ComfyUI LLM Party extends the node-based ComfyUI environment by providing a suite of LLM-powered nodes designed for orchestrating text interactions alongside visual AI workflows. It offers chat nodes to engage with large language models, memory nodes for context retention, and routing nodes for managing multi-agent dialogues. Users can chain language generation, summarization, and decision-making operations within their pipelines, merging textual AI and image generation. The extension also supports custom prompt templates, variable management, and condition-based branching, allowing creators to automate narrative generation, image captioning, and dynamic scene descriptions. Its modular design enables seamless integration with existing nodes, empowering artists and developers to build sophisticated AI Agent workflows without programming expertise.

Divine Agent is a comprehensive AI agent platform that simplifies the design, development, and deployment of autonomous digital workers. Through its intuitive visual workflow builder, users can define agent behavior as a sequence of nodes, connect to any REST or GraphQL API, and select from supported LLMs like OpenAI and Google PaLM. The built-in memory module preserves context across sessions, while real-time analytics track usage, performance, and errors. Once tested, agents can be deployed as HTTP endpoints or integrated with channels like Slack, email, and custom applications, enabling rapid automation of customer support, sales, and knowledge tasks.

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.

LLM-Agent provides a structured architecture for building intelligent agents using LLMs. It includes a toolkit for defining custom tools, memory modules for context preservation, and executors that orchestrate complex chains of actions. Agents can call APIs, run local processes, query databases, and manage conversational state. Prompt templates and plugin hooks allow fine-tuning of agent behavior. Designed for extensibility, LLM-Agent supports adding new tool interfaces, custom evaluators, and dynamic routing of tasks, enabling automated research, data analysis, code generation, and more.

MCP Agent provides a robust foundation for building intelligent AI-driven assistants by offering modular components for integrating language models, custom tools, and data sources. Its core functionalities include dynamic tool invocation based on user intents, context-aware memory management for long-term conversations, and a flexible plugin system that simplifies extending capabilities. Developers can define pipelines to process inputs, trigger external APIs, and manage asynchronous workflows, all while maintaining transparent logs and metrics. With support for popular LLMs, configurable templates, and role-based access controls, MCP Agent streamlines the deployment of scalable, maintainable AI agents in production environments. Whether for customer support chatbots, RPA bots, or research assistants, MCP Agent accelerates development cycles and ensures consistent performance across use cases.

At its core, Memary provides a modular memory management system tailored for large language model agents. By abstracting memory interactions through a common API, it supports multiple storage backends, including in-memory dictionaries, Redis for distributed caching, and vector stores like Pinecone or FAISS for semantic search. Users define schema-based memories (episodic, semantic, or long-term) and leverage embedding models to populate vector stores automatically. Retrieval functions allow contextually relevant memory recall during conversations, enhancing agent responses with past interactions or domain-specific data. Designed for extensibility, Memary can integrate custom memory backends and embedding functions, making it ideal for developing robust, stateful AI applications such as virtual assistants, customer service bots, and research tools requiring persistent knowledge over time.

OpenAI Agents Chatbot allows developers to integrate and manage multiple specialized AI agents (e.g., tools, knowledge retrieval, memory modules) into a single conversational application. features chain-of-thought orchestration, session-based memory, configurable tool endpoints, and seamless OpenAI API interactions. Users can customize each agent’s behavior, deploy locally or in cloud environments, and extend the framework with additional modules. This accelerates development of advanced chatbots, virtual assistants, and task automation systems.

Pebbling AI is a dedicated memory infrastructure designed to enhance AI agent capabilities. By offering vector storage integrations, retrieval-augmented generation support, and customizable memory pruning, it ensures efficient long-term context handling. Developers can define memory schemas, build knowledge graphs, and set retention policies to optimize token usage and relevance. With analytics dashboards, teams monitor memory performance and user engagement. The platform supports multi-agent coordination, allowing separate agents to share and access common knowledge. Whether building conversational bots, virtual assistants, or automated workflows, Pebbling AI streamlines memory management to deliver personalized, context-rich experiences.

Personal AI Assistant is a modular AI agent built in Python to deliver conversational chat, context-aware memory, and automated task execution. It features a plugin system for web browsing, file management, email sending, and calendar scheduling. Backed by OpenAI or local language models and SQLite-based memory storage, it preserves conversation history and adapts responses over time. Developers can extend capabilities with custom modules, creating a tailored assistant for productivity, research, or home automation.

Rusty Agent is a lightweight yet powerful Rust library designed to simplify the creation of autonomous AI agents that leverage large language models. It introduces core abstractions such as Agents, Tools, and Memory modules, allowing developers to define custom tool integrations—e.g., HTTP clients, knowledge bases, calculators—and orchestrate multi-step conversations programmatically. Rusty Agent supports dynamic prompt building, streaming responses, and contextual memory storage across sessions. It integrates seamlessly with OpenAI API (GPT-3.5/4) and can be extended for additional LLM providers. Its strong typing and performance benefits of Rust ensure safe, concurrent execution of agent workflows. Use cases include automated data analysis, interactive chatbots, task automation pipelines, and more—empowering Rust developers to embed intelligent language-driven agents into their applications.

