Skip to main Communities My dashboard Log in Sign up Published April 15, 2026 | Version 1.0.2 Dataset Open The 2025 AI Agent Index Authors/Creators Staufer, Leon (Project leader) 1 Feng, K. J. Kevin (Annotator) Wei, Kevin (Annotator) 2 Bailey, Luke (Annotator) 3 Duan, Yawen (Annotator) 4 Yang, Mick (Annotator) 5 Ozisik, Ayse Pinar (Annotator) 6 Casper, Stephen (Supervisor) 6 Kolt, Noam (Supervisor) 7 Show affiliations 1. University of Cambridge 2.
Harvard Law School 3. Stanford University 4.
Concordia AI 5. University of Pennsylvania 6. MIT Computer Science and Artificial Intelligence Laboratory 7. Hebrew University of Jerusalem Description This dataset contains structured annotations for 30 prominent AI agents released or actively developed in 2025, compiled as part of the AI Agent Index project. The AI Agent Index is a systematic effort to catalogue and characterise real-world AI agents across dimensions relevant to accountability, safety, and transparency. File Contents File Description 2025_annotations.json Full annotations in nested JSON format, preserving the hierarchical section structure with inline source links and archived URLs. Text is in Markdown format. 2025_annotations.csv Flattened tabular version with one row per agent and one column per field. Text is in Markdown format. Data Structure Each agent record is organised into seven thematic sections: Inclusion criteria: the signals used to select the agent (search volume, market cap, GitHub stars, developer importance) Product overview: agent name, description, release date, advertised use case, pricing, target users, website, and category Company & accountability: developer identity, legal entity, place of incorporation, profit status, parent company, governance documents, AI safety frameworks, and standards compliance Technical capabilities & system architecture: underlying model, documentation, observation space, action space, memory architecture, user interface design, user roles, and openness of components Autonomy & control: autonomy level ( L1–L5 scale ), human approval requirements, execution monitoring and traceability, emergency stop mechanisms, and usage statistics Ecosystem interaction: agent self-identification to humans and systems, interoperability standards (MCP, A2A, ACP, AGNTCY), and web conduct Safety, evaluation & impact: technical guardrails, sandboxing approaches, risk evaluations, internal and third-party safety testing, benchmark results, vulnerability disclosure programmes, and known incidents More Information For further details about the AI Agent Index project, methodology, and interactive data explorer, visit https://aiagentindex.mit.edu/ Files 2025_annotations.csv Files (551.4 kB) Name Size Download all 2025_annotations.csv md5:c125725c6c5491f9011119589684b5b8 245.7 kB Preview Download 2025_annotations.json md5:5225322f409698084e4b80be85980821 305.7 kB Preview Download Additional details Related works Is new version of Dataset: 10.5281/zenodo.18701930 (DOI) Is supplement to Conference paper: arXiv:2602.17753 (arXiv) Dates Created 2025-12-31 The 2025 AI Agent Index reflects a snapshot in time as of December 31, 2025. Updated 2026-04-15 Small fixes and corrections Software Repository URL https://aiagentindex.mit.edu/ 1K Views 617 Downloads Show more details All versions This version Views Total views 1,035 455 Downloads Total downloads 617 228 Data volume Total data volume 196.7 MB 71.3 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Details DOI DOI Badge DOI 10.5281/zenodo.19592546 Markdown [](https://doi.org/10.5281/zenodo.19592546) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19592546.svg :target: https://doi.org/10.5281/zenodo.19592546 HTML <a href="https://doi.org/10.5281/zenodo.19592546"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19592546.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19592546.svg Target URL https://doi.org/10.5281/zenodo.19592546 Resource type Dataset Publisher Zenodo Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Citation Export Technical metadata Created April 15, 2026 Modified April 15, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies