Agentic Workflow is a declarative framework enabling developers to define complex AI workflows by chaining multiple LLM-based agents, each with customizable roles, prompts, and execution logic. It provides built-in support for task orchestration, state management, error handling, and plugin integrations, allowing seamless interaction between agents and external tools. The library uses Python and YAML-based configurations to abstract agent definitions, supports asynchronous execution flows, and offers extensibility through custom connectors and plugins. As an open-source project, it includes detailed examples, templates, and documentation to help teams accelerate development and maintain complex AI agent ecosystems.

CL4R1T4S empowers developers to build AI agents by offering core abstractions: Agent, Memory, Tools, and Chain. Agents can use LLMs to process input, call external functions, and maintain context across sessions. Memory modules allow storing conversation history or domain knowledge. Tools can wrap API calls, allowing agents to fetch data or perform actions. Chains define sequential steps for complex tasks like document analysis, data extraction, or iterative querying. The framework handles prompt templates, function calling, and error handling transparently. With CL4R1T4S, teams can prototype chatbots, automations, and decision support systems, leveraging Clojure’s functional paradigm and rich ecosystem.

Multi-Agent ColComp is an extensible, open-source framework for orchestrating a team of AI agents to work together on complex tasks. Developers can define distinct agent roles, configure communication channels, and share contextual data through a unified memory store. The library includes plug-and-play components for negotiation, coordination, and consensus building. Example setups demonstrate collaborative text generation, distributed planning, and multi-agent simulation. Its modular design supports easy extension, enabling teams to prototype and evaluate multi-agent strategies rapidly in research or production environments.

Noema Declarative AI allows developers and researchers to specify AI agents and their workflows in a high-level, declarative manner. By writing YAML or JSON configuration files, you define agents, prompts, tools, and memory modules. The Noema runtime then parses these definitions, loads language models, executes each step of your pipeline, handles state and context, and returns structured results. This approach reduces boilerplate, improves reproducibility, and separates logic from execution, making it ideal for prototyping chatbots, automation scripts, and research experiments.

Emma-X provides a modular agent orchestration platform for building conversational AI assistants using large language models. Developers can define agent behaviors via JSON configurations, select LLM providers like OpenAI, Hugging Face, or local endpoints, and attach external tools such as search, database, or custom APIs. The built-in memory layer preserves context across sessions, while the UI components handle chat rendering, file uploads, and interactive prompts. Plugin hooks allow real-time data fetching, analytics, and custom action buttons. Emma-X ships with example agents for customer support, content creation, and code generation. Its open architecture lets teams extend agent capabilities, integrate with existing web applications, and quickly iterate on conversation flows without deep LLM expertise.

Junjo Python API is an SDK that empowers developers to integrate AI agents into Python applications. It provides a unified interface for defining agents, connecting to LLMs, orchestrating tools like web search, databases, or custom functions, and maintaining conversational memory. Developers can build chains of tasks with conditional logic, stream responses to clients, and handle errors gracefully. The API supports plugin extensions, multilingual processing, and real-time data retrieval, enabling use cases from automated customer support to data analysis bots. With comprehensive documentation, code samples, and Pythonic design, Junjo Python API reduces time-to-market and operational overhead of deploying intelligent agent-based solutions.