Add AI Gateway integration docs

docs/nav.json

@@ -258,7 +258,29 @@
         "label": "AI Gateway",
         "items": [
           { "label": "Overview", "slug": "reference/advanced/gateway/index" },
-          { "label": "Embeddings", "slug": "reference/advanced/gateway/embeddings" }
+          { "label": "Embeddings", "slug": "reference/advanced/gateway/embeddings" },
+          {
+            "label": "Integrations",
+            "items": [
+              { "label": "Overview", "slug": "reference/advanced/gateway/integrations/index" },
+              { "label": "Agno", "slug": "reference/advanced/gateway/integrations/agno" },
+              { "label": "Genkit", "slug": "reference/advanced/gateway/integrations/genkit" },
+              { "label": "Google ADK", "slug": "reference/advanced/gateway/integrations/google-adk" },
+              { "label": "Haystack", "slug": "reference/advanced/gateway/integrations/haystack" },
+              { "label": "Instructor", "slug": "reference/advanced/gateway/integrations/instructor" },
+              { "label": "LangChain", "slug": "reference/advanced/gateway/integrations/langchain" },
+              { "label": "LlamaIndex", "slug": "reference/advanced/gateway/integrations/llamaindex" },
+              { "label": "Mastra", "slug": "reference/advanced/gateway/integrations/mastra" },
+              { "label": "Microsoft Agent Framework", "slug": "reference/advanced/gateway/integrations/microsoft-agent-framework" },
+              { "label": "OpenAI Agents SDK", "slug": "reference/advanced/gateway/integrations/openai-agents-sdk" },
+              { "label": "OpenAI SDK", "slug": "reference/advanced/gateway/integrations/openai-sdk" },
+              { "label": "Semantic Kernel", "slug": "reference/advanced/gateway/integrations/semantic-kernel" },
+              { "label": "Smolagents", "slug": "reference/advanced/gateway/integrations/smolagents" },
+              { "label": "Strands Agents", "slug": "reference/advanced/gateway/integrations/strands-agents" },
+              { "label": "Vercel AI SDK", "slug": "reference/advanced/gateway/integrations/vercel-ai-sdk" },
+              { "label": "VoltAgent", "slug": "reference/advanced/gateway/integrations/voltagent" }
+            ]
+          }
         ]
       }
     ]

docs/navigation.yml

@@ -587,6 +587,59 @@ navigation:
           - page: "Embeddings"
             path: "reference/advanced/gateway/embeddings.md"
             slug: "manage/ai-gateway/embeddings"
+          - section: "Integrations"
+            contents:
+              - page: "Overview"
+                path: "reference/advanced/gateway/integrations/index.md"
+                slug: "manage/ai-gateway/integrations"
+              - page: "Agno"
+                path: "reference/advanced/gateway/integrations/agno.md"
+                slug: "manage/ai-gateway/integrations/agno"
+              - page: "Genkit"
+                path: "reference/advanced/gateway/integrations/genkit.md"
+                slug: "manage/ai-gateway/integrations/genkit"
+              - page: "Google ADK"
+                path: "reference/advanced/gateway/integrations/google-adk.md"
+                slug: "manage/ai-gateway/integrations/google-adk"
+              - page: "Haystack"
+                path: "reference/advanced/gateway/integrations/haystack.md"
+                slug: "manage/ai-gateway/integrations/haystack"
+              - page: "Instructor"
+                path: "reference/advanced/gateway/integrations/instructor.md"
+                slug: "manage/ai-gateway/integrations/instructor"
+              - page: "LangChain"
+                path: "reference/advanced/gateway/integrations/langchain.md"
+                slug: "manage/ai-gateway/integrations/langchain"
+              - page: "LlamaIndex"
+                path: "reference/advanced/gateway/integrations/llamaindex.md"
+                slug: "manage/ai-gateway/integrations/llamaindex"
+              - page: "Mastra"
+                path: "reference/advanced/gateway/integrations/mastra.md"
+                slug: "manage/ai-gateway/integrations/mastra"
+              - page: "Microsoft Agent Framework"
+                path: "reference/advanced/gateway/integrations/microsoft-agent-framework.md"
+                slug: "manage/ai-gateway/integrations/microsoft-agent-framework"
+              - page: "OpenAI Agents SDK"
+                path: "reference/advanced/gateway/integrations/openai-agents-sdk.md"
+                slug: "manage/ai-gateway/integrations/openai-agents-sdk"
+              - page: "OpenAI SDK"
+                path: "reference/advanced/gateway/integrations/openai-sdk.md"
+                slug: "manage/ai-gateway/integrations/openai-sdk"
+              - page: "Semantic Kernel"
+                path: "reference/advanced/gateway/integrations/semantic-kernel.md"
+                slug: "manage/ai-gateway/integrations/semantic-kernel"
+              - page: "Smolagents"
+                path: "reference/advanced/gateway/integrations/smolagents.md"
+                slug: "manage/ai-gateway/integrations/smolagents"
+              - page: "Strands Agents"
+                path: "reference/advanced/gateway/integrations/strands-agents.md"
+                slug: "manage/ai-gateway/integrations/strands-agents"
+              - page: "Vercel AI SDK"
+                path: "reference/advanced/gateway/integrations/vercel-ai-sdk.md"
+                slug: "manage/ai-gateway/integrations/vercel-ai-sdk"
+              - page: "VoltAgent"
+                path: "reference/advanced/gateway/integrations/voltagent.md"
+                slug: "manage/ai-gateway/integrations/voltagent"
   - section: "Observability"
     slug: ""
     contents:

