Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/simonmesmith/agentflow
Complex LLM Workflows from Simple JSON.
https://github.com/simonmesmith/agentflow
Last synced: about 1 month ago
JSON representation
Complex LLM Workflows from Simple JSON.
- Host: GitHub
- URL: https://github.com/simonmesmith/agentflow
- Owner: simonmesmith
- License: mit
- Created: 2023-08-02T20:57:16.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2023-08-11T18:07:43.000Z (11 months ago)
- Last Synced: 2024-03-17T09:12:26.313Z (3 months ago)
- Language: Python
- Homepage:
- Size: 78.1 KB
- Stars: 256
- Watchers: 3
- Forks: 21
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-langchain - AgentFlow
- awesome-langchain-zh - AgentFlow
README
# Agentflow: Complex LLM Workflows from Simple JSON
Agentflow is a powerful yet user-friendly tool to run workflows powered by LLMs. You can:
* **Write workflows in plain English** in human-readable JSON files.
* **Use variables for dynamic outputs** that change based on user input.
* **Build and execute custom functions** to go beyond text generation.
## Why Agentflow?
Agentflow fills the gap between chat and autonomous interfaces:
* **Chat (e.g. ChatGPT) can't run workflows** because they're conversational.
* **Autonomous (e.g. Auto-GPT) run them unreliably** because they have too much freedom.
Agentflow offers a balanced solution: Workflows that LLMs follow step-by-step.
## Install and Use
Agentflow is currently in development. To try it:
1. Sign up for the [OpenAI API](https://platform.openai.com/overview) and get an [API key](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key)
2. Clone or download this repository.
3. Create a `.env` file from [example.env](https://github.com/simonmesmith/agentflow/blob/main/example.env) and add your OpenAI API key.
4. Run `pip install -r requirements.txt` to install dependencies.
Now you can run flows from the command line, like this:
```bash
python -m run --flow=example
```
### Optional Arguments
#### Use `variables` to pass variables to your flow
```bash
python -m run --flow=example_with_variables --variables 'market=college students' 'price_point=$50'
```
#### Use `v` (verbose) to see task completion in real-time
```bash
python -m run --flow=example -v
```
## Create New Flows
Copy [example.json](https://github.com/simonmesmith/agentflow/blob/main/agentflow/flows/example.json) or [example_with_variables.json](https://github.com/simonmesmith/agentflow/blob/main/agentflow/flows/example_with_variables.json) or create a flow from scratch in this format:
```json
{
"system_message": "An optional message that guides the model's behavior.",
"tasks": [
{
"action": "Instruct the LLM here!"
},
{
"action": "Actions can have settings, including function calls and temperature, like so:",
"settings": {
"function_call": "save_file",
"temperature": 0.5
}
},
{
"action": "..."
}
]
}
```
## Create New Functions
Copy [save_file.py](https://github.com/simonmesmith/agentflow/blob/main/agentflow/functions/save_file.py) and modify it, or follow these instructions (replace "function_name" with your function name):
1. **Create `function_name.py` in the [functions](https://github.com/simonmesmith/agentflow/tree/main/agentflow/functions) folder**.
2. **Create a class within called `FunctionName`** that inherits from `BaseFunction`.
3. **Add `get_definition()` and `execute()` in the class**. See descriptions of these in `BaseFunction`.
That's it! You can now use your function in `function_call` as shown above. However, you should probably:
4. **Add tests in [tests](https://github.com/simonmesmith/agentflow/tree/main/tests)**! Then you'll know if workflows are failing because of your function.
## License
Agentflow is licensed under the [MIT License](https://github.com/simonmesmith/agentflow/blob/main/LICENSE).