open-webuiComplete Open WebUI API integration for managing LLM models, chat completions, Ollama proxy operations, file uploads, knowledge bases (RAG), image generation, audio processing, and pipelines. Use this skill when interacting with Open WebUI instances via REST API - listing models, chatting with LLMs, uploading files for RAG, managing knowledge collections, or executing Ollama commands through the Open WebUI proxy. Requires OPENWEBUI_URL and OPENWEBUI_TOKEN environment variables or explicit parameters.
Install via ClawdBot CLI:
clawdbot install 0x7466/open-webuiComplete API integration for Open WebUI - a unified interface for LLMs including Ollama, OpenAI, and other providers.
Activate this skill when the user wants to:
Do NOT activate for:
export OPENWEBUI_URL="http://localhost:3000" # Your Open WebUI instance URL
export OPENWEBUI_TOKEN="your-api-key-here" # From Settings > Account in Open WebUIExample requests that SHOULD activate this skill:
Example requests that should NOT activate this skill:
OPENWEBUI_URL and OPENWEBUI_TOKEN are setUse the CLI tool or direct API calls:
# Using the CLI tool (recommended)
python3 scripts/openwebui-cli.py --help
python3 scripts/openwebui-cli.py models list
python3 scripts/openwebui-cli.py chat --model llama3.2 --message "Hello"
# Using curl (alternative)
curl -H "Authorization: Bearer $OPENWEBUI_TOKEN" \
"$OPENWEBUI_URL/api/models"| Endpoint | Method | Description |
|----------|--------|-------------|
| /api/chat/completions | POST | OpenAI-compatible chat completions |
| /api/models | GET | List all available models |
| /ollama/api/chat | POST | Native Ollama chat completion |
| /ollama/api/generate | POST | Ollama text generation |
| Endpoint | Method | Description |
|----------|--------|-------------|
| /ollama/api/tags | GET | List Ollama models |
| /ollama/api/pull | POST | Pull/download a model |
| /ollama/api/delete | DELETE | Delete a model |
| /ollama/api/embed | POST | Generate embeddings |
| /ollama/api/ps | GET | List loaded models |
| Endpoint | Method | Description |
|----------|--------|-------------|
| /api/v1/files/ | POST | Upload file for RAG |
| /api/v1/files/{id}/process/status | GET | Check file processing status |
| /api/v1/knowledge/ | GET/POST | List/create knowledge collections |
| /api/v1/knowledge/{id}/file/add | POST | Add file to knowledge base |
| Endpoint | Method | Description |
|----------|--------|-------------|
| /api/v1/images/generations | POST | Generate images |
| /api/v1/audio/speech | POST | Text-to-speech |
| /api/v1/audio/transcriptions | POST | Speech-to-text |
Always confirm before:
DELETE /ollama/api/delete) - Irreversiblesk-...XXXX formatpython3 scripts/openwebui-cli.py models listpython3 scripts/openwebui-cli.py chat \
--model llama3.2 \
--message "Explain the benefits of RAG" \
--streampython3 scripts/openwebui-cli.py files upload \
--file /path/to/document.pdf \
--processpython3 scripts/openwebui-cli.py knowledge add-file \
--collection-id "research-papers" \
--file-id "doc-123-uuid"python3 scripts/openwebui-cli.py ollama embed \
--model nomic-embed-text \
--input "Open WebUI is great for LLM management"python3 scripts/openwebui-cli.py ollama pull \
--model llama3.2:70b
# Agent must confirm: "This will download ~40GB. Proceed? [y/N]"python3 scripts/openwebui-cli.py ollama status| Error | Cause | Solution |
|-------|-------|----------|
| 401 Unauthorized | Invalid or missing token | Verify OPENWEBUI_TOKEN |
| 404 Not Found | Model/endpoint doesn't exist | Check model name spelling |
| 422 Validation Error | Invalid parameters | Check request body format |
| 400 Bad Request | File still processing | Wait for processing completion |
| Connection refused | Wrong URL | Verify OPENWEBUI_URL |
Files uploaded for RAG are processed asynchronously. Before adding to knowledge:
/api/v1/files/{id}/process/status until status: "completed"Pulling models (e.g., 70B parameters) can take hours. Always:
Chat completions support streaming. Use --stream flag for real-time output or collect full response for non-streaming.
The included CLI tool (scripts/openwebui-cli.py) provides:
Run python3 scripts/openwebui-cli.py --help for full usage.
Generated Mar 1, 2026
Researchers use the skill to upload academic papers to a knowledge base and query them via RAG-enabled chat completions, enabling quick literature reviews and data extraction. It supports multiple LLM models through Open WebUI for summarizing and analyzing research content efficiently.
Businesses integrate the skill to manage knowledge collections with support documents, allowing AI agents to provide accurate, context-aware responses via chat completions. It leverages Ollama proxy for fast embeddings and model management to handle high-volume inquiries.
Content creators utilize the skill for image generation and audio processing through Open WebUI APIs, automating tasks like generating visuals for marketing or transcribing audio files. It supports pipelines for streamlined workflows in media projects.
IT teams employ the skill to list, pull, and delete LLM models via Ollama proxy endpoints, ensuring efficient resource management in development environments. It includes status checks and model loading for maintaining AI infrastructure.
Offer a subscription-based service that integrates Open WebUI skill into existing business platforms, providing API management, custom knowledge bases, and support for multiple LLM providers. Revenue comes from monthly fees and premium features like advanced RAG.
Provide consulting services to help organizations deploy and customize the skill for specific use cases, such as setting up RAG systems or optimizing chat completions. Revenue is generated through project-based contracts and ongoing maintenance.
Sell training programs and support packages to educate users on leveraging the skill for tasks like file uploads, model management, and pipeline creation. Revenue streams include one-time training sessions and annual support subscriptions.
💬 Integration Tip
Ensure OPENWEBUI_URL and OPENWEBUI_TOKEN are properly configured as environment variables for seamless authentication, and test connection with basic endpoints like /api/models before complex operations.
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