https://github.com/chenxingqiang/agent-flow-project
A Python, JavaScript, CSS project focusing on Project Structure, Create and initialize agent, Workflow execution, Configuration, Core component tests.
https://github.com/chenxingqiang/agent-flow-project
agent agentflow css flow html javascript jinja project python shell tex
Last synced: 3 months ago
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A Python, JavaScript, CSS project focusing on Project Structure, Create and initialize agent, Workflow execution, Configuration, Core component tests.
- Host: GitHub
- URL: https://github.com/chenxingqiang/agent-flow-project
- Owner: chenxingqiang
- Created: 2024-12-04T05:24:53.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-02-17T02:27:08.000Z (4 months ago)
- Last Synced: 2025-03-15T18:53:25.844Z (4 months ago)
- Topics: agent, agentflow, css, flow, html, javascript, jinja, project, python, shell, tex
- Language: Python
- Size: 2.04 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AgentFlow
AgentFlow is a flexible and extensible framework for building and managing AI agents and workflows. It provides a modular architecture for creating, configuring, and orchestrating AI agents with advanced features for workflow management and testing.
## Latest Version
Current version: v0.1.1
- Fixed workflow transform functions to handle step and context parameters
- Added feature engineering and outlier removal transforms
- Improved test suite and type hints
- Enhanced error handling and validation
## Project Structure
```
agentflow/
├── agents/ # Agent implementations
│ ├── agent.py # Base agent implementation
│ └── agent_types.py # Agent type definitions
├── core/ # Core framework components
│ ├── base_types.py # Base type definitions
│ ├── config.py # Configuration management
│ ├── workflow.py # Workflow engine
│ └── workflow_executor.py # Workflow execution
├── ell2a/ # ELL2A integration
│ └── types/ # Message and content types
├── transformations/ # Data transformation tools
│ └── text.py # Text processing utilities
└── tests/ # Comprehensive test suite
├── unit/ # Unit tests
├── core/ # Core component tests
└── performance/ # Performance tests
```
## Features
- **Flexible Agent Architecture**
- Configurable agent types and behaviors
- Support for research and data science agents
- Extensible agent factory system
- **Advanced Workflow Management**
- Step-based workflow execution
- Dependency management
- Error handling and retry policies
- Performance monitoring
- **Robust Testing Framework**
- Unit and integration tests
- Performance testing
- Test-driven development support
- **Data Transformation Tools**
- Feature engineering
- Outlier removal
- Text processing utilities
- **Type Safety**
- Comprehensive type hints
- Pydantic model validation
- Runtime type checking
## Installation
```bash
pip install agentflow
```
## Quick Start
```python
from agentflow import Agent, AgentConfig, WorkflowConfig, WorkflowStep, WorkflowStepType
# Create workflow configuration
workflow_config = WorkflowConfig(
id="test-workflow",
name="test_workflow",
steps=[
WorkflowStep(
id="step-1",
name="transform_step",
type=WorkflowStepType.TRANSFORM,
description="Data transformation step",
config={
"strategy": "standard",
"params": {
"method": "standard",
"with_mean": True,
"with_std": True
}
}
)
]
)
# Create agent configuration
agent_config = AgentConfig(
name="test_agent",
type="data_science",
workflow=workflow_config
)
# Create and initialize agent
agent = Agent(config=agent_config)
await agent.initialize()
# Process data
result = await agent.execute({"data": your_data})
```
## Configuration
Agents and workflows can be configured using Python dictionaries or YAML files:
```yaml
AGENT:
name: data_science_agent
type: data_science
version: 1.0.0
mode: sequential
MODEL:
provider: openai
name: gpt-4
temperature: 0.7
max_tokens: 4096
WORKFLOW:
id: transform-workflow
name: Data Transformation Workflow
max_iterations: 5
timeout: 30
steps:
- id: step-1
name: feature_engineering
type: transform
description: Feature engineering step
config:
strategy: standard
params:
method: standard
with_mean: true
with_std: true
```
## Testing
Run the test suite:
```bash
# Run all tests
pytest
# Run specific test categories
pytest tests/unit/
pytest tests/core/
pytest tests/performance/
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Write tests for your changes
4. Implement your changes
5. Run the test suite
6. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.