Open WebUI Tools Collection

🚀 A modular collection of tools, function pipes, and filters to supercharge your Open WebUI experience.

Transform your Open WebUI instance into a powerful AI workstation with this comprehensive toolkit. From academic research and image generation to music creation and autonomous agents, this collection provides everything you need to extend your AI capabilities.

✨ What's Inside

This repository contains 15+ specialized tools and functions designed to enhance your Open WebUI experience:

🛠️ Tools

arXiv Search - Academic paper discovery (no API key required!)
Perplexica Search - Web search using Perplexica API with citations
Pexels Media Search - High-quality photos and videos from Pexels API
Native Image Generator - Direct Open WebUI image generation with Ollama model management
Hugging Face Image Generator - AI-powered image creation
ComfyUI ACE Step Audio - Advanced music generation
Flux Kontext ComfyUI - Professional image editing

🔄 Function Pipes

Planner Agent v2 - Autonomous task execution with tool support
arXiv Research MCTS - Advanced research with Monte Carlo Tree Search
Multi Model Conversations - Multi-agent discussions
Resume Analyzer - Professional resume analysis
Mopidy Music Controller - Music server management
Letta Agent - Autonomous agent integration
MCP Pipe - Model Context Protocol integration

🔧 Filters

Prompt Enhancer - Automatic prompt improvement
Semantic Router - Intelligent model selection
Full Document - File processing capabilities
Clean Thinking Tags - Conversation cleanup

🚀 Quick Start

Option 1: Open WebUI Hub (Recommended)

Visit https://openwebui.com/u/haervwe
Browse the collection and click "Get" for desired tools
Follow the installation prompts in your Open WebUI instance

Option 2: Manual Installation

Copy .py files from tools/, functions/, or filters/ directories
Navigate to Open WebUI Workspace > Tools/Functions/Filters
Paste the code, provide a name and description, then save

🎯 Key Features

🔌 Plug-and-Play: Most tools work out of the box with minimal configuration
🎨 Visual Integration: Seamless integration with ComfyUI workflows
🤖 AI-Powered: Advanced features like MCTS research and autonomous planning
📚 Academic Focus: arXiv integration for research and academic work
🎵 Creative Tools: Music generation and image editing capabilities
🔍 Smart Routing: Intelligent model selection and conversation management
📄 Document Processing: Full document analysis and resume processing

📋 Prerequisites

Open WebUI: Version 0.6.0+ recommended
Python: 3.8 or higher
Optional Dependencies:
- ComfyUI (for image/music generation tools)
- Mopidy (for music controller)
- Various API keys (Hugging Face, Tavily, etc.)

🔧 Configuration

Most tools are designed to work with minimal configuration. Key configuration areas:

API Keys: Required for some tools (Hugging Face, Tavily, etc.)
ComfyUI Integration: For image and music generation tools
Model Selection: Choose appropriate models for your use case
Filter Setup: Enable filters in your model configuration

📖 Detailed Documentation

arXiv Search Tool
Perplexica Search Tool
Pexels Media Search Tool
Native Image Generator
Hugging Face Image Generator
ComfyUI ACE Step Audio Tool
Flux Kontext ComfyUI Pipe
Planner Agent v2
arXiv Research MCTS Pipe
Multi Model Conversations Pipe
Resume Analyzer Pipe
Mopidy Music Controller
Letta Agent Pipe
MCP Pipe
Prompt Enhancer Filter
Semantic Router Filter
Full Document Filter
Clean Thinking Tags Filter
Using the Provided ComfyUI Workflows
Installation
Contributing
License
Credits
Support

🧪 Tools

arXiv Search Tool

Description

Search arXiv.org for relevant academic papers on any topic. No API key required!

Configuration

No configuration required. Works out of the box.

Usage

Example:

Search for recent papers about "tree of thought"

Returns up to 5 most relevant papers, sorted by most recent.