docs/reference/advanced/gateway/integrations/agno.md

@@ -0,0 +1,49 @@
+---
+title: "Connect Agno to the AI Gateway"
+description: "Send Agno model requests through the Logfire AI Gateway."
+---
+
+# Connect Agno to the AI Gateway
+
+Send requests from your Agno agents through the Logfire AI Gateway.
+
+[Agno](https://docs.agno.com/) is a Python framework for building multimodal and multi-agent AI systems. The example keeps the agent in Agno and points its `OpenAIChat` model at an OpenAI-compatible gateway route.
+
+## Before you start
+
+- Complete the [AI Gateway prerequisites](index.md#before-you-start), including setting `LOGFIRE_GATEWAY_API_KEY` in your terminal.
+- Use an existing Agno project with the packages imported below installed.
+
+!!! note "Model data passes through Logfire"
+    This configuration sends prompts, tool inputs, and model responses through the Logfire AI Gateway and the selected model provider. If gateway telemetry is enabled, Logfire stores the conversation content in your selected project. Calls to built-in providers count toward your gateway spend.
+
+## Configure Agno
+
+Set `api_key` to your gateway key and `base_url` to the OpenAI-compatible gateway route. Copy the route and a supported model name from the Gateway **Connect** tab.
+
+```python title="agno-gateway.py" hl_lines="10-11" skip-run="true" skip-reason="external-connection"
+import os
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+
+agent = Agent(
+    name='Weather Agent',
+    model=OpenAIChat(
+        id='gpt-5.4-mini',
+        api_key=os.environ['LOGFIRE_GATEWAY_API_KEY'],
+        base_url='https://gateway-us.pydantic.dev/proxy/openai',
+    ),
+)
+
+agent.print_response('What is the weather in London?')
+```
+
+## Verify it worked
+
+Run the example from your terminal. It prints the model response. That confirms the client reached the gateway. Organization admins can also open **AI Engineering** > **Gateway** > **Spending** to see usage for the key. If telemetry is enabled, open the selected project's **Live** view to inspect the request trace.
+
+## Troubleshooting
+
+- **Python raises `KeyError: 'LOGFIRE_GATEWAY_API_KEY'`:** set the environment variable in the same terminal where you run the example.
+- **The request returns an authentication or model error:** copy the URL and model name again from the Gateway **Connect** tab, and confirm that the selected route supports the OpenAI request format.

