Build with ADK streamlines the creation of AI agents by providing a CLI scaffolding tool, workflow definitions, LLM integration modules, testing utilities, logging, and deployment support. Developers can initialize agent projects, select AI models, configure prompts, connect external tools or APIs, run local tests, and push their agents to production or container platforms—all with simple commands. The modular architecture allows easy extension with plugins and supports multiple programming languages for maximum flexibility.

Easy-Agent accelerates AI agent development by providing a modular framework that integrates LLMs with external tools, in-memory session tracking, and configurable action flows. Developers start by defining a set of tool wrappers that expose APIs or executables, then instantiate an agent with desired reasoning strategies—such as single-step, multi-step chain-of-thought, or custom prompts. The framework manages context, invokes tools dynamically based on model output, and tracks conversation history through session memory. It supports asynchronous execution for parallel tasks and solid error handling to ensure robust agent performance. By abstracting complex orchestration, Easy-Agent empowers teams to deploy intelligent assistants for use cases like automated research, customer support bots, data extraction pipelines, and scheduling assistants with minimal setup.

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.

Restack Python SDK Examples offer a comprehensive set of demonstration projects illustrating how to leverage the Restack platform to build AI agents. Included are templates for chatbots, document analysis agents, and task automation workflows. Examples cover API configuration, tool integration (e.g., web search, memory storage), agent orchestration, error handling, and deployment scenarios. Developers can clone the repository, configure their API keys, and extend sample agents to suit custom use cases.

FireAct Agent is an open-source React framework designed for building AI-powered conversational agents. It offers a modular architecture that lets you define custom tools, manage session memory, and render chat UIs with rich message types. With TypeScript typings and server-side rendering support, FireAct Agent streamlines the process of connecting LLMs, invoking external APIs or functions, and maintaining conversational context across interactions. You can customize styling, extend core components, and deploy on any web environment.

FlyingAgent provides a modular architecture that leverages large language models to simulate autonomous agents capable of reasoning, planning, and executing actions across various domains. Agents maintain an internal memory for context retention and can integrate external toolkits for tasks like web browsing, data analysis, or third-party API calls. The framework supports multi-agent coordination, plugin-based extensions, and customizable decision-making policies. With its open design, developers can tailor memory backends, tool integrations, and task managers, enabling applications in customer support automation, research assistance, content generation pipelines, and digital workforce orchestration.

LangChain Google Gemini Agent is a Python-based library designed to simplify the creation of autonomous AI agents powered by Google’s Gemini language models. It combines LangChain’s modular approach—allowing prompt chains, memory management, and tool integrations—with Gemini’s advanced natural language understanding. Users can define custom tools for API calls, database queries, web scraping, and document summarization; orchestrate them via an agent that interprets user inputs, selects appropriate tool actions, and composes coherent responses. The result is a flexible agent capable of multi-step reasoning, live data access, and context-aware dialogues, ideal for building chatbots, research assistants, and automated workflows, and supports integration with popular vector stores and cloud services for scalability.

MindForge is a robust orchestration framework designed for building and deploying AI-driven agents with minimal boilerplate. It offers a modular architecture comprising a task planner, reasoning engine, memory manager, and tool execution layer. By leveraging LLMs, agents can parse user input, formulate plans, and invoke external tools—such as web scraping APIs, databases, or custom scripts—to accomplish complex tasks. Memory components store conversational context, enabling multi-turn interactions, while the decision engine dynamically selects actions based on defined policies. With plugin support and customizable pipelines, developers can extend functionality to include custom tools, third-party integrations, and domain-specific knowledge bases. MindForge simplifies AI agent development, facilitating rapid prototyping and scalable deployment in production environments.

MIU CS589 AI Agent API offers a standardized interface for building custom AI agents. Developers can define agent behaviors, integrate external tools or services, and handle streaming or batch responses via HTTP endpoints. The framework handles authentication, request routing, error handling and logging out of the box. It is fully extensible—users can register new tools, adjust agent memory, and configure LLM parameters. Suitable for experimentation, demos, and production prototypes, it simplifies multi-tool orchestration and accelerates AI agent development without locking you into a monolithic platform.

Microsoft Copilot Studio is a powerful platform that allows users to create, customize, and deploy AI agents tailored to specific organizational needs. It features intuitive templates and user-friendly tools for accessing and fine-tuning language models, as well as seamless API integrations. The platform facilitates the automation of tasks, enhancing productivity across various applications. Users can optimize their AI agent's performance through easy testing and adjustment of parameters, ensuring the solutions meet their exact requirements.

