

Agentk
Overview :
AgentK is a self-evolving modular self-agent general artificial intelligence (AGI) model composed of multiple collaborating agents that can construct new agents based on user needs to accomplish tasks. Built on the LangGraph and LangChain frameworks, it possesses self-testing and self-repair capabilities, designed to be a minimal set of agents and tools for self-guidance and evolution of intelligence.
Target Users :
AgentK is primarily designed for developers and AI researchers who can leverage it for rapid prototyping and testing of AI models. Its self-evolving nature makes it particularly suitable for complex applications that need to continuously learn and adapt to new tasks.
Use Cases
Developers use AgentK to construct intelligent systems that can automatically collect and analyze market data.
Researchers utilize AgentK to experiment with and validate theories of artificial intelligence.
Businesses adopt AgentK to automate complex business processes, enhancing efficiency and decision-making quality.
Features
Hermes: Interacts with humans, understands goals, manages task creation and allocation, and coordinates activities of other agents.
AgentSmith: Responsible for creating and maintaining the architecture of other agents, ensuring they have the necessary tools and are functionally tested.
ToolMaker: Developer of internal tools within the system, creating and refining tools needed by agents to complete tasks, ensuring system flexibility and comprehensiveness.
WebResearcher: Knowledge gatherer that conducts in-depth online research to provide the system with up-to-date information, assisting agents in making informed decisions and executing tasks effectively.
Support for LangGraph and LangChain frameworks: Built on these two frameworks, providing robust underlying support.
Runs in Docker containers: AgentK operates independently in Docker containers, facilitating management and deployment.
How to Use
1. Install the latest version of Docker.
2. Copy `.env.template` to create a `.env` file.
3. Set the environment variables in the `.env` file.
4. Run `./agentk` to start AgentK.
5. Interact with AgentK via the provided API or interface to define tasks and objectives.
6. Observe how AgentK creates and manages agents to accomplish tasks.
7. Adjust and optimize AgentK as needed.
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