Portkey-AI/gateway
gateway
A blazing fast AI Gateway with integrated guardrails. Route to 1,600+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.
Usage guide
gateway is an open-source project around ai-gateway, generative-ai, hacktoberfest with 12,230 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating gateway for TypeScript AI workflows.
- Comparing a GitHub project with 12,230 stars and current repository activity.
Pros
- gateway has visible GitHub traction with 12,230 stars. Topics: ai-gateway, gateway, generative-ai.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
gateway should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
gateway architecture preview
gateway's main path starts at the entry surface, runs through MCP tool router, combines OpenAI, Runtime context, MCP tools / APIs / webhooks, and returns User-facing result.
Entry
CLI / terminal entry
gateway is primarily entered through a developer command or terminal workflow.
npx @portkey-ai/gateway
Runtime
MCP tool router
The router exposes tools and context through Model Context Protocol boundaries.
MCP
Model
OpenAI
Model calls are likely routed through OpenAI based on README and topic signals.
OpenAI
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
MCP tools / APIs / webhooks
Tool adapters let the runtime act outside the model through MCP tools / APIs / webhooks.
MCP tools, APIs / webhooks
Output
User-facing result
The final output is returned to the user, workflow, API caller, or downstream system.
output
Featured video
Gateway Worship Español
Danzando | Christine D’Clario, Travy Joe, Daniel Calveti y Gateway Worship Español
180,829,922 views · 2022-04-29
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- Node.js and the package manager used by the project
- Local build tools for compiling the project
- A clean working directory for the first test run
Check the runtime environment
gateway depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/Portkey-AI/gateway.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npx @portkey-ai/gatewayAdoption guidance and sources
Practical use cases
A blazing fast AI Gateway with integrated guardrails. Route to 1,600+
This is one of the documented reasons to evaluate gateway before choosing a stack.
Focus area: ai-gateway
This is one of the documented reasons to evaluate gateway before choosing a stack.
MCP project comparison
Compare gateway with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official gateway setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented gateway example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is gateway?
gateway is an open-source mcp project. A blazing fast AI Gateway with integrated guardrails. Route to 1,600+ LLMs, 50+ AI Guardrails with 1 fast & friendly API.
How do I install gateway?
Start with the official README. The first detected setup step is: git clone https://github.com/Portkey-AI/gateway.git.
Is gateway beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can gateway be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does gateway need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt gateway?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.