Example arXiv search result in Open WebUI

Perplexica Search Tool

Description

Search the web for factual information, current events, or specific topics using the Perplexica API. This tool provides comprehensive search results with citations and sources, making it ideal for research and information gathering. Perplexica is an open-source AI-powered search engine and alternative to Perplexity AI that must be self-hosted locally. It uses advanced language models to provide accurate, contextual answers with proper source attribution.

Configuration

BASE_URL (str): Base URL for the Perplexica API (default: http://host.docker.internal:3001)
OPTIMIZATION_MODE (str): Search optimization mode - "speed" or "balanced" (default: balanced)
CHAT_MODEL (str): Default chat model for search processing (default: llama3.1:latest)
EMBEDDING_MODEL (str): Default embedding model for search (default: bge-m3:latest)
OLLAMA_BASE_URL (str): Base URL for Ollama API (default: http://host.docker.internal:11434)

Prerequisites: You must have Perplexica installed and running locally at the configured URL. Perplexica is a self-hosted open-source search engine that requires Ollama with the specified chat and embedding models. Follow the installation instructions in the Perplexica repository to set up your local instance.

Usage

Example:

Search for "latest developments in AI safety research 2024"

Returns comprehensive search results with proper citations
Automatically emits citations for source tracking in Open WebUI
Provides both summary and individual source links

Features

Web Search Integration: Direct access to current web information
Citation Support: Automatic citation generation for Open WebUI
Model Flexibility: Configurable chat and embedding models
Real-time Status: Progress updates during search execution
Source Tracking: Individual source citations with metadata

Pexels Media Search Tool

Description

Search and retrieve high-quality photos and videos from the Pexels API. This tool provides access to Pexels' extensive collection of free stock photos and videos, with comprehensive search capabilities, automatic citation generation, and direct image display in chat. Perfect for finding professional-quality media for presentations, content creation, or creative projects.

Configuration

PEXELS_API_KEY (str): Free Pexels API key from https://www.pexels.com/api/ (required)
DEFAULT_PER_PAGE (int): Default number of results per search (default: 5, recommended for LLMs)
MAX_RESULTS_PER_PAGE (int): Maximum allowed results per page (default: 15, prevents overwhelming LLMs)
DEFAULT_ORIENTATION (str): Default photo orientation - "all", "landscape", "portrait", or "square" (default: "all")
DEFAULT_SIZE (str): Default minimum photo size - "all", "large" (24MP), "medium" (12MP), or "small" (4MP) (default: "all")

Prerequisites: Get a free API key from Pexels API and configure it in the tool's Valves settings.

Usage

Photo Search Example:

Search for photos of "modern office workspace"

Video Search Example:

Search for videos of "ocean waves at sunset"

Curated Photos Example:
```
Get curated photos from Pexels
```

Features

Three Search Functions: search_photos, search_videos, and get_curated_photos
Direct Image Display: Images are automatically formatted with markdown for immediate display in chat
Advanced Filtering: Filter by orientation, size, color, and quality
Attribution Support: Automatic citation generation with photographer credits
Rate Limit Handling: Built-in error handling for API limits and invalid keys
LLM Optimized: Results are limited and formatted to prevent overwhelming language models
Real-time Status: Progress updates during search execution

Native Image Generator

Description

Generate images using Open WebUI's native image generation middleware configured in admin settings. This tool leverages whatever image generation backend you have configured (such as AUTOMATIC1111, ComfyUI, or OpenAI DALL-E) through Open WebUI's built-in image generation system, with optional Ollama model management to free up VRAM when needed.

Configuration

unload_ollama_models (bool): Whether to unload all Ollama models from VRAM before generating images (default: False)
ollama_url (str): Ollama API URL for model management (default: http://host.docker.internal:11434)

Prerequisites: You must have image generation configured in Open WebUI's admin settings under Settings > Images. This tool works with any image generation backend you have set up (AUTOMATIC1111, ComfyUI, OpenAI, etc.).

