MCP server de AISIX AI Gateway
Commit 13 nov 2026Proporciona una única API compatible con OpenAI para enrutar, gobernar, asegurar y observar el tráfico a través de múltiples proveedores de LLM y MCP servers.
También conocido como api7/aisix, aisix
- Stars
- 219
- Herramientas
- —
- Descargas, 30 días
- —
- Última versión
- 1.3.0
- Último commit
- 13 nov 2026
- Licencia
- Apache-2.0
¿Qué es AISIX AI Gateway MCP?
AISIX AI Gateway es un MCP server. Proporciona una única API compatible con OpenAI para enrutar, gobernar, asegurar y observar el tráfico a través de múltiples proveedores de LLM y MCP servers.
Resumen generado a partir del README del proyecto
Acerca del MCP server de AISIX AI Gateway
AISIX OSS es un AI gateway nativo de Rust diseñado para que los equipos de plataforma gestionen el tráfico de LLM y agentes de IA a través de un único punto de control. Proporciona una API compatible con OpenAI que actúa como una interfaz unificada para varios proveedores, incluyendo OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI y DeepSeek. Al desplegar este único binario estático en tu propia infraestructura, puedes enrutar, asegurar y observar las solicitudes sin que tu tráfico en vivo pase por un plano de control de terceros.
El gateway gestiona tareas operativas complejas como el enrutamiento semántico, la ejecución de modelos en conjunto (ensemble) y el failover automatizado utilizando estrategias como round robin o de menor latencia. Incluye guardrails integrados para la detección de PII y el cumplimiento de políticas de contenido, además de capacidades de caché utilizando backends de memoria o Redis. Los usuarios gestionan el sistema mediante un archivo resources.yaml declarativo o un clúster de etcd, lo que permite recargas de configuración en caliente vía SIGHUP sin necesidad de reiniciar el servicio.
Qué puedes hacer con él
- 1Acceso unificado a modelosUn desarrollador puede cambiar entre diferentes proveedores de LLM simplemente cambiando el nombre del modelo en su código actual de OpenAI SDK.
- 2Gestión segura de MCP serversPuedes exponer múltiples MCP servers a través del gateway manteniendo un control estricto sobre los namespaces de las herramientas y las credenciales de los usuarios.
- 3Enrutamiento inteligente de solicitudesUn cliente de IA puede enviar prompts automáticamente al modelo más adecuado basándose en el significado real de la entrada del usuario.
- 4Cumplimiento de seguridad de contenidoLos ingenieros de plataforma pueden implementar la redacción automatizada de información sensible y bloquear contenido prohibido utilizando diversas herramientas de moderación.
Escrito a partir del README del proyecto. Cada caso de uso se verifica con una cita del mismo.
Cómo instalar AISIX AI Gateway MCP
Formatos verificados el 10 nov 2026Añadir a CursorAñadir a GooseInstalar AISIX AI Gateway MCP en Claude Code
$ claude mcp add aisix -- docker run -i --rm ghcr.io/api7/aisix:1.3.0Instalar AISIX AI Gateway MCP en Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"aisix": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/api7/aisix:1.3.0"
]
}
}
}Instalar AISIX AI Gateway MCP en VS Code
.vscode/mcp.json
{
"servers": {
"aisix": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/api7/aisix:1.3.0"
]
}
}
}Instalar AISIX AI Gateway MCP en VS Code CLI
$ code --add-mcp '{"name":"aisix","type":"stdio","command":"docker","args":["run","-i","--rm","ghcr.io/api7/aisix:1.3.0"]}'Instalar AISIX AI Gateway MCP en Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"aisix": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/api7/aisix:1.3.0"
]
}
}
}Instalar AISIX AI Gateway MCP en Codex
$ codex mcp add aisix -- docker run -i --rm ghcr.io/api7/aisix:1.3.0Instalar AISIX AI Gateway MCP en Codex config.toml
~/.codex/config.toml
[mcp_servers.aisix] command = "docker" args = ["run", "-i", "--rm", "ghcr.io/api7/aisix:1.3.0"]
Instalar AISIX AI Gateway MCP en Gemini CLI
$ gemini mcp add -s user aisix docker -- run -i --rm ghcr.io/api7/aisix:1.3.0Instalar AISIX AI Gateway MCP en Gemini settings.json
~/.gemini/settings.json
{
"mcpServers": {
"aisix": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/api7/aisix:1.3.0"
]
}
}
}Configuración
CALLER_API_KEYOPENAI_API_KEY
| Nombre | Descripción | Tipo |
|---|---|---|
| CALLER_API_KEY | — | |
| OPENAI_API_KEY | — |
