updated 2026-10-09 16:07 UTC

Weights & Biases Weave

Repository Website

Weave is a toolkit for developing AI-powered applications, built by Weights & Biases.

Observability & Evaluation Python Listed Apache-2.0

Is Weights & Biases Weave safe? Strong public trust posture, backed by multiple independent signals.

OSSF Scorecard 6.3 / 10
Provenance Attested
Signed commits 89%
Last push today
Advisories None known
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In detail: Weights & Biases Weave scores 84.3/100 (Grade A), ranked #101 of 1371 tracked open-source AI agent projects, on evidence coverage A (4 of 5 independent signal types). The public evidence: its packages ship with cryptographic provenance; OSSF Scorecard rates its supply-chain practices 6.3/10; 89% of recent commits are signed; no published advisory affects its latest release (weave); last pushed 2026-10-09. Every point is earned from checkable signals — never paid placement. How scoring works →

How Weights & Biases Weave could raise its score

Each line changes one public signal and recomputes with the live scoring function. The gains don't add up exactly, because the score is capped near the top.

Already done? Suggest a correctionAdd the trust badge

Chain of custody

Scanners check what the code says. This traces who ships Weights & Biases Weave and whether that has changed: from the source repository, through how changes are reviewed and released, to the package you install. These are the checks for OWASP ASI04 Agentic Supply Chain Vulnerabilities.

  1. Source Signed

    github.com/wandb/weave, Apache-2.0, last pushed 2026-10-09. 89% of the last 100 commits carry a verified signature, so changes trace to a verified account.

  2. Review and release Mixed

    OpenSSF Scorecard rates the repository's practices 6.3/10 (scanned Oct 8, 2026). The checks that decide who can get a change released:

    • Code Review8
    • Branch Protection4
    • Signed Releasesn/a
    • Dangerous Workflow10
    • Token Permissions0
    • Pinned Dependencies5
    All 18 Scorecard checks
    Binary-Artifacts 10
    Branch-Protection 4
    CI-Tests 10
    CII-Best-Practices 0
    Code-Review 8
    Contributors 10
    Dangerous-Workflow 10
    Dependency-Update-Tool 10
    Fuzzing 0
    License 10
    Maintained 10
    Packaging 10
    Pinned-Dependencies 5
    SAST 0
    Security-Policy 10
    Signed-Releases -1
    Token-Permissions 0
    Vulnerabilities 0

  3. Published packages Partly attested

    • npm weave Source link points back to this repo Build provenance attested
    • PyPI weave Source link isn't a GitHub repo Build provenance attested

  4. What you install today No known advisories

    Latest release checked: weave 0.16.9. No published advisory affects it (OSV, checked 2026-10-09).

Changes to this chain

No change to package provenance, package source links, Scorecard coverage or license in HVTracker's daily snapshots of Weights & Biases Weave.

Evidence behind this score

Coverage A: 4 of 5 independent evidence types found.

  • GitHub repository data
  • Package downloads
  • Supply-chain checks
  • Public actions (missing)
  • Community mentions

Verify this score yourself

Every build re-issues Weights & Biases Weave’s score as an Ed25519-signed credential, valid for 7 days. You can check it offline against HVTracker’s published key; if anyone changes a number after signing, verification fails.

Signed credential (JSON) How to verify

What this score doesn’t check

It covers who ships the code, not what the code does. It doesn’t read tool descriptions or prompts for injected instructions, watch runtime behaviour, or find bugs nobody has disclosed yet. For that, run a content scanner before you connect it, such as Cisco MCP Scanner or Snyk Agent Scan.

How Weights & Biases Weave compares in Observability & Evaluation

  1. #2 MLflow 89.6 +5.3
  2. #3 OpenLLMetry 86.1 +1.8
  3. #4 Weights & Biases Weave 84.3 this agent
  4. #5 LangWatch 83.1 −1.2
  5. #6 Langfuse 79.8 −4.5

Bars show each HVTrust score; the tick marks Weights & Biases Weave’s 84.3.

Where the 84.3 comes from

HVTrust dimensions vs the Observability & Evaluation average

84.3 / 100 · 100.0% confidence

Weights & Biases Weave Observability & Evaluation average (26 agents)

Safety / Integrity50% OSSF Scorecard · 30% provenance · 20% signed commits
19.8 / 25
8.6 above avg 11.2
Identity / Provenance60% listing status · 40% build provenance
18.0 / 18
4.7 above avg 13.3
Transparency50% declared license · 50% OSSF Scorecard
13.9 / 17
1.9 above avg 12.0
Maintenance60% last-push freshness · 40% commit activity
19.6 / 20
4.3 above avg 15.3
AdoptionLog-scaled stars · package downloads
14.4 / 20
1.0 above avg 13.4

