Merge branch 'main' into whatsapp-integration

Author: dirkbrnd

cookbook/examples/apps/github_mcp_agent/agents.py

@@ -1,27 +1,29 @@
 import os
 from textwrap import dedent
+
 from agno.agent import Agent
 from agno.tools.mcp import MCPTools
 from mcp import ClientSession, StdioServerParameters
 from mcp.client.stdio import stdio_client
 
+
 async def run_github_agent(message):
     if not os.getenv("GITHUB_TOKEN"):
         return "Error: GitHub token not provided"
-    
+
     try:
         server_params = StdioServerParameters(
             command="npx",
             args=["-y", "@modelcontextprotocol/server-github"],
         )
-        
+
         # Create client session
         async with stdio_client(server_params) as (read, write):
             async with ClientSession(read, write) as session:
                 # Initialize MCP toolkit
                 mcp_tools = MCPTools(session=session)
                 await mcp_tools.initialize()
-                
+
                 # Create agent
                 agent = Agent(
                     tools=[mcp_tools],
@@ -36,7 +38,7 @@ async def run_github_agent(message):
                     markdown=True,
                     show_tool_calls=True,
                 )
-                
+
                 # Run agent
                 response = await agent.arun(message)
                 return response.content

cookbook/examples/apps/github_mcp_agent/app.py

@@ -1,49 +1,58 @@
 import asyncio
 import os
+
 import streamlit as st
 from agents import run_github_agent
+
 # Page config
 st.set_page_config(page_title="🐙 GitHub MCP Agent", page_icon="🐙", layout="wide")
 
 # Title and description
 st.markdown("<h1 class='main-header'>🐙 GitHub MCP Agent</h1>", unsafe_allow_html=True)
-st.markdown("Explore GitHub repositories with natural language using the Model Context Protocol")
+st.markdown(
+    "Explore GitHub repositories with natural language using the Model Context Protocol"
+)
 
 # Setup sidebar for API key
 with st.sidebar:
     st.header("🔑 Authentication")
-    github_token = st.text_input("GitHub Token", type="password", 
-                                help="Create a token with repo scope at github.com/settings/tokens")
-    
+    github_token = st.text_input(
+        "GitHub Token",
+        type="password",
+        help="Create a token with repo scope at github.com/settings/tokens",
+    )
+
     if github_token:
         os.environ["GITHUB_TOKEN"] = github_token
-    
+
     st.markdown("---")
     st.markdown("### Example Queries")
-    
+
     st.markdown("**Issues**")
     st.markdown("- Show me issues by label")
     st.markdown("- What issues are being actively discussed?")
-    
+
     st.markdown("**Pull Requests**")
     st.markdown("- What PRs need review?")
     st.markdown("- Show me recent merged PRs")
-    
+
     st.markdown("**Repository**")
     st.markdown("- Show repository health metrics")
     st.markdown("- Show repository activity patterns")
-    
+
     st.markdown("---")
-    st.caption("Note: Always specify the repository in your query if not already selected in the main input.")
+    st.caption(
+        "Note: Always specify the repository in your query if not already selected in the main input."
+    )
 
 # Query input
 col1, col2 = st.columns([3, 1])
 with col1:
     repo = st.text_input("Repository", value="agno-agi/agno", help="Format: owner/repo")
 with col2:
-    query_type = st.selectbox("Query Type", [
-        "Issues", "Pull Requests", "Repository Activity", "Custom"
-    ])
+    query_type = st.selectbox(
+        "Query Type", ["Issues", "Pull Requests", "Repository Activity", "Custom"]
+    )
 
 # Create predefined queries based on type
 if query_type == "Issues":
@@ -55,8 +64,11 @@
 else:
     query_template = ""
 
-query = st.text_area("Your Query", value=query_template, 
-                     placeholder="What would you like to know about this repository?")
+query = st.text_area(
+    "Your Query",
+    value=query_template,
+    placeholder="What would you like to know about this repository?",
+)
 
 # Run button
 if st.button("🚀 Run Query", type="primary", use_container_width=True):
@@ -71,15 +83,15 @@
                 full_query = f"{query} in {repo}"
             else:
                 full_query = query
-                
+
             result = asyncio.run(run_github_agent(full_query))
-        
+
         # Display results in a nice container
         st.markdown("### Results")
         st.markdown(result)
 
 # Display help text for first-time users
-if 'result' not in locals():
+if "result" not in locals():
     st.markdown(
         """<div class='info-box'>
         <h4>How to use this app:</h4>
@@ -96,10 +108,10 @@
             <li>More specific queries yield better results</li>
             <li>This app requires Node.js to be installed (for the npx command)</li>
         </ul>
-        </div>""", 
-        unsafe_allow_html=True
+        </div>""",
+        unsafe_allow_html=True,
     )
 
