inferless

Enterprise-grade AI agent platform automating sales, customer service, HR, and other business processes

An AI development and MLOps platform for customizing, deploying, and continuously optimizing large language models.

Defense Situational Awareness Platform integrating multi-domain sensor data for threat detection and tracking

Sales automation platform that handles appointments, customer follow-ups, and syncs calendars with CRM systems.

Prompt engineering tool that transforms simple ideas into structured instructions and generates diverse prompt types

Enterprise agent deployment platform supporting autonomous decision-making and multi-task automation.

Dagger is an open-source platform designed to build and run composable workflows by transforming code into containerized operations. It excels in environments requiring high reproducibility, modularity, and observability, making it particularly well-suited for complex systems such as AI agents and CI/CD pipelines. Dagger supports multiple programming languages and seamlessly integrates with any compute platform, enabling developers to build reproducible, scalable workflows with built-in caching, tracing, and real-time debugging capabilities.

Dedalus Labs offers a developer-centric platform for seamlessly building, deploying, and managing professional AI agents. Acting as a universal MCP gateway, it supports any LLM and any MCP server, whether self-hosted or managed by Dedalus. The platform eliminates typical infrastructure burdens such as configuration, hosting, auto-scaling, and load balancing, enabling users to rapidly move from concept to production. Its open marketplace hosts a variety of MCP tools, allowing users to combine cloud-hosted services with custom Python functions. Dedalus Labs emphasizes flexibility, multi-model access, and real-time evolution of AI agents to meet diverse use cases across industries.

Zyphra is an AI company based in Palo Alto focused on building Maia, the next-generation multimodal agent system for enterprises, integrating advanced neural network architectures, long-term memory, and reinforcement learning. The company specializes in developing efficient, compact, and powerful language models optimized for consumer-grade and edge devices. Additionally, Zyphra has developed large-scale, high-quality open datasets such as Zyda-2, which significantly enhance language model performance through rigorous filtering, deduplication, and GPU-accelerated data processing.

Unify AI provides developers with a centralized platform to access and benchmark multiple large language models (LLMs) from various providers through a single API. It dynamically routes requests to the best-performing model based on real-time metrics such as quality, speed, and cost. The platform continuously updates its benchmarks every 10 minutes, enabling optimized model selection according to user-defined constraints. Unify AI also supports integration with popular LLM orchestration tools, offering a modular and customizable environment for logging, evaluating, and optimizing LLM deployments.

smolagents is an open-source, lightweight AI agent framework developed by Hugging Face that simplifies the creation of efficient AI agents. It emphasizes code agents—those that directly write and execute Python code snippets—offering higher efficiency, accuracy, and flexibility compared to traditional JSON or text-based agents. With a core codebase of approximately 1,000 lines, it supports all major language models including those from Hugging Face, OpenAI, and Anthropic, integrated via LiteLLM. Deeply integrated with the Hugging Face Hub, it enables easy sharing and loading of tools and agents, fostering a collaborative ecosystem. Secure execution is ensured through sandboxed environments like E2B, making smolagents a versatile, future-ready solution for AI agent development across diverse scenarios.

Leeroo is an advanced AI system designed to transform knowledge into intelligent agents by orchestrating a diverse array of expert language models. Its core innovation, the Leeroo Orchestrator, dynamically selects the most suitable expert model for each task, optimizing accuracy, speed, and cost. Unlike traditional approaches that require high-end hardware, Leeroo enables deployment on accessible consumer-grade machines and in any cloud environment, offering scalability and privacy protection through local deployment options. The platform continuously improves through a self-play training loop, enhancing its ability to assign tasks to experts and achieving exceptional performance in STEM fields such as mathematics and computer science.