Verificaciones automáticas
Ejecutado 12 nov 2026Informe completo →Aún no verificado. Un servidor necesita una puntuación de 80, ningún hallazgo grave y suficientes datos para que podamos revisarlo. Cómo funcionan las verificaciones · Qué corregir para obtener más puntos
- Repositorio de origen vinculado y accesibleAprobado
- Licencia de código abierto (OSI)Aprobado
- Commit o lanzamiento en el último añoAprobado
- El nombre del paquete no es similar a un paquete popularNo verificado
- El endpoint remoto usa HTTPSNo verificado
- Las herramientas destructivas están etiquetadasNo verificado
- Sin instrucciones ocultas para el modeloAprobado
- No hay avisos críticos en el paquete ni en sus dependenciasNo verificado
README
Ver en GitHubAISIX OSS
The open-source, Rust-native AI gateway for LLMs and AI agents
One OpenAI-compatible API in front of every model. Route, govern, secure, cache, and observe all your LLM and AI-agent traffic from a single control point — shipped as one static binary with low per-request overhead. Run it in your infrastructure for free, forever.
Built by the original creators of Apache APISIX.
Start free · Documentation · Quickstart · AISIX Enterprise and AISIX Cloud · Roadmap
AISIX OSS is the open-source product built around the Rust-native AISIX gateway. The gateway puts a single, OpenAI-compatible API in front of every LLM provider — OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, DeepSeek, and any OpenAI-compatible endpoint. It gives platform teams one place to route, govern, secure, and observe LLM traffic, with first-class SSE streaming and low gateway overhead.
It runs as a single static binary — low cold-start, lock-free config reads, and hot
configuration reloads with no restarts: declare resources in one resources.yaml and
reload on SIGHUP, or point the gateway at etcd for a multi-replica cluster. Run the
gateway in your infrastructure, or connect it to AISIX Enterprise or
AISIX Cloud
for centralized management with team governance, budgets, audit, and a dashboard.
AISIX OSS (this repo) is the open-source product. It runs without a control plane using declarative configuration or etcd. The same gateway binary serves as the data plane in the commercial products: customers host the control plane with AISIX Enterprise, while API7 hosts it with AISIX Cloud. In all three products, the gateway runs in your environment and calls providers directly; live AI traffic does not pass through either control plane. The proxy API is identical throughout. Talk to us about AISIX Enterprise or AISIX Cloud →
⚡ Quickstart
One container. No control plane, no database, no configuration store — the gateway reads
every dynamic resource from one declarative resources.yaml.
# config.yaml
resources_file: /etc/aisix/resources.yaml
proxy:
addr: "0.0.0.0:3000"
admin:
enabled: false # a declarative gateway needs no admin listener
observability:
metrics:
prometheus:
enabled: true
addr: "0.0.0.0:9090"
# resources.yaml
_format_version: "1"
provider_keys:
- display_name: openai-main
provider: openai
api_key: ${OPENAI_API_KEY} # interpolated from the environment
models:
- display_name: my-model
provider: openai
model_name: gpt-4o-mini
provider_key: openai-main
api_keys:
- display_name: local-dev
key_env: CALLER_API_KEY # hashed at load; the plaintext is never stored
allowed_models: ["my-model"]
export OPENAI_API_KEY="YOUR_PROVIDER_KEY"
export CALLER_API_KEY="YOUR_CALLER_KEY"
docker run -d --name aisix \
-v "$(pwd)/config.yaml:/etc/aisix/config.yaml:ro" \
-v "$(pwd)/resources.yaml:/etc/aisix/resources.yaml:ro" \
-e OPENAI_API_KEY -e CALLER_API_KEY \
-p 3000:3000 -p 127.0.0.1:9090:9090 \
ghcr.io/api7/aisix:latest # proxy → :3000, metrics + status → :9090
# ^ the metrics/status listener is unauthenticated;
# keep it on loopback or a private network
Then call the gateway exactly like OpenAI:
curl http://localhost:3000/v1/chat/completions \
-H "Authorization: Bearer $CALLER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"my-model","messages":[{"role":"user","content":"hello"}]}'
Edit resources.yaml and send SIGHUP (docker kill -s HUP aisix) to apply changes with
no restart — an invalid file is rejected whole and the last good configuration keeps
serving. Check a file before booting with aisix validate --resources resources.yaml.
