Raygun

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Connect an agent via an official MCP server G

Agent access

Drive the product through a documented public API G

Agent access

Plug MCP servers into this product so it can use their tools G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Build against official SDKs G

Agent access

Operate the product with natural-language commands G

Agentic features

Run the product headlessly / in CI for automation G

Agent access

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Rely on versioned APIs with a documented deprecation policy G

Api quality

Set up automations that run autonomously in the background G

Agentic features

Subscribe to events via webhooks G

Agent access

Use an official CLI G

Agent access

Test against a sandbox environment without touching production data G

Api quality

The platform's AI analyzes an issue — stack trace, breadcrumbs, related commits — and proposes a root cause I can act on C

Root cause

Instrument my web frontend, backend services, and mobile apps with official SDKs that capture uncaught errors automatically C

Sdk coverage

Triage issues — assign an owner, resolve, ignore, or snooze — and the platform reopens a resolved issue automatically when it regresses C

Triage

An agent can pull my top production issues with stack traces via API or MCP, triage them, and file the real bugs into my tracker C

Agent access

Define rules that trigger actions automatically on events G

Route alerts by project, environment, and severity to Slack, PagerDuty, or email with per-rule thresholds — new issue, frequency spike, affected-user count C

Alert routing

The platform groups thousands of duplicate events into one issue via stack-trace fingerprinting, and I can customize the grouping when it gets it wrong C

Grouping

Upload JavaScript source maps from CI — via CLI or bundler plugin — so production stack traces show my original source, not minified frames C

Sourcemaps

Associate errors with releases and commits so each issue shows the release it first appeared in and the suspect commit that likely caused it C

Release tracking

Export all of my data in open formats and leave G

List, query, and update issues and fetch raw events through a documented REST API with scoped tokens C

Api access

Prevent my data from being used to train AI models G

Self-host the core product G

Scrub PII from error events — server-side scrubbing rules plus SDK-level filtering — before sensitive payloads ever persist C

Pii scrubbing

Capture native mobile crashes — iOS, Android, NDK — with the device, OS, and app-version context needed to reproduce them C

Mobile crashes

Control data retention and deletion G

Every issue quantifies real impact — how many users and sessions are affected — so I prioritize by blast radius, not raw event counts C

User impact

Search and filter events across projects by tag, release, environment, and custom properties with a real query syntax C

Event search

See error trends across projects and teams on dashboards — top regressions, new issues per release, volume over time C

Dashboards

Fight alert fatigue with spike protection, per-key rate limits, and mute/ignore rules so one bad deploy doesn't page the whole team all night C

Noise control

Perform bulk operations across many items at once G

The platform connects to my repos so an issue shows suspect commits and code owners, and I can jump from a stack frame to the exact line on my default branch C

Scm context

Upload dSYM, ProGuard, and native debug symbols so mobile and native crashes symbolicate to real function names and lines C

Symbolication

Attach breadcrumbs, custom tags, and user context to every event so a stack trace arrives with the state that produced it C

Event context

Do everything through the API that I can do in the UI G

Opt out of telemetry and usage tracking G

I get alerted when an error that was fixed comes back in a newer release, distinct from ordinary new-issue noise C

Regressions

Link an error to Jira, GitHub Issues, or Linear with two-way status sync, so fixing the ticket resolves the error and a regression reopens it C

Issue trackers

The event-ingestion protocol is documented and open enough that custom clients and compatible SDKs can send events without an official SDK C

Ingest protocol

The platform's AI can draft a code fix for an error and open a pull request against my repo for human review C

Autofix

Watch release health — crash-free sessions and users, adoption per release — and compare a canary release against the last stable one C

Release health

Cap event quotas per project and set spend limits so an error storm burns a predictable budget, never a surprise invoice G

Quotas

Choose where my data is stored (region/residency) G

Read the product's source under an open license G

Schedule recurring jobs or workflows G

Merge issues that are really the same bug and split ones the fingerprinter wrongly collapsed C

Triage

Collect user feedback — a crash-report dialog or feedback widget — attached to the exact error event the user hit C

User feedback

Version, review, and roll back my automations G