| Name | agentdk JSON |
| Version |
0.3.0
JSON |
| download |
| home_page | None |
| Summary | Agent Development Kit for building intelligent agents with LangGraph and MCP integration, Support Agent Async in jupyter notebook/IPython |
| upload_time | 2025-07-15 12:45:51 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT |
| keywords |
agents
ai
automation
langgraph
llm
mcp
multi-agent
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 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
```bash
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
```bash
# 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:
```bash
# 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
```bash
# 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
```bash
$ 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:
```python
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:
```json
{
"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:
```python
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:
```python
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`:
```python
# 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):**
```json
{
"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:**
```json
{
"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:
```json
{
"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"**
```bash
# 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"**
```bash
# 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"**
```bash
# Reinstall with CLI dependencies
pip install agentdk[all]
# or for UV
uv sync --extra all
```
**"Examples not found after pip install"**
```bash
# 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
```python
# 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
```python
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
```python
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](LICENSE) file for details.
## Links
- **Homepage**: [https://github.com/breadpowder/agentdk](https://github.com/breadpowder/agentdk)
- **Bug Reports**: [GitHub Issues](https://github.com/breadpowder/agentdk/issues)
- **Contributing**: See [CONTRIBUTING.md](CONTRIBUTING.md)
---
Built with ❤️ for the LangGraph and MCP community.
Raw data
{
"_id": null,
"home_page": null,
"name": "agentdk",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "agents, ai, automation, langgraph, llm, mcp, multi-agent",
"author": null,
"author_email": "Zineng Yuan <zineng.yuan01@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/60/0c/e7ddc66a3c8472a7d7b33d61150aad9a78e298572b96ed6b274acd138fc1/agentdk-0.3.0.tar.gz",
"platform": null,
"description": "# AgentDK - Agent Development Kit\n\nA Python framework for building intelligent agents with LangGraph + MCP integration. Create data analysis agents, multi-agent workflows, and persistent CLI interactions.\n\n## \ud83d\ude80 Key Features\n\n- **\ud83e\udd16 Agent Workflows**: Individual agents and multi-agent supervisor patterns \n- **\ud83d\udd0c MCP Integration**: Model Context Protocol servers for standardized tool access\n- **\ud83e\udde0 Memory & Sessions**: Conversation continuity and user preferences\n- **\ud83d\udda5\ufe0f CLI Interface**: Interactive sessions with `agentdk run`\n\n## \ud83d\udce6 Installation\n\nChoose your installation method based on your needs:\n\n### Option 1: PyPI Install (Library Usage)\n\n**Best for**: Using AgentDK as a library in your projects, creating custom agents\n\n```bash\npip install agentdk[all]\n```\n\nThis installs AgentDK with all dependencies and includes working examples.