base on Build custom inference engines for models, agents, multi-modal systems, RAG, pipelines and more. <div align='center'> <h2> The framework to build custom inference engines with expert control. <br/> Engines for models, agents, MCP, multi-modal, RAG, and pipelines. <br/> No MLOps. No YAML. </h2> <img alt="Lightning" src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/ls_banner2.png" width="800px" style="max-width: 100%;"> &nbsp; </div> <div align='center'> <pre> ✅ Build your own inference engine ✅ 2× faster than FastAPI ✅ Agents, RAG, pipelines, more ✅ Custom logic + control ✅ Any PyTorch model ✅ Self-host or managed ✅ Multi-GPU autoscaling ✅ Batching + streaming ✅ BYO model or vLLM ✅ No MLOps glue code ✅ Easy setup in Python ✅ Serverless support </pre> <div align='center'> [](https://pepy.tech/projects/litserve) [](https://discord.gg/WajDThKAur)  [](https://codecov.io/gh/Lightning-AI/litserve) [](https://github.com/Lightning-AI/litserve/blob/main/LICENSE) </div> </div> <div align="center"> <div style="text-align: center;"> <a target="_blank" href="#quick-start" style="margin: 0 10px;">Quick start</a> • <a target="_blank" href="#featured-examples" style="margin: 0 10px;">Examples</a> • <a target="_blank" href="#features" style="margin: 0 10px;">Features</a> • <a target="_blank" href="#performance" style="margin: 0 10px;">Performance</a> • <a target="_blank" href="#host-anywhere" style="margin: 0 10px;">Hosting</a> • <a target="_blank" href="https://lightning.ai/docs/litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme" style="margin: 0 10px;">Docs</a> </div> </div> &nbsp; <div align="center"> <a target="_blank" href="https://lightning.ai/docs/litserve/home/get-started?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme"> <img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/get-started-badge.svg" height="36px" alt="Get started"/> </a> </div> &nbsp; # Looking for GPUs and an inference platform? Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) - purpose-built for PyTorch and PyTorch Lightning. - [GPUs](https://lightning.ai/pricing?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme) from $0.19. - [Clusters](https://lightning.ai/clusters?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): frontier-grade training/inference clusters. - [AI Studio (vibe train)](https://lightning.ai/studios?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): workspaces where AI helps you debug, tune and vibe train. - [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): workspaces where AI helps you optimize, and deploy models. - [Notebooks](https://lightning.ai/notebooks?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): Persistent GPU workspaces where AI helps you code and analyze. - [Inference](https://lightning.ai/deploy?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme): Deploy models as inference APIs. # Why LitServe? LitServe lets you build your own inference engine. Serving engines such as vLLM serve specific model types (LLMs) with rigid abstractions. LitServe gives you the low-level control to serve any model (vision, audio, text, multi-modal), and define exactly how inference works - from batching, caching, streaming, and routing, to multi-model orchestration and custom logic. LitServe is perfect for building inference APIs, agents, chatbots, MCP servers, RAG, pipelines and more. Self host LitServe or deploy in one-click to [Lightning AI](https://lightning.ai/litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme). &nbsp; # Quick start Install LitServe via pip ([more options](https://lightning.ai/docs/litserve/home/install)): ```bash pip install litserve ``` [Example 1](#inference-engine-example): Toy inference pipeline with multiple models. [Example 2](#agent-example): Minimal agent to fetch the news (with OpenAI API). ([Advanced examples](#featured-examples)): ### Inference engine example ```python import litserve as ls # define the api to include any number of models, dbs, etc... class InferenceEngine(ls.LitAPI): def setup(self, device): self.text_model = lambda x: x**2 self.vision_model = lambda x: x**3 def predict(self, request): x = request["input"] # perform calculations using both models a = self.text_model(x) b = self.vision_model(x) c = a + b return {"output": c} if __name__ == "__main__": # 12+ features like batching, streaming, etc... server = ls.LitServer(InferenceEngine(max_batch_size=1), accelerator="auto") server.run(port=8000) ``` Deploy for free to [Lightning cloud](#hosting-options) (or self host anywhere): ```bash # Deploy for free with autoscaling, monitoring, etc... lightning deploy server.py --cloud # Or run locally (self host anywhere) lightning deploy server.py # python server.py ``` Test the server: Simulate an http request (run this on any terminal): ```bash curl -X POST http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"input": 4.0}' ``` ### Agent example ```python import re, requests, openai import litserve as ls class NewsAgent(ls.LitAPI): def setup(self, device): self.openai_client = openai.OpenAI(api_key="OPENAI_API_KEY") def predict(self, request): website_url = request.get("website_url", "https://text.npr.org/") website_text = re.sub(r'<[^>]+>', ' ', requests.get(website_url).text) # ask the LLM to tell you about the news llm_response = self.openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": f"Based on this, what is the latest: {website_text}"}], ) output = llm_response.choices[0].message.content.strip() return {"output": output} if __name__ == "__main__": server = ls.LitServer(NewsAgent()) server.run(port=8000) ``` Test it: ```bash curl -X POST http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"website_url": "https://text.npr.org/"}' ``` &nbsp; # Key benefits A few key benefits: - **Deploy any pipeline or model**: Agents, pipelines, RAG, chatbots, image models, video, speech, text, etc... - **No MLOps glue:** LitAPI lets you build full AI systems (multi-model, agent, RAG) in one place ([more](https://lightning.ai/docs/litserve/api-reference/litapi?