It is a Python library called DAGent designed to help developers quickly create AI agents using their existing Python code. DAGent structures AI agents into workflows by organizing functions as nodes in a directed acyclic graph (DAG). The agentic behavior is achieved through the use of large language models (LLMs) to infer which function to execute, abstracted by a “Decision Node.” This approach allows developers to build AI agents without significant overhead, leveraging their current codebase.
DAGent supports the use of different LLM models for inference and tool description generation. Developers can specify the model when calling functions like `call_llm` or `call_llm_tool`, or when compiling a DecisionNode. For example, the library can integrate with models like `groq/llama3-70b-8192`. The library also includes features such as `FunctionNode` and `Tool` to define specific tasks and tools within the workflow. Additionally, the `prev_output` parameter allows functions to utilize outputs from previous nodes.
The project encourages user feedback, which is taken seriously, and provides documentation for exploring all available qualifiers. A quickstart example is available in the repository under `dagent/examples/quickstart_simple_agent.py`. DAGent is developed by Extensible-AI and is open-source, with contributions welcomed on GitHub. The repository includes resources like installation instructions, basic usage, and diagrams to help users get started.
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