{"data":{"node":{"slug":"jina-ai-serve","name":"serve","tagline":"Build multimodal AI applications with cloud-native stack","github_url":"https://github.com/jina-ai/serve","owner":"jina-ai","repo":"serve","owner_avatar_url":"https://avatars.githubusercontent.com/u/60539444?v=4","primary_language":"Python","stars":21861,"forks":2240,"topics":["cloud-native","cncf","deep-learning","docker","fastapi","framework","generative-ai","grpc","jaeger","kubernetes","llmops","machine-learning","microservice","mlops","multimodal","neural-search","opentelemetry","orchestration","pipeline","prometheus"],"archived":false,"github_pushed_at":"2025-03-24T13:59:54+00:00","maintenance_label":"Dormant","stars_delta_30d":-2,"url":"https://www.graphcanon.com/tools/jina-ai-serve","markdown_url":"https://www.graphcanon.com/tools/jina-ai-serve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jina-ai-serve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jina-ai-serve"},"categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"cloud-native","name":"cloud-native"},{"slug":"cncf","name":"cncf"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"docker","name":"docker"},{"slug":"fastapi","name":"fastapi"},{"slug":"framework","name":"framework"},{"slug":"generative-ai","name":"generative-ai"},{"slug":"grpc","name":"grpc"}],"edges":[{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"bytedance-ui-tars-desktop","name":"UI-TARS-desktop","tagline":"Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra","github_url":"https://github.com/bytedance/UI-TARS-desktop","owner":"bytedance","repo":"UI-TARS-desktop","owner_avatar_url":"https://avatars.githubusercontent.com/u/4158466?v=4","primary_language":"TypeScript","stars":38634,"forks":3897,"topics":["agent","agent-tars","browser-use","computer-use","cowork","gui-agent","gui-operator","mcp","mcp-server","multimodal","tars","ui-tars","vision","vlm"],"archived":false,"github_pushed_at":"2026-08-05T02:48:59+00:00","maintenance_label":"Active","stars_delta_30d":526,"url":"https://www.graphcanon.com/tools/bytedance-ui-tars-desktop","markdown_url":"https://www.graphcanon.com/tools/bytedance-ui-tars-desktop.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bytedance-ui-tars-desktop","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bytedance-ui-tars-desktop"}},{"type":"alternative","direction":"out","explanation":"Both Jina-Serve and BentoML are frameworks for building and deploying AI services, but they differ in their architectural approaches and the protocols they support (e.g., gRPC vs HTTP/REST).","successor_context":null,"tool":{"slug":"bentoml-bentoml","name":"BentoML","tagline":"The easiest way to serve AI apps and models","github_url":"https://github.com/bentoml/BentoML","owner":"bentoml","repo":"BentoML","owner_avatar_url":"https://avatars.githubusercontent.com/u/49176046?v=4","primary_language":"Python","stars":8847,"forks":1032,"topics":["ai-inference","deep-learning","generative-ai","inference-platform","llm","llm-inference","llm-serving","llmops","machine-learning","ml-engineering","mlops","model-inference-service","model-serving","multimodal","python"],"archived":false,"github_pushed_at":"2026-09-07T17:43:01+00:00","maintenance_label":"Active","stars_delta_30d":119,"url":"https://www.graphcanon.com/tools/bentoml-bentoml","markdown_url":"https://www.graphcanon.com/tools/bentoml-bentoml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bentoml-bentoml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bentoml-bentoml"}},{"type":"integrates_with","direction":"out","explanation":"Jina-Serve can leverage transformers from Hugging Face to build its AI applications, as it supports multiple ML frameworks and data types.","successor_context":null,"tool":{"slug":"huggingface-transformers","name":"transformers","tagline":"🤗 Transformers: state-of-the-art machine learning models for text, vision, audio, and multimodal models","github_url":"https://github.com/huggingface/transformers","owner":"huggingface","repo":"transformers","owner_avatar_url":"https://avatars.githubusercontent.com/u/25720743?v=4","primary_language":"Python","stars":166279,"forks":34619,"topics":["audio","deep-learning","deepseek","gemma","glm","hacktoberfest","llm","machine-learning","model-hub","natural-language-processing","nlp","pretrained-models","python","pytorch","pytorch-transformers","qwen","speech-recognition","transformer","vlm"],"archived":false,"github_pushed_at":"2026-09-18T08:16:29+00:00","maintenance_label":"Very active","stars_delta_30d":2158,"url":"https://www.graphcanon.com/tools/huggingface-transformers","markdown_url":"https://www.graphcanon.com/tools/huggingface-transformers.