https://github.com/ako1983/airops_integration_agent
A prototype AI agent that helps users configure integration actions using natural language requests and available context variables. Built with LangGraph, Claude AI, and includes observability through LangSmith and Weights & Biases.
https://github.com/ako1983/airops_integration_agent
agentic-ai claude-ai integration langchain langgraph llm llm-agents llm-evaluation
Last synced: about 1 month ago
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A prototype AI agent that helps users configure integration actions using natural language requests and available context variables. Built with LangGraph, Claude AI, and includes observability through LangSmith and Weights & Biases.
- Host: GitHub
- URL: https://github.com/ako1983/airops_integration_agent
- Owner: ako1983
- Created: 2025-05-19T01:38:12.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-06T18:13:41.000Z (4 months ago)
- Last Synced: 2025-06-17T16:44:24.369Z (4 months ago)
- Topics: agentic-ai, claude-ai, integration, langchain, langgraph, llm, llm-agents, llm-evaluation
- Language: Python
- Homepage:
- Size: 228 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AirOps Integration Agent
## Overview
The AirOps Integration Agent is a prototype system that enables users to configure and execute integration actions (e.g., create a Webflow item, send a Slack notification, post to WordPress) using natural language requests. It leverages workflow context and available integration actions to generate structured, executable workflows.
## Features
- **Natural Language Understanding**: Parses user requests to extract intent, platform, entity type, and parameters.
- **Action Selection**: Matches parsed requests to the most relevant integration action using scoring logic.
- **Schema Retrieval**: Retrieves the schema for the selected action to understand required parameters.
- **Parameter Generation & Validation**: Extracts and validates parameters from the user request and context.
- **Workflow Generation**: Assembles a workflow definition, including necessary data transformations.
- **Observability & Tracking**: Integrates with LangSmith and Weights & Biases for tracing and performance monitoring.
- **Testing**: Includes scripts to test the agent with sample requests and output the generated workflows.
## Project Structure
```
airops_integration_agent/
├── config/
│ └── observability.py
├── data/
│ ├── integration_actions.txt / .json
│ ├── workflow_context.json
│ └── sample_requests.json
├── src/
│ ├── nlp.py
│ ├── action_selector.py
│ ├── graph.py
│ ├── models/
│ │ ├── action.py
│ │ ├── request.py
│ │ └── workflow.py
│ ├── nodes/
│ │ ├── query_refiner.py
│ │ ├── planner.py
│ │ ├── schema_retriever.py
│ │ ├── generator.py
│ │ ├── validator.py
│ │ ├── repair.py
│ │ ├── workflow_generator.py
│ │ └── final_output.py
│ └── tests/
│ ├── test_integration.py
│ └── test_nlp.py
├── utils/
│ ├── helpers.py
│ └── tracking.py
├── main.py
├── requirements.txt
└── README.md
```
## How It Works
1. **User Request Parsing**: The agent parses a natural language request to identify the target platform, action intent, entity type, and parameters.
2. **Action Selection**: It selects the most relevant integration action from available options.
3. **Schema Retrieval**: Retrieves the schema for the selected action to determine required parameters.
4. **Parameter Generation & Validation**: Extracts parameters from the request/context and validates them.
5. **Workflow Generation**: Generates a workflow definition, including any necessary data transformations.
6. **Execution & Output**: Outputs the workflow or requests clarification if needed.
## Example Usage
Run the test script to see the agent in action:
```bash
python main.py
```
This will process sample requests and print the generated workflows and any clarification questions.
## Extending the Agent
- **Add new integrations**: Update `data/integration_actions.txt` or `.json` with new actions.
- **Improve NLP**: Enhance `src/nlp.py` for more robust request parsing.
- **Add nodes**: Implement new workflow steps in `src/nodes/` as needed.
## Observability & Tracking
- **LangSmith**: Used for tracing LLM calls and agent steps.
- **Weights & Biases (wandb)**: Used for performance tracking.
## Requirements
- Python 3.10+
- See `requirements.txt` for dependencies.
## Contributing
1. Fork the repo and create a feature branch.
2. Add or update tests in `src/tests/`.
3. Submit a pull request with a clear description.
## License
MIT License
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For more details, see the code and docstrings in each module.