docs/reference/advanced/gateway/integrations/genkit.md

@@ -0,0 +1,107 @@
+---
+title: "Connect Genkit to the AI Gateway"
+description: "Send Genkit model requests through the Logfire AI Gateway."
+---
+
+# Connect Genkit to the AI Gateway
+
+Send requests from your Genkit application through the Logfire AI Gateway.
+
+[Genkit](https://genkit.dev/) is Google's open-source framework for building AI applications. Both examples configure Genkit's OpenAI-compatible plugin with a gateway key and route.
+
+## Before you start
+
+- Complete the [AI Gateway prerequisites](index.md#before-you-start), including setting `LOGFIRE_GATEWAY_API_KEY` in your terminal.
+- Use an existing Genkit project with the packages imported by your chosen example installed.
+
+!!! note "Model data passes through Logfire"
+    This configuration sends prompts, tool inputs, and model responses through the Logfire AI Gateway and the selected model provider. If gateway telemetry is enabled, Logfire stores the conversation content in your selected project. Calls to built-in providers count toward your gateway spend.
+
+## TypeScript
+
+Set `apiKey` to your gateway key and `baseURL` to the OpenAI-compatible gateway route. Copy the route and a supported model name from the Gateway **Connect** tab.
+
+```typescript title="genkit-gateway.mts" hl_lines="15-16" skip-run="true" skip-reason="external-connection"
+import { openAICompatible } from '@genkit-ai/compat-oai';
+import { genkit } from 'genkit';
+import { z } from 'zod';
+
+const envSchema = z.object({
+  LOGFIRE_GATEWAY_API_KEY: z.string(),
+});
+
+const env = envSchema.parse(process.env);
+
+const ai = genkit({
+  plugins: [
+    openAICompatible({
+      name: 'openai',
+      apiKey: env.LOGFIRE_GATEWAY_API_KEY,
+      baseURL: 'https://gateway-us.pydantic.dev/proxy/openai',
+    }),
+  ],
+});
+
+const { text } = await ai.generate({
+  model: 'openai/gpt-5.4-mini',
+  prompt: 'What is the weather in London?',
+});
+
+console.log(text);
+```
+
+## Go
+
+Pass the same gateway key and route to Genkit's Go plugin.
+
+```go title="genkit-gateway.go" hl_lines="25 27" skip-run="true" skip-reason="external-connection"
+package main
+
+import (
+	"context"
+	"log"
+	"os"
+	"time"
+
+	"github.com/firebase/genkit/go/ai"
+	"github.com/firebase/genkit/go/genkit"
+	"github.com/firebase/genkit/go/plugins/compat_oai/openai"
+	"github.com/openai/openai-go/option"
+)
+
+func main() {
+	apiKey := os.Getenv("LOGFIRE_GATEWAY_API_KEY")
+	if apiKey == "" {
+		log.Fatal("LOGFIRE_GATEWAY_API_KEY is required")
+	}
+
+	ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
+	defer cancel()
+
+	g := genkit.Init(ctx, genkit.WithPlugins(&openai.OpenAI{
+		APIKey: apiKey,
+		Opts: []option.RequestOption{
+			option.WithBaseURL("https://gateway-us.pydantic.dev/proxy/openai"),
+		},
+	}))
+
+	response, err := genkit.Generate(ctx, g,
+		ai.WithPrompt("What is the weather in London?"),
+		ai.WithModelName("openai/gpt-5.4-mini"),
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	log.Println(response.Text())
+}
+```
+
+## Verify it worked
+
+Run either example from your terminal. It prints the model response. That confirms the client reached the gateway. Organization admins can also open **AI Engineering** > **Gateway** > **Spending** to see usage for the key. If telemetry is enabled, open the selected project's **Live** view to inspect the request trace.
+
+## Troubleshooting
+
+- **The example reports that `LOGFIRE_GATEWAY_API_KEY` is missing:** set the environment variable in the same terminal where you run the example.
+- **The request returns an authentication or model error:** copy the URL and model name again from the Gateway **Connect** tab, and confirm that the selected route supports the OpenAI request format.

docs/reference/advanced/gateway/integrations/google-adk.md

@@ -0,0 +1,138 @@
+---
+title: "Connect Google ADK to the AI Gateway"
+description: "Send Google ADK model requests through the Logfire AI Gateway."
+---
+
+# Connect Google ADK to the AI Gateway
+
+Send requests from your Google Agent Development Kit (ADK) agents through the Logfire AI Gateway.
+
+[Google ADK](https://adk.dev/) is Google's framework for building multi-step and multi-agent AI systems. The Python example uses LiteLLM, a library that translates between model-provider APIs. The Go example connects directly to the gateway's Google Vertex route.
+
+## Before you start
+
+- Complete the [AI Gateway prerequisites](index.md#before-you-start), including setting `LOGFIRE_GATEWAY_API_KEY` in your terminal.
+- Use an existing Google ADK project with the packages imported by your chosen example installed.
+
+!!! note "Model data passes through Logfire"
+    This configuration sends prompts, tool inputs, and model responses through the Logfire AI Gateway and the selected model provider. If gateway telemetry is enabled, Logfire stores the conversation content in your selected project. Calls to built-in providers count toward your gateway spend.
+
+## Python
+
+The LiteLLM client uses the OpenAI-compatible gateway route. Copy the route and a supported model name from the Gateway **Connect** tab.
+
+```python title="google-adk-gateway.py" hl_lines="9-10" skip-run="true" skip-reason="external-connection"
+import asyncio
+import os
+
+from google.adk.agents import LlmAgent
+from google.adk.models.lite_llm import LiteLlm
+from google.adk.runners import InMemoryRunner
+from google.genai import types
+
+os.environ['OPENAI_API_KEY'] = os.environ['LOGFIRE_GATEWAY_API_KEY']
+os.environ['OPENAI_BASE_URL'] = 'https://gateway-us.pydantic.dev/proxy/openai'
+
+agent = LlmAgent(
+    model=LiteLlm(model='openai/gpt-5.4-mini'),
+    name='weather_agent',
+    instruction='You are a concise weather assistant.',
+)
+
+
+async def main() -> None:
+    runner = InMemoryRunner(agent=agent)
+    session = await runner.session_service.create_session(
+        app_name=runner.app_name,
+        user_id='gateway-example-user',
+    )
+    content = types.Content(
+        role='user',
+        parts=[types.Part.from_text(text='What is the weather in London?')],
+    )
+    async for event in runner.run_async(
+        user_id=session.user_id,
+        session_id=session.id,
+        new_message=content,
+    ):
+        if event.is_final_response() and event.content and event.content.parts:
+            print(event.content.parts[0].text)
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
+```
+
+## Go
+
+The Go client uses the provider-native Google Vertex route. Copy the Google Vertex URL and a supported Gemini model from the Gateway **Connect** tab.
+
+```go title="google-adk-gateway.go" hl_lines="17 25 28 30" skip-run="true" skip-reason="external-connection"
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+	"net/http"
+	"os"
+	"time"
+
+	adkmodel "google.golang.org/adk/v2/model"
+	"google.golang.org/adk/v2/model/gemini"
+	"google.golang.org/genai"
+)
+
+func main() {
+	apiKey := os.Getenv("LOGFIRE_GATEWAY_API_KEY")
+	if apiKey == "" {
+		log.Fatal("LOGFIRE_GATEWAY_API_KEY is required")
+	}
+
+	ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
+	defer cancel()
+
+	model, err := gemini.NewModel(ctx, "gemini-2.5-flash", &genai.ClientConfig{
+		Backend: genai.BackendVertexAI,
+		HTTPOptions: genai.HTTPOptions{
+			BaseURL: "https://gateway-us.pydantic.dev/proxy/google-vertex",
+			Headers: http.Header{
+				"Authorization": []string{"Bearer " + apiKey},
+			},
+		},
+	})
+	if err != nil {
+		panic(err)
+	}
+
+	request := &adkmodel.LLMRequest{
+		Contents: []*genai.Content{
+			genai.NewContentFromText("What is the weather in London?", genai.RoleUser),
+		},
+	}
+
+	for response, err := range model.GenerateContent(ctx, request, false) {
+		if err != nil {
+			panic(err)
+		}
+		if response.Content == nil {
+			continue
+		}
+		for _, part := range response.Content.Parts {
+			if part.Text != "" {
+				fmt.Println(part.Text)
+			}
+		}
+	}
+}
+```
+
+## Verify it worked
+
+Run either example from your terminal. It prints the model response. That confirms the client reached the gateway. Organization admins can also open **AI Engineering** > **Gateway** > **Spending** to see usage for the key. If telemetry is enabled, open the selected project's **Live** view to inspect the request trace.
+
+## Troubleshooting
+
+- **The example reports that `LOGFIRE_GATEWAY_API_KEY` is missing:** set the environment variable in the same terminal where you run the example.
+- **The Python request fails:** copy an OpenAI-compatible URL and supported model from the Gateway **Connect** tab.
+- **The Go request fails:** copy the Google Vertex URL and supported Gemini model from the **Connect** tab; an OpenAI-compatible route does not work with the provider-native Go client.