Orra.dev is a comprehensive AI agent creation platform designed to simplify the end-to-end lifecycle of intelligent assistants. By combining a visual workflow builder with seamless integrations to leading LLM providers and enterprise systems, Orra.dev allows teams to prototype conversation logic, refine agent behavior, and launch production-ready bots across multiple channels within minutes. Features include access to pre-built templates for FAQ bots, e-commerce assistants, and code review agents, along with customizable triggers, API connectors, and user role management. With built-in testing suites, collaborative versioning, and performance dashboards, organizations can iterate on agent responses, monitor user interactions, and optimize workflows based on real-time data, accelerating deployment and reducing maintenance overhead.

BeeAI Framework Py Starter is an open-source Python project designed to bootstrap AI agent creation. It includes core modules for agent orchestration, a plugin system to extend functionality, and adapters for connecting to popular LLM APIs. Developers can define tasks, manage conversational memory, and integrate external tools through simple configuration files. The framework emphasizes modularity and ease of use, enabling rapid prototyping of chatbots, automated assistants, and data-processing agents without boilerplate code.

ByteChef offers a modular architecture to build, test, and deploy AI agents. Developers define agent profiles, attach custom skill plugins, and orchestrate multi-agent workflows through a visual web IDE or SDK. It integrates with major LLM providers (OpenAI, Cohere, self-hosted models) and external APIs. Built-in debugging, logging, and observability tools streamline iteration. Projects can be deployed as Docker services or serverless functions, enabling scalable, production-ready AI agents for customer support, data analysis, and automation.

FreeThinker provides a modular architecture for defining AI agents that can autonomously execute tasks by leveraging large language models, memory modules, and external tools. Developers can configure agents via Python or YAML, plug in custom tools for web search, data processing, or API calls, and utilize built-in planning strategies. The framework handles step-by-step execution, context retention, and result aggregation so agents can operate hands-free on research, automation, or decision-support workflows.

openai-agents-python is a comprehensive Python package designed to help developers construct fully autonomous AI agents. It provides abstractions for agent planning, tool integration, memory states, and execution loops. Users can register custom tools, specify agent goals, and let the framework orchestrate step-by-step reasoning. The library also includes utilities for testing and logging agent actions, making it easier to iterate on behaviors and troubleshoot complex multi-step tasks.

LAWLIA provides a structured interface to define agent behaviors, plugin tools, and memory management for conversational or autonomous workflows. Developers can integrate with major LLM APIs, configure prompt templates, and register custom tools like search, calculators, or database connectors. Through its Agent class, LAWLIA handles planning, action execution, and response interpretation, allowing multi-turn interactions and dynamic tool invocation. Its modular design supports extending capabilities via plugins, enabling agents for customer support, data analysis, code assistance, or content generation. The framework streamlines agent development by managing context, memory, and error handling under a unified API.

Llama-Agent is a developer-focused toolkit for creating intelligent AI agents powered by large language models. It offers tool integration to call external APIs or functions, memory management to store and retrieve context, and chain-of-thought planning to break down complex tasks. Agents can execute actions, interact with custom environments, and adapt through a plugin system. As an open-source project, it supports easy extension of core components, enabling rapid experimentation and deployment of automated workflows across various domains.

At its core, SpongeCake is a high-level abstraction layer over Langchain designed to accelerate AI agent development. It offers built-in support for registering tools—like web search, database connectors, or custom APIs—managing prompt templates, and persisting conversational memory. With both code-based and YAML-based configurations, teams can declaratively define agent behaviors, chain multi-step workflows, and enable dynamic tool selection. The included CLI facilitates local testing, debugging, and deployment, making SpongeCake ideal for building chatbots, task automators, and domain-specific assistants without repetitive boilerplate.

Agent Forge streamlines the entire lifecycle of AI agent development by offering CLI scaffold commands to generate boilerplate code, conversation templates, and configuration settings. Developers can define agent roles, attach LLM providers, and integrate external tools such as vector databases, REST APIs, and custom plugins using YAML or JSON descriptors. The framework enables local execution, interactive testing, and packaging agents as Docker images or serverless functions for easy deployment. Built-in logging, environment profiles, and VCS hooks simplify debugging, collaboration, and CI/CD pipelines. This flexible architecture supports creating chatbots, autonomous research assistants, customer support bots, and automated data processing workflows with minimal setup.

ADK-Golang is an open-source Agent Development Kit for the Go ecosystem. It provides a modular framework to register and manage tools (APIs, databases, external services), build dynamic prompt templates, and maintain conversation memory for multi-turn interactions. With built-in orchestration patterns and logging support, developers can easily configure, test, and deploy AI agents that perform tasks such as data retrieval, automated workflows, and contextual chat. ADK-Golang abstracts low-level API calls and streamlines end-to-end agent lifecycles—from initialization and planning to execution and response handling—entirely in Go.