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huggingface-transformers","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huggingface-transformers"}},{"type":"integrates_with","direction":"out","explanation":"Jina-Serve could likely integrate with MLFlow for tracking and managing the lifecycle of AI models, offering a comprehensive solution for deploying and monitoring ML services.","successor_context":null,"tool":{"slug":"mlflow-mlflow","name":"mlflow","tagline":"Open source AI engineering platform for agents, LLMs, and ML models","github_url":"https://github.com/mlflow/mlflow","owner":"mlflow","repo":"mlflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/39938107?v=4","primary_language":"Python","stars":28010,"forks":6323,"topics":["agentops","agents","ai","ai-governance","apache-spark","evaluation","langchain","llm-evaluation","llmops","machine-learning","ml","mlflow","mlops","model-management","observability","open-source","openai","prompt-engineering"],"archived":false,"github_pushed_at":"2026-09-18T08:08:32+00:00","maintenance_label":"Very active","stars_delta_30d":895,"url":"https://www.graphcanon.com/tools/mlflow-mlflow","markdown_url":"https://www.graphcanon.com/tools/mlflow-mlflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mlflow-mlflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mlflow-mlflow"}},{"type":"integrates_with","direction":"out","explanation":"Jina-Serve (serve), as a framework for deploying scalable AI services with support for multiple protocols and orchestration tools, can be used to deploy Langchain-Chatchat, which is an application for RAG and AI agent functionalities. This 'integrates with' relationship means that serve provides the deployment infrastructure necessary to run Langchain-Chatchat efficiently.","successor_context":null,"tool":{"slug":"chatchat-space-langchain-chatchat","name":"Langchain-Chatchat","tagline":"Local knowledge-based LLM RAG and Agent app with Langchain","github_url":"https://github.com/chatchat-space/Langchain-Chatchat","owner":"chatchat-space","repo":"Langchain-Chatchat","owner_avatar_url":"https://avatars.githubusercontent.com/u/139558948?v=4","primary_language":"Python","stars":38644,"forks":6265,"topics":["chatbot","chatchat","chatglm","chatgpt","embedding","faiss","fastchat","gpt","knowledge-base","langchain","langchain-chatglm","llama","llm","milvus","ollama","qwen","rag","retrieval-augmented-generation","streamlit","xinference"],"archived":false,"github_pushed_at":"2025-11-10T09:27:42+00:00","maintenance_label":"Slowing","stars_delta_30d":122,"url":"https://www.graphcanon.com/tools/chatchat-space-langchain-chatchat","markdown_url":"https://www.graphcanon.com/tools/chatchat-space-langchain-chatchat.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/chatchat-space-langchain-chatchat","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=chatchat-space-langchain-chatchat"}},{"type":"alternative","direction":"out","explanation":"Both Jina-Serve and sglang are serving frameworks for large language models and multimodal models, each offering their own approach to deployment and scalability.","successor_context":null,"tool":{"slug":"sgl-project-sglang","name":"sglang","tagline":"SGLang is a high-performance serving framework for large language models and multimodal models.","github_url":"https://github.com/sgl-project/sglang","owner":"sgl-project","repo":"sglang","owner_avatar_url":"https://avatars.githubusercontent.com/u/147780389?v=4","primary_language":"Python","stars":36123,"forks":8969,"topics":["attention","blackwell","cuda","deepseek","diffusion","glm","gpt-oss","inference","llama","llm","minimax","moe","qwen","qwen-image","reinforcement-learning","transformer","vlm","wan"],"archived":false,"github_pushed_at":"2026-09-18T08:40:33+00:00","maintenance_label":"Very active","stars_delta_30d":4669,"url":"https://www.graphcanon.com/tools/sgl-project-sglang","markdown_url":"https://www.graphcanon.com/tools/sgl-project-sglang.