Usage

Example:

Generate an image of "a serene mountain landscape at sunset"

Uses whatever image generation backend is configured in Open WebUI admin settings
Automatically manages model resources if Ollama unloading is enabled
Returns markdown-formatted image links for immediate display

Features

Native Integration: Uses Open WebUI's native image generation middleware without external dependencies
Backend Agnostic: Works with any image generation backend configured in admin settings (AUTOMATIC1111, ComfyUI, OpenAI, etc.)
Memory Management: Optional Ollama model unloading to optimize VRAM usage
Flexible Model Support: You can prompt de agent to change the image generation model, providing the name is given to it.
Real-time Status: Provides generation progress updates via event emitter
Error Handling: Comprehensive error reporting and recovery

Hugging Face Image Generator

Description

Generate high-quality images from text descriptions using Hugging Face's Stable Diffusion models.

Configuration

API Key (Required): Obtain a Hugging Face API key from your HuggingFace account and set it in the tool's configuration in Open WebUI.
API URL (Optional): Uses Stability AI's SD 3.5 Turbo model as default. Can be customized to use other HF text-to-image model endpoints.

Usage

Example:

Create an image of "beautiful horse running free"

Multiple image format options: Square, Landscape, Portrait, etc.

Example image generated with Hugging Face tool

ComfyUI ACE Step Audio Tool

Description

Generate music using the ACE Step AI model via ComfyUI. This tool lets you create songs from tags and lyrics, with full control over the workflow JSON and node numbers. Designed for advanced music generation and can be customized for different genres and moods.

Configuration

comfyui_api_url (str): ComfyUI API endpoint (e.g., http://localhost:8188)
model_name (str): Model checkpoint to use (default: ACE_STEP/ace_step_v1_3.5b.safetensors)
workflow_json (str): Full ACE Step workflow JSON as a string. Use {tags}, {lyrics}, and {model_name} as placeholders.
tags_node (str): Node number for the tags input (default: "14")
lyrics_node (str): Node number for the lyrics input (default: "14")
model_node (str): Node number for the model checkpoint input (default: "40")

Usage

Import the ACE Step workflow:
- In ComfyUI, go to the workflow import section and load extras/ace_step_api.json.
- Adjust nodes as needed for your setup.
Configure the tool in Open WebUI:
- Set the comfyui_api_url to your ComfyUI backend.
- Paste the workflow JSON (from the file or your own) into workflow_json.
- Set the correct node numbers if you modified the workflow.
Generate music:
- Provide tags and (optionally) lyrics.
- The tool will return a link to the generated audio file.

Example:

Generate a song in the style of "funk, pop, soul" with the following lyrics: "In the shadows where secrets hide..."

Returns a link to the generated audio or a status message. Advanced users can fully customize the workflow for different genres, moods, or creative experiments.

🔄 Function Pipes

Flux Kontext ComfyUI Pipe

Description

Connects Open WebUI to the Flux Kontext image-to-image editing model via ComfyUI. This pipe enables advanced image editing, style transfer, and creative transformations using the Flux Kontext workflow.

Configuration

ComfyUI_Address (str): Address of the running ComfyUI server (default: http://127.0.0.1:8188)
ComfyUI_Workflow_JSON (str): The entire ComfyUI workflow in JSON format (default provided, or use extras/flux_context_owui_api_v1.json)
Prompt_Node_ID (str): Node ID for the text prompt (default: "6")
Image_Node_ID (str): Node ID for the input image (default: "196")
Seed_Node_ID (str): Node ID for the sampler (default: "194")
enhance_prompt (bool): Use a vision model to enhance the prompt based on the input image (default: False).
vision_model_id (str): The model ID to use for vision-based prompt enhancement (required if enhance_prompt is enabled).
enhancer_system_prompt (str): System prompt used to guide the vision model when enhancing the prompt. This allows you to customize the instructions given to the vision-language model for prompt engineering. By default, it provides detailed instructions for visual prompt enhancement, but you can modify it to fit your workflow or style.
unload_ollama_models (bool): Unload all Ollama models from VRAM before running (default: False)
ollama_url (str): Ollama API URL for unloading models (default: http://host.docker.internal:11434)
max_wait_time (int): Max wait time for generation in seconds (default: 1200)

