Models & Developer Platforms Tools Directory

Explore open models, datasets, and apps to kickstart your next experiment.

Provide search, embedding, and reranking models for AI apps to improve retrieval and comprehension.

Provide real-world tool environments for AI agents to support training and complex task evaluation.

Leverage enterprise AI models and solutions for search, generation, and knowledge applications.

Select and deploy open-weight models, accessing diverse AI models through a unified interface.

Call AI models via an inference platform to power applications with model execution capabilities.

Trace and evaluate AI app calls to improve agent quality using operational data.

Run open models on desktop to handle document and code tasks with local AI.

Run and manage open models locally, providing a unified API for applications.

Run, fine-tune, and deploy open models in the cloud, powering AI applications with scalable compute.

Run community AI models via API to integrate image, audio, and other capabilities into your products.

Call image, video, and audio models via generative media APIs.

Deploy and run open models with inference and fine-tuning services for AI applications.

Access language, image, and voice models via cloud services to build AI applications.

Explore GLM models and enterprise AI services to integrate LLM capabilities into your business.

Build multimodal AI products using language, voice, and video models.

Explore multimodal foundation models and development platforms to integrate AI into your apps.

Explore Tencent Hunyuan models and open capabilities to build language and multimodal applications.

Test Gemini models and prompts, build prototypes, and export application integration code.

Build, train, and deploy neural network models using open-source deep learning frameworks.

Develop models through an open-source machine learning platform, enabling training and cross-platform deployment.

Build models using high-level deep learning APIs to simplify experimentation and training workflows.

Perform classification, regression, and clustering in Python to build machine learning pipelines.

Track model experiments and evaluate AI applications to manage development workflows for models and agents.