Item detail
github.com

data-context-hq/datacontext

data-context-hq/datacontext is a package in RepoRadar's AI Infrastructure section, holding Silver tier and a 'try now' verdict. Its strongest signal is workflow potential, scored 9.0 out of 10.

Score7.9
Popularity1.0
Riskconditional
TierSilver
Score breakdown
Usefulness8.0
Novelty7.0
Momentum5.0
Maturity5.8
Open-source/build8.4
Evidence8.0
Workflow potential9.0
Setup ease6.4

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Useful for teams that need to know which actor, tenant, session, or tool caused a data read when AI agents start touching real pipelines and warehouses.

Who should use it

Data and platform teams adding AI-driven automation to warehouse and application workflows Developers who need session-level attribution around database and connector calls Teams instrumenting SQLAlchemy, Postgres, BigQuery, Dagster, Snowflake, or dbt paths for agent usage Operators who want observability before letting autonomous workflows touch production data

Who should skip it

Move on from data-context-hq/datacontext if the licensing terms, language support, or platform requirements do not fit your project.

About this signal

data-context-hq/datacontext is tracked by RepoRadar as a package in the AI Infrastructure section. First seen 2026-06-28; the source record was last checked on 2026-06-28. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. The standout signals for data-context-hq/datacontext are workflow potential (9.0) and open-source/build quality (8.4), while momentum (5.0) trails — that balance shapes where it fits best. This page summarizes the public evidence on the linked source page and states where additional review is still needed.

How this item is evaluated

The data-context-hq/datacontext record combines a 7.9/10 composite score with separate popularity (1.0), risk (conditional), and setup (moderate) signals. See the scoring methodology for the current weights and evidence definitions.

Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.

Risk explanation

Instrumentation can capture query context, actor identifiers, and session metadata, so scrub sensitive tenant or customer details before exporting events beyond a trusted environment; The package is still early and spans many optional integrations, so validate overhead and event shape in staging before relying on it for production audit flows.

Evidence links
Closest alternatives / related signals
observability data-infrastructure python agents auditability apache-2.0
Verification record

What RepoRadar actually verified

Tested in a bounded workflow

Bounded representative workflow retained by RepoRadar verification harness. Last checked 2026-07-13T10:41:00.293618Z.

passed · cohort-20260712-datacontext-sqlite-attribution-workflow

Tester
RepoRadar automated local verification harness
Started
2026-07-13T10:40:59.616042Z
Completed
2026-07-13T10:41:00.293618Z
Environment
Windows 10 AMD64; Python 3.11.9; credential-stripped child environment; disposable home/cache
Install/setup time
1 minute(s)
Evidence scope
Bounded representative workflow
Cleanup
Per-check temporary home and work directory removed. Shared cohort package cache removed.
Actions exercised
  • Created a disposable home, work directory, and isolated package cache with credential-like environment variables excluded.
  • Created 1 synthetic fixture file(s) inside the disposable work directory; retained hashes prove the exact inputs.
  • Configured a local JSONL sink and request context, then created and queried a one-row in-memory SQLite incident table.
  • Wrapped the real query in trace_query and asserted the emitted event, top-level service_name, success status, fingerprint, and sanitized query text.
  • Executed bounded check: Trace a real in-memory SQLite query through DataContext and validate its sanitized attribution event.
  • Captured the complete sanitized stdout, stderr, exit status, artifact checks, and 0.68-second wall time.
Observed results
  • Command exited 0 after 0.68 seconds.
  • DataContext emitted one successful attributed query event with a SHA-256 fingerprint while excluding the literal ID from query_text.
  • Expected marker 'CHECK_OK event=datacontext.query status=ok sanitized=true' was observed in retained output.
  • Validated datacontext.jsonl: 4 required marker(s) present and 1 excluded marker(s) absent; size and SHA-256 are retained.
Observed strengths
  • The library linked request context to a real database operation and produced a structured, sanitized audit event without an exporter service.
Friction
  • Manual tracing requires supplying both database metadata and the query shape around the actual driver call.
  • Setup or runtime emitted 1 stderr line(s); the complete warnings/errors are preserved in the retained log.
Limitations
  • The file-sink fixture validates manual request context, query tracing, fingerprinting, and default sanitization only; it does not test framework auto-instrumentation, OpenTelemetry export, or production databases.
  • This credential-free disposable workflow does not establish production scale, model quality, reliability under sustained use, or team adoption.

Pricing assessment: The SQLite trace and file sink ran from the local open-source package with no observability backend or paid service.

Privacy assessment: The in-memory row stayed local, and the retained event used sanitized query text rather than the literal query value.

Open retained test log →

Verification sources

Longitudinal intelligence

How this decision record is moving

Raw history JSON →

37 dated snapshots retained from 2026-06-28 through 2026-08-13; see the snapshot index for explicit coverage gaps. Stars, version, release, pricing, integration, risk, maintenance, verdict, score, and momentum fields remain explicit even when a source has not reported them. Repository momentum is a normalized 0–10 RepoRadar signal; GitHub stars appear only where the popularity monitor retained exact timestamped observations.

RepoRadar score7.9 current · +0.0 net
Repository momentum5.5 current · +0.5 net
GitHub stars (observed)39 current · +0 net
GitHub stars39 exact observation
VersionNot reported by source
Last releaseNot reported by source
Maintenancemaintained
Current riskconditional
Current verdicttry now
Pricing baselineNo structured commercial pricing baseline
Pricing checkedNot applicable or not recorded
Pricing freshnessNo dated commercial pricing review
Integrations baselineNo structured integrations recorded

Recent dated points

DateScoreMomentumStarsRiskVerdictMaintenance
2026-08-137.95.539conditionaltry nowmaintained
2026-08-127.95.539conditionaltry nowmaintained
2026-08-117.95.539conditionaltry nowmaintained
2026-08-107.95.539conditionaltry nowmaintained
2026-08-097.95.539conditionaltry nowmaintained
2026-08-087.95.539conditionaltry nowmaintained
2026-08-077.95.539conditionaltry nowmaintained
2026-08-067.95.0Not recordedconditionaltry nownot recorded
2026-08-057.95.0Not recordedconditionaltry nownot recorded
2026-08-047.95.539conditionaltry nowmaintained
2026-08-037.95.539conditionaltry nowmaintained
2026-08-027.95.539conditionaltry nowmaintained

Why the record changed

verification changed

Verification changed: Discovered → Tested in a bounded workflow.