Usage

Import the Flux Kontext workflow:
- In ComfyUI, import extras/flux_context_owui_api_v1.json as a workflow.
- Adjust node IDs if you modify the workflow.
Configure the pipe in Open WebUI:
- Set the ComfyUI_Address to your ComfyUI backend.
- Paste the workflow JSON into ComfyUI_Workflow_JSON.
- Set the correct node IDs for prompt, image, and sampler.
Edit images:
- Provide a prompt and an input image.
- (Optional) Enable enhance_prompt and specify a vision_model_id to automatically improve your prompt using a vision-language model and the input image. The enhanced prompt will be used for image editing and shown in the chat.
- The pipe will return the edited image.

Example:

Edit this image to look like a medieval fantasy king, preserving facial features.
# (If enhance_prompt is enabled, the vision model will refine this prompt based on the image)

Example of Flux Kontext ComfyUI Pipe output

Planner Agent v2

Now with tool calling.

This powerful agent allows you to define a goal, and it will autonomously generate and execute a plan to achieve it. The Planner is a generalist agent, capable of handling any text-based task, making it ideal for complex, multi-step requests that would typically require multiple prompts and manual intervention.

It features advanced capabilities like:

Automatic Plan Generation: Breaks down your goal into a sequence of actionable steps with defined dependencies.
Adaptive Execution: Executes each step, dynamically adjusting to the results of previous actions.
Reflection and Refinement: Analyzes the output of each step, identifies potential issues, and iteratively refines the output through multiple attempts.
Robust Error Handling: Includes retries and fallback mechanisms to ensure successful execution even with occasional API errors.
Detailed Execution Summary: Provides a comprehensive report of the plan execution, including timings and potential issues.

Features:

General Purpose: Can handle a wide range of text-based tasks, from creative writing and code generation to research summarization and problem-solving.
Multi-Step Task Management: Excels at managing complex tasks that require multiple steps and dependencies.
Integration with Native Open WebUI Tools: Agents can use asigned tools to complete the task.
Context Awareness: Maintains context throughout the execution process, ensuring that each step builds upon the previous ones.
Output Optimization: Employs a reflection mechanism to analyze and improve the output of each step through multiple iterations.

Configuration

MODEL: The model ID for the main planning LLM.
ACTION_MODEL: The model ID for executing most actions.
WRITER_MODEL: The model ID for text-heavy actions like writing documentation.
MAX_RETRIES: The number of times an action can be retried if it fails.
AUTOMATIC_TAKS_REQUIREMENT_ENHANCEMENT: Whether to use an LLM to enhance the requirements for each task.

Usage

Example:

Create a fully-featured Single Page Application (SPA) for Conway's Game of Life, including a responsive UI. No frameworks, only clean HTML, JS, and CSS.

Example of Planner Agent in action

arXiv Research MCTS Pipe

Description

Search arXiv.org for relevant academic papers and iteratively refine a research summary using a Monte Carlo Tree Search (MCTS) approach.

Configuration

model: The model ID from your LLM provider
tavily_api_key: Required. Obtain your API key from tavily.com
max_web_search_results: Number of web search results to fetch per query
max_arxiv_results: Number of results to fetch from the arXiv API per query
tree_breadth: Number of child nodes explored per MCTS iteration
tree_depth: Number of MCTS iterations
exploration_weight: Controls balance between exploration and exploitation
temperature_decay: Exponentially decreases LLM temperature with tree depth
dynamic_temperature_adjustment: Adjusts temperature based on parent node scores
maximum_temperature: Initial LLM temperature (default 1.4)
minimum_temperature: Final LLM temperature at max tree depth (default 0.5)

Usage

Example:

Do a research summary on "DPO laser LLM training"

Example of arXiv Research MCTS Pipe output

Multi Model Conversations Pipe

Description

Simulate conversations between multiple language models, each acting as a distinct character. Configure up to 5 participants.

