base on Open-source LLMOps platform for hosting and scaling AI in your own infrastructure 🏓🦙 # Paddler Digital products and their users need privacy, reliability, cost control and an option to be independent from third party vendors. Paddler is an open-source LLMOps platform for organizations that host and scale open-source models in their own infrastructure. ## Key features <img align="right" src="https://github.com/user-attachments/assets/19e74262-1918-4b1d-9b4c-bcb4f0ab79f5"> * Inference through a built-in [llama.cpp](https://github.com/ggml-org/llama.cpp) engine * Load balancing * Works through agents that can be added dynamically, allowing integration with autoscaling tools * Request buffering, enabling scaling from zero hosts * Dynamic model swapping * Built-in web admin panel for management, monitoring and testing * Observability metrics ## For whom? * Product teams that need LLM inference and embeddings in their features * DevOps/LLMOps teams that need to run and deploy LLMs at scale * Organizations handling sensitive data with high compliance and privacy requirements (medical, financial, etc.) * Organizations wanting to achieve predictable LLM costs instead of being exposed to per-token pricing * Product leaders who need reliable model performance to maintain consistent user experience of their AI-based features ## Documentation Visit our [documentation page](https://paddler.intentee.com/docs/introduction/what-is-paddler/) to install Paddler and get started with it. [API documentation](https://paddler.intentee.com/api/introduction/using-paddler-api/) is also available. [Video overview](https://www.youtube.com/watch?v=aT6QCL8lk08) ## Installation There are multiple ways to install Paddler, but the goal is to obtain the `paddler` binary and make it available in your system. You can: * Option 1: Download the latest release from our [GitHub releases](https://github.com/intentee/paddler/releases) * Option 2: Build Paddler from source ### Using Paddler The entire Paddler functionality is available through the `paddler` command. You can run `paddler --help` to see the available commands and options. Read more about [installation and initial setup](https://paddler.intentee.com/docs/introduction/installation/) ## How does it work? Paddler is built for an easy set up. It comes as a self-contained binary with only two deployable components, the `balancer` and the `agents`. The `balancer` exposes the following: - Inference service (used by applications that connect to it to obtain tokens or embeddings) - Management service, which manages the Paddler's setup internally - Web admin panel that lets you view and test your Paddler setup `Agents` are usually deployed on separate instances. They further distribute the incoming requests to `slots`, which are responsible for generating tokens and embeddings. Paddler uses a built-in llama.cpp engine for inference, but has its own implementation of llama.cpp slots which keep their own context and KV cache. ### Web admin panel Paddler comes with a built-in web admin panel. You can use it to monitor your Paddler fleet: <img width="1587" height="732" alt="paddler-web-admin-panel" src="https://github.com/user-attachments/assets/de26312e-e83e-4def-8326-0aa5d559396c" /> Add and update your model and customize the chat template and inference parameters: <img width="1422" height="1584" alt="paddler-model" src="https://github.com/user-attachments/assets/dd9d7eb0-a990-4b1c-b523-7286956baeb2" /> And use GUI to test the inference: <img width="1413" height="984" alt="paddler-prompt" src="https://github.com/user-attachments/assets/30b35b5a-c3de-4acc-a602-c7ffaa21d0a6" /> ## Starting out * [Setup a basic LLM cluster](https://paddler.intentee.com/docs/starting-out/set-up-a-basic-llm-cluster/) * [Use Paddler's web admin panel](https://paddler.intentee.com/docs/starting-out/using-web-admin-panel/) * [Generate tokens and embeddings](https://paddler.intentee.com/docs/starting-out/generating-tokens-and-embeddings/) * [Use function calling](https://paddler.intentee.com/docs/starting-out/using-function-calling/) * [Create a multi agent fleet](https://paddler.intentee.com/docs/starting-out/multi-agent-fleet/) * [Go beyond a single device](https://paddler.intentee.com/docs/starting-out/going-beyond-a-single-device/) ## Why the Name We initially wanted to use [Raft](https://raft.github.io/) consensus algorithm (thus Paddler, because it paddles on a Raft), but eventually dropped that idea. The name stayed, though. Later, people started sending us the "that's a paddlin'" clip from The Simpsons, and we just embraced it. ## Community and contributions We keep everything simple and on GitHub. Please use GitHub discussions for community conversations, and feel free to contribute. ", Assign "at most 3 tags" to the expected json: {"id":"14666","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"