tracing

TraceMotive v0.2.0, a local AI-agent debugging tool, has been released with improvements like persistent storage, one-command startup, and trace comparison features, addressing feedback from the initial version.

OpenObserve is an open-source observability platform built in Rust that supports logs, metrics, distributed tracing, and RUM. Its storage cost is 140x lower than Elasticsearch, it can be deployed as a single file, and it serves as an open-source alternative to Datadog.

Alan Maguire led a session at the Linux Storage, Filesystem, Memory-Management, and BPF Summit proposing additions to BTF to store information about inlined functions, enabling kernel tracing of such functions via kprobes.

Argues that the choice of AI agent framework (LangGraph, CrewAI, etc.) matters less than evals, tracing, and guardrails for production reliability, offering practical advice for developers building agent stacks.

Progress AI Observability is a product for tracing, evaluating, and improving AI agents in production.

Future AGI is an open-source platform combining evaluations, tracing, simulations, guardrails, and optimization to help teams ship self-improving AI agents, with a nightly release available for early testing.

Google Devs shares how to use Google ADK, Gemini Live, and LangSmith to build voice agents with full traceability of interactions, including tool calls and token costs.

TraceLLM brings OpenTelemetry-style observability to production AI applications, enabling tracing and monitoring for LLM-based systems.

A tutorial by Darsh on how to trace and monitor local AI agents using LangSmith, LangChain, Ollama, and Qwen, enabling inspection of model and tool calls, latency, and usage.

LangSmith officially launched Python-side voice tracing support, covering four mainstream frameworks: Pipecat, LiveKit, OpenAI Realtime, and Gemini Live. It brings voice conversations into the same observable, evaluable workflow as text agents, solving the pain point of poor debuggability in voice agents.

LangChain launches LangSmith tracing for voice frameworks (Pipecat, LiveKit, OpenAI Realtime, Gemini Live), enabling full audio monitoring, STT/TTS latency tracking, interruption detection, and VAD analysis with minimal code.

LangChain released a tracing plugin that converts every Cursor AI agent session into a structured trace in LangSmith, including model runs, tool calls, and nested subagent work, part of a series comparing coding-agent traces.

Discusses a common failure mode in AI agents where the model claims to have executed a tool call without actually firing it, and advocates for trusting execution receipts over agent narration to ensure reliability in production.

LangChain released a tracing plugin for Codex sessions in LangSmith, enabling detailed traces of every turn including tool calls and token usage, configurable with just two blocks and a flag.

The article compares popular developer tools for agent reliability across four layers: tracing/evals, runtime guardrails, and gateway. It finds that no single open-source tool covers all layers, and most developers use a combination.

LangChain released a plugin that logs all Claude Code sessions into LangSmith for debugging, with a quick three-command setup.

A guide on running agent evaluations using Harbor framework and LangSmith sandboxes with full trace support.

Coding agent costs are rising due to fragmented logging across tools like Claude Code, Cursor, and Copilot; LangChain's LangSmith provides unified tracing and cost visibility to help teams monitor and optimize spend.

A deep dive into the Linux graphics stack, tracing from GPU triangle drawing through Mesa3D, GLFW, OpenGL, Vulkan, Wayland, and Linux DRM to understand how the system works.

Arize Phoenix announces trace-level annotations now display as peer columns in the trace header and as a section in the project stats panel, with live updates during streaming.