agentdk 0.3.0

Agent Development Kit for building intelligent agents with LangGraph and MCP integration, Support Agent Async in jupyter notebook/IPython

AgentDK - Agent Development Kit

A Python framework for building intelligent agents with LangGraph + MCP integration. Create data analysis agents, multi-agent workflows, and persistent CLI interactions.

🚀 Key Features

• 🤖 Agent Workflows: Individual agents and multi-agent supervisor patterns
• 🔌 MCP Integration: Model Context Protocol servers for standardized tool access
• 🧠 Memory & Sessions: Conversation continuity and user preferences
• 🖥️ CLI Interface: Interactive sessions with agentdk run

📦 Installation

Choose your installation method based on your needs:

Option 1: PyPI Install (Library Usage)

Best for: Using AgentDK as a library in your projects, creating custom agents

pip install agentdk[all]

This installs AgentDK with all dependencies and includes working examples.

Option 2: GitHub Clone (Development & Examples)

Best for: Exploring examples, contributing, or development with database setup

# Clone repository
git clone https://github.com/breadpowder/agentdk.git
cd agentdk

# Create UV environment (recommended)
uv venv --python 3.11
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate   # Windows

# Install with all dependencies
uv sync --extra all

# Set up examples environment with database
cd examples
./setup.sh  # Sets up MySQL database with Docker

🏁 Quick Start

After PyPI Install

Set your API key and try a simple agent:

# Set your API key
export OPENAI_API_KEY="your-key"
# or export ANTHROPIC_API_KEY="your-key"

# Try the included EDA agent example
agentdk run -m agentdk.examples.subagent.eda_agent

# Or run the multi-agent supervisor
agentdk run -m agentdk.examples.agent_app

After GitHub Clone

# Set your API key
export OPENAI_API_KEY="your-key"

# Run examples from the examples directory
cd examples
agentdk run subagent/eda_agent.py
agentdk run agent_app.py

# Interactive sessions with memory
agentdk run subagent/eda_agent.py --resume

Interactive Session Example

$ agentdk run -m agentdk.examples.subagent.eda_agent
✅ Using OpenAI gpt-4o-mini
Agent ready. Type 'exit' to quit.

[user]: How many customers are in the database?
[eda_agent]: Let me check the customer count...

[user]: exit
Session saved. Resume with: agentdk run <path> --resume

🛠️ How to Define Your Own Agents

1. Simple Agent (Database Analysis)

Create a basic agent that connects to a database via MCP:

from agentdk.builder.agent_builder import buildAgent

def create_my_agent(llm, mcp_config_path=None, **kwargs):
    """Create a database analysis agent."""
    return buildAgent(
        agent_class="SubAgentWithMCP",
        llm=llm,
        mcp_config_path=mcp_config_path or "mcp_config.json",
        name="my_agent",
        prompt="You are a helpful database analyst. Help users explore and analyze data.",
        **kwargs
    )

MCP Configuration Setup

Create mcp_config.json for database access:

{
  "mysql": {
    "command": "uv",
    "args": ["--directory", "../mysql_mcp_server", "run", "mysql_mcp_server"],
    "env": {
      "MYSQL_HOST": "localhost",
      "MYSQL_PORT": "3306",
      "MYSQL_USER": "your_user",
      "MYSQL_PASSWORD": "your_password",  
      "MYSQL_DATABASE": "your_database"
    }
  }
}

Path Resolution Rules:

• Relative paths (like "../mysql_mcp_server") are resolved relative to the config file location
• Absolute paths work from any location
• AgentDK searches for configs in this order:
  1. Explicit path provided to agent
  2. Same directory as your agent file
  3. Current working directory
  4. Parent directory
  5. Examples directory (if exists)

2. Multi-Agent Supervisor Pattern

Combine multiple specialized agents:

from agentdk.agent.base_app import RootAgent
from agentdk.agent.app_utils import create_supervisor_workflow

class MyApp(RootAgent):
    """Multi-agent application with supervisor workflow."""
    
    def create_workflow(self, llm):
        # Create specialized agents
        data_agent = create_my_agent(llm, "config/mcp_config.json")
        research_agent = create_research_agent(llm)
        
        # Create supervisor that routes between agents
        return create_supervisor_workflow([data_agent, research_agent], llm)

# Usage
app = MyApp(llm=your_llm, memory=True)
result = app("Analyze our customer data and research market trends")

3. Agent Without MCP (Custom Tools)

For agents with custom Python functions:

from agentdk.agent.factory import create_agent

def my_custom_tool(query: str) -> str:
    """Custom tool implementation."""
    return f"Processed: {query}"