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sgl-project-sglang","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sgl-project-sglang"}},{"type":"alternative","direction":"out","explanation":"VLLM serves a similar purpose of easy LLM serving but may have different design philosophies or performance characteristics compared to Jina-Serve.","successor_context":null,"tool":{"slug":"vllm-project-vllm","name":"vllm","tagline":"A high-throughput and memory-efficient inference and serving engine for LLMs","github_url":"https://github.com/vllm-project/vllm","owner":"vllm-project","repo":"vllm","owner_avatar_url":"https://avatars.githubusercontent.com/u/136984999?v=4","primary_language":"Python","stars":92075,"forks":22363,"topics":["amd","blackwell","cuda","deepseek","deepseek-v3","gpt","gpt-oss","inference","kimi","llama","llm","llm-serving","model-serving","moe","openai","pytorch","qwen","qwen3","tpu","transformer"],"archived":false,"github_pushed_at":"2026-09-18T07:28:54+00:00","maintenance_label":"Very active","stars_delta_30d":4228,"url":"https://www.graphcanon.com/tools/vllm-project-vllm","markdown_url":"https://www.graphcanon.com/tools/vllm-project-vllm.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/vllm-project-vllm","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=vllm-project-vllm"}},{"type":"related","direction":"out","explanation":null,"successor_context":null,"tool":{"slug":"ray-project-ray","name":"ray","tagline":"Unified framework for scaling AI and Python applications","github_url":"https://github.com/ray-project/ray","owner":"ray-project","repo":"ray","owner_avatar_url":"https://avatars.githubusercontent.com/u/22125274?v=4","primary_language":"Python","stars":43863,"forks":8052,"topics":["data-science","deep-learning","deployment","distributed","hyperparameter-optimization","hyperparameter-search","large-language-models","llm","llm-inference","llm-serving","machine-learning","optimization","parallel","python","pytorch","ray","reinforcement-learning","rllib","serving","tensorflow"],"archived":false,"github_pushed_at":"2026-09-18T06:15:07+00:00","maintenance_label":"Very active","stars_delta_30d":337,"url":"https://www.graphcanon.com/tools/ray-project-ray","markdown_url":"https://www.graphcanon.com/tools/ray-project-ray.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ray-project-ray","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ray-project-ray"}},{"type":"integrates_with","direction":"in","explanation":"BentoML, as a framework for building online serving systems optimized for AI models, can integrate with Jina-Serve (referred to here as 'serve') to expand its service capabilities by leveraging Jina-Serve's support for gRPC, HTTP, and WebSockets protocols, thus enabling more versatile deployment options.","successor_context":null,"tool":{"slug":"bentoml-bentoml","name":"BentoML","tagline":"The easiest way to serve AI apps and models","github_url":"https://github.com/bentoml/BentoML","owner":"bentoml","repo":"BentoML","owner_avatar_url":"https://avatars.githubusercontent.com/u/49176046?v=4","primary_language":"Python","stars":8847,"forks":1032,"topics":["ai-inference","deep-learning","generative-ai","inference-platform","llm","llm-inference","llm-serving","llmops","machine-learning","ml-engineering","mlops","model-inference-service","model-serving","multimodal","python"],"archived":false,"github_pushed_at":"2026-09-07T17:43:01+00:00","maintenance_label":"Active","stars_delta_30d":119,"url":"https://www.graphcanon.com/tools/bentoml-bentoml","markdown_url":"https://www.graphcanon.com/tools/bentoml-bentoml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bentoml-bentoml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bentoml-bentoml"}},{"type":"integrates_with","direction":"in","explanation":"EmbedAnything generates embeddings from diverse data types and streams them to a vector database, while serve (Jina-Serve) is a framework for deploying scalable AI services. The 'integrates with' relationship exists because the embeddings generated by EmbedAnything can be utilized within the scalable services deployed using serve, enabling effective multimodal data processing and serving in