scenario-go serves as a robust framework for constructing AI agents in Go by allowing developers to author scenario definitions that specify step-by-step interactions with large language models. Each scenario can incorporate prompt templates, custom functions, and memory storage to maintain conversational state across multiple turns. The toolkit integrates with leading LLM providers via RESTful APIs, enabling dynamic input-output cycles and conditional branching based on AI responses. With built-in logging and error handling, scenario-go simplifies debugging and monitoring of AI workflows. Developers can compose reusable scenario components, chain multiple AI tasks, and extend functionality through plugins. The result is a streamlined development experience for building chatbots, data extraction pipelines, virtual assistants, and automated customer support agents fully in Go.

Wizard Language is a declarative domain-specific language built on TypeScript for authoring AI assistants as wizards. Developers define intent-driven steps, prompts, tool invocations, memory stores, and branching logic in a concise DSL. Under the hood, Wizard Language compiles these definitions into orchestrated LLM calls, managing context, asynchronous flows, and error handling. It accelerates prototyping of chatbots, data retrieval assistants, and automated workflows by abstracting prompt engineering and state management into reusable components.

AgentMesh provides a modular infrastructure for developers to create networks of AI agents, each focusing on a specific task or domain. Agents can be dynamically discovered and registered at runtime, exchange messages asynchronously, and follow configurable routing rules. The framework handles retries, fallbacks, and error recovery, allowing multi-agent pipelines for data processing, decision support, or conversational use cases. It integrates easily with existing LLMs and custom models via a simple plugin interface.

AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.

AtomicAgent provides a structured framework for defining, composing, and executing AI agent tasks. Core modules include a tool registry to register and invoke external services, a memory manager to persist conversational or task context, and an orchestration engine that drives LLM interactions step by step. Developers can define reusable tools, configure decision logic, and leverage asynchronous execution for long-running tasks. AtomicAgent’s modular design promotes maintainability, testability, and rapid iteration of complex AI-driven workflows, from chatbots to data processing pipelines.

Council provides a structured environment for designing AI agents by defining roles, chaining tasks, and integrating external tools or APIs. Users can configure memory stores, manage agent state, and implement custom reasoning pipelines. Council’s plugin architecture allows seamless integration with NLP services, data sources, and third-party tools, enabling you to rapidly prototype and deploy multi-agent systems that coordinate to perform complex tasks reliably.

Exo is a developer-centric framework enabling the creation of AI-driven agents capable of communicating with users, invoking external APIs, and preserving conversational context. At its core, Exo uses TypeScript definitions to describe tools, memory layers, and dialogue management. Users can register custom actions for tasks like data retrieval, scheduling, or API orchestration. The framework automatically handles prompt templates, message routing, and error handling. Exo’s memory module can store and recall user-specific information across sessions. Developers deploy agents in Node.js or serverless environments with minimal configuration. Exo also supports middleware for logging, authentication, and metrics. Its modular design ensures components can be reused across multiple agents, accelerating development and reducing redundancy.

Modus is a developer-focused framework that simplifies the creation of AI agents by providing core components for LLM integration, memory storage, and tool orchestration. It supports plugin-based tool libraries, enabling agents to perform tasks like data retrieval, analysis, and action execution. With built-in memory modules, agents can maintain conversational context and learn over interactions. Its extensible architecture accelerates AI development and deployment across various applications.

FastMCP is an open-source Python framework for building MCP (Model Context Protocol) servers and clients that empower LLMs with external tools, data sources, and custom prompts. Developers define tool classes and resource handlers in Python, register them with the FastMCP server, and deploy using transport protocols like HTTP, STDIO, or SSE. The framework’s client library offers an asynchronous interface for interacting with any MCP server, facilitating seamless integration of AI agents into applications.