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 — feature-flag evaluate endpoint + 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 — audit/recent-changes API |
| 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) | A/B test APIs + 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 | All features on every plan; priced by events + retention |
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.
Where Agentry complements a flag provider
Agentry does not replace LaunchDarkly's production flag runtime. Its owner-authenticated flag endpoints are control-plane tools for interactive setup and review; deployed code still needs a scoped runtime flag provider or an app-owned deterministic allocator. Agentry's role is to verify and analyze the exposure, outcome, error, and deploy signals around a rollout.
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. With the signals wired to Agentry, an agent can read trusted deploy and audit context, query error fingerprints around the change, and prepare an evidence-first review. The experiment-analysis workflow shows the evidence and review loop without pretending Agentry owns runtime assignment.
Safe aggregate results can be published only when they are backed by a reviewed public-query blueprint. Custom experiment queries remain private until that publication boundary exists.
Migrating from LaunchDarkly
Keep LaunchDarkly or another scoped provider on the runtime path. Add Agentry exposure and outcome telemetry around that assignment so an agent can verify the data, compare variants, and correlate product movement with errors and deploys. Do not proxy an Agentry owner key through deployed application code.
These workflows cover the LaunchDarkly use cases most teams care about:
-
Analyze an experiment your product already runs
Verify exposure, assignment, outcomes, samples, and guardrails before preparing a decision memo.
-
Track adoption of a newly-launched feature behind a flag
Rollout percentage + adoption funnel + error correlation, in one place.
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Browse all A/B testing workflows →
Every workflow that uses flags + experimentation patterns Agentry supports.
Try Agentry against your real data.
Keep your scoped flag runtime. Add Agentry to verify exposure, outcomes, errors, and deploy context around the rollout.
Agentry gives coding agents production context from errors, product analytics, and deploys through one HTTP API. Fetch and read https://agentry.sh/install.md completely, then install Agentry's full first-time telemetry baseline in this repository. I authorize device-code login, saving the owner key only to ~/.agentry/credentials.json, creating one Agentry project for this repository, and read-only inspection of shipped source plus hidden CI/provider configuration. Deeply analyze the product's business logic and complete value flow before choosing telemetry. Save the complete source-backed plan and show me its exact contents and hash. Until I explicitly approve that plan or request a replacement, do not edit app/CI source, place runtime/CI credentials, commit, exercise product traffic, start proof, push, or deploy. After approval, I authorize only the reviewed targets: place the required scoped browser/server/CI credentials through the established environment or secret mechanism, preserve existing telemetry, implement and test the baseline, commit it, push that reviewed commit when the shipped CI/provider path requires it, exercise safe proof paths with test/non-customer data, and perform one deployment through the reviewed shipped CI/provider path. Ask first if proof would charge money, contact a third party, change real customer data, or require new external access. After the plan is saved, immediately before every onboarding state-changing POST, GET current onboarding state, perform only its single returned next_action, then read state again; do not batch or infer later stages. Continue until status is verified, installation_complete is true, and next_action is null. Keep all secrets, source snapshots, proof markers, and scratch files outside the repository.
- 1. Open your repo in Codex, Claude Code, Cursor etc.
- 2. Paste the install prompt.
- 3. Your agent reads the install doc and shows you an implementation plan for approval.