Neuron AI is an end-to-end serverless platform for creating, deploying, and managing intelligent AI agents. It supports major LLM providers (OpenAI, Anthropic, Hugging Face) and enables multi-model pipelines, conversation context handling, and automated workflows via a low-code interface or SDKs. With built-in data ingestion, vector search, and plugin integration, Neuron simplifies knowledge sourcing and service orchestration. Its auto-scaling infrastructure and monitoring dashboards ensure performance and reliability, making it ideal for enterprise-grade chatbots, virtual assistants, and automated data processing bots.

Yoo.ai is designed to streamline the end-to-end lifecycle of enterprise AI agents. Users can customize conversational flows using visual low-code interfaces, configure memory layers to maintain context across sessions, and connect to CRM, knowledge bases, and third-party APIs for real-time data. The platform offers built-in security controls, role-based access, and on-premises or cloud deployment options to meet compliance requirements. Advanced workflow automation enables agents to trigger business processes, send notifications, and generate reports. Yoo.ai also provides analytics dashboards to track user interactions, identify conversation bottlenecks, and continuously improve agent performance. Developers can extend capabilities with custom Python or Node.js functions, integrate with Slack, Microsoft Teams, and web chat widgets, and leverage versioning, A/B testing, and automated monitoring for scalable, reliable deployments.

Doraemon-Agent is an open-source Python platform and framework designed for developers to build sophisticated AI agents. It allows you to integrate custom plugins and external tools, maintain long-term memory across sessions, and execute chain-of-thought planning with multiple steps. Developers can configure agent roles, manage context, log interactions, and extend functionality through a plugin architecture. It simplifies the creation of autonomous assistants for tasks like data analysis, research support, or customer service automation.

EspressoAI provides developers with a structured environment to design, configure, and deploy AI agents powered by large language models. It supports tool registration and invocation from within agent workflows, manages conversational context via built-in memory modules, and allows chaining of prompts for multi-step reasoning. Developers can integrate external APIs, custom plugins, and conditional logic to tailor agent behavior. The framework’s modular design ensures extensibility, enabling teams to swap components, add new capabilities, or adapt to proprietary LLMs without rewriting core logic.

IoA provides a flexible architecture for defining, coordinating, and executing multiple AI agents in a unified workflow. Key components include a planner that decomposes high-level goals, an executor that dispatches tasks to specialized agents, and memory modules for context management. It supports integration with external APIs and toolkits, real-time monitoring, and customizable skill plugins. Developers can rapidly prototype autonomous assistants, customer support bots, and data processing pipelines by combining ready-made modules or extending them with custom logic.

Magma Deploy is an AI agent deployment platform that simplifies the end-to-end process of building, scaling, and monitoring intelligent assistants. Users define retrieval-augmented workflows visually, connect to any vector database, choose from OpenAI or open-source models, and configure dynamic routing rules. The platform handles embedding generation, context management, auto-scaling, and usage analytics, allowing teams to focus on agent logic and user experience rather than backend infrastructure.

Pillar is a comprehensive AI agent framework designed to simplify the development and deployment of intelligent multi-step workflows. It features a modular architecture with planners for task decomposition, memory stores for context retention, and executors that perform actions via external APIs or custom code. Developers can define agent pipelines in YAML or JSON, integrate any LLM provider, and extend functionality through custom plugins. Pillar handles asynchronous execution and context management out of the box, reducing boilerplate code and accelerating time-to-market for AI-driven applications such as chatbots, data analysis assistants, and automated business processes.

SimplerLLM provides developers a minimalistic API to compose LLM chains, define agent actions, and orchestrate tool calls. With built-in abstractions for memory retention, prompt templates, and output parsing, users can rapidly assemble conversational agents that maintain context across interactions. The framework seamlessly integrates with OpenAI, Azure, and HuggingFace models, and supports pluggable toolkits for searches, calculators, and custom APIs. Its lightweight core minimizes dependencies, allowing agile development and easy deployment on cloud or edge. Whether building chatbots, QA assistants, or task automators, SimplerLLM simplifies end-to-end LLM agent pipelines.

Wumpus LLM Agent is designed to simplify development of advanced Socratic AI agents by providing prebuilt orchestration utilities, structured prompting templates, and seamless tool integration. Users define agent personas, tool sets, and conversation flows, then leverage built-in chain-of-thought management for transparent reasoning. The framework handles context switching, error recovery, and memory storage, enabling multi-step decision processes. It includes a plugin interface for APIs, databases, and custom functions, allowing agents to browse the web, query knowledge bases, or execute code. With comprehensive logging and debugging, developers can trace each reasoning step, fine-tune agent behavior, and deploy on any platform that supports Python 3.7+.

FastAPI LangGraph Agent Template offers a comprehensive foundation for developing LLM-driven agents within a FastAPI application. It includes predefined LangGraph nodes for common tasks like text completion, embedding, and vector similarity search while allowing developers to create custom nodes and pipelines. The template manages conversation history via memory modules that persist context across sessions and supports environment-based configuration for different deployment stages. Built-in Docker files and CI/CD-friendly structure ensure seamless containerization and deployment. Logging and error-handling middleware enhance observability, while the modular codebase simplifies extending functionality. By combining FastAPI's high-performance web framework with LangGraph's orchestration capabilities, this template streamlines the agent development lifecycle from prototyping to production.

