Migrate from LiteLLM

Switch from self-hosted LiteLLM to managed Vichar. 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. Vichar gives you the same unified API with built-in analytics, caching, and a dashboard—without the infrastructure overhead.

Let your AI agent do the migration

Copy this prompt into Claude Code, Cursor, or any coding agent — it reads our docs and handles the migration from LiteLLM for you.

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.vichar.io/v1";

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

Why Teams Switch to Vichar

What You Get	LiteLLM (Self-Hosted)	Vichar
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 (managed)

Self-hosting also means you own the patch cycle. On March 24, 2026 two malicious litellm releases (1.82.7 and 1.82.8) reached PyPI and were pulled after about 40 minutes; they harvested credentials from unpinned pip installs, while pinned Docker deployments were unaffected. Pin versions wherever you self-host — Vichar included — or use the managed gateway and skip the upkeep.

Still want to self-host? Vichar is open source under AGPLv3 — see Self-Hosting—same features, your infrastructure.

For a detailed breakdown, see Getting Started.

Migration Steps

Get Your Vichar API Key

Map Your Models

Vichar supports two model ID formats:

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

gpt-6-astra
claude-sonnet-5
gemini-3.1-pro-preview

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

openai/gpt-6-astra
anthropic/claude-sonnet-5
google-ai-studio/gemini-3.1-pro-preview

This means many LiteLLM model names work directly with Vichar:

LiteLLM Model	Vichar Model
gpt-6-astra	gpt-6-astra or openai/gpt-6-astra
anthropic/claude-sonnet-5	claude-sonnet-5 or anthropic/claude-sonnet-5
gemini/gemini-3.1-pro-preview	gemini-3.1-pro-preview or google-ai-studio/gemini-3.1-pro-preview
bedrock/anthropic.claude-sonnet-5	claude-sonnet-5 or aws-bedrock/claude-sonnet-5

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-6-astra",
    messages=[{"role": "user", "content": "Hello!"}]
)

# After (Vichar) - model name can stay the same!
client = OpenAI(
    base_url="https://api.vichar.io/v1",
    api_key=os.environ["LLM_GATEWAY_API_KEY"]
)

response = client.chat.completions.create(
    model="gpt-6-astra",  # or "openai/gpt-6-astra" 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 Vichar:

import litellm

# Before (direct LiteLLM)
response = litellm.completion(
    model="gpt-6-astra",
    messages=[{"role": "user", "content": "Hello!"}]
)

# After (via Vichar) - same model name works
response = litellm.completion(
    model="gpt-6-astra",  # or "openai/gpt-6-astra" to target a specific provider
    messages=[{"role": "user", "content": "Hello!"}],
    api_base="https://api.vichar.io/v1",
    api_key=os.environ["LLM_GATEWAY_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 (Vichar) - same model name works
const client = new OpenAI({
	baseURL: "https://api.vichar.io/v1",
	apiKey: process.env.LLM_GATEWAY_API_KEY,
});

const completion = await client.chat.completions.create({
	model: "gpt-6-astra", // or "openai/gpt-6-astra" 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-6-astra",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

# After (Vichar) - same model name works
curl https://api.vichar.io/v1/chat/completions \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
# Use "openai/gpt-6-astra" to target a specific provider

Migrate Configuration

LiteLLM Config (Before)

# litellm_config.yaml
model_list:
  - model_name: gpt-6-astra
    litellm_params:
      model: openai/gpt-6-astra
      api_key: sk-...
  - model_name: claude-sonnet-5
    litellm_params:
      model: anthropic/claude-sonnet-5
      api_key: sk-ant-...

Vichar (After)

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

Streaming Support

Vichar supports streaming identically to LiteLLM:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.vichar.io/v1",
    api_key=os.environ["LLM_GATEWAY_API_KEY"]
)

stream = client.chat.completions.create(
    model="openai/gpt-6-astra",
    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

Vichar supports function calling:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.vichar.io/v1",
    api_key=os.environ["LLM_GATEWAY_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-6-astra",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools
)

Removing LiteLLM Infrastructure

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

Update all clients to use Vichar endpoints
Monitor the Vichar 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

Self-Hosting Vichar

If you prefer self-hosting like LiteLLM, Vichar is available under AGPLv3:

git clone https://github.com/vicharai/api
cd llmgateway
pnpm install
pnpm run setup
pnpm dev

This gives you the same benefits as LiteLLM's self-hosted proxy with Vichar's analytics and caching features.

Full Comparison

Want to see a detailed breakdown of all features? See Quick Start.

Migrate from LiteLLM

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