Configuration

number_of_participants: Set the number of participants (1-5)
rounds_per_user_message: How many rounds of replies before the user can send another message
participant_[1-5]_model: Model for each participant
participant_[1-5]_alias: Display name for each participant
participant_[1-5]_system_message: Persona and instructions for each participant
all_participants_appended_message: Global instruction appended to each prompt
temperature, top_k, top_p: Standard model parameters

Usage

Example:

Start a conversation between three AI agents about climate change.

Example of Multi Model Conversations Pipe

Resume Analyzer Pipe

Description

Analyze resumes and provide tags, first impressions, adversarial analysis, potential interview questions, and career advice.

Configuration

model: The model ID from your LLM provider
dataset_path: Local path to the resume dataset CSV file
rapidapi_key (optional): For job search functionality
web_search: Enable/disable web search for relevant job postings
prompt_templates: Customizable templates for all steps

Usage

Requires the Full Document Filter (see below) to work with attached files.
Example:

Analyze this resume:
[Attach resume file]

Screenshots of Resume Analyzer Pipe output

Mopidy Music Controller

Description

Control your Mopidy music server to play songs from the local library or YouTube, manage playlists, and handle various music commands.

Configuration

model: The model ID from your LLM provider
mopidy_url: URL for the Mopidy JSON-RPC API endpoint (default: http://localhost:6680/mopidy/rpc)
youtube_api_key: YouTube Data API key for search
temperature: Model temperature (default: 0.7)
max_search_results: Maximum number of search results to return (default: 5)
use_iris: Toggle to use Iris interface or custom HTML UI (default: True)
system_prompt: System prompt for request analysis

Usage

Example:
```
Play the song "Imagine" by John Lennon
```
Quick text commands: stop, halt, play, start, resume, continue, next, skip, pause

Example of Mopidy Music Controller Pipe

Letta Agent Pipe

Description

Connect with Letta agents, enabling seamless integration of autonomous agents into Open WebUI conversations. Supports task-specific processing and maintains conversation context while communicating with the agent API.

Configuration

agent_id: The ID of the Letta agent to communicate with
api_url: Base URL for the Letta agent API (default: http://localhost:8283)
api_token: Bearer token for API authentication
task_model: Model to use for title/tags generation tasks
custom_name: Name of the agent to be displayed
timeout: Timeout to wait for Letta agent response in seconds (default: 400)

Usage

Example:

Chat with the built in Long Term memory Letta MemGPT agent.

Example of Letta Agent Pipe

MCP Pipe

Description

The MCP Pipe integrates the Model Context Protocol (MCP) into Open WebUI, enabling seamless connections between AI assistants and various data sources, tools, and development environments. Note: This implementation only works with Python-based MCP servers. NPX or other server types are not supported by default.

MCP is a universal, open standard that replaces fragmented integrations with a single protocol for connecting AI systems with data sources. This allows you to:

Connect to multiple MCP servers simultaneously (Python servers only)
Access tools and prompts from connected servers
Process queries using context-aware tools
Support data repositories, business tools, and development environments
Automatically discover tools and prompts
Stream responses from tools
Maintain conversation context across different data sources

Prerequisites

Open WebUI: Make sure you are running a compatible version (0.5.0+ recommended)
Python MCP servers: You must have one or more MCP-compatible servers installed and accessible (see open-webui/openapi-servers for examples)
MCP configuration file: A config.json file must be placed in the /data/ folder inside your Open WebUI installation
Python environment: Any additional MCP servers you add must be installed in the Open WebUI Python environment

Step-by-Step Setup

Install or set up your MCP servers
- Example: mcp_server_time for time and timezone conversion, mcp_server_tavily for web search
- Install via pip or clone and install as needed

Create the MCP configuration file

Place a config.json file in the /data/ directory of your Open WebUI installation

Example config.json:

{
    "mcpServers": {
        "time_server": {
            "command": "python",
            "args": ["-m", "mcp_server_time", "--local-timezone=America/New_York"],
            "description": "Provides Time and Timezone conversion tools."
        },
        "tavily_server": {
            "command": "python",
            "args": ["-m", "mcp_server_tavily", "--api-key=tvly-xxx"],
            "description": "Provides web search capabilities tools."
        }
    }
}

