20 Open-Source Datasets for Generative AI and Agentic AI

The fields of generative AI (GenAI) and agentic AI are transforming everything from creative content generation to autonomous decision-making. At the heart of these innovations lie vast open-source datasets that fuel model training, testing, and deployment. In this article, we present a curated list of the top open-source datasets for generative and agentic AI that you can use to train your models. These span multiple modalities – from extensive collections of text and richly annotated images to specialized resources for building intelligent agents and solving complex reasoning tasks.

1. The Pile

The Pile is an extensive, diverse dataset comprising roughly 800GB of text drawn from sources like ArXiv, GitHub, Wikipedia, and more. It has been meticulously compiled to offer a wide spectrum of writing styles and subject matter, making it ideal for training large-scale language models. Researchers and developers leverage The Pile to improve natural language understanding and generation by exposing models to a broad contextual landscape.

Best For:

• Training large-scale language models.
• Developing sophisticated natural language understanding systems.
• Fine-tuning models for domain-specific text generation.

Link: EleutherAI – The Pile

2. Common Crawl

Common Crawl aggregates billions of web pages scraped on a monthly basis, offering a true web-scale dataset. Its vast collection captures diverse content from across the internet, making it a foundational resource for training robust language models. The dataset is invaluable for tasks ranging from language modeling to large-scale information retrieval due to its comprehensive and continuously updated nature.

Best For:

• Building web-scale language models.
• Enhancing information retrieval and search engine capabilities.
• Analyzing content trends and user behavior online.

Link: Common Crawl

3. WikiText

WikiText is an open-source language modeling dataset derived from high-quality Wikipedia articles. It retains the rich structure and linguistic complexity found in editorial content, offering models a challenging environment to learn long-range dependencies. It also features a far larger vocabulary and retains the original case, punctuation and numbers. The WikiText-2 dataset is over 2 times larger than the first, and WikiText-103 is over 110 times larger.

Best For:

• Training language models with a focus on long-range context.
• Benchmarking next-word prediction and text generation tasks.
• Fine-tuning models for summarization and translation applications.

Link: WikiText on Hugging Face

4. OpenWebText

OpenWebText is an open-source effort to recreate the WebText dataset originally used by OpenAI for language modeling. Compiled from web pages linked on Reddit, it provides a diverse collection of high-quality internet text. This dataset is especially valuable for training models that require a broad spectrum of language styles and contemporary online discourse, making it ideal for research in large-scale text generation.

Best For:

• Training web-scale language models using diverse online text.
• Fine-tuning models for text generation and summarization tasks.
• Researching natural language understanding with up-to-date web data.

Link: OpenWebText on GitHub

5. LAION-5B

LAION-5B is an enormous dataset containing 5.85 billion image-text pairs, providing an unprecedented resource for multimodal AI. Its scale and diversity support the training of cutting-edge text-to-image models like Stable Diffusion and DALL·E. The integration of visual and textual data allows researchers to build systems that effectively translate language into visual content.

Best For:

• Training text-to-image generative models.
• Developing multimodal content synthesis systems.
• Creating advanced image captioning and visual storytelling applications.

Link: LAION-5B

Also Read: 20 Most Liked Datasets on HuggingFace

6. MS COCO

MS COCO offers a rich collection of images accompanied by detailed annotations for object detection, segmentation, and captioning. The dataset’s complexity challenges models to understand and generate comprehensive descriptions of visual scenes. It is widely used in both academic and industrial settings to drive advancements in image understanding and generation.

Best For:

• Developing robust object detection and segmentation models.
• Training models for image captioning and visual description.
• Creating context-aware image synthesis systems.

Link: MS COCO

7. Open Images Dataset

The Open Images Dataset is a large-scale, community-driven collection of images annotated with labels, bounding boxes, and segmentation masks. Its extensive coverage and diverse content make it ideal for training general-purpose image generation and recognition models. The dataset supports innovative applications in computer vision by providing detailed visual context across numerous object categories. The V7 version of the dataset has dense annotations for over 1.9M images and labels for over 9M images.

