{"id":"src_QUSn3AJMqTRqR6awJ3qii","slug":"changelog","name":"Changelog","type":"feed","url":"https://axiom.co/changelog","orgId":"org_LkyjTUw3Ctj1N2XLvTTbP","productId":null,"productSlug":null,"org":{"id":"org_LkyjTUw3Ctj1N2XLvTTbP","slug":"axiom","name":"Axiom"},"isPrimary":true,"isHidden":false,"discovery":"curated","metadata":"{\"feedUrl\":\"https://axiom.co/changelog/feed.xml\",\"feedType\":\"atom\",\"feedDiscoveredAt\":\"2026-04-09T16:23:15.535Z\",\"noFeedFound\":false,\"feedEtag\":\"W/\\\"z0w0l85k8k11c7\\\"\",\"feedContentDepth\":\"summary-only\",\"enrichment\":{\"consecutiveFailures\":0},\"sourceActor\":{\"nextAlarmAt\":\"2026-07-14T04:21:03.249Z\",\"lastAlarmAt\":\"2026-07-14T00:21:04.556Z\",\"managed\":true}}","notice":null,"kind":"platform","stars":null,"starsFetchedAt":null,"releaseCount":112,"releasesLast30Days":2,"avgReleasesPerWeek":0.2,"latestVersion":null,"latestDate":"2026-07-02T00:00:00.000Z","changelogUrl":null,"hasChangelogFile":false,"lastFetchedAt":"2026-07-13T12:12:36.416Z","lastPolledAt":"2026-07-14T00:25:29.131Z","changeDetectedAt":null,"trackingSince":"2021-10-20T00:00:00.000Z","releases":[{"id":"rel_Copnl8vaYEjrmKiSy64Iq","version":null,"type":"feature","title":"APL and MPL in the Grafana data source","summary":"The Axiom Grafana data source now runs both APL and MPL queries with a unified v2 query model, CodeMirror language support, and automatic migration of saved queries. APL gains four weighted aggregations, the MCP server adds monitor and notifier management, and metrics datasets get a prebuilt Kubernetes dashboard on arrival.","titleGenerated":"Axiom Grafana data source now speaks APL and MPL; new weighted aggregations and MCP monitor management","titleShort":"Grafana data source learns APL and MPL; weighted aggregations ship","breaking":"none","importance":4,"content":"This week the Axiom data source for Grafana learned to speak APL and MPL, APL picked up four new weighted aggregations, the Axiom MCP server gained monitor and notifier management, and metrics datasets get a Kubernetes dashboard the moment the data arrives.\n\n## APL and MPL in the Grafana data source\n\nThe Axiom data source for Grafana now runs both APL and MPL queries. Previously the plugin spoke a legacy APL-only query shape; the new version shares a single v2 query model across both languages and migrates your saved queries for you.\n\n- **Query in APL or MPL:** Write APL for logs, traces, and events, or MPL for metrics, with CodeMirror language support in the panel editor.\n- **Grafana-native results:** Query results come back as Grafana data frames, with dedicated builders for time series, log lines, traces, tables, and logs-volume panels, so results render the way Grafana expects.\n- **Template variables:** APL and MPL variable queries now drive dashboard variables, including dataset, metric, and tag-value selectors.\n- **Fits your Grafana setup:** The plugin supports newer Grafana releases and React 19, and honors your configured Grafana HTTP client options rather than its own transport.\n\nUpdate to the latest version of the Axiom data source to pick these up. If you query an edge deployment, there is now a documented migration path for the edge URL and credentials.\n\n## Four new weighted aggregations in APL\n\nAPL gains weighted variants of four aggregations: `histogramw`, `variancew`, `stdevw`, and `topkw`. They join the existing `percentilew` and `avgw`, so the full set of weighted aggregations is now available end to end.\n\nA weighted aggregation takes a value and an integer weight that stands in for a repetition count. This is useful when each row already represents many observations, such as pre-aggregated counters, and you want the statistics computed as if the underlying events were expanded. Rows with a weight of 0 or less are skipped, and a missing weight defaults to 1.\n\n## More of Axiom for your AI agents\n\nThe Axiom MCP server now covers alerting, not just querying.\n\n- **Monitor and notifier management:** New tools let an agent create, update, and delete monitors, and list, update, and delete notifiers, so an agent can set up an alert and its delivery in one flow.