Full walkthrough: the
Gateway Quickstart ·
every field: the resources file reference.
For a multi-replica cluster, point the gateway at etcd instead — resources_file and
etcd are mutually exclusive.
✨ Why AISIX
- One API, every model. Speak the OpenAI or Anthropic wire format in; the gateway
translates to whichever provider each model points at. Point an OpenAI or Claude SDK at
one
base_urland switch models without changing code. - A real gateway, in Rust. Single static binary, low cold-start, lock-free config reads on the hot path, native streaming.
- Open source, free forever. Apache-2.0 licensed and built to run in your infrastructure. Choose AISIX Enterprise or AISIX Cloud when you want centralized management through a control plane and dashboard.
- Production controls built in. Routing & failover, rate limits, guardrails, caching, and observability ship in the box. (Budgets and spend caps are AISIX Enterprise and AISIX Cloud features — the gateway enforces the control plane's decisions.)
🧩 Features — available today
Covered by 183 end-to-end scenario files (496 cases) that run against real gateway processes.
- OpenAI-compatible proxy (
:3000) —chat/completions,completions,responses,embeddings,rerank,images/{generations,edits},audio/{speech,transcriptions,translations},videos(submit → poll → fetch),files,batches,fine_tuning/jobs,realtime,GET /v1/models, plus a root-level/passthrough/:provider/*escape hatch. Native SSE streaming, tool/function calling, JSON mode, vision/multimodal input, and reasoning-content support. - Anthropic Messages API —
POST /v1/messagesas a first-class route, working against any configured upstream: requests and responses (including streaming) are translated both ways when a model points at a non-Anthropic provider. - Routing & failover — virtual/routing models with six strategies:
round_robin(smooth weighted round-robin),consistent_hash(session affinity keyed by header / cookie / API key / client IP),failover, plus metric-basedleast_cost,least_latency, andleast_busy. Per-targetprioritytiers (active/backup pools), retry budgets, cooldowns, tag-conditional targets, and per-attempt timeouts. - Ensemble models — fan one request out to a panel of models concurrently, then have a judge model synthesize a single answer, with a minimum-successful-responses threshold.
- Semantic routing — one virtual model that dispatches by the meaning of each request: it embeds the prompt, scores it against per-route example utterances, and routes to the best match (or a default). See the semantic routing docs.
- Rate limiting & concurrency — RPS/RPM/RPH/RPD + TPM/TPD + concurrency caps,
AND-combined across caller keys, models, and policy scopes (
api_key/model/team/member/team_member). Counters are per-process by default, or shared across replicas with the Redis backend. - Guardrails — content-policy enforcement on input and output, in-process or through a
provider: keyword/regex, built-in PII detection and redaction, Presidio, Lakera, OpenAI
Moderation, AWS Bedrock Guardrails, Azure AI Content Safety (Prompt Shield + text
moderation), and two Alibaba Cloud services. A block returns
422 content_filter; monitor mode records what would have happened without blocking. - Caching — exact-match response cache with per-policy TTL and model/key scope matchers; memory and Redis backends; cost-saved telemetry on every hit. Separately, automatic prompt caching can be enabled per direct Anthropic model to inject cache breakpoints, so callers get provider-side prompt discounts without changing their requests.
