Dubbo-Go-Pixiu 1.1.0 Release doc 发版文档 #840

Alanxtl · 2025-12-16T02:40:26Z

Alanxtl
Dec 16, 2025
Collaborator

Apache Dubbo-Go-Pixiu 1.1.0 Released

Toward a Next-Generation AI Gateway and Cloud-Native Application Gateway

A production-ready API & AI Gateway for the AI era

The Apache Dubbo-Go-Pixiu 1.1.0 release is now officially available 🎉

This is a major milestone release that marks Pixiu’s evolution from a traditional API Gateway into:

A Next-Generation AI Gateway + Kubernetes Application Gateway

If you are working on LLM integration, AI gateways, streaming inference, MCP, or cloud-native traffic governance, Pixiu 1.1.0 is designed for you.

What’s New in Pixiu 1.1.0 — At a Glance

Pixiu 1.1.0 focuses on three major capability pillars:

AI Gateway @Alanxtl
Milestones: [AI] AI gateway milestones #696
- Unified LLM access and governance
- Unified observability ability
- Native streaming inference (HTTP / SSE)
- Retry, fallback, and token-based cost governance
MCP Gateway @Similarityoung
Milestones: [AI] MCP Milestones #703
- Native Model Context Protocol (MCP) Server
- Tool-based service exposure for AI Agents
Cloud-Native Kubernetes Ingress / Application Gateway @mfordjody
- Re-architected Kubernetes Ingress & routing model
- Modern Application Gateway design

Performance Improvements

Pixiu 1.1.0 introduces a major performance optimization in the core router @Alanxtl:

Route read latency improved by ~3–5× under medium to large route scales
Route reload latency improved by ~4–5×, with significantly fewer allocations
Memory allocation on the hot read path reduced by orders of magnitude (from KB-level to hundreds of bytes)

These improvements substantially enhance Ingress routing, dynamic configuration updates, and high-concurrency AI gateway traffic, and are introduced by:

Core router performance optimization
feat: update route mechanism #777

Pixiu as a Production-Ready AI Gateway

With 1.1.0, Pixiu becomes a first-class AI Gateway, with a clear goal:

Enable enterprises to access, govern, and observe all AI capabilities through a single gateway

Key AI Gateway Capabilities

Unified LLM Proxy (OpenAI-compatible, private models, self-hosted models)
Native HTTP / SSE streaming inference
Model-level retry and fallback strategies
Token counting, logging, and billing extensions
API key management, health checks, and observability

AI Gateway Usage Example (LLM Proxy)

The following example shows how to use Pixiu as an LLM Gateway, exposing a unified /chat/completions endpoint while forwarding requests to upstream model providers.

Example Configuration

static_resources:
  listeners:
    - name: "llm_proxy"
      protocol_type: "HTTP"
      address:
        socket_address:
          address: "0.0.0.0"
          port: 8888

      filter_chains:
        filters:
          - name: dgp.filter.httpconnectionmanager
            config:
              route_config:
                routes:
                  - match:
                      prefix: "/chat/completions"
                    route:
                      cluster: "chat"
                      cluster_not_found_response_code: 505

              http_filters:
                - name: dgp.filter.llm.proxy
                  config:
                    maxIdleConns: 100
                    maxIdleConnsPerHost: 100
                    maxConnsPerHost: 100
                    scheme: "https"

                - name: dgp.filter.llm.tokenizer
                  config:
                    log_to_console: true

      config:
        idle_timeout: 5s
        read_timeout: 50s
        write_timeout: 50s

  clusters:
    - name: "chat"
      lb_policy: "lb"
      endpoints:
        - id: 1
          socket_address:
            domains:
              - "api.deepseek.com"
          llm_meta:
            retry_policy:
              name: "CountBased"
              times: 3

metric:
  enable: true
  prometheus_port: 2222

Request Example

curl -X POST http://127.0.0.1:8888/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-chat",
    "messages": [
      { "role": "user", "content": "Hello Pixiu AI Gateway" }
    ],
    "stream": false
  }'

Notes

Pixiu exposes a single unified AI endpoint, hiding upstream vendor differences.
Retry policies are defined at the model (cluster) level.
Native support for streaming (HTTP / SSE) is available for chat and agent scenarios.
Tokenizer filters can be extended for billing, quotas, or auditing.