Agent Script provides a declarative scripting layer over large language models, enabling you to write YAML or JSON scripts that define agent workflows, tool calls, and memory usage. You can plug in OpenAI, local LLMs, or other providers, connect external APIs as tools, and configure long-term memory backends. The framework handles context management, asynchronous execution, and detailed logging out of the box. With minimal code, you can prototype chatbots, RPA workflows, data extraction agents, or custom control loops, making it easy to build, test, and deploy AI-powered automations.

agent-steps is a Python step orchestration framework designed to streamline the development of AI agents by breaking complex tasks into discrete, reusable steps. Each step encapsulates a specific action—such as invoking a language model, performing data transformations, or external API calls—and can pass context to subsequent steps. The library supports synchronous and asynchronous execution, enabling scalable pipelines. Built-in logging and debugging utilities provide transparency into step execution, while its modular architecture promotes maintainability. Users can define custom step types, chain them into workflows, and integrate them easily into existing Python applications. agent-steps is suitable for building chatbots, automated data pipelines, decision support systems, and other multi-step AI-driven solutions.

Swarms SDK simplifies creation, configuration, and execution of collaborative multi-agent systems using large language models. Developers define agents with distinct roles—researcher, synthesizer, critic—and group them into swarms that exchange messages via a shared bus. The SDK handles scheduling, context persistence, and memory storage, enabling iterative problem solving. With native support for OpenAI, Anthropic, and other LLM providers, it offers flexible integrations. Utilities for logging, result aggregation, and performance evaluation help teams prototype and deploy AI-driven workflows for brainstorming, content generation, summarization, and decision support.

Dive is a Python-based open-source framework designed for creating and running autonomous AI agents that can perform multi-step tasks with minimal manual intervention. By defining agent profiles in simple YAML configuration files, developers can specify APIs, tools, and memory modules for tasks such as data retrieval, analysis, and pipeline orchestration. Dive manages context, state, and prompt engineering, allowing flexible workflows with built-in error handling and logging. Its pluggable architecture supports a wide range of language models and retrieval systems, making it easy to assemble agents for customer service automation, content generation, and DevOps processes. The framework scales from prototype to production, offering CLI commands and API endpoints to integrate agents seamlessly into existing systems.

Esquilax is a lightweight TypeScript framework designed for building and orchestrating complex AI agent workflows. It provides developers with a clear API to declaratively define agents, assign memory modules, and integrate custom plugin actions such as API calls or database queries. With built-in support for context handling and multi-agent coordination, Esquilax streamlines the creation of chatbots, digital assistants, and automated processes. Its event-driven architecture allows tasks to be chained or triggered dynamically, while logging and debugging tools offer full visibility into agent interactions. By abstracting away boilerplate code, Esquilax helps teams rapidly prototype scalable AI-driven applications.

ExampleAgent is a developer-focused toolkit designed to accelerate the creation of AI-driven assistants. It integrates directly with OpenAI’s GPT models to handle natural language understanding and generation, and offers a pluggable system for adding custom tools or APIs. The framework manages conversation context, memory, and error handling, enabling agents to perform information retrieval, task automation, and decision-making workflows. With clear code templates, documentation, and examples, teams can rapidly prototype domain-specific agents for chatbots, data extraction, scheduling, and more.

Geers AI Lang Graph provides a graph-based abstraction layer for building AI agents that coordinate multiple LLM calls and manage structured knowledge. By defining nodes and edges representing prompts, data, and memory, developers can create dynamic workflows, track context across interactions, and visualize execution flows. The framework supports plugin integrations for various LLM providers, custom prompt templating, and exportable graphs. It simplifies iterative agent design, improves context retention, and accelerates prototyping of conversational assistants, decision-support bots, and research pipelines.

The Gemini Agent Cookbook is a curated open-source toolkit offering a variety of hands-on examples for constructing intelligent agents powered by Google’s Gemini language models. It includes sample code for orchestrating multi-step reasoning chains, dynamically invoking external APIs, integrating toolkits for data retrieval, and managing conversation flows. The cookbook demonstrates best practices for error handling, context management, and prompt engineering, supporting use cases like autonomous chatbots, task automation, and decision support systems. It guides developers through building custom agents that can interpret user requests, fetch real-time data, perform computations, and generate formatted outputs. By following these recipes, engineers can accelerate agent prototyping and deploy robust AI-driven applications in diverse domains.

GPT-agents is a developer-focused toolkit that streamlines the creation and orchestration of autonomous AI agents using GPT. It offers built-in Agent classes, a modular tool integration system, and persistent memory management to support ongoing context. The framework handles conversational planning loops and multi-agent collaboration, allowing you to assign objectives, schedule sub-tasks, and chain agents on complex workflows. It supports customizable tools, model selection, and error handling to deliver robust, scalable automation for various domains.

LangChain is a developer-focused framework designed to streamline the creation of intelligent AI agents and applications. It provides abstractions for chains of LLM calls, agentic behavior with tool integrations, memory management for context persistence, and customizable prompt templates. With built-in support for document loaders, vector stores, and various model providers, LangChain allows you to construct retrieval-augmented generation pipelines, autonomous agents, and conversational assistants that can interact with APIs, databases, and external systems in a unified workflow.