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AISIX AI Gateway

Self-hosted MCP gateway that registers upstream MCP servers and fronts them behind one governed Streamable HTTP endpoint: per-tool access control by caller API key, guardrails over tool arguments and results, rate limits, and usage logs. The same Rust gateway also proxies LLM and A2A agent traffic.

Host

GitHub
github.com

Protocols

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Tools

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Uptime

0% over 30 probes

About this host

Join the world's most widely adopted, AI-powered developer platform where millions of developers, businesses, and the largest open source community build software that advances humanity. the operator’s own description, from their site

Also on this host

17,285 other resources published on github.com, newest first.

• hf-cliHugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging F
• hf-cloud-aws-context-discoveryDiscover the user''s local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task.
• hf-cloud-python-env-setupSet up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3.
• hf-cloud-sagemaker-deployment-plannerPlan and coordinate the deployment of a model to Amazon SageMaker AI.
• hf-cloud-sagemaker-iam-preflightEnsure a usable SageMaker execution role exists before deploying or training.
• hf-cloud-sagemaker-production-defaultsCreate a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default.
• hf-cloud-serving-image-selectionPick the right serving container for a SageMaker model deployment and find its current image URI.
• hf-memHugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

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