For more information, refer to [Pixiu LLM Samples](https://github.com/apache/dubbo-go-pixiu-samples/blob/main/llm)

Native Model Context Protocol (MCP) Support

Pixiu 1.1.0 introduces first-class MCP support, allowing Pixiu to run directly as an MCP Server.

This enables a new integration model:

Business services can be exposed as MCP tools and invoked directly by AI Agents

MCP Gateway Capabilities

MCP Server Filter
Tool-based service definitions
Path / query / body parameter injection and validation
Streaming invocation support
Optional integration with service registries (e.g., Nacos)

MCP Gateway Usage Example (MCP Server)

The following example shows how to expose HTTP APIs as MCP Tools.

static_resources:
  listeners:
    - name: "net/http"
      protocol_type: "HTTP"
      address:
        socket_address:
          address: "0.0.0.0"
          port: 8888
      filter_chains:
        filters:
          - name: "dgp.filter.httpconnectionmanager"
            config:
              route_config:
                routes:
                  # MCP endpoint route
                  - match:
                      prefix: "/mcp"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 505
                  # API routes for server testing
                  - match:
                      prefix: "/api"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 404
                  # Root route
                  - match:
                      prefix: "/"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 404
              http_filters:
                # MCP Server Filter - Tools Testing Configuration
                - name: "dgp.filter.mcp.mcpserver"
                  config:
                    server_info:
                      name: "MCP Tools Test Server"
                      version: "1.0.0"
                      description: "MCP Server for testing all tools functionality"
                      instructions: "Use these tools to interact with the mock server API. Test all CRUD operations and search functionality."

                    # Tools Configuration - Testing all server endpoints
                    tools:
                      # Tool 1: Get User by ID
                      - name: "get_user"
                        description: "Get user information by ID with optional profile details"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/{id}"
                          timeout: "10s"
                        args:
                          - name: "id"
                            type: "integer"
                            in: "path"
                            description: "User ID to retrieve"
                            required: true
                          - name: "include_profile"
                            type: "boolean"
                            in: "query"
                            description: "Include user profile information"
                            required: false
                            default: false

                      # Tool 2: Search Users
                      - name: "search_users"
                        description: "Search users by name or email with pagination"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/search"
                          timeout: "10s"
                        args:
                          - name: "q"
                            type: "string"
                            in: "query"
                            description: "Search query (name or email)"
                            required: true
                          - name: "page"
                            type: "integer"
                            in: "query"
                            description: "Page number for pagination"
                            required: false
                            default: 1
                          - name: "limit"
                            type: "integer"
                            in: "query"
                            description: "Number of results per page (1-100)"
                            required: false
                            default: 10

                      # Tool 3: Create User
                      - name: "create_user"
                        description: "Create a new user account"
                        cluster: "mock-server"
                        request:
                          method: "POST"
                          path: "/api/users"
                          timeout: "10s"
                          headers:
                            Content-Type: "application/json"
                        args:
                          - name: "name"
                            type: "string"
                            in: "body"
                            description: "User's full name"
                            required: true
                          - name: "email"
                            type: "string"
                            in: "body"
                            description: "User's email address"
                            required: true
                          - name: "age"
                            type: "integer"
                            in: "body"
                            description: "User's age"
                            required: false

                      # Tool 4: Get User Posts
                      - name: "get_user_posts"
                        description: "Get all posts by a specific user with status filtering"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/{user_id}/posts"
                          timeout: "10s"
                        args:
                          - name: "user_id"
                            type: "integer"
                            in: "path"
                            description: "User ID to get posts for"
                            required: true
                          - name: "status"
                            type: "string"
                            in: "query"
                            description: "Filter posts by status"
                            required: false
                            default: "published"
                            enum: ["published", "draft", "all"]

                      # Tool 5: Health Check
                      - name: "health_check"
                        description: "Check the health and status of the server service"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/health"
                          timeout: "5s"

                      # Tool 6: Get Server Info
                      - name: "get_server_info"
                        description: "Get basic server information and available endpoints"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/"
                          timeout: "5s"

                # HTTP Proxy Filter
                - name: "dgp.filter.http.httpproxy"

  clusters:
    - name: "mock-server"
      type: "STATIC"
      lb_policy: "ROUND_ROBIN"
      endpoints:
        - socket_address:
            address: "127.0.0.1"
            port: 8081

How It Works

Each tool maps to a backend HTTP API.
Pixiu handles parameter binding, validation, and request forwarding.
AI Agents interact with Pixiu using standard MCP semantics.

For more information, refer to [Pixiu MCP Samples](https://github.com/apache/dubbo-go-pixiu-samples/blob/main/mcp)

Open Policy Agent (OPA) Support

Pixiu 1.1.0 introduces comprehensive support for Open Policy Agent (OPA) as a new HTTP filter plugin. This feature enhances Pixiu's access control capabilities and provides more granular traffic governance and policy evaluation. @nanjiek

Key Highlights of OPA Support

OPA Filter Plugin: Enables OPA policies to be used for traffic control and governance within Pixiu.
External OPA Server Support: In addition to embedded OPA evaluation, Pixiu now supports calling external OPA servers for remote policy decisions, providing flexibility for centralized policy management in large-scale systems.
Policy Evaluation: Requests are passed to OPA, which evaluates whether the request should be allowed based on the defined policy.
Highly Configurable: Supports flexible configurations, including different authentication methods (e.g., Bearer token), timeouts, and decision paths.
Enhanced Testing & Documentation: New test cases ensure the correctness of OPA plugins, and detailed documentation has been updated for configuration and usage.

Kubernetes Ingress & Application Gateway Upgrade

Pixiu 1.1.0 includes a significant re-architecture of its Ingress and routing system:

Redesigned routing mechanism
Modern Application Gateway architecture
Support for a new Ingress Controller
Fine-grained policy and traffic governance

Pixiu is not just a gateway—it is the cloud-native traffic entry point and control plane.

Ingress Usage

Pixiu recommends using Gateway API Extensions for Kubernetes integration:

👉 Gateway API Extensions

Dynamic Governance & Engineering Improvements

Dynamic Capabilities

Nacos-based service and AI endpoint discovery
Configuration center listening and hot reload
Dynamic route and cluster generation

Engineering Quality

Unified error code system
Unified metrics and observability filters
Multiple concurrency, memory, and stability fixes
Go version upgraded to 1.25

Documentation & Ecosystem

Fully rewritten README (English & Chinese)
Refactored admin and governance documentation
Added MCP and OPA usage guides
Continuous improvements to the Admin Console

Community & Contributors

Pixiu 1.1.0 is a community-driven release.
Special thanks to all contributors (alphabetical order):

@1kasa
@Alanxtl
@baerwang
@FoghostCn
@KamToHung
@ma642
@mark4z
@marsevilspirit
@mfordjody
@mutezebra
@nanjiek
@No-SilverBullet
@PhilYue
@Similarityoung
@testwill
@yuluo-yx

Get Started

Project Repository
https://github.com/apache/dubbo-go-pixiu
Official Samples
https://github.com/apache/dubbo-go-pixiu-samples

Final Words

Pixiu 1.1.0 is not just a version upgrade—
it is a foundation for production-grade AI Gateway adoption.

One Gateway. All AI. Cloud-Native Ready.

Apache Dubbo-Go-Pixiu 1.1.0 正式发布

迈向下一代 AI 网关与云原生应用网关

面向 AI 时代的生产级 API & AI 网关

Apache Dubbo-Go-Pixiu 1.1.0 现已正式发布 🎉

这是一次重要的里程碑版本，标志着 Pixiu 从传统 API 网关正式演进为：

下一代 AI 网关 + Kubernetes 应用网关

如果你正在从事 LLM 集成、AI 网关、流式推理、MCP 或云原生流量治理，那么 Pixiu 1.1.0 正是为你而设计。

Pixiu 1.1.0 新特性一览

Pixiu 1.1.0 聚焦三大核心能力支柱：

AI 网关 @Alanxtl
Milestones: [AI] AI gateway milestones #696
- 统一的 LLM 接入与治理
- 统一的可观测性能力
- 原生流式推理支持（HTTP / SSE）
- 重试、回退及基于 Token 的成本治理
MCP 网关 @Similarityoung
Milestones: [AI] MCP Milestones #703
- 原生 Model Context Protocol（MCP）Server
- 面向 AI Agent 的工具化服务暴露
云原生 Kubernetes Ingress / Application 网关 @mfordjody
- 全新重构的 Kubernetes Ingress 与路由模型
- 现代化 Application 网关架构设计

性能优化

Pixiu 1.1.0 在核心 Router 上引入了重要性能优化 @Alanxtl：

在中大型路由规模下，路由读取性能提升约 3–5 倍
路由刷新（reload）延迟降低约 4–5 倍，同时显著减少内存分配
核心读路径的内存分配从 KB 级降至百字节级

这些改进显著增强了 Ingress 路由、动态配置更新以及高并发 AI 网关核心流量路径 的性能表现，主要来自以下改动：

核心 Router 性能优化
feat: update route mechanism #777

Pixiu：生产级 AI 网关

在 1.1.0 版本中，Pixiu 正式成为生产级 AI 网关，其核心目标是：

让企业通过一个统一网关访问、治理并观测所有 AI 能力

AI 网关核心能力

统一 LLM 代理（兼容 OpenAI 接口，支持私有模型、自托管模型）
原生 HTTP / SSE 流式推理
模型级别的重试与回退策略
Token 统计、日志与计费扩展能力
API Key 管理、健康检查与可观测性

AI 网关使用示例（LLM Proxy）

以下示例展示了如何使用 Pixiu 作为 LLM 网关，通过统一的 /chat/completions 接口，将请求转发至上游模型服务。

示例配置

static_resources:
  listeners:
    - name: "llm_proxy"
      protocol_type: "HTTP"
      address:
        socket_address:
          address: "0.0.0.0"
          port: 8888

      filter_chains:
        filters:
          - name: dgp.filter.httpconnectionmanager
            config:
              route_config:
                routes:
                  - match:
                      prefix: "/chat/completions"
                    route:
                      cluster: "chat"
                      cluster_not_found_response_code: 505

              http_filters:
                - name: dgp.filter.llm.proxy
                  config:
                    maxIdleConns: 100
                    maxIdleConnsPerHost: 100
                    maxConnsPerHost: 100
                    scheme: "https"

                - name: dgp.filter.llm.tokenizer
                  config:
                    log_to_console: true

      config:
        idle_timeout: 5s
        read_timeout: 50s
        write_timeout: 50s

  clusters:
    - name: "chat"
      lb_policy: "lb"
      endpoints:
        - id: 1
          socket_address:
            domains:
              - "api.deepseek.com"
          llm_meta:
            retry_policy:
              name: "CountBased"
              times: 3

metric:
  enable: true
  prometheus_port: 2222

请求示例

curl -X POST http://127.0.0.1:8888/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-chat",
    "messages": [
      { "role": "user", "content": "Hello Pixiu AI Gateway" }
    ],
    "stream": false
  }'

说明

Pixiu 对外暴露统一的 AI 接口，屏蔽上游模型厂商差异
重试策略在模型（Cluster）级别定义
原生支持流式（HTTP / SSE），适用于对话与 Agent 场景
Tokenizer 过滤器可扩展用于计费、配额或审计

更多信息请参考：
[Pixiu LLM 示例](https://github.com/apache/dubbo-go-pixiu-samples/blob/main/llm)

原生 Model Context Protocol（MCP）支持

Pixiu 1.1.0 引入MCP 支持，可直接作为 MCP Server 运行。

这开启了一种全新的集成模式：

业务服务可以以 MCP 工具（Tool）的形式暴露，并被 AI Agent 直接调用

MCP 网关能力

MCP Server 过滤器
基于工具（Tool）的服务定义
Path / Query / Body 参数注入与校验
流式调用支持
可选服务注册中心集成（如 Nacos）

MCP 网关使用示例（MCP Server）

以下示例展示了如何将 HTTP API 暴露为 MCP 工具。

static_resources:
  listeners:
    - name: "net/http"
      protocol_type: "HTTP"
      address:
        socket_address:
          address: "0.0.0.0"
          port: 8888
      filter_chains:
        filters:
          - name: "dgp.filter.httpconnectionmanager"
            config:
              route_config:
                routes:
                  # MCP endpoint route
                  - match:
                      prefix: "/mcp"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 505
                  # API routes for server testing
                  - match:
                      prefix: "/api"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 404
                  # Root route
                  - match:
                      prefix: "/"
                    route:
                      cluster: "mock-server"
                      cluster_not_found_response_code: 404
              http_filters:
                # MCP Server Filter - Tools Testing Configuration
                - name: "dgp.filter.mcp.mcpserver"
                  config:
                    server_info:
                      name: "MCP Tools Test Server"
                      version: "1.0.0"
                      description: "MCP Server for testing all tools functionality"
                      instructions: "Use these tools to interact with the mock server API. Test all CRUD operations and search functionality."

