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Weave logo

Weave

Weights and Biases toolkit for tracking, evaluating, and debugging LLM applications and agent workflows.

by Weights and Biases·added 2026-04-27·
CLI
HarnessCLI
Review first — review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Canonical URL
https://heyclau.de/entry/tools/weave
Source URLs
https://docs.wandb.ai/weave, https://github.com/JSONbored/awesome-claude/blob/main/content/tools/weave.mdx, https://wandb.ai/site/weave
Brand
Weave
Brand domain
wandb.ai
Brand asset source
brandfetch
Author
Weights and Biases
Claim status
unclaimed
Last verified
2026-04-27

Schema details

Install type
copy
Troubleshooting
No
Tool listing metadata
Pricing
freemium
Disclosure
editorial
Application category
DeveloperApplication
Operating system
Web
Full copyable content
## Editorial notes

Weave belongs in the directory for teams already using W&B or needing experiment tracking around LLM behavior.

## Disclosure

Editorial listing. No paid placement or affiliate link is used.

About this resource

Editorial notes

Weave belongs in the directory for teams already using W&B or needing experiment tracking around LLM behavior.

Disclosure

Editorial listing. No paid placement or affiliate link is used.

#evaluation#observability

Source citations

Add this badge to your README

Show that Weave is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.

Listed on HeyClaude
[![Listed on HeyClaude](https://heyclau.de/badge/tools/weave.svg)](https://heyclau.de/entry/tools/weave)

How it compares

Weave side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

Field
Weave logoWeave

Weights and Biases toolkit for tracking, evaluating, and debugging LLM applications and agent workflows.

Open dossier
AgentOps logoAgentOps

Open-source observability platform and SDK for tracing, debugging, replaying, and cost-monitoring AI agent and LLM application runs.

Open dossier
Arize Phoenix logoArize Phoenix

Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments.

Open dossier
Helicone logoHelicone

Open-source LLM observability platform for logging, metrics, cost tracking, feedback, and gateway workflows.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety · Privacy · Safety ✓ Privacy ✓ Safety · Privacy · Safety · Privacy ✓
BrandWeave logoWeaveAgentOps logoAgentOpsArize Phoenix logoArize PhoenixHelicone logoHelicone
Categorytoolstoolstoolstools
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorWeights and BiasesAgentOpsArize AIHelicone
Added2026-04-272026-06-032026-04-272026-04-27
Platforms
CLI
CLI
CLI
CLI
Source repo————
Safety notes— missing✓AgentOps instruments LLM calls, tools, operations, and agent workflows, so enable it intentionally in environments where captured traces are allowed. Cost and latency dashboards are useful for operations, but alerting and budget decisions still need human-reviewed thresholds. Self-hosted deployments require normal backend hardening for database access, secrets, authentication, and retained trace data.— missing— missing
Privacy notes— missing✓Traces can include prompts, completions, tool inputs, tool outputs, errors, costs, tokens, tags, and application metadata. The docs say AgentOps automatically collects basic host environment details such as OS, Python version, anonymized hostname, and SDK version. Hosted dashboard use sends telemetry to AgentOps infrastructure; self-hosted use still requires retention, access-control, and log-review policies.— missing✓When used as a proxy, Helicone sits in the request path and logs your LLM prompts, responses, and metadata (Helicone cloud or your self-hosted instance); review what request data is captured, keep secrets out of logged payloads, or use the self-hosted/async logging options.
Prerequisites— none listed
  • Python or TypeScript/JavaScript application using a supported LLM provider or agent framework.
  • AgentOps project/API key for hosted dashboard use, or a reviewed self-hosted deployment plan.
  • A telemetry policy for which prompts, responses, tool calls, metadata, and host details may be captured.
— none listed— none listed
Install————
Config————
Citations
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