cloud-","successor_context":null,"tool":{"slug":"starlightsearch-embedanything","name":"EmbedAnything","tagline":"Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust","github_url":"https://github.com/StarlightSearch/EmbedAnything","owner":"StarlightSearch","repo":"EmbedAnything","owner_avatar_url":"https://avatars.githubusercontent.com/u/165606246?v=4","primary_language":"Rust","stars":1307,"forks":142,"topics":["ai","cloud","generative-ai","hacktoberfest","high-performance","indexing","inference","information-retrieval","large-language-models","local","machine-learning","onnxruntime","pipeline","production-ready","python","rag","rust","search","server","vector-database"],"archived":false,"github_pushed_at":"2026-08-12T08:56:59+00:00","maintenance_label":"Steady","stars_delta_30d":21,"url":"https://www.graphcanon.com/tools/starlightsearch-embedanything","markdown_url":"https://www.graphcanon.com/tools/starlightsearch-embedanything.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/starlightsearch-embedanything","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=starlightsearch-embedanything"}},{"type":"related","direction":"in","explanation":"BentoML and Serve both cater to building multimodal AI applications, but they do so differently. BentoML focuses on serving models via APIs and Docker containers, while Serve emphasizes cloud-native infrastructure for deployment.","successor_context":null,"tool":{"slug":"bentoml-bentoml","name":"BentoML","tagline":"The easiest way to serve AI apps and models","github_url":"https://github.com/bentoml/BentoML","owner":"bentoml","repo":"BentoML","owner_avatar_url":"https://avatars.githubusercontent.com/u/49176046?v=4","primary_language":"Python","stars":8847,"forks":1032,"topics":["ai-inference","deep-learning","generative-ai","inference-platform","llm","llm-inference","llm-serving","llmops","machine-learning","ml-engineering","mlops","model-inference-service","model-serving","multimodal","python"],"archived":false,"github_pushed_at":"2026-09-07T17:43:01+00:00","maintenance_label":"Active","stars_delta_30d":119,"url":"https://www.graphcanon.com/tools/bentoml-bentoml","markdown_url":"https://www.graphcanon.com/tools/bentoml-bentoml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bentoml-bentoml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bentoml-bentoml"}}],"neighbours":[{"slug":"triton-inference-server-server","name":"server","tagline":"Optimized cloud and edge inferencing solution","github_url":"https://github.com/triton-inference-server/server","owner":"triton-inference-server","repo":"server","owner_avatar_url":"https://avatars.githubusercontent.com/u/68086070?v=4","primary_language":"Python","stars":10995,"forks":1837,"topics":["cloud","datacenter","deep-learning","edge","gpu","inference","machine-learning"],"archived":false,"github_pushed_at":"2026-09-17T14:35:44+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/triton-inference-server-server","markdown_url":"https://www.graphcanon.com/tools/triton-inference-server-server.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/triton-inference-server-server","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=triton-inference-server-server","shared_categories":["inference-serving"]},{"slug":"bentoml-bentoml","name":"BentoML","tagline":"The easiest way to serve AI apps and models","github_url":"https://github.com/bentoml/BentoML","owner":"bentoml","repo":"BentoML","owner_avatar_url":"https://avatars.githubusercontent.com/u/49176046?v=4","primary_language":"Python","stars":8847,"forks":1032,"topics":["ai-inference","deep-learning","generative-ai","inference-platform","llm","llm-inference","llm-serving","llmops","machine-learning","ml-engineering","mlops","model-inference-service","model-serving","multimodal","python"],"archived":false,"github_pushed_at":"2026-09-07T17:43:01+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/bentoml-bentoml","markdown_url":"https://www.graphcanon.com/tools/bentoml-bentoml.