\n\n### Option 2: GitHub Clone (Development & Examples)\n\n**Best for**: Exploring examples, contributing, or development with database setup\n\n```bash\n# Clone repository\ngit clone https://github.com/breadpowder/agentdk.git\ncd agentdk\n\n# Create UV environment (recommended)\nuv venv --python 3.11\nsource .venv/bin/activate # Linux/Mac\n# .venv\\Scripts\\activate # Windows\n\n# Install with all dependencies\nuv sync --extra all\n\n# Set up examples environment with database\ncd examples\n./setup.sh # Sets up MySQL database with Docker\n```\n\n## \ud83c\udfc1 Quick Start\n\n### After PyPI Install\n\nSet your API key and try a simple agent:\n\n```bash\n# Set your API key\nexport OPENAI_API_KEY=\"your-key\"\n# or export ANTHROPIC_API_KEY=\"your-key\"\n\n# Try the included EDA agent example\nagentdk run -m agentdk.examples.subagent.eda_agent\n\n# Or run the multi-agent supervisor\nagentdk run -m agentdk.examples.agent_app\n```\n\n### After GitHub Clone\n\n```bash\n# Set your API key\nexport OPENAI_API_KEY=\"your-key\"\n\n# Run examples from the examples directory\ncd examples\nagentdk run subagent/eda_agent.py\nagentdk run agent_app.py\n\n# Interactive sessions with memory\nagentdk run subagent/eda_agent.py --resume\n```\n\n### Interactive Session Example\n\n```bash\n$ agentdk run -m agentdk.examples.subagent.eda_agent\n\u2705 Using OpenAI gpt-4o-mini\nAgent ready. Type 'exit' to quit.\n\n[user]: How many customers are in the database?\n[eda_agent]: Let me check the customer count...\n\n[user]: exit\nSession saved. Resume with: agentdk run <path> --resume\n```\n\n## \ud83d\udee0\ufe0f How to Define Your Own Agents\n\n### 1. Simple Agent (Database Analysis)\n\nCreate a basic agent that connects to a database via MCP:\n\n```python\nfrom agentdk.builder.agent_builder import buildAgent\n\ndef create_my_agent(llm, mcp_config_path=None, **kwargs):\n \"\"\"Create a database analysis agent.\"\"\"\n return buildAgent(\n agent_class=\"SubAgentWithMCP\",\n llm=llm,\n mcp_config_path=mcp_config_path or \"mcp_config.json\",\n name=\"my_agent\",\n prompt=\"You are a helpful database analyst. Help users explore and analyze data.\",\n **kwargs\n )\n```\n\n#### MCP Configuration Setup\n\nCreate `mcp_config.json` for database access:\n\n```json\n{\n \"mysql\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"../mysql_mcp_server\", \"run\", \"mysql_mcp_server\"],\n \"env\": {\n \"MYSQL_HOST\": \"localhost\",\n \"MYSQL_PORT\": \"3306\",\n \"MYSQL_USER\": \"your_user\",\n \"MYSQL_PASSWORD\": \"your_password\", \n \"MYSQL_DATABASE\": \"your_database\"\n }\n }\n}\n```\n\n**Path Resolution Rules:**\n- **Relative paths** (like `\"../mysql_mcp_server\"`) are resolved relative to the config file location\n- **Absolute paths** work from any location\n- AgentDK searches for configs in this order:\n 1. Explicit path provided to agent\n 2. Same directory as your agent file\n 3. Current working directory\n 4. Parent directory\n 5. Examples directory (if exists)\n\n### 2. Multi-Agent Supervisor Pattern\n\nCombine multiple specialized agents:\n\n```python\nfrom agentdk.agent.base_app import RootAgent\nfrom agentdk.agent.app_utils import create_supervisor_workflow\n\nclass MyApp(RootAgent):\n \"\"\"Multi-agent application with supervisor workflow.\"\"\"\n \n def create_workflow(self, llm):\n # Create specialized agents\n data_agent = create_my_agent(llm, \"config/mcp_config.json\")\n research_agent = create_research_agent(llm)\n \n # Create supervisor that routes between agents\n return create_supervisor_workflow([data_agent, research_agent], llm)\n\n# Usage\napp = MyApp(llm=your_llm, memory=True)\nresult = app(\"Analyze our customer data and research market trends\")\n```\n\n### 3. Agent Without MCP (Custom Tools)\n\nFor agents with custom Python functions:\n\n```python\nfrom agentdk.agent.factory import create_agent\n\ndef my_custom_tool(query: str) -> str:\n \"\"\"Custom tool implementation.