ByteChef offers a modular architecture to build, test, and deploy AI agents. Developers define agent profiles, attach custom skill plugins, and orchestrate multi-agent workflows through a visual web IDE or SDK. It integrates with major LLM providers (OpenAI, Cohere, self-hosted models) and external APIs. Built-in debugging, logging, and observability tools streamline iteration. Projects can be deployed as Docker services or serverless functions, enabling scalable, production-ready AI agents for customer support, data analysis, and automation.

Core Team AI provides an instant, collaborative AI team that includes specialized roles: Co-founder, CPO, CTO, CFO, CLO, and CMO. These AI leaders work together in real time, sharing insights and solving challenges to help startups evolve rapidly. The AI team integrates proven startup methodologies, ensuring that every conversation is organized and actionable. Founders can shape their vision with faster decision-making, on-demand support, and a team synchronized across various business functions.

Ducky empowers teams to build, train, and deploy custom AI agents without writing code. You can ingest documents, spreadsheets, or CRM records as knowledge sources and configure intent recognition, entity extraction, and multi-step workflows via a drag-and-drop interface. Ducky supports integration with REST APIs, databases, and webhooks, and offers multi-channel deployment through web chat widgets, Slack, and Chrome extension. Real-time analytics give insights into conversation volume, user satisfaction, and agent performance. Role-based access controls and versioning ensure enterprise-grade governance while maintaining rapid iteration cycles.

GPTMe provides a robust platform for orchestrating AI agents that retain conversational context, integrate external tools, and expose a consistent API. Developers install a lightweight Python package, define agents with plug-and-play memory backends, register custom tools (e.g., web search, database queries, file operations), and spin up a local or cloud service. GPTMe handles session tracking, multi-step reasoning, prompt templating, and model switching, delivering production-ready assistants for customer service, productivity, data analysis, and more.

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.

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.

Twilio AI Assistants are designed to help businesses streamline their customer communication by utilizing AI technologies to automate responses across various channels, including SMS and voice. These assistants are capable of understanding user queries and providing relevant information, thus improving overall customer satisfaction and operational efficiency. With Twilio, companies can easily implement AI assistants tailored to their unique business needs, ensuring consistent and timely responses to customer inquiries.

Union.ai offers a robust solution for orchestrating AI and data workflows. It integrates various compute and orchestration tools to streamline AI product development. By providing a cohesive platform, Union.ai reduces the time, cost, and operational complexities involved in deploying AI solutions. Organizations can effectively manage their AI and data pipelines, ensuring reliable, scalable, and efficient delivery of AI-backed applications.

AI-Agents provides a modular architecture for defining and running Python-based AI agents. Developers can configure agent behaviors, integrate external APIs or tools, and manage agent memory across sessions. It leverages popular LLMs, supports multi-agent collaboration, and enables plugin-based extensions for complex workflows like data analysis, automated support, and personalized assistants.

AI Refinery provides businesses with a suite of tools to facilitate the integration of artificial intelligence into existing processes. It streamlines the adoption of AI technologies, allowing organizations to improve operational efficiency, enhance customer experiences, and drive innovation. The platform includes features for automating workflows, optimizing decision-making processes, and enabling smarter data analysis, all tailored to specific business needs.

AI Agent with MCP is a comprehensive framework designed to streamline the development of advanced AI agents capable of maintaining long-term context, performing multi-step reasoning, and adapting strategies based on memory. It leverages a modular design comprising Memory Manager, Conditional Planner, and Prompt Manager, allowing custom integrations and extension with various LLMs. The Memory Manager persistently stores past interactions, ensuring context retention. The Conditional Planner evaluates conditions at each step and dynamically selects the next action. The Prompt Manager formats inputs and chains tasks seamlessly. Built in Python, it integrates with OpenAI GPT models via API, supports retrieval-augmented generation, and facilitates conversational agents, task automation, or decision support systems. Extensive documentation and examples guide users through setup and customization.

AutoML-Agent provides a versatile Python-based framework that orchestrates every stage of the machine learning lifecycle through an intelligent agent interface. Starting with automated data ingestion, it performs exploratory analysis, missing value handling, and feature engineering using configurable pipelines. Next, it conducts model architecture search and hyperparameter optimization powered by large language models to suggest optimal configurations. The agent then runs experiments in parallel, tracking metrics and visualizations to compare performance. Once the best model is identified, AutoML-Agent streamlines deployment by generating Docker containers or cloud-native artifacts compatible with common MLOps platforms. Users can further customize workflows via plugin modules and monitor model drift over time, ensuring robust, efficient, and reproducible AI solutions in production environments.

GenAI Processors provides a library of reusable, configurable processors to build end-to-end generative AI workflows. Developers can ingest documents, break them into semantic chunks, generate embeddings, store and query vectors, apply retrieval strategies, and dynamically construct prompts for large language model calls. Its plug-and-play design allows easy extension of custom processing steps, seamless integration with Google Cloud services or external vector stores, and orchestration of complex RAG pipelines for tasks such as question answering, summarization, and knowledge retrieval.

At its core, LionAGI provides a modular architecture for defining and executing dependent task stages, breaking complex problems into logical components that can be processed sequentially or in parallel. Each stage can leverage a custom prompt, memory storage, and decision logic to adapt behavior based on previous results. Developers can integrate any supported LLM API or self-hosted model, configure observation spaces, and define action mappings to create agents that plan, reason, and learn over multiple cycles. Built-in logging, error recovery, and analytics tools enable real-time monitoring and iterative refinement. Whether automating research workflows, generating reports, or orchestrating autonomous processes, LionAGI accelerates the delivery of intelligent, adaptable AI agents with minimal boilerplate.

Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.

Odyssey provides a flexible architecture for building collaborative multi-agent systems. It includes core components such as the Task Manager for defining and distributing subtasks, Memory Modules for storing context and conversation histories, Agent Controllers for coordinating LLM-powered agents, and Tool Managers for integrating external APIs or custom functions. Developers can configure workflows via YAML files, select prebuilt LLM kernels (e.g., GPT-4, local models), and seamlessly extend the framework with new tools or memory backends. Odyssey logs interactions, supports asynchronous task execution, and enables iterative refinement loops, making it ideal for research, prototyping, and production-ready multi-agent applications.

Steamship is a robust platform designed to simplify the creation, deployment, and management of AI agents. It offers developers a managed stack for language AI packages, supporting full-lifecycle development from serverless hosting to vector storage solutions. With Steamship, users can easily build, scale, and customize AI tools and applications, providing a seamless experience for integrating AI capabilities into their projects.