Verify before you commit
Use this page to sanity-check Deepsource quickly. We surface the evidence tier, freshness, and failure posture here, then put the official links where you can actually act on them, especially on mobile.
Evidence
Assessed
Docs reviewed · Mar 21, 2026
Freshness
Updated 2026-03-21T02:16:06.331927+00:00
Mar 21, 2026
Failures
Clear
No active failures listed
| Dimension | Score | Bar |
|---|---|---|
| Execution Score Measures reliability, idempotency, error ergonomics, latency distribution, and schema stability. | 7.2 | |
| Access Readiness Score Measures how easily an agent can onboard, authenticate, and start using this service autonomously. | 6.7 | |
| Aggregate AN Score Composite score: 70% execution + 30% access readiness. | 7.0 | |
No active failure modes reported.
Published review summaries with trust provenance attached to each card.
Docs-backed Built from public docs and product materials.
Test-backed Backed by guided testing or evaluator-run checks.
Runtime-verified Verified from authenticated runtime evidence.
Authentication uses API keys for REST API and GraphQL access. The key model is straightforward for automation integration. Teams should use dedicated service keys for agent automation to maintain clear attribution in the audit trail for automated issue resolution.
Documentation covers the GraphQL API with adequate depth. The autofix documentation explaining which analyzers support autofix and the fix generation process is particularly relevant for agents building automated quality improvement workflows. Teams evaluating DeepSource versus Code Climate or Codacy for the autofix capability will find it the primary differentiator.
DeepSource is a code review platform with a distinctive autofix capability — beyond identifying code issues, DeepSource can automatically generate pull requests that fix detected issues. For agents involved in code quality automation, the autofix feature reduces the gap between issue detection and remediation: agents can trigger analysis, review detected issues, and initiate automated fix PRs for common patterns without manual developer intervention for routine code improvements.
The API covers repositories, issues, occurrences, and analysis runs. The GraphQL API provides flexible querying of issue data across projects and repositories. Agents can retrieve open issues, filter by analyzer (security, style, performance), and query analysis run history. The autofix API enables agents to trigger fix generation for specific issues, creating a closed-loop quality improvement workflow.
Reliability is appropriate for a code quality service with batch analysis processing. Analysis runs are triggered by code pushes and take variable time depending on codebase size. Agents querying analysis results should account for the processing delay between push and result availability, implementing polling or webhook-based patterns for analysis completion.
Trust shortcuts
This score is documentation-derived. Treat it as a docs-based evaluation of API design, auth, error handling, and documentation quality.
Read how the score works, how disputes are handled, and how Rhumb scored itself before launch.
Overall tier
7.0 / 10.0
No alternatives captured yet.