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Basic Information

Isek is a framework for building decentralized agent-to-agent (A2A) networks that enable intelligent, collaborative, self-organizing agents to discover peers, share context, and cooperatively solve tasks without centralized orchestration. The project targets developers and researchers who want to prototype, deploy, and manage multi-node agent societies that integrate large language models. It provides core modules for agent logic, node orchestration, inter-agent protocols, memory and state, model backends, team formation, and tools for composing multi-agent workflows. The repository includes a developer-friendly CLI, examples that range from single agents to team-based scenarios, and documentation to help users set up environment variables for model access. Isek emphasizes model-agnostic intelligence and distributed deployments from local clusters to global swarms.

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Features
Isek offers autonomous peer discovery and decentralized cooperation so agents can recruit peers and collaborate without a single point of failure. It supports distributed deployment for multi-node and cloud scenarios and integrates LLM backends such as OpenAI through a model-agnostic interface. The codebase is modular and extensible, with directories for agent logic, node orchestration, communication protocol, memory, team coordination, and tools. A CLI simplifies setup, example listing, and running prebuilt scenarios. The project includes examples demonstrating single-agent and team-agent behaviors, a local registry called isek_center for coordination, and utilities to manage state and extend functionality. The README highlights cross-language prerequisites including Python 3.10+ and Node.js 18+ for P2P features.
Use Cases
Isek helps developers and researchers create and experiment with decentralized multi-agent systems by providing reusable components and workflows for agent discovery, context sharing, and cooperative problem solving. Its model-agnostic design lets teams plug in different LLMs or backends, and the CLI plus example scripts accelerate prototyping and demonstration of agent behaviors. The framework"s modular structure makes it straightforward to extend agents with custom tools, memory, and protocols, and to deploy networks across local clusters or distributed environments. Documentation, a local registry module, and example levels for single and team agents lower the barrier to testing coordination patterns. The project is open-source under the MIT license and invites contributions, which is useful for collaborative research and iterative development.

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