Plan Agent with Meta-Agent provides a layered AI agent architecture: the Plan Agent generates structured strategies to achieve high-level goals, while the Meta-Agent oversees execution, adjusts plans in real-time, and delegates subtasks to specialized sub-agents. It features plug-and-play tool connectors (e.g., web APIs, databases), persistent memory for context retention, and configurable logging for performance analysis. Users can extend the framework with custom modules to suit diverse automation scenarios, from data processing to content generation and decision support.

Real-Agents is designed to simplify the creation and orchestration of AI-powered agents that can perform complex tasks autonomously. Built on Python and compatible with major large language models, the framework features a modular design comprising core components for language understanding, reasoning, memory storage, and tool execution. Developers can rapidly integrate external services like web APIs, databases, and custom functions to extend agent capabilities. Real-Agents supports memory mechanisms to retain context across interactions, enabling multi-turn conversations and long-running workflows. The platform also includes utilities for logging, debugging, and scaling agents in production environments. By abstracting low-level details, Real-Agents streamlines the development cycle, allowing teams to focus on task-specific logic and deliver powerful automated solutions.

SelfYAI offers a comprehensive, no-code interface for designing, training, and deploying AI agents tailored to your specific business needs. Users can import data from CRM systems, spreadsheets, and databases, then configure custom workflows and conversational flows with simple drag-and-drop tools. Agents maintain context using memory modules and can be deployed across websites, Slack, Teams, and API endpoints. Built-in analytics track interaction volume, resolution rates, and user feedback, supporting iterative improvements. With robust security features and role-based access controls, SelfYAI ensures data privacy and compliance while scaling AI-driven automation effortlessly.

Thufir is a Python-based open-source agent framework designed to facilitate the creation of autonomous AI agents capable of complex task planning and execution. At its core, Thufir provides a planning engine that decomposes high-level objectives into actionable steps, a memory module for storing and retrieving contextual information across sessions, and a plug-and-play tool interface allowing agents to interact with external APIs, databases, or code execution environments. Developers can leverage Thufir’s modular components to customize agent behaviors, define custom tools, manage agent state, and orchestrate multi-agent workflows. By abstracting away low-level infrastructure concerns, Thufir accelerates the development and deployment of intelligent agents for use cases like virtual assistants, workflow automation, research, and digital workers.

Whiz is designed to provide a robust foundation for developing intelligent agents that can perform complex conversational and task-oriented workflows. Using Whiz, developers define "tools"—Python functions or external APIs—that the agent can invoke when processing user queries. A built-in memory module captures and retrieves conversation context, enabling coherent multi-turn interactions. A dynamic planning engine decomposes goals into actionable steps, while a flexible interface allows injecting custom policies, tool registries, and memory backends. Whiz supports embedding-based semantic search to fetch relevant documents, logging for auditability, and asynchronous execution for scaling. Fully open-source, Whiz can be deployed anywhere Python runs, enabling rapid prototyping of customer support bots, data analysis assistants, or specialized domain agents with minimal boilerplate.

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.

AgentScope is a developer-focused framework designed to simplify the creation of intelligent agents by providing modular components for dynamic planning, contextual memory storage, and tool/API integration. It supports multiple LLM backends (OpenAI, Anthropic, Hugging Face) and offers customizable pipelines for task execution, answer synthesis, and data retrieval. AgentScope’s architecture enables rapid prototyping of conversational bots, workflow automation agents, and research assistants, all while maintaining extensibility and scalability.

AgentForge provides a structured environment for defining, combining, and orchestrating individual AI skills into cohesive autonomous agents. It supports conversation memory for context retention, plugin integration for external services, multi-agent communication, task scheduling, and error handling. Developers can configure custom skill handlers, leverage built-in modules for natural language understanding, and integrate with popular LLMs like OpenAI’s GPT series. AgentForge’s modular design accelerates development cycles, facilitates testing, and simplifies deployment of chatbots, virtual assistants, data analysis agents, and domain-specific automation bots.

Agentic-Systems is designed to streamline the development of sophisticated autonomous AI applications by offering a modular architecture composed of agent, tool, and memory components. Developers can define custom tools that encapsulate external APIs or internal functions, while memory modules retain contextual information across agent iterations. The built-in orchestration engine schedules tasks, resolves dependencies, and manages multi-agent interactions for collaborative workflows. By decoupling agent logic from execution details, the framework enables rapid experimentation, easy scaling, and fine-grained control over agent behavior. Whether prototyping research assistants, automating data pipelines, or deploying decision-support agents, Agentic-Systems provides the necessary abstractions and templates to accelerate end-to-end AI solution development.

Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.