Migrate from LiteLLM

Switch from self-hosted LiteLLM to managed LangRouter. Same API format, zero infrastructure to maintain.

Running your own LiteLLM proxy works—until it doesn't. Scaling, monitoring, and keeping it running becomes another job. LangRouter gives you the same unified API with built-in analytics, caching, and a dashboard—without the infrastructure overhead.

Quick Migration

Both services use OpenAI-compatible endpoints, so migration is a two-line change:

- const baseURL = "http://localhost:4000/v1";  // LiteLLM proxy
+ const baseURL = "https://api.langrouter.ai/v1";

- const apiKey = process.env.LITELLM_API_KEY;
+ const apiKey = process.env.LANGROUTER_API_KEY;

Why Teams Switch to LangRouter

What You Get	LiteLLM (Self-Hosted)	LangRouter
OpenAI-compatible API	Yes	Yes
Infrastructure to manage	Yes (you run it)	No (we run it)
Managed cloud option	No	Yes
Analytics dashboard	Basic	Per-request detail
Response caching	Manual setup	Built-in, automatic
Cost tracking	Via callbacks	Native, real-time
Provider key management	Config file	Web UI with rotation
Uptime & scaling	You handle it	99.9% SLA (Pro/Ent)

Migration Steps

Get Your LangRouter API Key

Map Your Models

LangRouter supports two model ID formats:

Root Model IDs (without provider prefix) - Uses smart routing to automatically select the best provider based on uptime, throughput, price, and latency:

gpt-5.2
claude-opus-4-5-20251101
gemini-3-flash-preview

Provider-Prefixed Model IDs - Routes to a specific provider with automatic failover if uptime drops below 90%:

openai/gpt-5.2
anthropic/claude-opus-4-5-20251101
google-ai-studio/gemini-3-flash-preview

This means many LiteLLM model names work directly with LangRouter:

LiteLLM Model	LangRouter Model
gpt-5.2	gpt-5.2 or openai/gpt-5.2
claude-opus-4-5-20251101	claude-opus-4-5-20251101 or anthropic/claude-opus-4-5-20251101
gemini/gemini-3-flash-preview	gemini-3-flash-preview or google-ai-studio/gemini-3-flash-preview
bedrock/claude-opus-4-5-20251101	claude-opus-4-5-20251101 or aws-bedrock/claude-opus-4-5-20251101

For more details on routing behavior, see the routing documentation.

Update Your Code

Python with OpenAI SDK

from openai import OpenAI

# Before (LiteLLM proxy)
client = OpenAI(
    base_url="http://localhost:4000/v1",
    api_key=os.environ["LITELLM_API_KEY"]
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# After (LangRouter) - model name can stay the same!
client = OpenAI(
    base_url="https://api.langrouter.ai/v1",
    api_key=os.environ["LANGROUTER_API_KEY"]
)

response = client.chat.completions.create(
    model="gpt-4",  # or "openai/gpt-4" to target a specific provider
    messages=[{"role": "user", "content": "Hello!"}]
)

Python with LiteLLM Library

If you're using the LiteLLM library directly, you can point it to LangRouter:

import litellm

# Before (direct LiteLLM)
response = litellm.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# After (via LangRouter) - same model name works
response = litellm.completion(
    model="gpt-4",  # or "openai/gpt-4" to target a specific provider
    messages=[{"role": "user", "content": "Hello!"}],
    api_base="https://api.langrouter.ai/v1",
    api_key=os.environ["LANGROUTER_API_KEY"]
)

TypeScript/JavaScript

import OpenAI from "openai";

// Before (LiteLLM proxy)
const client = new OpenAI({
	baseURL: "http://localhost:4000/v1",
	apiKey: process.env.LITELLM_API_KEY,
});

// After (LangRouter) - same model name works
const client = new OpenAI({
	baseURL: "https://api.langrouter.ai/v1",
	apiKey: process.env.LANGROUTER_API_KEY,
});

const completion = await client.chat.completions.create({
	model: "gpt-4", // or "openai/gpt-4" to target a specific provider
	messages: [{ role: "user", content: "Hello!" }],
});

cURL

# Before (LiteLLM proxy)
curl http://localhost:4000/v1/chat/completions \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# After (LangRouter) - same model name works
curl https://api.langrouter.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
# Use "openai/gpt-4" to target a specific provider

Migrate Configuration

LiteLLM Config (Before)

# litellm_config.yaml
model_list:
  - model_name: gpt-4
    litellm_params:
      model: gpt-4
      api_key: sk-...
  - model_name: claude-3
    litellm_params:
      model: claude-3-sonnet-20240229
      api_key: sk-ant-...

LangRouter (After)

With LangRouter, you don't need a config file. Provider keys are managed in the web dashboard, or you can use the default LangRouter keys.

If you want to use your own provider keys, configure them in the dashboard under Settings > Provider Keys.

Streaming Support

LangRouter supports streaming identically to LiteLLM:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.langrouter.ai/v1",
    api_key=os.environ["LANGROUTER_API_KEY"]
)

stream = client.chat.completions.create(
    model="openai/gpt-4",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Function/Tool Calling

LangRouter supports function calling:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.langrouter.ai/v1",
    api_key=os.environ["LANGROUTER_API_KEY"]
)

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            },
            "required": ["location"]
        }
    }
}]

response = client.chat.completions.create(
    model="openai/gpt-4",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools
)

Removing LiteLLM Infrastructure

After verifying LangRouter works for your use case, you can decommission your LiteLLM proxy:

Update all clients to use LangRouter endpoints
Monitor the LangRouter dashboard for successful requests
Shut down your LiteLLM proxy server
Remove LiteLLM configuration files

What Changes After Migration

No servers to babysit — We handle scaling, uptime, and updates
Real-time cost visibility — See what every request costs, broken down by model
Automatic caching — Repeated requests hit cache, reducing your spend
Web-based management — No more editing YAML files for config changes
New models immediately — Access new releases within 48 hours, no deployment needed

Need Help?

Browse available models at langrouter.ai/models
Read the API documentation
Contact support at [email protected]

Migrate from LiteLLM

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