litellm/docs/my-website/docs/generateContent.md

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/generateContent

Use LiteLLM to call Google AI's generateContent endpoints for text generation, multimodal interactions, and streaming responses.

Overview

Feature	Supported	Notes
Cost Tracking	✅
Logging	✅	works across all integrations
End-user Tracking	✅
Streaming	✅
Fallbacks	✅	between supported models
Loadbalancing	✅	between supported models
Metadata Tracking	✅	passes trace ID, metadata to observability callbacks (e.g. S3, Langfuse)

Usage

---

LiteLLM Python SDK

Non-streaming example

from litellm.google_genai import agenerate_content
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Hello, can you tell me a short joke?")
    ],
    role="user",
)

response = await agenerate_content(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=100,
)
print(response)

Streaming example

from litellm.google_genai import agenerate_content_stream
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Write a long story about space exploration")
    ],
    role="user",
)

response = await agenerate_content_stream(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=500,
)

async for chunk in response:
    print(chunk)

Sync non-streaming example

from litellm.google_genai import generate_content
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Hello, can you tell me a short joke?")
    ],
    role="user",
)

response = generate_content(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=100,
)
print(response)

Sync streaming example

from litellm.google_genai import generate_content_stream
from google.genai.types import ContentDict, PartDict
import os

# Set API key
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"

contents = ContentDict(
    parts=[
        PartDict(text="Write a long story about space exploration")
    ],
    role="user",
)

response = generate_content_stream(
    contents=contents,
    model="gemini/gemini-2.0-flash",
    max_tokens=500,
)

for chunk in response:
    print(chunk)

LiteLLM Proxy Server

1. Setup config.yaml

model_list:
    - model_name: gemini-flash
      litellm_params:
        model: gemini/gemini-2.0-flash
        api_key: os.environ/GEMINI_API_KEY

2. Start proxy

litellm --config /path/to/config.yaml

3. Test it!

from google.genai import Client
import os

# Configure Google GenAI SDK to use LiteLLM proxy
os.environ["GOOGLE_GEMINI_BASE_URL"] = "http://localhost:4000"
os.environ["GEMINI_API_KEY"] = "sk-1234"

client = Client()

response = client.models.generate_content(
    model="gemini-flash",
    contents=[
        {
            "parts": [{"text": "Write a short story about AI"}],
            "role": "user"
        }
    ],
    config={"max_output_tokens": 100}
)

Generate Content

curl -L -X POST 'http://localhost:4000/v1beta/models/gemini-flash:generateContent' \
-H 'content-type: application/json' \
-H 'authorization: Bearer sk-1234' \
-d '{
  "contents": [
    {
      "parts": [
        {
          "text": "Write a short story about AI"
        }
      ],
      "role": "user"
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 100
  }
}'

Stream Generate Content

curl -L -X POST 'http://localhost:4000/v1beta/models/gemini-flash:streamGenerateContent' \
-H 'content-type: application/json' \
-H 'authorization: Bearer sk-1234' \
-d '{
  "contents": [
    {
      "parts": [
        {
          "text": "Write a long story about space exploration"
        }
      ],
      "role": "user"
    }
  ],
  "generationConfig": {
    "maxOutputTokens": 500
  }
}'

Related

• Use LiteLLM with gemini-cli