AgentKit provides a framework for building multifunctional LLM agents by visually constructing complex "thought processes" using directed acyclic graphs (DAGs) of natural language prompts. It targets users who want to design agent logic without extensive coding, enabling the creation of sophisticated workflows by chaining simple prompt-based nodes.

How It Works

The core of AgentKit is its graph-based approach, where each node represents a specific subtask defined by a natural language prompt. These nodes are linked via dependency specifications, forming a DAG that dictates the execution order. This explicit construction of a "thought process" allows for modularity and the integration of diverse functionalities. AgentKit supports dynamic modification of the DAG at inference time, enabling advanced capabilities like branching based on LLM responses.

Quick Start & Requirements

• Install via pip: pip install agentkit-llm (or with extras like [all] for full LLM support).
• Prerequisites: Python, and optionally API keys for supported LLMs (OpenAI, Anthropic, Ollama).
• Documentation: https://agentkit.readthedocs.io/

Highlighted Details

• Visual, code-free agent construction using prompt chains.
• Supports dynamic DAG modification for advanced logic.
• Integrates with various LLM providers including OpenAI, Anthropic, and Ollama.
• Tracks token usage per node.

Maintenance & Community

The project is associated with authors from Carnegie Mellon University and has an ArXiv paper detailing its methodology.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive license suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

The framework relies heavily on LLM API calls, and performance/reliability is dependent on the chosen LLM provider and network connectivity. Debugging complex graph evaluations may require setting verbose=True on nodes and LLM_API_FUNCTION.debug=True.