OpenProject vs OpenTelemetry

OpenProject 17.8: Create and update with AI in OpenProject (Enterprise add-on)

OpenProject 17.8 takes the next step in connecting AI assistants with your project work. The MCP Server introduced in OpenProject 17.2 can now create and update work packages, add comments, and manage...

News from the Product Desk: The future of agile reporting in OpenProject

High-performing agile teams do not just deliver work, they reflect on it. That habit of stopping after every sprint to ask what went well, what did not, and what should we change is the heartbeat of continuous…

OpenTelemetry Go Logs API and SDK reach release candidate status

OpenTelemetry Go v1.47.0-rc.1 is here. This release promotes the Logs API and SDK to release candidate (RC), the final stage before we provide stable v1 compatibility guarantees. We believe the design is ready, and now we need the community to test …

LLMOps for Compound AI Systems: Observability & Cost

The article argues that most GenAI pilots fail in production due to insufficient system-level engineering rather than poor models. It presents an LLMOps playbook for compound AI systems (embedders, retrievers, vector stores, re-rankers, validators, tool calls, and multiple LLMs) centered on five controls: a model gateway for routing and budgeting, pipeline-level traces for end-to-end observability, semantic caching keyed by query embeddings, lightweight eval gates for safety and quality, and tiered scaling of heavy infrastructure. A concrete engineering example reports a 38% reduction in token spend and 25% lower median latency after implementing a gateway, semantic cache, and tracing. The post includes a short pseudocode example (using qdrant-style vector operations) and an operational checklist for iterating LLMOps as an operating model.

• The article defines five LLMOps controls: model gateway, pipeline-level traces, semantic caching, eval gates, and tiered scaling.
• Author recommends using OpenTelemetry-compatible spans to instrument embed, search, rerank, prompt build, LLM call, and tool call stages.
• A cited engineering example achieved a 38% reduction in token spend and 25% lower median latency after implementing three LLMOps controls.

agent-cost: Measure LLM Usage, Separate Task Attribution

The author describes agent-cost, a small tooling primitive that reads local logs from LLM CLIs (e.g., Claude Code and Codex) to produce auditable, machine-readable usage facts (model, token kind, timestamp, count) and an estimated price. The tool is designed to run with no network calls at runtime, carry a versioned price catalog (with SHA-256 digest), and keep session measurement distinct from task attribution. Unknown or unsupported pricing and ambiguous session-to-task bindings are surfaced (labels like "unpriced" or "lower_bound") rather than silently allocated. The author re-ran the published coding-agent-cost 0.1.0 package and notes a catalog version 2026-07-29 and workflows that validate the measure/v1 protocol and data quality.

• agent-cost reads local logs from LLM CLIs (examples: Claude Code and Codex) and normalizes usage events into facts containing model, token kind, timestamp, and count.
• At runtime agent-cost makes no network calls and declares no Python runtime dependencies; installation from PyPI still requires trust in the supply chain.
• agent-cost carries a versioned pricing catalog with a SHA-256 digest and marks unknown models/prices as 'unpriced' or 'lower_bound' instead of inventing values.