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme)). - **Instant setup:** Connect models, DBs, and data in a few lines with `setup()` ([more](https://lightning.ai/docs/litserve/api-reference/litapi?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme#setup)). - **Optimized:** autoscaling, GPU support, and fast inference included ([more](https://lightning.ai/docs/litserve/api-reference/litserver?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme)). - **Deploy anywhere:** self-host or one-click deploy with Lightning ([more](https://lightning.ai/docs/litserve/features/deploy-on-cloud?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme)). - **FastAPI for AI:** Built on FastAPI but optimized for AI - 2× faster with AI-specific multi-worker handling ([more]((#performance))). - **Expert-friendly:** Use vLLM, or build your own with full control over batching, caching, and logic ([more](https://lightning.ai/lightning-ai/studios/deploy-a-private-llama-3-2-rag-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme)). > ⚠️ Not a vLLM or Ollama alternative out of the box. LitServe gives you lower-level flexibility to build what they do (and more) if you need it. &nbsp; # Featured examples Here are examples of inference pipelines for common model types and use cases. <pre> <strong>Toy model:</strong> <a target="_blank" href="#define-a-server">Hello world</a> <strong>LLMs:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-llama-3-2-vision-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Llama 3.2</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/openai-fault-tolerant-proxy-server?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">LLM Proxy server</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-ai-agent-with-tool-use?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Agent with tool use</a> <strong>RAG:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-private-llama-3-2-rag-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">vLLM RAG (Llama 3.2)</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-private-llama-3-1-rag-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">RAG API (LlamaIndex)</a> <strong>NLP:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-any-hugging-face-model-instantly?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Hugging face</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-hugging-face-bert-model?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">BERT</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-text-embedding-api-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Text embedding API</a> <strong>Multimodal:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-open-ai-clip-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">OpenAI Clip</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-multi-modal-llm-with-minicpm?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">MiniCPM</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-phi3-5-vision-api-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Phi-3.5 Vision Instruct</a>, <a target="_blank" href="https://lightning.ai/bhimrajyadav/studios/deploy-and-chat-with-qwen2-vl-using-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Qwen2-VL</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-multi-modal-llm-with-pixtral?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Pixtral</a> <strong>Audio:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-open-ai-s-whisper-model?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Whisper</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-an-music-generation-api-with-meta-s-audio-craft?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">AudioCraft</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-an-audio-generation-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">StableAudio</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-noise-cancellation-api-with-deepfilternet?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Noise cancellation (DeepFilterNet)</a> <strong>Vision:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-private-api-for-stable-diffusion-2?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Stable diffusion 2</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-an-image-generation-api-with-auraflow?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">AuraFlow</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-an-image-generation-api-with-flux?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Flux</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-super-resolution-image-api-with-aura-sr?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Image Super Resolution (Aura SR)</a>, <a target="_blank" href="https://lightning.ai/bhimrajyadav/studios/deploy-background-removal-api-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Background Removal</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-controlled-image-generation-api-controlnet?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Control Stable Diffusion (ControlNet)</a> <strong>Speech:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-a-voice-clone-api-coqui-xtts-v2-model?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Text-speech (XTTS V2)</a>, <a target="_blank" href="https://lightning.ai/bhimrajyadav/studios/deploy-a-speech-generation-api-using-parler-tts-powered-by-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Parler-TTS</a> <strong>Classical ML:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-random-forest-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Random forest</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-xgboost-with-litserve?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">XGBoost</a> <strong>Miscellaneous:</strong> <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-an-media-conversion-api-with-ffmpeg?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">Media conversion API (ffmpeg)</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/deploy-both-pytorch-and-tensorflow-in-a-single-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">PyTorch + TensorFlow in one API</a>, <a target="_blank" href="https://lightning.ai/lightning-ai/studios/openai-fault-tolerant-proxy-server?