- MCP gateway — front registered upstream MCP servers at
/mcpwith gateway-held credentials, per-server tool namespaces, and per-caller access. It serves every Streamable HTTP revision from2025-03-26through stateless2026-07-28without downstream sessions. Upstreams useinitializeby default orserver/discoverwithprotocol_version: "2026-07-28". CI runs the official MCP suite's applicable tools-only protocol scenarios. Also exposes a REST API as MCP tools from its OpenAPI description. - A2A agent gateway — front A2A (Agent-to-Agent) agents at
/a2a/:agent, serving each agent's card with URLs rewritten to the gateway, over JSON-RPC 2.0. - Inbound authentication — caller API keys (SHA-256 hashed, model allowlists, expiry, rotation), or OIDC/JWT bearer tokens validated against registered providers (Entra ID, Okta, Google Workspace, or any OIDC issuer) with JWKS caching.
- Observability — Prometheus
/metrics, structured per-request access logs, usage events, OTLP/GenAI span export (Langfuse, Honeycomb, Grafana Cloud, or any OTLP receiver), plus dedicated Datadog and Aliyun SLS log exporters and object-storage (S3/GCS/Azure Blob) telemetry. - Declarative configuration — one
resources.yamlcarries all ten resource collections (provider keys, models, caller keys, guardrails, MCP servers, A2A agents, cache policies, observability exporters, rate-limit policies, OIDC providers), validated against the same JSON Schemas the gateway uses at runtime.aisix validatechecks a file offline;SIGHUPreloads it atomically. - Operational endpoints —
/livezand/readyzon the proxy listener;/status/config,/status/ready,/status/models, and Prometheus/metricson a dedicated metrics listener (:9090). The admin listener (:3001) additionally serves a read-only resource surface, OpenAPI 3 with a Scalar UI, and a playground. Resources are managed declaratively — through theresources_file(reloaded on SIGHUP) or direct etcd writes — not through the admin listener; its former write endpoints were removed.
🔌 Supported providers
AISIX dispatches through five native adapter families — distinct wire-protocol bridges, not one generic relabel. Whatever the upstream protocol, the client-facing API stays OpenAI-shaped.
| Adapter family | Reaches | Wire shape · auth |
|---|---|---|
openai |
OpenAI + any OpenAI-compatible vendor — DeepSeek, Groq, Mistral, Together, Fireworks, Perplexity, vLLM, Ollama, or self-hosted OpenAI-compatible endpoints | OpenAI chat completions · Bearer |
anthropic |
Anthropic Claude | Anthropic Messages · x-api-key |
bedrock |
AWS Bedrock — Anthropic, Meta Llama, Mistral, Cohere, Amazon Titan/Nova, AI21 | Bedrock Converse + /invoke · SigV4 |
vertex |
Google Vertex AI (Gemini) | Vertex :generateContent · OAuth2 |
azure-openai |
Azure OpenAI | Azure deployments · api-key / Entra ID |
Plus specialized handling for vendor quirks (e.g. DeepSeek reasoning content) and dedicated rerank / embeddings vendors (Cohere, Jina). Details in adapter protocol families.
☁️ Choose an AISIX Product
Same gateway binary, same proxy API — in every form the gateway runs in your environment. AISIX Enterprise adds a customer-hosted commercial control plane. AISIX Cloud adds an API7-hosted commercial control plane.
| AISIX OSS (this repo) | AISIX Enterprise | AISIX Cloud | |
|---|---|---|---|
| License | Apache-2.0 | Commercial | Commercial |
| Control plane | None | Customer-hosted | API7-hosted |
| Configuration | Declarative resources.yaml, or etcd for a cluster |
Dashboard + AISIX Cloud Admin API | Dashboard + AISIX Cloud Admin API |
| Tenancy | Single instance / namespace | Organization-scoped environments, teams, and members | Organization-scoped environments, teams, and members |
| Provider keys | ${VAR} references in resources.yaml; plaintext credentials in etcd |
Encrypted at rest; operator-triggered in-place rotation | Encrypted at rest; operator-triggered in-place rotation |
| Budgets | — (rate and token limits only) | Per key, provider, environment, organization, and team | Per key, provider, environment, organization, and team |
| RBAC and audit | Local admin key; no organization layer | Organization roles, invitations, and audit history | Organization roles, invitations, and audit history |
| Operations | You operate the gateway | You operate the control plane and gateways | API7 operates the control plane; you operate gateways |
→ Want centralized governance, budgets, and a dashboard? Talk to API7 about AISIX Enterprise or AISIX Cloud, or book a demo.
