It is an open-source developer platform designed to reliably integrate probabilistic large language model (LLM) reasoning into existing systems using a code-first and API-driven approach.
It is an open-source developer platform designed to reliably integrate probabilistic large language model (LLM) reasoning into existing systems using a code-first and API-driven approach. Inferable enables developers to connect distributed systems by registering them as tools that agents can use, creating powerful agents capable of multi-step reasoning and action-taking. These agents interact with tools, which are asynchronous functions with schema inputs, running on the user’s infrastructure either as part of their codebase or as separate services deployed in their virtual private cloud (VPC).
Inferable provides a vertically integrated agent orchestration system, leveraging a distributed message queue with at-least-once delivery guarantees to reliably connect tools and agents. Developers can compose probabilistic agent interactions with deterministic, durable workflows using a “workflow as code” approach, enabling the creation of robust, production-ready processes. The platform supports familiar programming patterns, allowing users to create tools, agents, and workflows without learning new frameworks.
The platform includes a durable execution engine for composing agent actions with deterministic code, enabling the creation of complex workflows. It offers first-class support for Node.js, Golang, and C#, with plans to expand to more languages. Functions run on the user’s infrastructure, ensuring that LLMs cannot perform actions beyond what the functions allow. The SDK long-polls for instructions, eliminating the need for incoming connections or load balancers.
Inferable provides end-to-end observability into AI workflows and function calls without requiring configuration. It enforces structured outputs and allows developers to compose, pipe, and chain agents using language primitives. The platform includes a built-in ReAct agent for solving complex problems through step-by-step reasoning and function calls.
Inferable is completely open-source and can be self-hosted, giving users full control over their data and compute. It is enterprise-ready, offering features like distributed orchestration for tools, durable workflows as code, on-premise execution, and composable agents. The platform ensures data privacy and security, with no requirement for incoming connections to user infrastructure and no retention of processed data. Users can control which models are used and can integrate their own models both within and outside the managed runtime.
It is a framework and suite of applications designed for developing and deploying large language model (LLM) applications based on Qwen (version 2.0 or higher).
It is a platform designed to create, deploy, and manage AI agents at scale, enabling the development of production applications backed by agent microservices with REST APIs.
It is a multi-agent framework designed to assign different roles to GPTs (Generative Pre-trained Transformers) to form a collaborative entity capable of handling complex tasks.
It is a composable framework called FloAI that simplifies the creation of AI agent architectures by providing a flexible, modular approach to building agent-based applications.
It is an experimental open-source project called Multi-GPT, designed to make GPT-4 fully autonomous by enabling multiple specialized AI agents, referred to as "expertGPTs," to collaborate on tasks.
It is an autonomous system powered by large language models (LLMs) that, given high-level instructions, can plan, use tools, carry out steps of processing, and take actions to achieve specific goals.
It is the Large Language Model Automatic Computer (L2MAC), a pioneering framework designed to function as a practical, general-purpose stored-program automatic computer based on the von Neumann architecture.
It is an AI super assistant that provides access to state-of-the-art (SOTA) large language models (LLMs) and enables users to build, automate, and optimize AI-driven solutions for a wide range of applications.
It is a project titled "Natural Language-Based Societies of Mind (NLSOM)" that explores the concept of intelligence through diverse, interconnected agents working collaboratively in a natural language-based framework.
It is a framework called QuantaLogic ReAct Agent, designed to build advanced AI agents by integrating large language models (LLMs) with a robust tool system, enabling them to understand, reason about, and execute complex tasks through natural language interaction.
It is a visionary platform and ecosystem designed to redefine the future of legacy media through innovation, market-moving insights, and advanced artificial intelligence.
It is a unified observability and evaluation platform for AI designed to accelerate the development of AI applications and agents while optimizing their performance in production.
It is an AI-powered customer service solution designed to streamline operations, enhance customer satisfaction, and provide 24/7 support through intelligent virtual agents.
It is a platform designed for building, deploying, and managing AI Agents with a focus on reliability, accuracy, and seamless integration across systems.
It is a sales automation platform called Salesforge that blends human expertise with AI agents to create and manage effective sales outreach campaigns.
It is a description of Oraczen, a company that provides AI solutions designed to empower enterprises by addressing inefficiencies in traditional monolithic systems.
It is an AI-powered agent designed to handle every aspect of customer security reviews, including sharing security information and answering security questionnaires.
