Agentic AI Workflow is an extensible Python library designed to orchestrate multiple AI agents for complex task automation. It includes a planning agent to break down objectives into actionable steps, execution agents to perform those steps via connected LLMs, and a reflection agent to review outcomes and refine strategies. Developers can customize prompt templates, memory modules, and connector integrations for any major language model. The framework provides reusable components, logging, and performance metrics to streamline the creation of autonomous research assistants, content pipelines, and data processing workflows.

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.

AWS Agentic Workflows is a serverless orchestration framework that lets you chain AI tasks into end-to-end workflows. Using Amazon Bedrock foundation models, you can invoke AI agents to perform natural language processing, classification, or custom tasks. AWS Step Functions manages state transitions, retries, and parallel execution. Lambda functions can preprocess inputs and post-process outputs. CloudWatch provides logs and metrics for real-time monitoring and debugging. This enables developers to build reliable, scalable AI pipelines without managing servers or infrastructure.

Agree.com offers a comprehensive solution for managing contracts, negotiations, and payments in one place. With AI-powered workflows, users can create, edit, and sign agreements securely. Integrated invoicing ensures faster payment collection, while the platform syncs transactions with major accounting software. Designed for individuals, teams, and enterprises, Agree.com facilitates collaboration, compliance, and revenue optimization through its intuitive features.

AI Agent Example is a hands-on demonstration repository for developers and researchers interested in building intelligent agents powered by large language models. The project includes sample code for agent planning, memory storage, and tool invocation, showcasing how to integrate external APIs or custom functions. It features a simple conversational interface that interprets user intents, formulates action plans, and executes tasks by calling predefined tools. Developers can follow clear patterns to extend the agent with new capabilities, such as scheduling events, web scraping, or automated data processing. By providing a modular architecture, this template accelerates experimentation with AI-driven workflows and personalized digital assistants while offering insights into agent orchestration and state management.

AirOps leverages scalable AI workflows to optimize business operations through easy-to-use tools. Users can create, customize, and deploy AI applications without extensive programming knowledge. With a library of templates and powerful integrations, AirOps supports various use cases, from content creation to data analytics, ensuring users can harness AI's full potential.

Autogpt is a developer-focused Rust framework for constructing autonomous AI agents. It offers typed interfaces to the OpenAI API, built-in memory handling, context chaining, and extensible plugin support. Agents can be configured to perform chained prompts, maintain conversation state, and execute dynamic tasks programmatically. Suitable for embedding in CLI tools, backend services, or research prototypes, Autogpt simplifies orchestration of complex AI workflows while leveraging Rust’s performance and safety guarantees.

Swarms is a developer-focused framework enabling the creation, orchestration, and execution of multi-agent AI workflows. You define agents with specific roles, configure their behavior via LLM prompts, and link them to external tools or APIs. Swarms manages inter-agent communication, task planning, and memory persistence. Its plugin architecture allows seamless integration of custom modules—such as retrievers, databases, or monitoring dashboards—while built-in connectors support popular LLM providers. Whether you need coordinated data analysis, automated customer support, or complex decision-making pipelines, Swarms provides the building blocks to deploy scalable, autonomous agent ecosystems.

LangGraph provides a graph-based abstraction for designing AI agent workflows. Developers define nodes that represent prompts, tools, data sources, or decision logic, then connect these nodes with edges to form a directed graph. At runtime, LangGraph traverses the graph, executing LLM calls, API requests, and custom functions in sequence or in parallel. Built-in support for caching, error handling, logging, and concurrency ensures robust agent behavior. Extensible node and edge templates let users integrate any external service or model, making LangGraph ideal for building chatbots, data pipelines, autonomous workers, and research assistants without complex boilerplate code.

Empler AI revolutionizes how professionals and businesses operate by harnessing the power of AI. The extension provides versatile tools like content rewriting, summarization, translation, and grammar checking. Users can easily access a vast library of AI templates, helping them craft emails, reports, and documents more efficiently. By simplifying complex tasks, Empler AI empowers users to focus on what truly matters—growing their business.

FlowOps delivers a visual, no-code environment where users define AI agents as sequential workflows. Through its intuitive drag-and-drop builder, you can assemble modules for LLM interactions, vector store lookups, external API calls, and custom code execution. Advanced features include conditional branching, looping constructs, and error handling to build robust pipelines. It integrates with popular LLM providers (OpenAI, Anthropic), databases (Pinecone, Weaviate), and REST services. Once designed, workflows can be deployed instantly as scalable APIs with built-in monitoring, logging, and version control. Collaboration tools allow teams to share and iterate on agent designs. FlowOps is ideal for creating chatbots, automated document extractors, data analysis workflows, and end-to-end AI-driven business processes without writing a single line of infrastructure code.