Replace tvly-xxx with your actual Tavily API key
Add additional servers as needed, following the same structure

Install any required MCP servers
- For each server listed in your config, ensure it is installed in the Open WebUI Python environment
- Example: pip install mcp_server_time or clone and install from source
Restart Open WebUI
- This ensures the new configuration and servers are loaded
Configure the MCP Pipe in Open WebUI
- Set the valves as needed (see below)

Configuration Valves

MODEL: (default: "Qwen2_5_16k:latest") The LLM model to use for MCP queries
OPENAI_API_KEY: Your OpenAI API key for API access (if using OpenAI-compatible models)
OPENAI_API_BASE: (default: "http://0.0.0.0:11434/v1") Base URL for API requests
TEMPERATURE: (default: 0.5) Controls randomness in responses (0.0-1.0)
MAX_TOKENS: (default: 1500) Maximum tokens to generate
TOP_P: (default: 0.8) Top-p sampling parameter
PRESENCE_PENALTY: (default: 0.8) Penalty for repeating topics
FREQUENCY_PENALTY: (default: 0.8) Penalty for repeating tokens

Example Usage

# Example usage in your prompt
Use the time_server to get the current time in New York.

You can also use the Tavily server for web search, or any other MCP server you have configured.
The MCP Pipe will automatically discover available tools and prompts from all configured servers.

Troubleshooting & Tips

Python servers only: This pipe does not support NPX or non-Python MCP servers. For NPX support, see the advanced MCP Pipeline below.
Server not found: Make sure the MCP server is installed and accessible in the Python environment used by Open WebUI
Config file not loaded: Double-check the location (/data/config.json) and syntax of your config file
API key issues: Ensure all required API keys (e.g., Tavily, OpenAI) are set correctly in the config and valves
Advanced features: For more advanced MCP features (including NPX server support), see the MCP Pipeline Documentation
Logs: Check Open WebUI logs for errors related to MCP server startup or communication

Reference: Advanced MCP Pipeline

If you need more advanced features, such as NPX server support, see the documentation in Pipelines/MCP_Pipeline/README_MCP_Pipeline.md in this repository.

🔧 Filters

Prompt Enhancer Filter

Description

Uses an LLM to automatically improve the quality of your prompts before they are sent to the main language model.

Configuration

user_customizable_template: Tailor the instructions given to the prompt-enhancing LLM
show_status: Displays status updates during the enhancement process
show_enhanced_prompt: Outputs the enhanced prompt to the chat window
model_id: Select the specific model to use for prompt enhancement

Usage

Enable in your model configuration's filters section.
The filter will automatically process each user message before it's sent to the main LLM.

Semantic Router Filter

Description

Acts as a model router. Analyzes the user's message and available models, then automatically selects the most appropriate model, pipe, or preset for the task.

Configuration

Configure banned models, vision model routing, and whether to show the selection reasoning in chat.

Usage

Enable in your model configuration's filters section.

Full Document Filter

Description

Allows Open WebUI to process entire attached files (such as resumes or documents) as part of the conversation. Cleans and prepends the file content to the first user message, ensuring the LLM receives the full context.

Configuration

priority (int): Priority level for the filter operations (default: 0)
max_turns (int): Maximum allowable conversation turns for a user (default: 8)

User Valves

max_turns (int): Maximum allowable conversation turns for a user (default: 4)

Usage

Enable the filter in your model configuration.
When you attach a file in Open WebUI, the filter will automatically clean and inject the file content into your message.
No manual configuration is needed for most users.

Example:

Analyze this resume:
[Attach resume file]

Clean Thinking Tags Filter

Description

Checks if an assistant's message ends with an unclosed or incomplete "thinking" tag. If so, it extracts the unfinished thought and presents it as a user-visible message.

Configuration

No configuration required.

Usage

Works automatically when enabled.