                    # Tools Configuration - Testing all server endpoints
                    tools:
                      # Tool 1: Get User by ID
                      - name: "get_user"
                        description: "Get user information by ID with optional profile details"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/{id}"
                          timeout: "10s"
                        args:
                          - name: "id"
                            type: "integer"
                            in: "path"
                            description: "User ID to retrieve"
                            required: true
                          - name: "include_profile"
                            type: "boolean"
                            in: "query"
                            description: "Include user profile information"
                            required: false
                            default: false

                      # Tool 2: Search Users
                      - name: "search_users"
                        description: "Search users by name or email with pagination"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/search"
                          timeout: "10s"
                        args:
                          - name: "q"
                            type: "string"
                            in: "query"
                            description: "Search query (name or email)"
                            required: true
                          - name: "page"
                            type: "integer"
                            in: "query"
                            description: "Page number for pagination"
                            required: false
                            default: 1
                          - name: "limit"
                            type: "integer"
                            in: "query"
                            description: "Number of results per page (1-100)"
                            required: false
                            default: 10

                      # Tool 3: Create User
                      - name: "create_user"
                        description: "Create a new user account"
                        cluster: "mock-server"
                        request:
                          method: "POST"
                          path: "/api/users"
                          timeout: "10s"
                          headers:
                            Content-Type: "application/json"
                        args:
                          - name: "name"
                            type: "string"
                            in: "body"
                            description: "User's full name"
                            required: true
                          - name: "email"
                            type: "string"
                            in: "body"
                            description: "User's email address"
                            required: true
                          - name: "age"
                            type: "integer"
                            in: "body"
                            description: "User's age"
                            required: false

                      # Tool 4: Get User Posts
                      - name: "get_user_posts"
                        description: "Get all posts by a specific user with status filtering"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/users/{user_id}/posts"
                          timeout: "10s"
                        args:
                          - name: "user_id"
                            type: "integer"
                            in: "path"
                            description: "User ID to get posts for"
                            required: true
                          - name: "status"
                            type: "string"
                            in: "query"
                            description: "Filter posts by status"
                            required: false
                            default: "published"
                            enum: ["published", "draft", "all"]

                      # Tool 5: Health Check
                      - name: "health_check"
                        description: "Check the health and status of the server service"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/api/health"
                          timeout: "5s"

                      # Tool 6: Get Server Info
                      - name: "get_server_info"
                        description: "Get basic server information and available endpoints"
                        cluster: "mock-server"
                        request:
                          method: "GET"
                          path: "/"
                          timeout: "5s"

                # HTTP Proxy Filter
                - name: "dgp.filter.http.httpproxy"

  clusters:
    - name: "mock-server"
      type: "STATIC"
      lb_policy: "ROUND_ROBIN"
      endpoints:
        - socket_address:
            address: "127.0.0.1"
            port: 8081

工作原理

每个 Tool 映射到一个后端 HTTP API
Pixiu 负责参数绑定、校验与请求转发
AI Agent 通过标准 MCP 语义与 Pixiu 交互

更多信息请参考：
[Pixiu MCP 示例](https://github.com/apache/dubbo-go-pixiu-samples/blob/main/mcp)

Open Policy Agent (OPA) 支持

Pixiu 1.1.0 引入了对 Open Policy Agent (OPA) 策略引擎的全面支持，作为新的 HTTP 过滤器插件。这一功能增强了 Pixiu 的访问控制能力，提供了更细粒度的流量治理和策略评估。 @nanjiek

