observability

Monocle is a framework for tracing GenAI app code. This repo contains implementation of Monocle for GenAI apps written in Python.

AURA is a production-tested SRE agent platform you can deploy in minutes. AURA handles the guardrails, APIs, state management, streaming, and failure handling required to put AI to work safely on production infrastructure.

Graphsignal Profiler

High-scale LLM gateway, written in Rust. OpenTelemetry-based observability included

The implementation of Model Context Protocol (MCP) server for VictoriaMetrics

Open Source AI Tracing Framework built on Opentelemetry for AI Applications and Frameworks

🔥🔥🔥 Enterprise AI middleware, alternative to unifyapps, n8n, lyzr

🟪 Open-source runtime that ships any LangGraph or Google ADK agent as a production-ready FastAPI service. Bundled , AG-UI copilotkit API, chat UI, 15+ guardrails, MCP, OpenTelemetry, OIDC. One pip install. Self-hosted, no vendor lock-in.

Enterprise-ready Spring AI platform for RAG, tool calling, async ingestion, JWT/RBAC security, and observability.

Real-time guardrail that shows token spend & kills runaway LLM/agent loops.

Reduce logs to their semantic anomalies

OpenTelemetry-native LLM observability CLI. Point any OTEL exporter at it and watch your LLM/agent traces in real time.

In the midst of all the tools out there that you can possibly use to keep track of them. Here's a "shovel" that just works to try them all out.

Language AI Engineering Lab, a place where you can deeply understand and build modern Language AI systems, from fundamentals to production.

Multi-language agent runtime and library for execution scope management, lifecycle events, and middleware on tool and LLM calls.

A general-purpose Python framework for building LLM agents and multi-agent systems. "Four lines of code, an agent with memory."

AgC is the open-core platform that powers Open Agentic Compute — a new compute substrate purpose-built for deploying, running, and orchestrating AI agents at scale.

Engineering deterministic, production-grade systems around non-deterministic LLMs — FSM, durable execution, retries, DAGs, agent runtimes, model routing, edge inference, RAG, memory, multi-agent orchestration, security, and observability. 14 runnable proof-of-concept phases.

The lowest-overhead LLM router. Production-ready, highly available, one OpenAI-compatible endpoint in front of 80 providers and your own vLLM/SGLang — 0.76 µs per request, no I/O on the request path, cache-affinity routing, RBAC, budgets and a 13-screen UI in the binary.

Self improving agents through iterations