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.

AI Agents is a comprehensive Python framework designed to streamline the development of intelligent software agents. It offers plug-and-play toolkits for integrating external services such as web search, file I/O, and custom APIs. With built-in memory modules, agents maintain context across interactions, enabling advanced multi-step reasoning and persistent conversations. The framework supports multiple LLM providers, including OpenAI and open-source models, allowing developers to switch or combine models easily. Users define tasks, assign tools and memory policies, and the core engine orchestrates prompt construction, tool invocation, and response parsing for seamless agent operation.

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.

Agentfy provides a modular architecture for constructing AI agents by combining LLMs, memory backends, and tool integrations into a cohesive runtime. Developers declare agent behavior using Python classes, register tools (REST APIs, databases, utilities), and choose memory stores (local, Redis, SQL). The framework orchestrates prompts, actions, tool calls, and context management to automate tasks. Built-in CLI and Docker support enable one-step deployment to cloud, edge, or desktop environments.

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.

CL4R1T4S empowers developers to build AI agents by offering core abstractions: Agent, Memory, Tools, and Chain. Agents can use LLMs to process input, call external functions, and maintain context across sessions. Memory modules allow storing conversation history or domain knowledge. Tools can wrap API calls, allowing agents to fetch data or perform actions. Chains define sequential steps for complex tasks like document analysis, data extraction, or iterative querying. The framework handles prompt templates, function calling, and error handling transparently. With CL4R1T4S, teams can prototype chatbots, automations, and decision support systems, leveraging Clojure’s functional paradigm and rich ecosystem.

CUPCAKE AGI (Composable Utilitarian Pipeline for Creative, Knowledgeable, and Evolvable Autonomous General Intelligence) is a flexible Python framework that simplifies building autonomous agents by combining language models, memory, and external tools. It offers core modules including a goal planner, a model executor, and a memory manager to retain context across interactions. Developers can extend functionality via plugins to integrate APIs, databases, or custom toolkits. CUPCAKE AGI supports both synchronous and asynchronous workflows, making it ideal for research, prototyping, and production-grade agent deployments across diverse applications.

Dialogflow Fulfillment Library provides a structured way to connect your Dialogflow agent to custom backend logic via webhooks. It offers built-in response builders for cards, suggestion chips, quick replies, and payloads, as well as context management and parameter extraction. Developers can define intent handlers in a concise map, leverage middleware for preprocessing, and integrate with Actions on Google for voice applications. Deployment to Google Cloud Functions is straightforward, ensuring scalable, secure, and maintainable conversational services.

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.

Embabel Agent provides a structured approach for building AI agents in Node.js and browser environments. Developers define tools—such as HTTP fetchers, database connectors, or custom functions—and configure agent behaviors through simple JSON or JavaScript classes. The framework maintains conversation history, routes queries to the appropriate tool, and supports plugin extensions. Embabel Agent is ideal for creating chatbots with dynamic capabilities, automated assistants that interact with multiple APIs, and research prototypes that require on-the-fly orchestration of AI calls.

Ernie Bot Agent is a developer framework designed to streamline the creation of AI-driven conversational agents using Baidu ERNIE Bot. It provides abstractions for API calls, prompt templates, memory management, and tool integration. The SDK supports multi-turn conversations with context awareness, custom workflows for task execution, and a plugin system for domain-specific extensions. With built-in logging, error handling, and configuration options, it reduces boilerplate and enables rapid prototyping of chatbots, virtual assistants, and automation scripts.

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.

Kaizen is an advanced AI agent framework designed to simplify creation and management of autonomous LLM-driven agents. It provides a modular architecture for defining multi-step workflows, integrating external tools via APIs, and storing context in memory buffers to maintain stateful conversations. Kaizen's pipeline builder enables chaining prompts, executing code, and querying databases within a single orchestrated run. Built-in logging and monitoring dashboards offer real-time insights into agent performance and resource usage. Developers can deploy agents on cloud or on-premise environments with autoscaling support. By abstracting LLM interactions and operational concerns, Kaizen empowers teams to rapidly prototype, test, and scale AI-driven automation across domains like customer support, research, and DevOps.