\n- **Richer metrics discovery:** The `listMetrics` and `searchMetrics` tools now return each metric's type, temporality, and unit, which helps agents compose correct queries the first time.\n\nAxiom is also now available as an official Grok plugin. The plugin bundles the hosted MCP endpoint and Axiom's skills together, so `grok plugin install axiom` wires up both at once.\n\n## Prebuilt dashboards for Kubernetes and your integrations\n\nIntegration dashboards are now generated from your dataset schema instead of a fixed server response, which means they show up automatically for the data you actually send. There are templates for Vercel, AWS Lambda, Cloudflare Logpush, Cloudflare Workers, Convex, OpenTelemetry traces, Tailscale, Next.js, and Netlify.\n\nDynamic dashboards now understand metric-backed templates too. Send Kubernetes kubelet stats to a metrics dataset and Axiom builds a Kubernetes Stats overview dashboard for it, no manual assembly required.\n\n## More of our favorite changes\n\n- **Search inside trace logs:** In the trace view, the search box now filters the correlated logs, with a toggle to match the log message only or all attributes.\n- **Activity panel on events datasets:** The dataset detail page shows total event count and a chart of ingest activity over a time range you choose, in the right sidebar.\n- **Metrics in smart filters:** A dashboard smart filter's options can now be driven by an MPL metrics query, not just APL.\n- **Axiom on AWS Marketplace:** You can now purchase Axiom through AWS Marketplace. Link your AWS account from the usage and billing page to buy and manage compute credits through your existing AWS agreement.\n\n### Bug fixes\n\n- Fixed failed virtual columns causing errors when a query was retried.\n- Fixed sorting on a column produced by `getschema`.\n- Fixed Correlations not being found for very recent events.\n- Fixed dashboards created through MCP opening with unsaved changes.\n- Fixed empty APL URL variables being treated as declared instead of undeclared.","publishedAt":"2026-07-02T00:00:00.000Z","fetchedAt":"2026-07-13T12:12:27.903Z","url":"https://axiom.co/changelog/apl-and-mpl-in-the-grafana-data-source","media":[],"coverageCount":0},{"id":"rel_H5eCX0ujkRgalp2_VKjrv","version":null,"type":"feature","title":"Correlations","summary":"Correlations are now available in Axiom.\n\nCorrelations connect the logs, traces, and metrics that describe the same system. Instead of treating each s...","titleGenerated":null,"titleShort":null,"breaking":"unknown","importance":null,"content":"Correlations are now available in Axiom.\n\nCorrelations connect the logs, traces, and metrics that describe the same system. Instead of treating each signal as a separate silo, Axiom lets you move from the first clue to the surrounding system state without manual query stitching.\n\n## Move from logs to traces\n\nIn many incidents, the first clue is a log event: a timeout, an exception, a failed request, or some local detail that proves something went wrong.\n\nWith Correlations, a log event that includes trace context can open the matching trace in Axiom. No copying trace IDs. No switching datasets. No rebuilding the time range by hand.\n\n## Inspect related metrics\n\nA trace can show where one request slowed down. It cannot always show whether the service was already unhealthy.\n\nWith Correlations, Axiom can surface related workload metrics while you are inspecting the span. That context helps separate a single bad request from a broader service problem.\n\nFor more information, see [Correlations](/docs/query-data/correlations).","publishedAt":"2026-06-19T00:00:00.000Z","fetchedAt":"2026-06-22T16:06:20.311Z","url":"https://axiom.co/changelog/correlations","media":[],"coverageCount":0},{"id":"rel_IHLpcg4S7fH6DCXQXBRmZ","version":null,"type":"feature","title":"Metrics are now generally available","summary":"Hyper-cardinality, unified with logs and traces, and fully queryable by AI agents through MCP and a dedicated metrics skill.","titleGenerated":null,"titleShort":null,"breaking":"unknown","importance":null,"content":"Hyper-cardinality, unified with logs and traces, and fully queryable by AI agents through MCP and a dedicated metrics 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