Media Generation (Optional)
The tool server can call Google AI Studio / Vertex AI models to generate images and videos. When these variables are unset the agents fall back to DuckDuckGo image search (for images) and skip video generation entirely.
Image Generation Variables
| Variable | Description |
|---|---|
IMAGE_GENERATE_GCP_PROJECT_ID | Google Cloud project that owns the Vertex or GenAI resources for image generation. |
IMAGE_GENERATE_GCP_LOCATION | Region where the model is deployed (e.g., us-central1). |
IMAGE_GENERATE_GCS_OUTPUT_BUCKET | Bucket where generated assets are written before they are handed back to the agent. |
IMAGE_GENERATE_GOOGLE_AI_STUDIO_API_KEY | API key issued by Google AI Studio that authorizes direct calls to the image model. |
Video Generation Variables
| Variable | Description |
|---|---|
VIDEO_GENERATE_GCP_PROJECT_ID | Project ID that hosts your video-generation pipelines. |
VIDEO_GENERATE_GCP_LOCATION | Region for the video model (us-central1 works for most deployments). |
VIDEO_GENERATE_GCS_OUTPUT_BUCKET | Bucket for intermediate/exported video files. |
VIDEO_GENERATE_GOOGLE_AI_STUDIO_API_KEY | Google AI Studio key that has access to video models. |
Setup Checklist
- Enable the Vertex AI API inside the specified project(s) and grant the service account referenced by
GOOGLE_APPLICATION_CREDENTIALSpermission to use the models and write to the buckets. - Create the GCS buckets listed above (enable uniform bucket-level access and versioning if you need stronger controls).
- Generate an API key inside Google AI Studio that has the correct project/region scope and paste it into the relevant variable(s).
- Restart the tool server after updating the environment file; verify generation by asking the agent for a new image/video and checking the bucket for outputs.