ChatbotsList.com offers a curated collection of AI chatbots designed to assist, entertain, and make life easier. This platform serves as a comprehensive directory for users to discover chatbots tailored to various needs, from productivity and customer support to personal companionship. Whether you need a chatbot for your website, slack, or other platforms, ChatbotsList.com has something for everyone. Detailed descriptions, user reviews, and feature highlights make it simple to find the right chatbot that meets your specific requirements.

CoreLink-Persona-Engine is a modular framework that empowers developers to create AI agents with unique personas by defining personality traits, memory behaviors, and conversation flows. It provides a flexible plugin architecture to integrate knowledge bases, custom logic, and external APIs. The engine manages both short-term and long-term memory, enabling contextual continuity across sessions. Developers can configure persona profiles using JSON or YAML, connect to LLM providers like OpenAI or local models, and deploy agents on various platforms. With built-in logging and analytics, CoreLink facilitates monitoring agent performance and refining behavior, making it suitable for customer support chatbots, virtual assistants, role-playing applications, and research prototypes.

Arakoo.ai is an AI Agent platform designed to help businesses automate repetitive tasks and enhance customer interactions through intelligent virtual assistants. Users can select from a library of pre-built agent templates—such as support bots, sales assistants, and scheduling bots—or create custom agents using a visual workflow builder. The platform integrates with CRM systems, messaging apps, and ticketing tools, allowing agents to fetch data, answer queries, and escalate complex issues seamlessly. Arakoo.ai also offers analytics dashboards to track agent performance, conversation metrics, and user satisfaction. Advanced NLP capabilities ensure agents understand context and intent, while iterative training features enable continuous improvement based on real-world interactions.

Emma-X provides a modular agent orchestration platform for building conversational AI assistants using large language models. Developers can define agent behaviors via JSON configurations, select LLM providers like OpenAI, Hugging Face, or local endpoints, and attach external tools such as search, database, or custom APIs. The built-in memory layer preserves context across sessions, while the UI components handle chat rendering, file uploads, and interactive prompts. Plugin hooks allow real-time data fetching, analytics, and custom action buttons. Emma-X ships with example agents for customer support, content creation, and code generation. Its open architecture lets teams extend agent capabilities, integrate with existing web applications, and quickly iterate on conversation flows without deep LLM expertise.

IoA provides a flexible architecture for defining, coordinating, and executing multiple AI agents in a unified workflow. Key components include a planner that decomposes high-level goals, an executor that dispatches tasks to specialized agents, and memory modules for context management. It supports integration with external APIs and toolkits, real-time monitoring, and customizable skill plugins. Developers can rapidly prototype autonomous assistants, customer support bots, and data processing pipelines by combining ready-made modules or extending them with custom logic.

ProficientAI Agent Framework is an end-to-end solution for designing and deploying advanced AI agents. It allows users to define custom agent behaviors through modular tool definitions and function specifications, ensuring seamless integration with external APIs and services. The framework’s memory management subsystem provides short-term and long-term context storage, enabling coherent multi-turn conversations. Developers can easily switch between different language models or combine them for specialized tasks. Built-in monitoring and logging tools offer insights into agent performance and usage metrics. Whether you’re building customer support bots, knowledge base search assistants, or task automation workflows, ProficientAI simplifies the entire pipeline from prototype to production, ensuring scalability and reliability.

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.

Pentagi is a no-code AI agent platform that lets you create, train, and deploy intelligent conversational agents for various business scenarios. Using its visual flow builder, you define intents, entities, and response actions. Integrations with external APIs enable dynamic data retrieval and automated task execution. Deploy your agents on web chat widgets, messaging apps, or mobile SDKs, then monitor performance through a built-in analytics dashboard to optimize conversations and agent effectiveness.

SuperAgentX empowers businesses and developers to build autonomous AI agents through an intuitive, no-code interface. Users start by defining agent behaviors and workflows using a drag-and-drop editor, then integrate external services and APIs to enrich agent capabilities, such as CRM lookups, database queries, or third-party communication platforms. Advanced scheduling and automation features allow agents to execute tasks at specified times or triggers, while real-time monitoring and logging provide insights into agent activity. Deployed agents can be accessed via chat interfaces, REST endpoints, or embedded widgets, making them ideal for customer support bots, data retrieval assistants, and process automation across various industries.