Quick Trust Read

What Would Improve It
Improve adoption to lift the weakest part of the trust profile.
Recent Changes
2026-10-07
Rank Moved
Rank dropped 10 spots (#87 → #97)
2026-09-28
Rank Moved
Rank dropped 13 spots (#73 → #86)
2026-09-23
Rank Moved
Rank dropped 21 spots (#53 → #74)
Maintainer Checklist
Raise Scorecard signals Current OSSF Scorecard is 6.3/10. Tighten the weakest checks to improve public safety evidence.
Compare with Promptfoo Get email alerts for Weights & Biases Weave View Observability & Evaluation Report missing data
77.5
Activity sub-score · out of 100
#4
In Observability & Evaluation

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade A reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Evidence coverage A is separate — it grades how many independent signal types back the score (4 of 5), so a high score on thin evidence stays visible. Full methodology →

Signals refreshed 2026-10-09 16:07 UTC · Repo last pushed today

Rank Trend

2026-09-29 2026-10-09

Activity & Reach

Stars
1.1k
Forks
172
Last Push
2026-10-09
today
Commits (4 wk)
80
Downloads (7d)
212,213
pypi
HN mentions (30d)
0
Open Issues
87
Rank Change
▼3
was #98

Analysis

Activity Inputs

77.5 / 100
StarsRepository reach
18.3 / 30
FreshnessLast push recency
25.0 / 25
ActivityRecent commits
23.8 / 25
CommunityFork signal
10.4 / 20

Common questions about Weights & Biases Weave

Public supply-chain signals for Weights & Biases Weave are strong: it has multiple independent trust indicators in place. This does not replace your own security review, but Weights & Biases Weave carries less obvious unverified-evidence risk than projects with thin signals.
Does Weights & Biases Weave publish package provenance?
Yes. Weights & Biases Weave's package releases carry build provenance attestations, which cryptographically link the published package back to its source repository and CI workflow.
Does Weights & Biases Weave have an OpenSSF Scorecard?
Weights & Biases Weave has an OpenSSF Scorecard score of 6.3/10. The Scorecard checks for branch protection, signed releases, dependency updates, fuzzing, code review, and other supply-chain hygiene items. See the full check breakdown on this page.
Is Weights & Biases Weave actively maintained?
Actively maintained. The repository was pushed to within the last 1 day(s).
What license does Weights & Biases Weave use?
Weights & Biases Weave ships under Apache-2.0. A declared, OSI-approved license is one of the transparency signals HVTrust scores.
Are Weights & Biases Weave's commits signed?
89% of the last 100 commits to Weights & Biases Weave are verified-signed (GPG, SSH, S/MIME, or GitHub's signing flow). Signed commits help confirm that code was authored by who the commit claims.

Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.

Compare Weights & Biases Weave head-to-head

Weights & Biases Weave vs Promptfoo → Weights & Biases Weave vs MLflow → Weights & Biases Weave vs OpenLLMetry →

AI agent surface

MCP, providers, tool surface
Scored in HVTrust

These runtime-trust fields — detected from public repo docs and manifests — contribute a bounded adjustment to this project's HVTrust score alongside supply-chain evidence. The exact values each field can add or subtract are documented in the methodology → Compare this surface across every listed agent in the capability matrix →

MCP Server Support
high confidence
Implemented
Weights & Biases Weave appears to expose MCP server capabilities.
Detailed evidence is not shown in the public view.
External Service Dependencies
high confidence
9 detected
Public provider/service dependencies detected.
Credential signal: API keys or service config markers documented.
Tool / Plugin Surface
medium confidence
1 tags
Broad capability areas detected.
  • database
Detailed evidence is not shown in the public view.
Package Provenance Drift
medium confidence
Partial
Some package metadata matches; some source metadata is missing
Detailed evidence is not shown in the public view.
  • MCP signal live
  • External deps live
  • Tool / plugin surface live
  • Package provenance drift live

Maintain Weights & Biases Weave?

For maintainers

HVTrust scores Weights & Biases Weave from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.

Submit a correction → Add the trust badge

Reputation Timeline

Signal history
Rank 5
2026-10-07
Rank Moved
Rank dropped 10 spots (#87 → #97)
2026-09-28
Rank Moved
Rank dropped 13 spots (#73 → #86)
2026-09-23
Rank Moved
Rank dropped 21 spots (#53 → #74)
2026-09-21
Rank Moved
Rank rose 24 spots (#77 → #53)
2026-09-05
Rank Moved
Rank dropped 10 spots (#54 → #64)
GitHub → Website → PyPI: weave npm: weave

Embed Badge Badge guide for maintainers →

For maintainers
HVTrust 84.3 Grade A
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HTML:
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Other agents in Observability & Evaluation

Data sources
GitHub REST API (repo, commits, stars, forks, license) · npm Registry (downloads, provenance) · PyPI / pypistats (downloads, provenance) · OpenSSF Scorecard CLI · Algolia HN Search API
Each agent's signals refresh once daily across 6 staggered batches. Methodology v4.4 · Raw JSON