Free
$0Free plan available.
Botticelli is an open-source .NET Core framework for building universal bots integrated with databases, queue brokers, speech engines, and AI engines (such as GPT-J and ChatGPT). It supports cross-platform development, is reliable, easy to use, and easy to deploy with Docker support. It also facilitates integration with databases, AI solutions (Botticelli.AI), speech synthesizers (Botticelli.Talks), queue brokers (Botticelli.Bus), and scheduling (Botticelli.Scheduler).
To use Botticelli, clone the repository from GitHub using the command: `git clone https://github.com/devgopher/botticelli.git`. You can find additional documentation on the website to guide you through the process of building and deploying your bot.
Botticelli is an open-source .NET Core framework used to build universal bots that integrate with databases, queue brokers, speech engines, and AI engines.
Botticelli supports Telegram, WhatsApp, VK Messenger, and other standard messaging platforms.
You can get started by cloning the repository from GitHub using `git clone https://github.com/devgopher/botticelli.git` and following the provided documentation for further instructions.
Free plan available.
Use these comparison pages to understand the trade-offs between the models most relevant to Botticelli.
Compare GPT 5.4 and GPT 5.4 Pro across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and GPT 5.4 across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Opus across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
Compare GPT 5.5 and Claude 4.6 Sonnet across pricing, context window, capabilities, benchmarks, and API access to choose the better fit for long-context workloads versus long-context workloads.
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