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.

AgentInteraction is a developer-focused Python framework designed to simulate, coordinate, and evaluate multi-agent interactions using large language models. It allows users to define distinct agent roles, control conversational flow through a central manager, and integrate any LLM provider via a consistent API. With features like message routing, context management, and performance analytics, AgentInteraction streamlines experimentation with collaborative or competitive agent architectures, making it easy to prototype complex dialogue scenarios and measure success rates.

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.

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.

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.

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.

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.

Magi MDA is a developer-centric AI agent framework that simplifies the creation and deployment of autonomous agents. It exposes a set of core components—planners, executors, interpreters, and memories—that can be assembled into custom pipelines. Users can hook into popular LLM providers for text generation, add retrieval modules for knowledge augmentation, and integrate arbitrary tools or APIs for specialized tasks. The framework handles step-by-step reasoning, tool routing, and context management automatically, allowing teams to focus on domain logic rather than orchestration boilerplate.

Noema Declarative AI allows developers and researchers to specify AI agents and their workflows in a high-level, declarative manner. By writing YAML or JSON configuration files, you define agents, prompts, tools, and memory modules. The Noema runtime then parses these definitions, loads language models, executes each step of your pipeline, handles state and context, and returns structured results. This approach reduces boilerplate, improves reproducibility, and separates logic from execution, making it ideal for prototyping chatbots, automation scripts, and research experiments.

AgentSea AI Hub is a robust AI platform and framework that streamlines end-to-end agent development and management. It features a drag-and-drop visual builder for crafting agent personas, conversation flows, and custom skills without deep coding expertise. Developers can integrate external APIs, knowledge bases, and databases, while the built-in memory management module preserves context across sessions. The platform supports multi-channel deployment including web, mobile, chat, voice, and email, ensuring seamless user interactions. Detailed performance monitoring, A/B testing, and version control enable continuous improvement. With role-based access control and collaborative workspaces, teams can efficiently coordinate on complex agent projects. AgentSea AI Hub accelerates digital worker creation, automates repetitive tasks, and enhances customer engagement through intelligent automation.

Sherpa by CartographAI is a Python-based agent framework designed to streamline the creation of intelligent assistants and automated workflows. It enables developers to define agents that can interpret user input, select appropriate LLM endpoints or external APIs, and orchestrate complex tasks such as document summarization, data retrieval, and conversational Q&A. With its plugin architecture, Sherpa supports easy integration of custom tools, memory stores, and routing strategies to optimize response relevance and cost. Users can configure multi-step pipelines where each module performs a distinct function—like semantic search, text analysis, or code generation—while Sherpa manages context propagation and fallback logic. This modular approach accelerates prototype development, improves maintainability, and empowers teams to build scalable AI-driven solutions for diverse applications.

Simple-Agent is an open-source AI agent framework written in Python that leverages the OpenAI API to create modular conversational agents. It allows developers to define tool functions that the agent can invoke, maintain context memory across interactions, and customize agent behaviors via skill modules. The framework handles request routing, action planning, and tool execution so you can focus on domain-specific logic. With built-in logging and error handling, Simple-Agent accelerates the development of AI-powered chatbots, automated assistants, and decision-support tools. It offers easy integration with custom APIs and data sources, supports asynchronous tool calls, and provides a simple configuration interface. Use it to prototype AI agents for customer support, data analysis, automation, and more. The modular architecture makes it straightforward to add new capabilities without altering core logic. Backed by community contributions and documentation, Simple-Agent is ideal for both beginners and experienced developers aiming to deploy intelligent agents quickly.

Symbol-LLM offers a modular architecture for constructing AI agents powered by large language models augmented with symbolic memory stores. It features a planner module to break down complex tasks, an executor to invoke tools, and a memory system to maintain context across interactions. With built-in toolkits like web search, calculator and code runner, plus simple APIs for custom tool integration, Symbol-LLM enables developers and researchers to rapidly prototype and deploy sophisticated LLM-based assistants for various domains including research, customer support, and workflow automation.

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.

ChatStreamAiAgent provides developers with a lightweight Python toolkit to implement AI chat agents that stream token outputs as they are generated. It supports multiple LLM providers, asynchronous event hooks, and easy integration into web or console applications. With built-in context management and prompt templating, teams can rapidly prototype conversational assistants, customer support bots, or interactive tutorials while delivering low-latency, real-time responses.