🎨 Using the Provided ComfyUI Workflows

Importing a Workflow

Open ComfyUI.
Click the "Load Workflow" or "Import" button.
Select the provided JSON file (e.g., ace_step_api.json or flux_context_owui_api_v1.json).
Save or modify as needed.
Use the node numbers in your Open WebUI tool configuration.

Best Practices

Always check node numbers after importing, as they may change if you modify the workflow.
You can create and share your own workflows by exporting them from ComfyUI.

Why this matters

This approach allows you to leverage state-of-the-art image and music generation/editing models with full control and customization, directly from Open WebUI.

📦 Installation

From Open WebUI Hub (Recommended)

Visit https://openwebui.com/u/haervwe
Click "Get" for desired tool/pipe/filter.
Follow prompts in your Open WebUI instance.

Manual Installation

Copy .py files from tools/, functions/, or filters/ into Open WebUI via the Workspace > Tools/Functions/Filters section.
Provide a name and description, then save.

🤝 Contributing

Feel free to contribute to this project by:

Forking the repository
Creating your feature branch
Committing your changes
Opening a pull request

📄 License

MIT License

🙏 Credits

Developed by Haervwe
Credit to the amazing teams behind:
- https://github.com/ollama/ollama
- https://github.com/open-webui/open-webui
And all model trainers out there providing these amazing tools.

🎯 Usage Examples

Academic Research

# Search for recent papers on a topic
Search for recent papers about "large language model training"

# Conduct comprehensive research
Do a research summary on "DPO laser LLM training"

Creative Projects

# Generate images
Create an image of "beautiful horse running free"

# Create music
Generate a song in the style of "funk, pop, soul" with lyrics: "In the shadows where secrets hide..."

# Edit images
Edit this image to look like a medieval fantasy king, preserving facial features

Productivity

# Analyze documents
Analyze this resume: [Attach resume file]

# Plan complex tasks
Create a fully-featured Single Page Application (SPA) for Conway's Game of Life

Multi-Agent Conversations

# Start group discussions
Start a conversation between three AI agents about climate change

🌟 Community & Ecosystem

This collection is part of the broader Open WebUI ecosystem. Here's how you can get involved:

🔗 Open WebUI Hub: Discover more tools at openwebui.com
📚 Documentation: Learn more about Open WebUI at docs.openwebui.com
💡 Ideas: Share your ideas and feature requests
🐛 Bug Reports: Help improve the tools by reporting issues
🌟 Star the Repository: Show your support by starring this repo

💬 Support

For issues, questions, or suggestions, please open an issue on the GitHub repository.

Name		Name	Last commit message	Last commit date
Latest commit History 187 Commits
Extras		Extras
Pipelines		Pipelines
__pycache__		__pycache__
filters		filters
functions		functions
img		img
tools		tools
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

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Haervwe/open-webui-tools

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Open WebUI Tools Collection

✨ What's Inside

🛠️ Tools

🔄 Function Pipes

🔧 Filters

🚀 Quick Start

Option 1: Open WebUI Hub (Recommended)

Option 2: Manual Installation

🎯 Key Features

📋 Prerequisites

🔧 Configuration

📖 Detailed Documentation

Table of Contents

🧪 Tools

arXiv Search Tool

Description

Configuration

Usage

Perplexica Search Tool

Description

Configuration

Usage

Features

Pexels Media Search Tool

Description

Configuration

Usage

Features

Native Image Generator

Description

Configuration

Usage

Features

Hugging Face Image Generator

Description

Configuration

Usage

ComfyUI ACE Step Audio Tool

Description

Configuration

Usage

🔄 Function Pipes

Flux Kontext ComfyUI Pipe

Description

Configuration

Usage

Planner Agent v2

Configuration

Usage

arXiv Research MCTS Pipe

Description

Configuration

Usage

Multi Model Conversations Pipe

Description

Configuration

Usage

Resume Analyzer Pipe

Description

Configuration

Usage

Mopidy Music Controller

Description

Configuration

Usage

Letta Agent Pipe

Description

Configuration

Usage

MCP Pipe

Description

Prerequisites

Step-by-Step Setup

Configuration Valves

Packages