LiteLLM (LLM Proxy & Spend Tracking) Connector for AI agents.
10 live capabilities
Control your LLM gateway, manage model fallbacks, and track spending in real-time.
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Why people use LiteLLM (LLM Proxy & Spend Tracking)
LiteLLM (LLM Proxy & Spend Tracking) for managing LLM gateway costs
With this Connector, you can just ask your agent to do the work. You can check a team's budget, revoke a key, or add a new model to your proxy in a single sentence. It turns a tedious administrative chore into a quick conversation, giving you a clear view of your spend and infrastructure without the headache.
What Vinkius changes
You get a conversational control panel for your LLM gateway without leaving your workspace.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Adding a new model endpoint
A platform engineer needs to add a new Llama 4 endpoint to their Bedrock setup and wants to do it via a chat prompt instead of editing a YAML file.
- Real-world use case 02
Auditing a spending spike
An AI Ops lead notices a spike in spending and needs to find out which team is responsible using get_user_info.
- Real-world use case 03
Provisioning dev keys
A developer needs a new API key for a test microservice but does not have admin rights; they ask the agent to generate_key for them.
Complete set · 10capabilities
The complete LiteLLM (LLM Proxy & Spend Tracking) capability set.
These are the exact actions your AI can choose when you ask it to work with LiteLLM (LLM Proxy & Spend Tracking).
01—04
4 capabilities in this set.
Part of 10 available through LiteLLM (LLM Proxy & Spend Tracking).
- 01 Capability
Create model
Add new routing endpoints like Bedrock or Llama 4 to your proxy. This helps you expand your model options without manual config changes.
- 02 Capability
Create team
Set up new organizational divisions with specific cost limits. This ensures each department stays within its allotted budget.
- 03 Capability
Create user
Link specific identities to your proxy logs for better tracking. This lets you see exactly which user is making which request.
- 04 Capability
Delete key
Remove a proxy key immediately to stop unauthorized usage. This is your fastest way to respond to a security breach.
05—07
3 capabilities in this set.
Part of 10 available through LiteLLM (LLM Proxy & Spend Tracking).
- 05 Capability
Delete model
Delete a deployment path to prevent errors from reaching your app. Use this to prune broken or deprecated routes.
- 06 Capability
Generate key
Create a new API key for a specific team or microservice. This allows for easy permissioning without sharing master keys.
- 07 Capability
Get key info
Check the budget and configuration for an existing proxy key. Use this to verify that a key has the correct limits.
08—10
3 capabilities in this set.
Part of 10 available through LiteLLM (LLM Proxy & Spend Tracking).
- 08 Capability
Get model info
View the full fallback path for a specific model. This helps you understand your routing logic at a glance.
- 09 Capability
Get team info
See the internal logic and user bounds for a team. This provides a bird's-eye view of team-level permissions.
- 10 Capability
Get user info
Get the total USD consumed by a specific end-user. This is essential for auditing and individual cost tracking.
Set up in minutes
One URL. Then ask LiteLLM (LLM Proxy & Spend Tracking) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LiteLLM (LLM Proxy & Spend Tracking) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it LiteLLM (LLM Proxy & Spend Tracking), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable LiteLLM (LLM Proxy & Spend Tracking) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the LiteLLM (LLM Proxy & Spend Tracking) URL.
