korchasa awesome lists digest (14.06.2026)

Today’s update has one clear source of change: awesome-aiops added 15 projects. The selection leans toward infrastructure for agentic systems, observability, privacy, and practical LLM operations in production. This is not just a list of new repositories; it shows how AIOps practice is absorbing agent runtimes, local cost control, data protection, and specialized deployment platforms.

iflytek/astron-agent — 8554 stars. Astron Agent is an enterprise-grade platform for building, orchestrating, and deploying production-ready AI agentic workflows with integrated RPA, model management, and team collaboration.

Open-Source-Legal/OpenContracts — 1352 stars. OpenContracts is an open-source document intelligence platform that builds a programmable citation graph from documents, enabling AI agents, structured extraction, and a Model Context Protocol (MCP…

LiteLLM-Labs/litellm-agent-platform — 810 stars. LiteLLM Agent Platform provides a unified API and management console for deploying, managing, and operating various AI agent runtimes, offering session management, scheduling, and memory.

ZhangJinHaHaHa/AgentLens — 589 stars. AgentLens is a decentralized infrastructure and marketplace for AI Agents that provides verifiable proof of capabilities, security, and track record using on-chain audit scores, Intel SGX TEE attes…

cloudshipai/station — 425 stars. Station is an open-source, self-hosted platform for building, testing, and deploying intelligent multi-agent AI systems with Git-backed workflows and full observability.

openinfer-project/openinfer — 383 stars. openinfer is a pure Rust + CUDA LLM inference engine designed for high performance and low latency serving, focusing on understanding every layer of the inference stack.

packyme/privacy-filter — 226 stars. Privacy Filter is a Go-based LLM gateway component for millisecond-latency PII and secret redaction from text, ensuring data privacy before interaction with large language models.

raketenkater/llm-server — 223 stars. An intelligent launcher and OpenAI-compatible server for GGUF models on llama.cpp, featuring auto-tuned flag optimization, multi-GPU tensor-split, MoE expert placement, and hardware-matched downloads.

alphadl/AdaRubrics — 216 stars. AdaRubric is a pipeline for evaluating LLM agent trajectories using task-adaptive rubrics and generating dense reward signals for DPO and RLHF, enhancing agent performance.

Tejas-TA/predikit — 206 stars. Predikit bridges traditional ML models (scikit-learn, XGBoost) with AI agents by generating OpenAI function schemas and LangChain tools, enabling seamless integration and callable interfaces.

Javis603/token-monitor — 191 stars. Token Monitor is a real-time, multi-device syncable desktop widget that tracks token usage, costs, and AI tool limits across various AI coding tools like Claude Code, Codex, and Cursor.

alibaba/UnifiedModel — 151 stars. UModel is a vendor-neutral semantic runtime for enterprise AI, data governance, and operational intelligence, turning fragmented data into a unified object graph usable by AI agents.

caura-ai/caura-memclaw — 107 stars. MemClaw is an open-source, governed, shared memory system for multi-agent AI fleets, enabling agents to learn, recall, and compound knowledge, improving collective intelligence.

VasiHemanth/tokentelemetry — 105 stars. TokenTelemetry is a local, open-source observability dashboard for AI coding and autonomous agents, tracking token usage, costs, tool calls, session traces, and reasoning steps across various LLMs …

petrobras/3W — 0 stars. 3W is an open project from Petrobras providing a dataset and toolkit for developing machine learning models to detect and classify undesirable events in offshore oil wells.

The main practical signal is that AIOps is moving beyond classic operations into managed agentic processes. At one end are orchestration and deployment platforms; at the other are focused components for privacy, trajectory evaluation, token monitoring, and domain datasets. That structure matters because it points to a full stack forming around AI systems that must be launched, measured, and protected.