Weave

Weave, a product of Weights & Biases (W&B), is an observability and continuous improvement platform specifically designed for production AI agents. It targets teams building and deploying agentic systems—such as coding agents like Claude Code—who face reliability collapses in production that offline evaluations cannot catch. Founded in 2017 and headquartered in San Francisco, Weave provides end-to-end monitoring, out-of-the-box signals, and a flexible evaluation framework to help agents learn and improve from real-world experience, enabling exponentially compounding reliability gains. It is not a generic observability tool but is tailored for multi-turn, multi-agent systems, making it suitable for developers and ML engineers who need to debug, evaluate, and iterate on complex agent behaviors in production environments.

Weave works by automatically logging every production interaction into trace trees, organizing them into sessions, turns, steps, tools, and sub-agents as first-class concepts—far beyond simple call traces. Built-in and custom signals automatically capture and classify agent interactions, surfacing failure modes without manual scoring of millions of traces. Alerts route through Slack notifications and trigger webhook automations to turn production insights into fast iterations. The evaluation framework uses a flexible imperative API for measuring improvements to harnesses and models, with powerful visual comparisons and leaderboards to catch regressions before they reach users. Weave also offers a Playground for testing prompts and models against production traces, pre-built guardrails for safety (toxicity, bias, PII, hallucinations) and quality (coherence, fluency, context relevance), and an MCP server that enables coding agents like Claude Code to autonomously read production data, run evaluations, and execute automatic iteration loops.

Weave competes directly with MLflow and LangSmith, but differentiates through its agent-native architecture and deep integration with the W&B ecosystem. Unlike MLflow, which is more focused on experiment tracking and model management, Weave is built from the ground up for agentic systems, offering session-level tracing and automatic self-improvement capabilities. Compared to LangSmith, Weave provides a more comprehensive platform that includes guardrails, leaderboards, and a playground, all within the same infrastructure used by W&B's broader AI developer platform. However, Weave is relatively newer in the agent observability space and may lack the extensive community and third-party integrations that LangSmith has cultivated. Its pricing starts at $249 per month, which is higher than some open-source alternatives like MLflow, but includes award-winning customer support and expert onboarding.

The honest trade-offs with Weave include its cost, which at $249 per month may be prohibitive for small teams or individual developers, especially compared to free or lower-cost alternatives. The platform is heavily integrated with the W&B ecosystem, which can create vendor lock-in for teams not already using W&B products. Some users report that AI-generated layouts and outputs can look generic and require manual editing, and the platform is not a replacement for professional UX design skills. Additionally, while Weave's autonomous improvement features are powerful, they require careful setup and prompt engineering; vague prompts can lead to average results, and the system's reliance on MCP server connectivity may add complexity for teams not familiar with that protocol.

Starting from $249 per month; plans include Pro, Elite, and Ultimate tiers with varying features like phone support, training, and analytics.