Comparison

Agentry vs LaunchDarkly

LaunchDarkly is the enterprise standard for feature flags: a mature SDK matrix, a sophisticated targeting engine, and the approval / audit workflows regulated industries require. Agentry covers the same core capability — server- and client-side evaluation, percentage rollouts, A/B tests — but through an AI agent in your editor, alongside errors, analytics, and deploys in one product. Pick LaunchDarkly when you need its compliance + governance story; pick Agentry when you want flags as part of the same investigation surface as everything else.

TL;DR

Pick LaunchDarkly if

  • You need SOC 2 / HIPAA / GDPR-DPA, SSO, and approval workflows wired to Jira or ServiceNow
  • You ship in 10+ languages including mobile native with offline evaluation
  • PMs and release managers need a dedicated UI for staged rollouts and scheduled promotions
  • Your targeting rules are complex (segments, prerequisites, contextual attributes at scale)

Pick Agentry if

  • You want to ask "did toggling flag X cause that error spike?" in one prompt
  • Engineering creates and rolls out flags directly — no PM-approval workflow
  • You'd rather have a thin fetch helper than per-language SDKs
  • Flags + errors + analytics + deploys in one product matters more than enterprise governance

Feature comparison

Capability LaunchDarkly Agentry
Server-side flag evaluation Yes — SDKs in 20+ languages Yes — agentry_evaluate_feature_flag + raw fetch
Client-side flag evaluation Yes — streaming SDKs (JS, mobile) Yes — via raw fetch (no streaming SDK)
Targeting rules engine Mature — segments, prereqs, contextual attrs Property-based + percentage; simpler model
Percentage rollouts Yes Yes
Audit log Yes — rich, with diff view Yes — agentry_recent_changes
Approval workflows (Jira/ServiceNow) Yes No
Per-language SDKs 20+ official SDKs No — ~25 lines of fetch
Mobile SDKs with offline evaluation Yes (iOS, Android, RN, Flutter) No
A/B test analysis Dedicated paid product (Experimentation) agentry_create_ab_test + HogQL via agent
SSO + SOC 2 / HIPAA Yes No
Integrated with errors + analytics + deploys No — flags only Yes — one query plane
Pricing model Per-MAU enterprise tiers Free during beta, usage-based later

When LaunchDarkly is the right call

LaunchDarkly is the right tool when feature flags are a governed change-management process inside your organisation, not just a code construct. If a flag flip has to be approved by a release manager, recorded against a Jira ticket, and rolled forward through a documented promotion path (dev → staging → prod with sign-offs at each stage), LaunchDarkly's workflow tooling is years ahead of anything else. The targeting engine — segments that reference other segments, prerequisite flags, contextual attributes evaluated in a strict order — handles complexity Agentry doesn't try to.

It's also the right call when you ship in many languages, especially mobile native. The SDK matrix is broad and battle-tested, and mobile SDKs with offline evaluation and local caching aren't something a fetch helper can replicate. If you're regulated (HIPAA, SOC 2, FedRAMP), LaunchDarkly is already on most approved-vendor lists; Agentry isn't.

If your release ritual is "open LaunchDarkly, find the flag, flip it after approval lands," stick with LaunchDarkly. That workflow is exactly what it's designed for.

When Agentry is the right call

Agentry is the right tool when flags are an engineering primitive your team uses in passing, not a process the organisation gates. Small and mid-size eng teams that ship without an approval workflow tend to find LaunchDarkly's governance layer adds more friction than it removes — and the per-MAU pricing on top doesn't help. Agentry exposes flag CRUD and evaluation through MCP tools (agentry_create_feature_flag, agentry_update_feature_flag, agentry_evaluate_feature_flag), so the agent creates, updates, and evaluates flags in the same conversation where you're writing the code that uses them.

The combined-signal angle is the bigger win. With LaunchDarkly, "did toggling the new-pricing flag yesterday cause the checkout error spike at 3pm?" requires correlating LaunchDarkly's audit log with Sentry's error stream with Mixpanel's funnel — three tools, three tabs, manual time alignment. In Agentry, that's one prompt: the agent reads agentry_recent_changes, queries error fingerprints around the flip, and tells you. The pricing-experiment recipe shows the same loop end-to-end.

Agentry also publishes flag-result dashboards via agentry_publish_query + the agp_ public key — useful for sharing experiment results outside the eng team without provisioning seats.

Migrating from LaunchDarkly

The mental model maps cleanly: a LaunchDarkly flag becomes an Agentry flag with the same key, the same variations, and a percentage or property-based rollout. Server-side evaluation is a single HTTP call (or agentry_evaluate_feature_flag from the agent). For migration, dual-evaluate both for a week — read from LaunchDarkly, mirror to Agentry, compare divergences — then flip the read path.

These recipes cover the LaunchDarkly use cases most teams care about:

Try Agentry against your real data.

Dual-write for a week. Compare. Switch if it's better. The agent handles install — you just paste one prompt.

Install https://agentry.sh/agentry.md and set it up
Agentry vs Sentry → Agentry vs PostHog → Agentry vs Mixpanel → Agentry vs Datadog → Agentry vs Amplitude → Agentry vs LogRocket → Agentry vs Highlight.io →