One endpoint for every local model
Put every local model (vLLM, Ollama, TensorRT-LLM) behind a single OpenAI-compatible endpoint, with queue-aware load balancing, sliding-window rate limits, and air-gapped Kubernetes deployment.
- 01
In-Memory Stream Engine
IN-MEMORY ZERO-COPYHandles request and response streams with a zero-copy, in-memory architecture; responses are streamed back to the client.
- 02
Universal OpenAI API
ZERO CODE REFACTORINGRoute all agent frameworks (LangChain, CrewAI, AutoGen) through standard
/v1/chat/completionsregardless of underlying inference engines. - 03
Queue-Aware Load Balancing
AUTOMATIC POD FAILOVERDirects traffic based on real-time GPU queue depth, KV-cache affinity, and auto-fails over to healthy replica nodes with zero dropped sockets.
- 04
Token Quotas & Limits
SLIDING-WINDOW RATE LIMITSEnforce per-agent token budgets, sliding-window rate limits, and department cost controls to prevent resource exhaustion.
One request, five stops.
- baseURL: "http://vllm-node-3:8000/v1"+ baseURL: "https://argate.internal/v1"
- 01Client
Your agent makes a standard
/v1/chat/completionscall. No change to LangChain, CrewAI or AutoGen code; only the baseURL changes. - 02Quota
Argate checks the token quota and sliding-window rate limit for the calling API key, agent or department.
- 03Security
The security layers run in the same pass: guardrails, PII redaction, human approval for risky tool calls.
- 04Routing
The request goes to the best vLLM, Ollama or TensorRT-LLM pod based on queue depth and KV-cache state. Unhealthy pods are skipped.
- 05Response
The model response streams back through the same endpoint. The client never needs to know how many models or nodes sit behind it.
Put your model fleet behind one endpoint
Deploy with a single Helm command and connect your entire cluster in minutes.
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