\"\"\"\n return f\"Processed: {query}\"\n\n# Create agent with custom tools\nagent = create_agent(\n agent_type=\"tools\",\n llm=your_llm,\n tools=[my_custom_tool],\n name=\"custom_agent\",\n prompt=\"You are a helpful assistant with custom tools.\"\n)\n\nresult = agent.query(\"Help me with something\")\n```\n\n### 4. CLI Integration\n\nMake your agent runnable with `agentdk run`:\n\n```python\n# my_agent.py\nfrom agentdk.core.logging_config import ensure_nest_asyncio\n\n# Enable async support\nensure_nest_asyncio()\n\ndef create_my_agent(llm=None, **kwargs):\n \"\"\"Factory function for CLI loading.\"\"\"\n return buildAgent(\n agent_class=\"SubAgentWithMCP\",\n llm=llm,\n mcp_config_path=\"config/mcp_config.json\",\n name=\"my_agent\",\n prompt=\"You are my custom agent.\",\n **kwargs\n )\n\n# CLI will auto-detect this function\n```\n\nThen run: `agentdk run my_agent.py`\n\n## \ud83d\udd27 MCP Configuration Guide\n\n### Config File Locations\n\nAgentDK searches for `mcp_config.json` in this priority order:\n\n1. **Explicit path**: `create_agent(mcp_config_path=\"/absolute/path/to/config.json\")`\n2. **Agent directory**: Same folder as your agent Python file\n3. **Working directory**: Where you run the command from\n4. **Parent directory**: One level up from working directory\n5. **Examples directory**: If `examples/` folder exists\n\n### Path Types\n\n**Relative Paths (Recommended):**\n```json\n{\n \"mysql\": {\n \"command\": \"uv\",\n \"args\": [\"--directory\", \"../mysql_mcp_server\", \"run\", \"mysql_mcp_server\"]\n }\n}\n```\n- Resolved relative to config file location\n- Portable across different systems\n- Works when moving project directories\n\n**Absolute Paths:**\n```json\n{\n \"mysql\": {\n \"command\": \"/usr/local/bin/mysql_mcp_server\",\n \"args\": [\"--host\", \"localhost\"]\n }\n}\n```\n- Fixed system paths\n- Not portable but explicit\n\n### Environment Variables\n\nAdd environment variables to your MCP server config:\n\n```json\n{\n \"mysql\": {\n \"command\": \"mysql_mcp_server\",\n \"args\": [\"--config\", \"mysql.conf\"],\n \"env\": {\n \"MYSQL_HOST\": \"localhost\",\n \"MYSQL_PORT\": \"3306\",\n \"MYSQL_USER\": \"agent_user\",\n \"MYSQL_PASSWORD\": \"secure_password\",\n \"MYSQL_DATABASE\": \"production_db\"\n }\n }\n}\n```\n\n## \ud83d\udcc1 Examples Directory\n\n**Note**: Examples are included in PyPI installs and available via `-m agentdk.examples`\n\n| File | Description | Run Command |\n|------|-------------|-------------|\n| `agent_app.py` | Multi-agent supervisor with EDA + research | `agentdk run -m agentdk.examples.agent_app` |\n| `subagent/eda_agent.py` | Database analysis agent with MySQL MCP | `agentdk run -m agentdk.examples.subagent.eda_agent` |\n| `subagent/research_agent.py` | Web research agent | `agentdk run -m agentdk.examples.subagent.research_agent` |\n\n**For GitHub installations:**\n| File | Description | Run Command |\n|------|-------------|-------------|\n| `setup.sh` | Environment setup with database | `./setup.sh` |\n| `agentdk_testing_notebook.ipynb` | Jupyter notebook examples | `jupyter lab` |\n\n## \ud83d\udd27 Troubleshooting\n\n### Common Issues\n\n**\"No valid MCP configuration found\"**\n```bash\n# Check your current directory and config location\nls -la mcp_config.json\n\n# Use absolute path\nagentdk run --mcp-config /full/path/to/mcp_config.json my_agent.py\n\n# Or ensure you're in the right directory\ncd /path/to/your/project\nagentdk run my_agent.py\n```\n\n**\"MySQL connection failed\"**\n```bash\n# For GitHub installations, ensure database is running\ncd examples\n./setup.sh\ndocker ps # Should show mysql container\n\n# Check your environment variables\necho $MYSQL_HOST $MYSQL_USER $MYSQL_PASSWORD\n```\n\n**\"agentdk command not found\"**\n```bash\n# Reinstall with CLI dependencies\npip install agentdk[all]\n# or for UV\nuv sync --extra all\n```\n\n**\"Examples not found after pip install\"**\n```bash\n# Use module syntax for PyPI installs\nagentdk run -m agentdk.examples.subagent.eda_agent\n\n# Or clone GitHub repo for development\ngit clone https://github.com/breadpowder/agentdk.git\n```\n\n### Environment Requirements\n- Python 3.11+\n- Docker (for database examples)\n- OpenAI or Anthropic API key\n\n## \ud83d\ude80 Advanced Usage\n\n### Memory and Sessions\n\n```python\n# Enable memory for conversation continuity\napp = MyApp(llm=your_llm, memory=True, user_id=\"analyst_001\")\n\n# Sessions persist across CLI runs\nagentdk run my_agent.py --resume --user-id analyst_001\n```\n\n### Custom Memory Configuration\n\n```python\nmemory_config = {\n \"provider\": \"mem0\",\n \"working_memory_limit\": 10,\n \"episodic_memory_limit\": 100\n}\n\napp = MyApp(llm=your_llm, memory=True, memory_config=memory_config)\n```\n\n### Jupyter Integration\n\n```python\nfrom agentdk.core.logging_config import ensure_nest_asyncio\n\n# Enable async support in notebooks\nensure_nest_asyncio()\n\n# Use agents in Jupyter\nagent = create_my_agent(llm)\nresult = agent.query(\"What data do we have?\")\n```\n\n## License\nMIT License - see [LICENSE](LICENSE) file for details.\n\n## Links\n- **Homepage**: [https://github.com/breadpowder/agentdk](https://github.com/breadpowder/agentdk)\n- **Bug Reports**: [GitHub Issues](https://github.com/breadpowder/agentdk/issues)\n- **Contributing**: See [CONTRIBUTING.md](CONTRIBUTING.md)\n\n---\n\nBuilt with \u2764\ufe0f for the LangGraph and MCP community.",
"bugtrack_url": null,
"license": "MIT",
"summary": "Agent Development Kit for building intelligent agents with LangGraph and MCP integration, Support Agent Async in jupyter notebook/IPython",
"version": "0.3.0",
"project_urls": {
"Bug Tracker": "https://github.com/breadpowder/agentdk/issues",
"Homepage": "https://github.com/breadpowder/agentdk"
},
"split_keywords": [
"agents",
" ai",
" automation",
" langgraph",
" llm",
" mcp",
" multi-agent"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "d3fd7844f2884fee90b965ee009dbfc485bdae5ab04ab2a957548e66be4a81e4",
"md5": "6e8e7baeef34d7363c972bb1e3059396",
"sha256": "8b8bce443ba31610c7c7b01f61e48eb9fc67b1c6ae9b1b523b617ee1f90f31a1"
},
"downloads": -1,
"filename": "agentdk-0.3.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6e8e7baeef34d7363c972bb1e3059396",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.11",
"size": 83405,
"upload_time": "2025-07-15T12:45:50",
"upload_time_iso_8601": "2025-07-15T12:45:50.646082Z",
"url": "https://files.pythonhosted.org/packages/d3/fd/7844f2884fee90b965ee009dbfc485bdae5ab04ab2a957548e66be4a81e4/agentdk-0.3.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "600ce7ddc66a3c8472a7d7b33d61150aad9a78e298572b96ed6b274acd138fc1",
"md5": "6e1be378b2b1ad28e5b8f7869fac1abb",
"sha256": "9cb27e4bdebced73f56fd3f2a54ac351e637607bac2df595a89c8fecbaf3aa81"
},
"downloads": -1,
"filename": "agentdk-0.3.0.tar.gz",
"has_sig": false,
"md5_digest": "6e1be378b2b1ad28e5b8f7869fac1abb",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.11",
"size": 84739,
"upload_time": "2025-07-15T12:45:51",
"upload_time_iso_8601": "2025-07-15T12:45:51.716201Z",
"url": "https://files.pythonhosted.org/packages/60/0c/e7ddc66a3c8472a7d7b33d61150aad9a78e298572b96ed6b274acd138fc1/agentdk-0.3.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-15 12:45:51",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "breadpowder",
"github_project": "agentdk",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "agentdk"
}