Best For:

• Training general-purpose image generation systems.
• Enhancing object detection and segmentation models.
• Building robust image recognition frameworks.

Link: Open Images Dataset

8. RedPajama‑1T

RedPajama‑1T is an open-source reproduction of LLaMA’s pretraining dataset, consisting of 1.2 trillion tokens from CommonCrawl, Wikipedia, Books, GitHub, arXiv, C4, and StackExchange. It applies filtering techniques, such as CCNet for web data, to enhance quality. The dataset is fully transparent, with all preprocessing scripts available for reproducibility.

Best For:

• Reproducing LLaMA’s training data
• Open-source LLM pretraining
• Multi-domain dataset curation

Link: RedPajama-1T

9. RedPajama‑V2

RedPajama‑V2 refines the 1T dataset by focusing on web data, sourced from 84 CommonCrawl snapshots, totaling over 100B text documents. It includes English, French, German, Spanish, and Italian, with 40+ quality annotations for filtering and optimization. This enables dynamic dataset curation for tailored pretraining.

Best For:

• High-quality dataset filtering
• Multilingual LLM development
• Custom pretraining dataset creation

Link: RedPajama‑V2

10. OpenAI WebGPT Dataset

The OpenAI WebGPT Dataset is tailored for training AI agents that interact dynamically with the web. It contains human-annotated data capturing real-world web browsing interactions, which are essential for developing retrieval-augmented generation systems. This resource empowers AI models to understand, navigate, and generate context-aware responses based on live web data.

Best For:

• Training web-browsing and information retrieval agents.
• Developing retrieval-augmented natural language processing systems.
• Enhancing AI’s ability to interact with and understand web content.

Link: OpenAI WebGPT Dataset

Also Read: 28 Websites to Find Datasets for your Projects

11. Obsidian Agent Dataset

The Obsidian Agent Dataset is a synthetic collection designed to simulate environments for autonomous decision-making. It focuses on agent-based reasoning and equips models with scenarios that test complex planning and decision-making skills. This dataset is pivotal for researchers developing AI agents that must operate autonomously in unpredictable settings.

Best For:

• Training autonomous decision-making models.
• Simulating agent-based reasoning in controlled environments.
• Experimenting with synthetic data for complex AI planning tasks.

Link: Obsidian Agent Dataset

12. WebShop Dataset

The WebShop Dataset is designed specifically for AI agents operating within the e-commerce domain. It features detailed product descriptions, user interaction logs, and browsing patterns that mimic real-world online shopping behavior. This dataset is ideal for developing intelligent agents capable of product research, recommendation, and automated purchase decision-making.

Best For:

• Building AI agents for e-commerce navigation and product research.
• Developing recommendation systems for online shoppers.
• Automating product comparison and purchase decision processes.

Link: WebShop Dataset

The Meta EAI Dataset is curated for training AI agents that interact with virtual and real-world environments. It provides detailed simulation scenarios that support the development of embodied AI—particularly for robotics and household task planning. By incorporating realistic interactive challenges, the dataset helps models learn effective planning and execution in dynamic environments.

Best For:

• Training interactive robotic agents for real-world tasks.
• Simulating household task planning and execution.
• Developing embodied AI applications in virtual environments.

Link: Meta EAI Dataset

14. MuJoCo

MuJoCo is a physics engine renowned for creating highly realistic simulations of physical interactions, particularly in robotics. It offers detailed, physics-based environments that enable AI models to learn complex motion and control tasks. This dataset is critical for researchers focused on developing models that require an accurate representation of real-world dynamics.

Best For:

• Training models for realistic robotic simulations.
• Developing advanced control systems in simulated environments.
• Benchmarking AI algorithms on physics-based tasks.

Link: MuJoCo

15. Robotics Datasets

Robotics datasets capture real-world sensor data and robot interactions, making them indispensable for embodied AI research. They offer rich, contextual information from varied robotic applications, ranging from industrial automation to service robots. These datasets enable the training of models that can navigate complex, physical environments with high reliability.

