Bring Llm Gateway
to LlamaIndex
Create your Vinkius account to connect Portkey to LlamaIndex and start using all 10 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Portkey MCP Server?
What you can do
Connect AI agents to the Portkey AI Gateway for enterprise-grade observability and management:
- Monitor logs and traces of all LLM calls passing through your gateway
- Analyze token usage, latency, and costs across models and teams
- Submit feedback (Likes/Dislikes) to improve model quality and agent performance
- Export logs for audit trails, compliance, and offline cost analysis
- Review gateway configurations including retry policies, fallbacks, and cache settings
- Manage virtual keys to track provider API key usage and limits
- Discover supported models from 1,600+ LLMs available via Portkey
- Enforce budget policies to prevent runaway AI costs per team or project
How it works
- Get your Portkey API key from the dashboard Settings
- Ask your AI agent to check usage, review costs, or manage policies
- Natural language commands replace manual Portkey dashboard navigation
- Unified observability across all your LLM providers (OpenAI, Anthropic, Google, etc.)
Who is this for?
Essential for AI platform engineers, LLM ops teams, FinOps analysts, AI governance officers, and engineering managers using multiple LLM providers. Let AI agents monitor gateway health, identify cost spikes, enforce budget policies, and optimize routing. Perfect for organizations spending $10k+/month on LLMs who need granular visibility into usage, latency, and model performance across the enterprise.
Built-in capabilities (10)
Requires policy name, budget limit (USD or token count), and optionally the target users or virtual keys to restrict. Returns the created policy details. Use this to enforce cost controls on specific teams or projects using the gateway. Create a new budget or usage policy for AI gateway access
Requires the policy ID. Use this when a project ends or budget constraints are no longer needed. Remove a budget or usage policy from Portkey
Optionally filters by date range, model, or user. Returns an export ID or download URL. Use this for audit trails, cost reporting, or offline analysis of AI usage patterns. Export AI gateway logs for external analysis or compliance reporting
Requires the log ID from list_logs results. Use this for deep debugging of specific AI interactions. Get detailed information about a specific AI gateway log entry
Virtual keys map to underlying provider keys (OpenAI, Anthropic, etc.) with metadata, usage limits, and policy associations. Returns key IDs, names, provider targets, current usage, and status. Use this to audit API key usage or identify keys approaching limits. List all virtual API keys managed by Portkey
Returns config IDs, names, creation dates, and associated virtual keys. Use this to review how LLM requests are routed or to audit gateway behavior. List all gateway configurations stored in Portkey
Returns log IDs, timestamps, model names, token usage, latency, costs, and status codes. Use this to monitor AI usage, identify expensive calls, or debug latency issues. Supports pagination via limit/offset. List recent AI gateway logs and traces from Portkey
). Returns model names, provider names, supported endpoints (chat, embeddings, etc.), and capabilities. Use this to discover which models are routable via your gateway. List all LLM models supported by the Portkey gateway
Returns policy names, limits, current consumption, and affected users/keys. Use this to review guardrails preventing runaway AI costs. List all budget and usage policies defined in Portkey
Requires the log ID, rating (LIKE, DISLIKE, or UNLIKE to remove), and optional text feedback. Use this to build RLHF datasets or monitor user satisfaction with AI outputs. Submit user feedback (Like/Dislike) for a specific AI response log
Why LlamaIndex?
LlamaIndex agents combine Portkey tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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Data-first architecture: LlamaIndex agents combine Portkey tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Portkey tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Portkey, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Portkey tools were called, what data was returned, and how it influenced the final answer
Portkey in LlamaIndex
Why run Portkey with Vinkius?
The Portkey connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 10 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Portkey using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Portkey and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Portkey to LlamaIndex through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Portkey for LlamaIndex
Every request between LlamaIndex and Portkey is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
Which LLM providers does Portkey support?
Portkey supports 1,600+ LLMs including OpenAI, Anthropic, Google, Mistral, Azure OpenAI, AWS Bedrock, Cohere, Hugging Face, and many more. Use the list_models tool to see the full catalog available via your gateway.
How does Portkey help control AI costs?
Portkey provides granular visibility into token usage, latency, and costs per model, team, or virtual key. You can create budget policies with hard limits to prevent runaway spending. The gateway also supports caching to reduce duplicate calls and fallbacks to cheaper models when appropriate.
Can I track feedback on AI responses?
Yes! Portkey allows you to submit Like/Dislike feedback for any logged LLM call. This data helps improve model selection, evaluate agent performance, and build RLHF datasets for fine-tuning.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Portkey tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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