🏗️ Architecture
A single Cargo workspace; the aisix-server crate builds one binary named aisix that
wires the crates together.
crates/
├── aisix-core Config, snapshot, resource model, resources.yaml source, errors
├── aisix-etcd Config provider + watch supervisor
├── aisix-gateway Hub & bridge, SSE parser, provider trait
├── aisix-proxy /v1/*, /mcp, /a2a handlers, routing, middleware
├── aisix-admin Read-only resource surface + playground + OpenAPI
├── aisix-provider-* openai · anthropic · azure-openai · bedrock · vertex
├── aisix-mcp MCP gateway — server registry, tool ACL, transports
├── aisix-a2a A2A agent gateway — agent cards, JSON-RPC bridge
├── aisix-ratelimit fixed-window + token accounting + concurrency (local | redis)
├── aisix-cache memory + redis backends
├── aisix-redis shared Redis connection for cache + rate limits
├── aisix-guardrails pre/post content-policy hooks
├── aisix-obs tracing, metrics, access log, exporters
└── aisix-server the `aisix` binary — bootstrap + CLI
🗺️ Roadmap
Highlights on the roadmap; tracked live in issues:
- Semantic (embedding-similarity) response caching
- More observability sinks — Langsmith, Helicone, Slack alerts
- Prompt templates managed as gateway resources
- Llama-Guard as a guardrail provider
Shipped since this list was last written: the MCP gateway, the A2A agent gateway, OIDC/JWT inbound auth, Redis-backed distributed rate limiting, and the Lakera, Presidio, PII, and OpenAI Moderation guardrails — see Features above.
🛠️ Development
Prerequisites: the Rust toolchain pinned in rust-toolchain.toml. Docker is only needed
for the tests that exercise etcd, Redis, or provider emulators.
cargo check --workspace
cargo fmt --check
cargo clippy --workspace -- -D warnings
cargo test --workspace
# Coverage (matches the CI gate)
cargo llvm-cov --workspace --lcov --output-path lcov.info
# Run locally against a resources.yaml (no etcd needed). Copy the Quickstart's two files
# and change resources_file to the local path, e.g. resources_file: ./resources.yaml
cargo run -p aisix-server --bin aisix -- --config config.local.yaml
# Check a resources file without starting a listener
cargo run -p aisix-server --bin aisix -- validate --resources resources.yaml
💬 Community
- Discord — discord.gg/dUmRZ7Rvf
- Issues & discussions — github.com/api7/aisix/issues
- Contributing — CONTRIBUTING.md
- Website — api7.ai/ai-gateway
If AISIX is useful to you, a ⭐ helps other engineers find it.
📄 License
Preguntas sobre AISIX AI Gateway MCP
¿Cómo agrego AISIX AI Gateway MCP a Claude Code?
Ejecuta claude mcp add aisix -- docker run -i --rm ghcr.io/api7/aisix:1.3.0 en tu terminal. Añade --scope user para que esté disponible en todos los proyectos.
¿Cómo agrego AISIX AI Gateway MCP a Cursor?
Abre ~/.cursor/mcp.json y añade el servidor en mcpServers, usando la configuración de Cursor que se muestra en la sección de instalación de arriba.
¿Cómo aplico cambios de configuración sin reiniciar el gateway?
Puedes recargar la configuración de forma atómica enviando una señal SIGHUP al proceso después de editar tu archivo resources.yaml.
¿AISIX es compatible con MCP servers?
Sí, actúa como un MCP gateway que gestiona MCP servers registrados mediante namespaces de herramientas por servidor y acceso controlado por usuario.
¿Puedo usar AISIX con modelos de Anthropic usando el formato de OpenAI?
Sí, el gateway traduce los formatos de OpenAI o Anthropic, lo que te permite apuntar un SDK de OpenAI al gateway para acceder a diferentes proveedores.
¿Qué tipo de seguridad y guardrails incluye?
Incluye cumplimiento de políticas de contenido, detección y redacción de PII, e integración con proveedores como OpenAI Moderation y AWS Bedrock Guardrails.