List of AutoGPT Customers
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Since 2010, our global team of researchers has been studying AutoGPT customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased AutoGPT for Generative AI Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using AutoGPT for Generative AI Platforms include: University Of Texas At Austin, a United States based Education organisation with 28761 employees and revenues of $4.35 billion, University of Georgia, a United States based Education organisation with 10856 employees and revenues of $1.90 billion, Karlsruher Institut für Technologie (KIT), a Germany based Education organisation with 9783 employees and revenues of $1.17 billion and many others.
Contact us if you need a completed and verified list of companies using AutoGPT, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The AutoGPT customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Karlsruher Institut für Technologie (KIT) | Education | 9783 | $1.2B | Germany | AutoGPT | AutoGPT | Generative AI Platforms | 2024 | n/a | In 2024, Karlsruher Institut für Technologie (KIT) implemented AutoGPT as the foundation for AutoGPT+P, an affordance-based task planning extension, in a robotics research deployment in Germany. The project positioned AutoGPT within the Generative AI Platforms category to target robotics planning tasks and to benchmark LLM-based planners under controlled experimental conditions. AutoGPT+P extended AutoGPT with affordance modeling and a task planning loop tailored for robotic action sequencing, implementing affordance-driven action selection, multi-step task decomposition, and iterative replanning to increase robustness in execution. These functional capabilities reflect common Generative AI Platforms workflows that orchestrate LLM output for structured decision making and control-oriented plan generation. The deployment and evaluation were research-focused and executed in robotics contexts in Germany, with experiments benchmarked against prior LLM-based planners. KIT researchers published experimental code and the dataset to enable reproducibility, and reported higher task success rates on the benchmark, for example 98% versus 81%. Governance and rollout emphasized reproducible research practices rather than enterprise production operations, with dataset release and evaluation methodology intended to permit verification by other research groups. The work demonstrates AutoGPT and the AutoGPT+P extension applied within Generative AI Platforms for robotics research, showing improved planning success and robustness compared with prior LLM-based planners. | |
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University of Georgia | Education | 10856 | $1.9B | United States | AutoGPT | AutoGPT | Generative AI Platforms | 2023 | n/a | In 2023, University of Georgia implemented AutoGPT in a public-health research deployment to automate infodemiology workflows for Alzheimer’s disease. Researchers at the University of Georgia led development of AD-AutoGPT, applying AutoGPT style autonomous agents to news collection, summarization, spatio-temporal extraction and visualization. The implementation used an autonomous LLM pipeline for public-health analytics that orchestrated agent-driven web ingestion, content filtering, abstractive summarization, named entity and spatio-temporal extraction, and a visualization layer for trend and topic mapping. Functional modules included autonomous news collection agents, summarization components, extraction engines for geotemporal markers, and a visualization module producing trend and topic maps, with AutoGPT operating in the Generative AI Platforms category. This was a United States based research deployment focused on infodemiology for Alzheimer’s disease and operated by university research teams. Operational coverage centered on researchers consuming trend and topic visualizations and downstream analytic outputs derived from public news sources, and the deployment reduced manual data collection and analysis effort while producing visualizations for AD discourse. Researchers maintained development and operational oversight during the research rollout, embedding the AutoGPT application into existing infodemiology workflows and researcher validation processes. Governance emphasized researcher led validation of autonomous agent outputs and visualization interpretation within a research context rather than enterprise production governance. | |
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University Of Texas At Austin | Education | 28761 | $4.4B | United States | AutoGPT | AutoGPT | Generative AI Platforms | 2023 | n/a | In 2023, researchers affiliated with the University Of Texas At Austin implemented AutoGPT as part of the AD-AutoGPT project to support Alzheimer’s disease infodemiology research. The work used AutoGPT within a Generative AI Platforms context to orchestrate prompt-driven autonomous agents for automated news retrieval, LDA topic modeling, and visualization workflows. The implementation constructed an autonomous pipeline architecture that combined agent orchestration, prompt orchestration, document ingestion, topic modeling using latent Dirichlet allocation, spatio-temporal trend extraction, and visualization modules. AutoGPT-derived agents were responsible for iterative prompt issuance and task chaining, the LDA component provided unsupervised topic extraction, and visualization components rendered topic and geographic trend outputs for analysis. Operationally the effort was academic and public health focused within the United States, executed by research teams at the University Of Texas At Austin for infodemiology and epidemiologic signal detection. The project demonstrates the ability to scale prompt-based autonomous pipelines for spatio-temporal and topic trend analysis, and documents an experimental research deployment pattern for Generative AI Platforms applied to public health news analytics. |
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