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/bentoml-bentoml","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=bentoml-bentoml","shared_categories":["inference-serving"]},{"slug":"tensorflow-serving","name":"serving","tagline":"Flexible, high-performance serving system for machine learning models","github_url":"https://github.com/tensorflow/serving","owner":"tensorflow","repo":"serving","owner_avatar_url":"https://avatars.githubusercontent.com/u/15658638?v=4","primary_language":"C++","stars":6362,"forks":2207,"topics":["cpp","deep-learning","deep-neural-networks","machine-learning","ml","neural-network","python","serving","tensorflow"],"archived":false,"github_pushed_at":"2026-09-16T22:46:53+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/tensorflow-serving","markdown_url":"https://www.graphcanon.com/tools/tensorflow-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/tensorflow-serving","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=tensorflow-serving","shared_categories":["inference-serving"]},{"slug":"kserve-kserve","name":"kserve","tagline":"Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes","github_url":"https://github.com/kserve/kserve","owner":"kserve","repo":"kserve","owner_avatar_url":"https://avatars.githubusercontent.com/u/83512434?v=4","primary_language":"Go","stars":5970,"forks":1689,"topics":["artificial-intelligence","cncf","genai","hacktoberfest","istio","k8s","knative","kserve","kubeflow","kubernetes","llm-inference","machine-learning","mlops","model-interpretability","model-serving","pytorch","service-mesh","tensorflow","vllm","xgboost"],"archived":false,"github_pushed_at":"2026-09-19T13:08:33+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/kserve-kserve","markdown_url":"https://www.graphcanon.com/tools/kserve-kserve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kserve-kserve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kserve-kserve","shared_categories":["inference-serving"]},{"slug":"pytorch-serve","name":"serve","tagline":"Serve, optimize and scale PyTorch models in production","github_url":"https://github.com/pytorch/serve","owner":"pytorch","repo":"serve","owner_avatar_url":"https://avatars.githubusercontent.com/u/21003710?v=4","primary_language":"Java","stars":4344,"forks":880,"topics":["cpu","deep-learning","docker","gpu","kubernetes","machine-learning","metrics","mlops","optimization","pytorch","serving"],"archived":true,"github_pushed_at":"2025-08-06T19:17:08+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/pytorch-serve","markdown_url":"https://www.graphcanon.com/tools/pytorch-serve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pytorch-serve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pytorch-serve","shared_categories":["inference-serving"]},{"slug":"superlinked-sie","name":"sie","tagline":"Open-source inference server and production cluster for all the models your agent needs.","github_url":"https://github.com/superlinked/sie","owner":"superlinked","repo":"sie","owner_avatar_url":"https://avatars.githubusercontent.com/u/94243920?v=4","primary_language":"Python","stars":3293,"forks":311,"topics":["bge","colbert","data-pipeline","deep-learning","embeddings","inference","inference-server","information-retrieval","llm","ml","mlops","natural-language-processing","nlp","python","reranking","retrieval","retrieval-augmented-generation","semantic-search","splade","vector-search"],"archived":false,"github_pushed_at":"2026-09-19T01:00:02+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/superlinked-sie","markdown_url":"https://www.graphcanon.com/tools/superlinked-sie.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/superlinked-sie","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=superlinked-sie","shared_categories":["inference-serving"]},{"slug":"michaelfeil-infinity","name":"infinity","tagline":"High-throughput, low-latency serving engine for text-embeddings, reranking models, CLIP, CLAP, and COLPAli","github_url":"https://github.com/michaelfeil/infinity","owner":"michaelfeil","repo":"infinity","owner_avatar_url":"https://avatars.githubusercontent.com/u/63565275?v=4","primary_language":"Python","stars":2942,"forks":207,"topics":["bert-embeddings","llm","text-embeddings"],"archived":false,"github_pushed_at":"2026-03-24T03:59:47+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/michaelfeil-infinity","markdown_url":"https://www.graphcanon.com/tools/michaelfeil-infinity.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/michaelfeil-infinity","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=michaelfeil-infinity","shared_categories":["inference-serving"]},{"slug":"langchain-ai-langserve","name":"langserve","tagline":"LangServe 🦜️🏓","github_url":"https://github.com/langchain-ai/langserve","owner":"langchain-ai","repo":"langserve","owner_avatar_url":"https://avatars.githubusercontent.com/u/126733545?v=4","primary_language":"JavaScript","stars":2328,"forks":270,"topics":["deployment","fastapi","langchain","langchain-python","llm","llms"],"archived":true,"github_pushed_at":"2026-05-05T19:49:07+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/langchain-ai-langserve","markdown_url":"https://www.graphcanon.com/tools/langchain-ai-langserve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/langchain-ai-langserve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=langchain-ai-langserve","shared_categories":["inference-serving"]},{"slug":"intentee-paddler","name":"paddler","tagline":"Open-source LLM/VLM load balancer and serving platform for self-hosting at scale","github_url":"https://github.com/intentee/paddler","owner":"intentee","repo":"paddler","owner_avatar_url":"https://avatars.githubusercontent.com/u/215040511?v=4","primary_language":"Rust","stars":1674,"forks":98,"topics":["ai","llamacpp","llm","llmops","load-balancer"],"archived":false,"github_pushed_at":"2026-09-17T17:41:40+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/intentee-paddler","markdown_url":"https://www.graphcanon.com/tools/intentee-paddler.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/intentee-paddler","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=intentee-paddler","shared_categories":["inference-serving"]},{"slug":"jina-ai-langchain-serve","name":"langchain-serve","tagline":"Self-host LLM Apps with Docker Compose or Kubernetes","github_url":"https://github.com/jina-ai/langchain-serve","owner":"jina-ai","repo":"langchain-serve","owner_avatar_url":"https://avatars.githubusercontent.com/u/60539444?v=4","primary_language":"Python","stars":1639,"forks":133,"topics":["autogpt","autonomous-agents","babyagi","chatbot","fastapi","gpt","langchain","llm","production","python","slack"],"archived":true,"github_pushed_at":"2023-09-20T04:01:50+00:00","maintenance_label":"Archived","url":"https://www.graphcanon.com/tools/jina-ai-langchain-serve","markdown_url":"https://www.graphcanon.com/tools/jina-ai-langchain-serve.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jina-ai-langchain-serve","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jina-ai-langchain-serve","shared_categories":["inference-serving"]},{"slug":"kubeai-project-kubeai","name":"kubeai","tagline":"AI Inference Operator for Kubernetes","github_url":"https://github.com/kubeai-project/kubeai","owner":"kubeai-project","repo":"kubeai","owner_avatar_url":"https://avatars.githubusercontent.com/u/232319222?v=4","primary_language":"Go","stars":1265,"forks":137,"topics":["ai","autoscaler","faster-whisper","inference-operator","k8s","kubernetes","llm","ollama","ollama-operator","openai-api","vllm","vllm-operator","whisper"],"archived":false,"github_pushed_at":"2026-09-17T23:56:08+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/kubeai-project-kubeai","markdown_url":"https://www.graphcanon.com/tools/kubeai-project-kubeai.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kubeai-project-kubeai","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kubeai-project-kubeai","shared_categories":["inference-serving"]},{"slug":"basetenlabs-truss","name":"truss","tagline":"The simplest way to serve AI/ML models in production","github_url":"https://github.com/basetenlabs/truss","owner":"basetenlabs","repo":"truss","owner_avatar_url":"https://avatars.githubusercontent.com/u/54861414?v=4","primary_language":"Python","stars":1203,"forks":126,"topics":["artificial-intelligence","easy-to-use","falcon","inference-api","inference-server","machine-learning","model-serving","open-source","packaging","stable-diffusion","whisper","wizardlm"],"archived":false,"github_pushed_at":"2026-09-18T22:12:03+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/basetenlabs-truss","markdown_url":"https://www.graphcanon.com/tools/basetenlabs-truss.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/basetenlabs-truss","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=basetenlabs-truss","shared_categories":["inference-serving"]}]}}