Continuum is an open-source Python framework that enables developers to construct intelligent agents by defining tasks, tools, and memory in a composable manner. Agents built with Continuum follow a plan-execute-observe loop, allowing interleaving of LLM reasoning with external API calls or scripts. Its pluggable architecture supports multiple memory stores (e.g., Redis, SQLite), custom tool libraries, and asynchronous execution. With a focus on flexibility, users can write custom agent policies, integrate third-party services like databases or webhooks, and deploy agents across environments. Continuum’s event-driven orchestration logs agent actions, facilitating debugging and performance tuning. Whether automating data ingestion, building conversational assistants, or orchestrating DevOps pipelines, Continuum provides a scalable foundation for production-grade AI agent workflows.

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.

Operit is a comprehensive open-source AI agent framework designed to streamline the creation of autonomous agents for various tasks. By integrating with LLMs like OpenAI’s GPT and local models, it enables dynamic reasoning across multi-step workflows. Users can define custom plugins to handle data fetching, web scraping, database queries, or code execution, while Operit manages session context, memory, and tool invocation. The framework offers a clear API for building, testing, and deploying agents with persistent state, configurable pipelines, and error-handling mechanisms. Whether you’re developing customer support bots, research assistants, or business automation agents, Operit’s extensible architecture and robust tooling ensure rapid prototyping and scalable deployments.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.

X AI Agent is a developer-focused framework that simplifies building custom AI agents using large language models. It provides native support for function calling, memory storage, tool and plugin integration, chain-of-thought reasoning, and orchestration of multi-step tasks. Users can define custom actions, connect external APIs, and maintain conversational context across sessions. The framework’s modular design ensures extensibility and allows seamless integration with popular LLM providers, enabling robust automation and decision-making workflows.