- Step 03
Save and start
Save the connection and enable LiteLLM (LLM Proxy & Spend Tracking) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"litellm-llm-proxy-spend-tracking": {
"url": "https://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using LiteLLM (LLM Proxy & Spend Tracking)
Open Agent mode in chat and ask: "Using LiteLLM (LLM Proxy & Spend Tracking), help me...". 10 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"litellm-llm-proxy-spend-tracking": {
"url": "https://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using LiteLLM (LLM Proxy & Spend Tracking)
Ask Copilot: "Using LiteLLM (LLM Proxy & Spend Tracking), help me...". 10 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"litellm-llm-proxy-spend-tracking": {
"url": "https://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using LiteLLM (LLM Proxy & Spend Tracking)
Open Cascade and ask: "Using LiteLLM (LLM Proxy & Spend Tracking), help me...". 10 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"litellm-llm-proxy-spend-tracking": {
"url": "https://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using LiteLLM (LLM Proxy & Spend Tracking)
Ask Cline: "Using LiteLLM (LLM Proxy & Spend Tracking), help me...". 10 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add litellm-llm-proxy-spend-tracking --transport http "https://edge.vinkius.com/vk_preview_km5X3hHTZMwK6l421yD2vEs8yb47UeU5zl6r4SJ6/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using LiteLLM (LLM Proxy & Spend Tracking)
Ask Claude: "Using LiteLLM (LLM Proxy & Spend Tracking), show me...". 10 tools are ready
Where the request belongs
Work LiteLLM can move forward.
For the AI Ops engineer who is tired of manual spend audits and the platform lead who needs to gatekeep LLM access across multiple departments.
Platform Engineer
Manages global gateway configs and audits fallback paths through natural conversation.
AI Ops Specialist
Monitors real-time spend and adjusts team budgets across multiple providers.
Backend Developer
Generates sub-keys for new microservices and verifies model routing availability.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsPortkey
AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.
Keywords AI
Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.
TrueFoundry
Universal LLM Gateway & ML deployment hub: invoke 1000+ proxy models and manage MCP service instances natively.
Helicone (LLM Observability)
Monitor LLM usage via Helicone. track requests, analyze costs, measure latency, and manage prompts.
Eden AI
Equip your AI agent to manage unified AI workflows, track providers, and monitor API usage via the Eden AI platform.
OpenAI
Manage OpenAI resources via API. list models, monitor fine-tunes, manage batches and inspect Assistants from any AI agent.
Bring your own AI
Change the model, client or framework. Keep LiteLLM connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about LiteLLM.
The practical details behind the request, access and result.
How does LiteLLM (LLM Proxy & Spend Tracking) help with costs?
It lets you set hard budgets for different teams and users. You can see exactly who is spending what in real-time, making it easy to catch overages before they become a problem.
Can I use LiteLLM (LLM Proxy & Spend Tracking) to manage multiple providers?
Yes, it connects to your proxy to manage fallbacks across providers like OpenAI, Anthropic, and others. You can see your routing paths and add new ones easily.
How do I secure my keys with LiteLLM (LLM Proxy & Spend Tracking)?
You can use the Connector to instantly delete leaked keys or generate new, isolated keys for specific microservices. This keeps your main infrastructure safe from unauthorized access.
Does LiteLLM (LLM Proxy & Spend Tracking) work with Claude or Cursor?
Yes, you can connect it to your preferred AI client to manage your LiteLLM gateway through natural conversation. It works with any MCP-compatible client.
Can I see which models my teams are using most?
Yes, by checking user info, you can see the total spend and which specific models are being consumed the most by different identities.
Is LiteLLM (LLM Proxy & Spend Tracking) good for large organizations?
It is ideal for teams that need organizational isolation. You can create team profiles to track costs and operational boundaries for different divisions.
Can I check the budget and rate limits for a specific proxy key?
Yes. Use the get_key_info capability with the specific Key ID. Your agent will retrieve the exact rate limits, budget constraints, and current RPM usage associated with that token.
How do I see the model fallback paths configured in my proxy?
The get_model_info capability allows your agent to extract the global model directory. You'll see the exact fallback chains (e.g., if OpenAI fails, use Anthropic) and the physical endpoints assigned to each model name.
Can my agent create a new team to track specific division costs?
Absolutely. Use the create_team capability and provide a JSON payload defining the team name and optional budget limits. Your agent will provision the new team identity in LiteLLM, allowing for precise organizational cost tracking.
One connection away
Give your agent a direct line to LiteLLM.
Connect LiteLLM once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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