#Llm-ops
Showing 20 of 20 repositories tagged #llm-ops, ranked by stars
🦍 The API and AI Gateway
Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
Julep — durable, composable AI agents. Flows that crash and resume, retry safely, and explain every step.
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
The platform for LLM evaluations and AI agent testing
The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.
AIConfig is a config-based framework to build generative AI applications.
国内首个企业级 IT 运维多 Agent 自动化平台 — 基于大语言模型的智能运维解决方案。ITOps Agent Platform通过可视化工作流编排，将多个AI Agent组合成智能运维自动化流水线，实现服务器管理、告警处理、故障诊断、日志分析、脚本管理、定时运维任务的自动化执行， 支持国内外主流大模型，旨在 Zabbix/Prometheus 告警自动修复闭环，Docker 一键部署，多平台兼容。
[DEPRECATED] Moved to microsoft/agent-governance-toolkit
A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline.
A living map of the AI agent security ecosystem.
Anthropic API reverse proxy with prompt-cache injection and request body transforms
Declarative governance framework for AI-augmented software development lifecycles
[⛔️ DEPRECATED] Friendli: the fastest serving engine for generative AI
lintlang is a static linter for AI agent configs, tool descriptions, and system prompts that runs zero-LLM quality gating in CI. Catches language-level failures (vague tool descriptions, missing stop conditions, schema gaps) before they reach runtime, with deterministic regex + structural detectors and no model calls.
AI lifecycle platform for classical ML and agentic systems. Versioned, encrypted registries for data, models, experiments, prompts, agents, MCPs, and skills. Pre/deploy/post-deployment patterns. Real-time monitoring and evaluation via Scouter. Rust core, governed by design
Multi-model AI agent runtime. Define agents in YAML, connect 6 LLM providers, orchestrate with ReAct/Plan&Execute/Fan-Out/Pipeline/Supervisor/Swarm patterns, and deploy as REST/WebSocket API with RAG, memory, MCP tools, guardrails, and OpenTelemetry observability.
Production operations framework for AI-powered SaaS. The architectural patterns, failure modes, and operational playbooks that determine whether your AI systems scale profitably or fail expensively.