OPA 支持亮点

OPA 过滤器插件：可以在 Pixiu 中通过配置启用 OPA 策略，进行请求的访问控制和治理决策。
外部 OPA Server 支持：除了内嵌式 OPA 策略执行，Pixiu 还支持通过外部 OPA Server 进行远程策略决策，这为大规模分布式系统中的集中化策略管理提供了灵活性。
策略评估：Pixiu 将 HTTP 请求作为输入传递给 OPA，OPA 根据用户定义的策略判断是否允许请求继续流转。
高度可配置：支持灵活的 OPA 配置，包括不同的认证方式（如 Bearer token）、超时设置以及决策路径选择。
增强的测试与文档：新增了测试用例来验证 OPA 插件的正确性，并更新了相关文档，提供了配置和使用 OPA 过滤器的详细指南。

Kubernetes Ingress 与 Application 网关升级

Pixiu 1.1.0 对 Ingress 与路由系统进行了重大重构：

全新路由机制设计
现代化 Application 网关架构
支持新的 Ingress Controller
更精细的策略与流量治理能力

Pixiu 不只是一个网关——
它是云原生流量入口与控制平面。

Ingress 使用方式

Pixiu 推荐使用 Gateway API Extensions 进行 Kubernetes 集成：

👉 Gateway API Extensions

动态治理与工程能力提升

动态能力

基于 Nacos 的服务与 AI Endpoint 发现
配置中心监听与热更新
动态路由与集群生成

工程质量

统一错误码体系
统一指标与可观测性过滤器
多项并发、内存与稳定性修复
Go 版本升级至 1.25

文档与生态

全新重写的 README（中 / 英）
重构 Admin 与治理文档
新增 MCP 与 OPA 使用指南
Admin Console 持续演进

社区与贡献者

Pixiu 1.1.0 是一次完全由社区驱动的版本发布。
特别感谢以下贡献者（按字母顺序）：

@1kasa
@Alanxtl
@baerwang
@FoghostCn
@KamToHung
@ma642
@mark4z
@marsevilspirit
@mfordjody
@mutezebra
@nanjiek
@No-SilverBullet
@PhilYue
@Similarityoung
@testwill
@yuluo-yx

快速开始

项目仓库
https://github.com/apache/dubbo-go-pixiu
官方示例
https://github.com/apache/dubbo-go-pixiu-samples

结语

Pixiu 1.1.0 不只是一次版本升级，
它是生产级 AI 网关落地的基础设施。

一个网关，连接所有 AI。云原生，已就绪。

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Dubbo-Go-Pixiu 1.1.0 Release doc 发版文档 #840

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Dubbo-Go-Pixiu 1.1.0 Release doc 发版文档 #840

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Alanxtl Dec 16, 2025 Collaborator

Apache Dubbo-Go-Pixiu 1.1.0 Released

Toward a Next-Generation AI Gateway and Cloud-Native Application Gateway

What’s New in Pixiu 1.1.0 — At a Glance

Performance Improvements

Pixiu as a Production-Ready AI Gateway

Key AI Gateway Capabilities

AI Gateway Usage Example (LLM Proxy)

Example Configuration

Request Example

Notes

Native Model Context Protocol (MCP) Support

MCP Gateway Capabilities

MCP Gateway Usage Example (MCP Server)

How It Works

Open Policy Agent (OPA) Support

Key Highlights of OPA Support

Kubernetes Ingress & Application Gateway Upgrade

Ingress Usage

Dynamic Governance & Engineering Improvements

Dynamic Capabilities

Engineering Quality

Documentation & Ecosystem

Community & Contributors

Get Started

Final Words

Apache Dubbo-Go-Pixiu 1.1.0 正式发布

迈向下一代 AI 网关与云原生应用网关

Pixiu 1.1.0 新特性一览

性能优化

Pixiu：生产级 AI 网关

AI 网关核心能力

AI 网关使用示例（LLM Proxy）

示例配置

请求示例

说明

原生 Model Context Protocol（MCP）支持

MCP 网关能力

MCP 网关使用示例（MCP Server）

工作原理

Open Policy Agent (OPA) 支持

OPA 支持亮点

Kubernetes Ingress 与 Application 网关升级

Ingress 使用方式

动态治理与工程能力提升

动态能力

工程质量

文档与生态

社区与贡献者

快速开始

结语

Replies: 0 comments

Alanxtl
Dec 16, 2025
Collaborator