Best Artificial Intelligence Software for FastAPI
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Compare the Top Artificial Intelligence Software that integrates with FastAPI as of June 2025
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This a list of Artificial Intelligence software that integrates with FastAPI. Use the filters on the left to add additional filters for products that have integrations with FastAPI. View the products that work with FastAPI in the table below.
What is Artificial Intelligence Software for FastAPI?
Artificial Intelligence (AI) software is computer technology designed to simulate human intelligence. It can be used to perform tasks that require cognitive abilities, such as problem-solving, data analysis, visual perception and language translation. AI applications range from voice recognition and virtual assistants to autonomous vehicles and medical diagnostics. Compare and read user reviews of the best Artificial Intelligence software for FastAPI currently available using the table below. This list is updated regularly.
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1
SuperAGI SuperCoder
SuperAGI
SuperAGI SuperCoder is an open-source autonomous system that combines AI-native dev platform & AI agents to enable fully autonomous software development starting with python language & frameworks SuperCoder 2.0 leverages LLMs & Large Action Model (LAM) fine-tuned for python code generation leading to one shot or few shot python functional coding with significantly higher accuracy across SWE-bench & Codebench As an autonomous system, SuperCoder 2.0 combines software guardrails specific to development framework starting with Flask & Django with SuperAGI’s Generally Intelligent Developer Agents to deliver complex real world software systems SuperCoder 2.0 deeply integrates with existing developer stack such as Jira, Github or Gitlab, Jenkins, CSPs and QA solutions such as BrowserStack /Selenium Clouds to ensure a seamless software development experienceStarting Price: Free -
2
BudgetML
ebhy
BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production quickly and cheaply. Cloud functions are limited in memory and cost a lot at scale. Kubernetes clusters are overkill for one single model. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers, etc., that are simply not within the scope of a typical data scientist. BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. It is by no means meant to be used in a full-fledged production-ready setup. It is simply a means to get a server up and running as fast as possible with the lowest costs possible.Starting Price: Free -
3
txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
4
Open Interpreter
Open Interpreter
Open Interpreter is an open source natural language interface for computers that enables users to execute code through conversational prompts in a terminal environment. It supports multiple programming languages, including Python, JavaScript, and Shell, allowing for a wide range of tasks such as data analysis, file management, and web browsing. It provides interactive mode commands to enhance user experience. Users can configure default behaviors using YAML files, facilitating flexible customization without altering command-line arguments each time. Open Interpreter can be integrated with FastAPI to create RESTful endpoints, enabling programmatic control over its functionalities. For safety, it prompts users for confirmation before executing code that interacts with the local environment, mitigating potential risks.Starting Price: Free -
5
Reflex
Pynecone
Reflex is an open source framework that empowers Python developers to build full-stack web applications entirely in pure Python, eliminating the need for JavaScript or complex frontend frameworks. With Reflex, you can write, test, and refine your app using just Python, making it fast, flexible, and scalable. It features an AI Builder that allows you to describe your app idea, and it will generate a working Python app instantly, complete with backend, frontend, and database integration. Reflex's architecture compiles the frontend down to a single-page Next.js app, while the backend is powered by FastAPI, with communication handled via WebSockets. This setup ensures that all the app logic and state management stay in Python and run on the server. The framework offers over 60 built-in components based on Radix UI and supports custom React components, enabling developers to create complex UIs without writing HTML or CSS.Starting Price: $20 per month -
6
Kodosumi
Masumi
Kodosumi is an open source, framework-agnostic runtime environment built on Ray for deploying, managing, and scaling agentic services at the enterprise level. It enables effortless deployment of AI agents with a single YAML config, offering minimal setup overhead and no vendor lock-in. Designed for handling bursty traffic and long-running workflows, it dynamically scales across Ray clusters to ensure consistent performance. Kodosumi integrates real-time logging and monitoring through the Ray dashboard, providing instant observability and streamlined debugging of complex flows. Core building blocks include autonomous agents (task performers), orchestrated flows, and deployable agentic services, all managed via a pragmatic web admin panel.Starting Price: Free -
7
JarvisLabs.ai
JarvisLabs.ai
We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.Starting Price: $1,440 per month
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