OpenAI API, indicates that the requested resource (e.g., model, deployment) doesn't exist or the API endpoint is incorrect. Verify that the specified `model` in your configuration (`config.yaml`) is a valid OpenAI model and that your Azure OpenAI deployment name (if applicable) is correctly configured. Double-check your API endpoint and API key for accuracy in `config.yaml` and ensure your resource is properly provisioned on the Azure OpenAI Service or OpenAI platform.

The "BadRequestError" often arises when using `max_tokens` with models that require `max_completion_tokens` or don't support `max_tokens` directly. To fix, replace `max_tokens` with `max_completion_tokens` in your OpenAI API call if the model supports it. If neither parameter works, adjust your parameters to match the specific model's documentation or remove the parameter and rely on default token limits.

BlockedPromptException usually arises when the language model flags your prompt or generated content as potentially harmful or violating its safety guidelines. To resolve this, rephrase your prompt to be less ambiguous, remove potentially sensitive keywords, and ensure it clearly aligns with ethical and safe AI practices; also, consider adjusting the model's safety settings (if available) to a less restrictive level if appropriate for your specific use case and risk tolerance.

RetryError in MetaGPT often arises from transient issues during API calls, such as rate limits or network instability, causing requests to fail and be retried. To fix this, implement robust error handling with exponential backoff retries using tenacity or similar libraries; also, verify API key validity and ensure the LLM server or service has sufficient capacity or is not experiencing outages.