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme">LLM proxy server</a> </pre> </pre> [Browse 100+ community-built templates](https://lightning.ai/studios?section=serving&utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) &nbsp; # Host anywhere Self-host with full control, or deploy with [Lightning AI](https://lightning.ai/?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) in seconds with autoscaling, security, and 99.995% uptime. **Free tier included. No setup required. Run on your cloud** ```bash lightning deploy server.py --cloud ``` https://github.com/user-attachments/assets/ff83dab9-0c9f-4453-8dcb-fb9526726344 &nbsp; # Features <div align='center'> | [Feature](https://lightning.ai/docs/litserve/features?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | Self Managed | [Fully Managed on Lightning](https://lightning.ai/deploy?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | |----------------------------------------------------------------------|-----------------------------------|------------------------------------| | Docker-first deployment | ✅ DIY | ✅ One-click deploy | | Cost | ✅ Free (DIY) | ✅ Generous [free tier](https://lightning.ai/pricing?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) with pay as you go | | Full control | ✅ | ✅ | | Use any engine (vLLM, etc.) | ✅ | ✅ vLLM, Ollama, LitServe, etc. | | Own VPC | ✅ (manual setup) | ✅ Connect your own VPC | | [(2x)+ faster than plain FastAPI](#performance) | ✅ | ✅ | | [Bring your own model](https://lightning.ai/docs/litserve/features/full-control?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Build compound systems (1+ models)](https://lightning.ai/docs/litserve/home?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [GPU autoscaling](https://lightning.ai/docs/litserve/features/gpu-inference?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Batching](https://lightning.ai/docs/litserve/features/batching?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Streaming](https://lightning.ai/docs/litserve/features/streaming?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Worker autoscaling](https://lightning.ai/docs/litserve/features/autoscaling?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Serve all models: (LLMs, vision, etc.)](https://lightning.ai/docs/litserve/examples?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Supports PyTorch, JAX, TF, etc...](https://lightning.ai/docs/litserve/features/full-control?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [OpenAPI compliant](https://www.openapis.org/) | ✅ | ✅ | | [Open AI compatibility](https://lightning.ai/docs/litserve/features/open-ai-spec?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [MCP server support](https://lightning.ai/docs/litserve/features/mcp?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Asynchronous](https://lightning.ai/docs/litserve/features/async-concurrency?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ✅ | ✅ | | [Authentication](https://lightning.ai/docs/litserve/features/authentication?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | ❌ DIY | ✅ Token, password, custom | | GPUs | ❌ DIY | ✅ 8+ GPU types, H100s from $1.75 | | Load balancing | ❌ | ✅ Built-in | | Scale to zero (serverless) | ❌ | ✅ No machine runs when idle | | Autoscale up on demand | ❌ | ✅ Auto scale up/down | | Multi-node inference | ❌ | ✅ Distribute across nodes | | Use AWS/GCP credits | ❌ | ✅ Use existing cloud commits | | Versioning | ❌ | ✅ Make and roll back releases | | Enterprise-grade uptime (99.95%) | ❌ | ✅ SLA-backed | | SOC2 / HIPAA compliance | ❌ | ✅ Certified & secure | | Observability | ❌ | ✅ Built-in, connect 3rd party tools| | CI/CD ready | ❌ | ✅ Lightning SDK | | 24/7 enterprise support | ❌ | ✅ Dedicated support | | Cost controls & audit logs | ❌ | ✅ Budgets, breakdowns, logs | | Debug on GPUs | ❌ | ✅ Studio integration | | [20+ features](https://lightning.ai/docs/litserve/features?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) | - | - | </div> &nbsp; # Performance LitServe is designed for AI workloads. Specialized multi-worker handling delivers a minimum **2x speedup over FastAPI**. Additional features like batching and GPU autoscaling can drive performance well beyond 2x, scaling efficiently to handle more simultaneous requests than FastAPI and TorchServe. Reproduce the full benchmarks [here](https://lightning.ai/docs/litserve/home/benchmarks?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) (higher is better). <div align="center"> <img alt="LitServe" src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/ls_charts_v6.png" width="1000px" style="max-width: 100%;"> </div> These results are for image and text classification ML tasks. The performance relationships hold for other ML tasks (embedding, LLM serving, audio, segmentation, object detection, summarization etc...). ***💡 Note on LLM serving:*** For high-performance LLM serving (like Ollama/vLLM), integrate [vLLM with LitServe](https://lightning.ai/lightning-ai/studios/deploy-a-private-llama-3-2-rag-api?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme), use [LitGPT](https://github.com/Lightning-AI/litgpt?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme#deploy-an-llm), or build your custom vLLM-like server with LitServe. Optimizations like kv-caching, which can be done with LitServe, are needed to maximize LLM performance. &nbsp; # Community LitServe is a [community project accepting contributions](https://lightning.ai/docs/litserve/community?utm_source=litserve_readme&utm_medium=referral&utm_campaign=litserve_readme) - Let's make the world's most advanced AI inference engine. 💬 [Get help on Discord](https://discord.com/invite/XncpTy7DSt) 📋 [License: Apache 2.0](https://github.com/Lightning-AI/litserve/blob/main/LICENSE) ", Assign "at most 3 tags" to the expected json: {"id":"11569","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"