Stage 01
Build trace and request context
OpenQCore · Realtime Runtime
Generation I · Low-Latency Execution Core
Realtime Runtime.
Low-Latency AI Execution.
Realtime Runtime is OpenQCore’s execution backbone for production chat systems, combining validation, backpressure control, deduplication, distributed locks, and resilient response orchestration.
Load Strategy
Backpressure-Aware
Execution Modes
Streaming + Non-Streaming
Safety Controls
Lock + Circuit Breaker + Dedup
Message Persistence
User + Assistant Durable Save
Request Lifecycle
Deterministic execution from ingress to cleanup.
Every request follows a guarded lifecycle: validation, overload protection, dedup checks, lock acquisition, runtime execution, persistence, and deterministic cleanup.
Stage 02
Track pending request + evaluate system load
Stage 03
Validate payload, attachments, and session access
Stage 04
Persist latest user message (if session exists)
Stage 05
Dedup check for non-stream requests
Stage 06
Acquire distributed lock and execute runtime
Stage 07
Persist assistant message and cache response
Stage 08
Release lock and untrack pending in finally block
Runtime Control Plane
Resilience primitives that keep execution stable.
Circuit Breaker Integration
Runtime execution is breaker-aware to isolate downstream failures and protect overall system availability.
Distributed Locking
A scoped lock key prevents duplicate in-flight processing for the same request identity.
Backpressure Management
Pending requests are tracked continuously and critical load states trigger controlled 503 rejection.
Execution Paths
Different response paths for different latency goals.
Streaming Path
- •Optimized for immediate token delivery
- •Returns StreamingResponse directly
- •Bypasses dedup cache return path
- •Still protected by validation + lock + cleanup
Non-Streaming Path
- •Checks dedup cache before execution
- •Executes runtime and extracts final content/payload
- •Persists assistant output for session continuity
- •Stores model output in dedup cache when possible
Observability & Safety
Traceable operations with measurable runtime behavior.
- •Structured event logging (start, complete, cleanup, errors)
- •Trace ID + Request ID propagation per execution
- •Metrics counters for requests, rejections, and error classes
- •HTTPException mapping with controlled status handling
- •Guaranteed finalization for lock release and pending cleanup
Run production AI workloads with realtime confidence.
Adopt a runtime architecture built for low latency, deterministic cleanup, resilient control flow, and operational reliability.