# Create agent with custom tools
agent = create_agent(
    agent_type="tools",
    llm=your_llm,
    tools=[my_custom_tool],
    name="custom_agent",
    prompt="You are a helpful assistant with custom tools."
)

result = agent.query("Help me with something")

4. CLI Integration

Make your agent runnable with agentdk run:

# my_agent.py
from agentdk.core.logging_config import ensure_nest_asyncio

# Enable async support
ensure_nest_asyncio()

def create_my_agent(llm=None, **kwargs):
    """Factory function for CLI loading."""
    return buildAgent(
        agent_class="SubAgentWithMCP",
        llm=llm,
        mcp_config_path="config/mcp_config.json",
        name="my_agent",
        prompt="You are my custom agent.",
        **kwargs
    )

# CLI will auto-detect this function

Then run: agentdk run my_agent.py

🔧 MCP Configuration Guide

Config File Locations

AgentDK searches for mcp_config.json in this priority order:

1. Explicit path: create_agent(mcp_config_path="/absolute/path/to/config.json")
2. Agent directory: Same folder as your agent Python file
3. Working directory: Where you run the command from
4. Parent directory: One level up from working directory
5. Examples directory: If examples/ folder exists

Path Types

Relative Paths (Recommended):

{
  "mysql": {
    "command": "uv",
    "args": ["--directory", "../mysql_mcp_server", "run", "mysql_mcp_server"]
  }
}

• Resolved relative to config file location
• Portable across different systems
• Works when moving project directories

Absolute Paths:

{
  "mysql": {
    "command": "/usr/local/bin/mysql_mcp_server",
    "args": ["--host", "localhost"]
  }
}

• Fixed system paths
• Not portable but explicit

Environment Variables

Add environment variables to your MCP server config:

{
  "mysql": {
    "command": "mysql_mcp_server",
    "args": ["--config", "mysql.conf"],
    "env": {
      "MYSQL_HOST": "localhost",
      "MYSQL_PORT": "3306",
      "MYSQL_USER": "agent_user",
      "MYSQL_PASSWORD": "secure_password",
      "MYSQL_DATABASE": "production_db"
    }
  }
}

📁 Examples Directory

Note: Examples are included in PyPI installs and available via -m agentdk.examples

File	Description	Run Command
agent_app.py	Multi-agent supervisor with EDA + research	agentdk run -m agentdk.examples.agent_app
subagent/eda_agent.py	Database analysis agent with MySQL MCP	agentdk run -m agentdk.examples.subagent.eda_agent
subagent/research_agent.py	Web research agent	agentdk run -m agentdk.examples.subagent.research_agent

For GitHub installations:

File	Description	Run Command
setup.sh	Environment setup with database	./setup.sh
agentdk_testing_notebook.ipynb	Jupyter notebook examples	jupyter lab

🔧 Troubleshooting

Common Issues

"No valid MCP configuration found"

# Check your current directory and config location
ls -la mcp_config.json

# Use absolute path
agentdk run --mcp-config /full/path/to/mcp_config.json my_agent.py

# Or ensure you're in the right directory
cd /path/to/your/project
agentdk run my_agent.py

"MySQL connection failed"

# For GitHub installations, ensure database is running
cd examples
./setup.sh
docker ps  # Should show mysql container

# Check your environment variables
echo $MYSQL_HOST $MYSQL_USER $MYSQL_PASSWORD

"agentdk command not found"

# Reinstall with CLI dependencies
pip install agentdk[all]
# or for UV
uv sync --extra all

"Examples not found after pip install"

# Use module syntax for PyPI installs
agentdk run -m agentdk.examples.subagent.eda_agent

# Or clone GitHub repo for development
git clone https://github.com/breadpowder/agentdk.git

Environment Requirements

• Python 3.11+
• Docker (for database examples)
• OpenAI or Anthropic API key

🚀 Advanced Usage

Memory and Sessions

# Enable memory for conversation continuity
app = MyApp(llm=your_llm, memory=True, user_id="analyst_001")

# Sessions persist across CLI runs
agentdk run my_agent.py --resume --user-id analyst_001

Custom Memory Configuration

memory_config = {
    "provider": "mem0",
    "working_memory_limit": 10,
    "episodic_memory_limit": 100
}

app = MyApp(llm=your_llm, memory=True, memory_config=memory_config)

Jupyter Integration

from agentdk.core.logging_config import ensure_nest_asyncio

# Enable async support in notebooks
ensure_nest_asyncio()

# Use agents in Jupyter
agent = create_my_agent(llm)
result = agent.query("What data do we have?")

License

MIT License - see LICENSE file for details.

Links

• Homepage: https://github.com/breadpowder/agentdk
• Bug Reports: GitHub Issues
• Contributing: See CONTRIBUTING.md

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