Best For:

• Training AI for real-world robotic interactions.
• Developing sensor-based decision-making systems.
• Benchmarking embodied AI performance in dynamic environments.

Link: Robotics Datasets

Also Read: 10 Open Source Datasets for LLM Training

16. Atari Games

Atari Games is a classic dataset used as a benchmark for reinforcement learning algorithms. It provides a suite of game environments that challenge AI models with sequential decision-making tasks. This dataset remains a popular tool for testing and advancing AI performance in diverse, dynamic scenarios.

Best For:

• Benchmarking reinforcement learning strategies.
• Testing AI performance in varied game environments.
• Developing algorithms for sequential decision-making.

Link: Atari Games

17. Web-crawled Interactions

Web-crawled interactions consist of large-scale user behavior data extracted from various online platforms. They capture authentic human interaction patterns and engagement metrics, offering valuable insights for training interactive agents. This dataset is particularly useful for developing AI that can understand and predict real-world user behavior on the web.

Best For:

• Training interactive agents based on real user behavior.
• Enhancing recommendation systems with dynamic interaction data.
• Analyzing engagement trends for conversational AI.

Link: Web-crawled Interactions

18. AI2 ARC Dataset

The AI2 ARC Dataset is a collection of challenging multiple-choice questions designed to assess an AI’s commonsense reasoning and problem-solving abilities. Its questions span a variety of topics and difficulty levels, making it a rigorous benchmark for reasoning models. Researchers utilize this dataset to push the boundaries of logical inference and to evaluate the depth of understanding in generative AI systems.

Best For:

• Benchmarking common sense reasoning capabilities.
• Training models to handle standardized test questions.
• Enhancing problem-solving and logical inference in AI systems.

Link: AI2 ARC Dataset

19. MS MARCO

Microsoft Machine Reading Comprehension (MS MARCO) is a large-scale dataset curated for tasks such as passage ranking, question answering, and information retrieval. It compiles real-world search queries and relevant passages to train and test retrieval-augmented generation systems. The dataset is instrumental in bridging the gap between information retrieval and generative models, leading to more context-aware search and answer generation.

Best For:

• Training retrieval-augmented generation (RAG) models.
• Developing advanced passage ranking and question-answering systems.
• Enhancing information retrieval pipelines with real-world data.

Link: MS MARCO

20. OpenAI Gym

OpenAI Gym is a standardized toolkit featuring a variety of simulated environments for developing and benchmarking reinforcement learning algorithms. It offers a range of scenarios—from simple control tasks to more complex simulations—ideal for training agentic behavior. Its ease of use and broad community support make it a staple in reinforcement learning research.

Best For:

• Benchmarking reinforcement learning algorithms.
• Developing simulated training environments for agents.
• Rapid prototyping of agentic behavior in controlled scenarios.

Link: OpenAI Gym

Also Read: A Guide to 400+ Categorized Large Language Model(LLM) Datasets

Summary Table

Here’s a summarized table of the above discussed open‐source datasets for generative and agentic AI. I’ve mentioned the approximate sample counts, file sizes, and developers for each, along with their download links.

Note: The number of samples and size of datasets can vary based on the version and preprocessing applied. Please refer to the official documentation via the provided download links for the latest and most precise information

Conclusion

The open-source datasets highlighted above provide a robust foundation for developing cutting-edge generative and agentic AI systems. Whether you’re working on natural language processing, computer vision, autonomous decision-making, or advanced reasoning, these resources offer the depth and diversity needed to drive innovation. By leveraging these datasets, researchers and developers can accelerate breakthroughs, refine model performance, and explore new frontiers in artificial intelligence.

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Sabreena is a GenAI enthusiast and tech editor who’s passionate about documenting the latest advancements that shape the world. She’s currently exploring the world of AI and Data Science as the Manager of Content & Growth at Analytics Vidhya.