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Software-Entwicklungskits

  • StableAgents enables creation and orchestration of autonomous AI agents with modular planning, memory, and tool integrations.
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    What is StableAgents?
    StableAgents provides a comprehensive toolkit to create autonomous AI agents that can plan, execute, and adapt complex workflows using large language models. It supports modular components including planners, memory stores, tools, and evaluators. Agents can access external APIs, perform retrieval-augmented tasks, and store conversation or interaction context. The framework comes with a CLI and Python SDK, enabling local development or cloud deployment. Through its plugin architecture, StableAgents integrates with popular LLM providers and vector databases and includes monitoring dashboards and logging for performance tracing.
  • Automatically scaffold Python-based AI agents using predefined templates, integrating LangChain, OpenAI and custom tools for rapid development.
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    What is AI Agent Code Generator?
    AI Agent Code Generator provides a command-line interface to scaffold Python projects for AI agents. Users select from multiple LangChain-based templates, configure their OpenAI API keys, and specify custom tools or functions. The tool then generates boilerplate code, project structure, and sample scripts to deploy conversational, information-retrieval, or task-automation agents. Developers can extend the generated code with additional plugins, modify prompts, and integrate new toolkits for specialized agent behavior, accelerating prototype and production development.
  • MASlite is a lightweight Python multi-agent system framework for defining agents, messaging, scheduling, and environment simulation.
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    What is MASlite?
    MASlite provides a clear API to create agent classes, register behaviors, and handle event-driven messaging between agents. It includes a scheduler to manage agent tasks, environment modeling to simulate interactions, and a plugin system to extend core capabilities. Developers can rapidly prototype multi-agent scenarios in Python by defining agent lifecycle methods, connecting agents via channels, and running simulations in a headless mode or integrating with visualization tools.
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