🚀 Excited to share my first open-source project: LatencyOps (v0.1.1)! Most AI applications send every single request to one fixed model with one hardcoded policy. But in production, a one-size-fits-all approach fails; whether you are orchestrating an advanced multi-agent network or scaling a high-volume data pipeline. LatencyOps is a provider-neutral Python toolkit that sits between your workflow and your model endpoints to make smart, per-request execution decisions under strict latency and quality constraints. No matter how your AI system is architected, LatencyOps is built to optimize it 🤖 1. For Multi-Agent Workflows: A Shared Control Plane Complex agent systems fail when every agent is forced through the same global model policy. A fast classification agent needs low latency; a deep research agent needs high quality. LatencyOps acts as an execution control plane, letting you assign a unique deadline, quality floor, and risk contract to each individual agent or sub-task while keeping your orchestration layer clean. 📊 2. For Standard Apps & Data-AI Pipelines: Smart Request Optimization You don't need agents to get massive value. If you run document analysis, evaluation jobs, or user-facing chat apps, your requests naturally vary. LatencyOps evaluates prompt token count, item difficulty, and required quality on the fly. It dynamically flags model tiers, precision changes, or early-exits per request—saving costs and protecting your SLAs. 🛠️ What Else is Under the Hood? Real-Time Signal Awareness: Factors in live queue pressure, KV-cache pressure, and provider uptime to steer your requests intelligently. Drop-In Compatibility: Composes natively with any OpenAI-compatible endpoint (OpenAI, LiteLLM, vLLM, SGLang, Hugging Face). Flexible Deployment: Run it in-process as a lightweight Python library, or deploy it as a local reference HTTP gateway for non-Python applications. Built-In Streaming Telemetry: Measures streaming TTFT (Time to First Token), TPOT (Time Per Output Token), end-to-end latency, p50, and p95 out of the box that is completely content-free, so your data stays private. Note: LatencyOps is currently an open-source alpha/reference toolkit. It is built to compose with your existing infrastructure, not replace model servers or agent frameworks. 📦 Get Started: pip install latencyops 💻 GitHub: lnkd.in/grhT5_6W 🐍 PyPI: lnkd.in/g38nABQM As my first open-source release, I would love the community's feedback, feature requests, or issue reports. Drop a comment below or check out the repo! 👇 lnkd.in/grhT5_6W #AIEngineering #LLMOps #OpenSource #Python #MultiAgentSystems #MachineLearning #Observability #InferenceOptimization