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https://github.com/extensible-ai/dagent
Build AI Agents with Your Existing Python Code!
https://github.com/extensible-ai/dagent
agent agents ai ai-agents dag framework llama llm ollama openai python
Last synced: 3 months ago
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Build AI Agents with Your Existing Python Code!
- Host: GitHub
- URL: https://github.com/extensible-ai/dagent
- Owner: Extensible-AI
- License: mit
- Created: 2024-05-31T19:53:12.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-26T14:55:40.000Z (3 months ago)
- Last Synced: 2024-10-27T10:13:27.036Z (3 months ago)
- Topics: agent, agents, ai, ai-agents, dag, framework, llama, llm, ollama, openai, python
- Language: Python
- Homepage:
- Size: 374 KB
- Stars: 50
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DAGent - Directed Acyclic Graphs (DAGs) as AI Agents
*DAGent is an opinionated Python library to create AI Agents quickly without overhead*
[](https://pepy.tech/project/dagent)
## Quickstart
### Installing
- `pip install dagent` or `rye add dagent`
- Make sure you have the API key of your choice available in system. The default is `OPENAI_API_KEY`
### Get right into it
See [dagent/examples/quickstart_simple_agent.py](dagent/examples/quickstart_simple_agent.py) for a quickstart example
### DAGent basics
The idea behind dagent is to structure AI agents in to a workflow. This is done through setting each function up as a node in a graph.
The agentic behavior is through the inferring of what function to run through the use of LLMs which is abstracted by a "Decision Node".
`Tool`
- A tool is just a function which the LLM can use.
- It is helpful to have docstrings and annotations to assist the llm infer what is happening. This is recommended for larger functions/tools.
`FunctionNode`
- Runs a python function
- Can be attached to a `DecisionNode` to be treated as a tool and allow an LLM to choose which function to run
`DecisionNode`
- This is where the llm picks a function to run from given options
- The `.compile()` method autogenerates and saves tool descriptions under Tool. Run with param `force_load=True` if there are errors or if an option of tool changes
- These tool/function descriptions get generated under a `Tool_JSON` folder. Feel free to edit tool descriptions if the agent is unreliable.
`prev_output` param for functions:
- If passing data from one function to another, make sure this param is in the function signature.
- If extra params get passed in/weird stuff happens add a `**kwargs` to see if there are any hidden params which were passed and need to be handled
### DAGent Diagram
```mermaid
graph TD
A[Function Node] --> B[Decision Node]
B --> C[Function Node]
B --> E[Function Node]
D --> F[Function Node]
E --> G[Decision Node]
F --> H[Function Node]
G --> K[Function Node]
G -- "Pick Function to Run" --> I[Function Node]
G --> J[Function Node]
I --> L[Function Node]
J --> M[Function Node]
K --> N[Function Node]
K -- "Run Both " --> S[Function Node]
%% Additional annotations
B -- "Use a Function as a tool" --> D[Function Node]
```
## Using Different Models
DAGent supports using different LLM models for inference and tool description generation. You can specify the model when calling `call_llm` or `call_llm_tool`, or when compiling the DecisionNode.
For example, to use the `groq/llama3-70b-8192` model:
```python
# Using groq with decision node
decision_node1 = DecisionNode('groq/llama3-70b-8192')
# Using ollama with decision node
decision_node2 = DecisionNode('ollama_chat/llama3.1', api_base="http://localhost:11434")
# Call llm function
output = decision_node2.run(messages=[{'role': 'user', 'content': 'add the numbers 2 and 3'}])
```
### Other things to know
- `prev_output` is needed in the function signature if you want to use the value from the prior function's value. Obviously the prior function should have returned something for this to work
- If there are errors with too many params being passed into a function node, add `**kwargs` to your function
- Args can be overriden at any time using the following (this merges the kwargs in the background with priority to the user):
```python
add_two_nums_node.user_params = {
# param_name : value
a : 10
}
```
## Feedback
If you are interested in providing feedback, please use [this form](https://docs.google.com/forms/d/14EHPUEYGVV-eNj6HyUaQYokHUtouYOeWfriwHaHOARE).
## Acknowledgements
Shoutout to:
- [@omkizzy](https://x.com/omkizzy)
- [@kaelan](https://github.com/Oasixer)