 # Footer
 st.markdown("---")
-st.write("Built with Streamlit, Agno, and Model Context Protocol ❤️")
+st.write("Built with Streamlit, Agno, and Model Context Protocol ❤️")

cookbook/examples/apps/mcp_agent/.gitignore

@@ -0,0 +1,3 @@
+output
+agents.db
+tmp

cookbook/examples/apps/mcp_agent/README.md

@@ -0,0 +1,122 @@
+# UAgI: Universal Agent Interface powered by MCP
+
+> [!IMPORTANT]
+> This is a work in progress (see [open issues](#-open-issues) below), please contribute and help improve.
+
+UAgI (Universal Agent Interface) is a powerful agent application that leverages the Model Context Protocol (MCP) to provide a unified interface for interacting with various MCP servers. This application allows you to connect to different data sources and tools through MCP servers, providing a seamless experience for working with external services.
+
+## 🌟 Features
+
+- **Multiple Model Support**: Works with various LLM providers including:
+  - OpenAI (o3-mini, gpt-4o, gpt-4.5)
+  - Anthropic (claude-3-7-sonnet, claude-3-7-sonnet-thinking)
+  - Google (gemini-2.0-flash, gemini-2.0-pro)
+  - Groq (llama-3.3-70b-versatile)
+
+- **MCP Server Integration**: Connect to the following MCP servers:
+  - GitHub: Access repositories, issues, and more
+  - Filesystem: Browse and manipulate files on your local system
+
+- **Knowledge Base**: Built-in knowledge of MCP documentation to help answer questions about the protocol
+
+- **Session Management**: Save and restore chat sessions using SQLite storage
+
+- **Chat History Export**: Export your conversations as markdown files
+
+- **Streamlit UI**: User-friendly interface with customizable settings
+
+## 🐞 Open Issues
+
+- Only works with 1 MCP server at a time
+- Changing MCP servers resets the agent
+- Only supports 2 MCP servers at the moment
+- Chat history is broken
+- MCP Cleanup is not working, so memory leaks are possible
+
+## 🚀 Quick Start
+
+### 1. Environment Setup
+
+Create and activate a virtual environment:
+```bash
+python3 -m venv .venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+```
+
+### 2. Install Dependencies
+
+```bash
+pip install -r cookbook/examples/apps/mcp_agent/requirements.txt
+```
+
+### 3. Configure API Keys
+
+Required:
+```bash
+export OPENAI_API_KEY=your_openai_key_here
+```
+
+Optional (for additional models):
+```bash
+export ANTHROPIC_API_KEY=your_anthropic_key_here
+export GOOGLE_API_KEY=your_google_key_here
+export GROQ_API_KEY=your_groq_key_here
+```
+
+For GitHub MCP server:
+```bash
+export GITHUB_TOKEN=your_github_token_here
+```
+
+### 4. Launch the Application
+
+```bash
+streamlit run cookbook/examples/apps/mcp_agent/app.py
+```
+
+Visit [localhost:8501](http://localhost:8501) to access the UAgI application.
+
+## 🔧 How It Works
+
+UAgI connects to MCP servers using the Model Context Protocol, which standardizes how applications provide context to LLMs. When you ask a question:
+
+1. The agent analyzes your request and determines which MCP tools might be helpful
+2. It connects to the appropriate MCP server (GitHub, Filesystem, etc.)
+3. The agent executes the necessary tools through the MCP server
+4. Results are processed and returned in a natural language response
+5. All interactions are saved in your session history
+
+## 📚 Understanding MCP
+
+The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools.
+
+MCP helps you build agents and complex workflows on top of LLMs by providing:
+- A growing list of pre-built integrations that your LLM can directly plug into
+- The flexibility to switch between LLM providers and vendors
+- Best practices for securing your data within your infrastructure
+
+## 🛠️ Customization
+
+### Adding New MCP Servers
+
+The application is designed to be extensible. To add new MCP servers:
+
+1. Update the `get_mcp_server_config()` function in `utils.py`
+2. Add server-specific example inputs in the `example_inputs()` function
+
+### Modifying Agent Behavior
+
+The agent configuration is in `agents.py`:
+- Adjust the agent description and instructions to change its behavior
+- Modify the knowledge base to include additional documentation
+- Add new tools or capabilities as needed
+
+## 📚 Documentation
+
+For more detailed information:
+- [Agno Documentation](https://docs.agno.com)
+- [Streamlit Documentation](https://docs.streamlit.io)
+
+## 🤝 Support
+
+Need help? Join our [Discord community](https://agno.link/discord)