Telegram AI Agent is a lightweight, open-source framework that empowers developers to create and deploy intelligent Telegram bots leveraging OpenAI’s GPT models. It provides persistent conversation memory, configurable prompt templates, and custom agent personalities. With support for multiple agents, plugin architectures, and easy environment configuration, users can extend bot capabilities with external APIs or databases. The framework handles message routing, command parsing, and state management, enabling smooth, context-aware interactions. Whether for customer support, educational assistants, or community management, Telegram AI Agent simplifies building robust, scalable bots that deliver human-like responses directly within Telegram’s messaging platform.

VillagerAgent provides a comprehensive toolkit for constructing AI agents that leverage large language models. At its core, developers define modular tool interfaces such as web search, data retrieval, or custom APIs. The framework manages agent memory by storing conversation context, facts, and session state for seamless multi-turn interactions. A flexible prompt templating system ensures consistent messaging and behavior control. Advanced features include orchestrating multiple agents to collaborate on tasks and scheduling background operations. Built in Python, VillagerAgent supports easy installation through pip and integrates with popular LLM providers. Whether building customer support bots, research assistants, or workflow automation tools, VillagerAgent streamlines the design, testing, and deployment of intelligent agents.

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.

Airkit.ai functions as an intelligent automation platform that allows businesses to create custom AI agents for managing customer interactions. Through its user-friendly interface, users can design chatbots that resolve queries, provide information, and facilitate transactions, all while enhancing customer satisfaction. By leveraging machine learning and natural language processing, Airkit.ai ensures that interactions are more intuitive and efficient, ultimately driving better business outcomes.

AnyAgent is a flexible agent framework that lets developers construct intelligent agents capable of reasoning, planning, and executing tasks across diverse domains. It offers a built-in planner for chaining actions, configurable memory stores for long-term context, and easy hookups to external tools and APIs. Through a simple declarative DSL, you can define custom skills, embed event logging, and swap between LLM backends seamlessly. Whether for customer support bots, data analysis assistants, or research prototypes, AnyAgent accelerates agent creation with robust architecture, modular components, and extensibility for real-world automation scenarios.

Azeerc-AI is a developer-focused framework that enables rapid construction of intelligent agents by orchestrating large language model (LLM) calls, tool integrations, and memory management. It provides a plugin architecture where you can register custom tools—such as web search, data fetchers, or internal APIs—then script complex, multi-step workflows. Built-in dynamic memory lets agents remember and retrieve past interactions. With minimal boilerplate, you can spin up conversational bots or task-specific agents, customize their behavior, and deploy them in any Python environment. Its extensible design fits use cases from customer support chatbots to automated research assistants.

Fast-LLM-Agent-MCP is a lightweight, open-source Python framework for building AI agents that combine memory management, chain-of-thought reasoning, and multi-step planning. Developers can integrate it with OpenAI, Azure OpenAI, local Llama, and other models to maintain conversational context, generate structured reasoning traces, and decompose complex tasks into executable subtasks. Its modular design allows custom tool integration and memory stores, making it ideal for applications like virtual assistants, decision support systems, and automated customer support bots.

Dev-Agent is an open-source AI agent framework that empowers developers to rapidly build and deploy autonomous agents. It combines a modular plugin architecture with easy-to-configure tool invocation, including HTTP endpoints, database queries, and custom scripts. Agents can leverage a persistent memory layer to reference past interactions, and orchestrate multi-step reasoning flows for complex tasks. With built-in support for OpenAI GPT models, users define agent behavior via simple JSON or YAML specs. The CLI tool manages authentication, session state, and logging. Whether creating customer support bots, data retrieval assistants, or automated CI/CD helpers, Dev-Agent reduces development overhead and enables seamless extension through community-driven plugins, offering flexibility and scalability for diverse AI-driven applications.

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.

NagaAgent is an open-source Python library designed to simplify the creation, orchestration, and scaling of AI agents. It provides a plug-and-play tool integration system, persistent conversational memory objects, and an asynchronous multi-agent controller. Developers can register custom tools as functions, manage agent state, and choreograph interactions between multiple agents. The framework includes logging, error-handling hooks, and configuration presets for rapid prototyping. NagaAgent is ideal for building complex workflows—customer support bots, data processing pipelines, or research assistants—without infrastructure overhead.

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.