Functionary provides a declarative way to register custom tools — JavaScript functions encapsulating API calls, database queries, or business logic. It wraps an LLM interaction to analyze user prompts, determine which tools to execute, and parse the tool outputs back into conversational responses. The framework supports memory, error handling, and chaining of actions, offering hooks for pre- and post-processing. Developers can quickly spin up agents capable of dynamic function orchestration without boilerplate, enhancing control over AI-driven workflows.

GenAI Processors provides a library of reusable, configurable processors to build end-to-end generative AI workflows. Developers can ingest documents, break them into semantic chunks, generate embeddings, store and query vectors, apply retrieval strategies, and dynamically construct prompts for large language model calls. Its plug-and-play design allows easy extension of custom processing steps, seamless integration with Google Cloud services or external vector stores, and orchestration of complex RAG pipelines for tasks such as question answering, summarization, and knowledge retrieval.

Glif serves as an AI sandbox where anyone can construct their AI-driven workflows, image generators, and interactive applications without coding. It blends creativity and technology by offering tools for generating captivating visuals and stories. Users initiate projects, explore various prompts, and build dynamic applications that suit their needs, all while having the freedom to experiment and innovate. From generative art to AI chatbots, Glif empowers users to turn their ideas into reality in an accessible manner.

Ollama Workflows is an open-source library of configurable AI agent pipelines built on top of the Ollama LLM framework. It offers dozens of ready-made workflows—like summarization, translation, code review, data extraction, email drafting, and more—that can be chained together in YAML or JSON definitions. Users install Ollama, clone the repository, select or customize a workflow, and run it via CLI. All processing happens locally on your machine, preserving data privacy while allowing you to iterate quickly and maintain consistent output across projects.

Hero is a consumer product designed for creating, organizing, and sharing custom link pages, referred to as Hero Pages. Users can compile lists of links, photos, text, and other content to share with friends, family, or colleagues. The platform is user-friendly and helps people keep track and disseminate information smoothly, making it ideal for personal, business, or community use.

InfantAgent offers a lightweight structure for designing and deploying intelligent agents in Python. It integrates with popular LLMs (OpenAI, Hugging Face), supports persistent memory modules, and enables custom tool chains. Out of the box, you get a conversational interface, task orchestration, and policy-driven decision making. The framework’s plugin architecture allows easy extension for domain-specific tools and APIs, making it ideal for prototyping research agents, automating workflows, or embedding AI assistants into applications.

Julep AI is an open-source platform designed to help data science teams quickly build, iterate on, and deploy multi-step AI workflows. With Julep, you can create scalable, durable, and long-running AI pipelines using agents, tasks, and tools. The platform's YAML-based configuration simplifies complex AI processes and ensures production-ready workflows. It supports rapid prototyping, modular design, and seamless integration with existing systems, making it ideal for handling millions of concurrent users while providing full visibility into AI operations.

Jupyter AI Agents is a framework that embeds autonomous AI assistants within Jupyter Notebook and JupyterLab environments. It allows users to create, configure, and run multiple agents capable of executing a range of tasks such as data analysis, code generation, debugging, web scraping, and knowledge retrieval. Each agent maintains contextual memory and can be chained together for complex workflows. With simple magic commands and Python APIs, users integrate agents seamlessly with existing Python libraries and datasets. Built on top of popular LLMs, it supports custom prompt templates, agent-to-agent communication, and real-time feedback. This platform transforms traditional notebook workflows by automating repetitive tasks, accelerating prototyping, and enabling interactive AI-driven exploration directly in the development environment.

RagFormation offers an end-to-end solution for implementing retrieval-augmented generation workflows. The platform ingests various data sources, including documents, web pages, and databases, and extracts embeddings using popular LLMs. It seamlessly connects with vector databases like Pinecone, Weaviate, or Qdrant to store and retrieve contextually relevant information. Users can define custom prompts, configure conversation flows, and deploy interactive chat interfaces or RESTful APIs for real-time question answering. With built-in monitoring, access controls, and support for multiple LLM providers (OpenAI, Anthropic, Hugging Face), RagFormation enables teams to rapidly prototype, iterate, and operationalize knowledge-driven AI applications at scale, minimizing development overhead. Its low-code SDK and comprehensive documentation accelerate integration into existing systems, ensuring seamless collaboration across departments and reducing time-to-market.