Open-source FullSession alternatives

PostHog is an open-source platform that bundles the tools product teams use to understand and improve what they ship. It captures event data, user sessions and errors in one place, and its newer self-driving mode aims to turn signals such as errors, rage clicks and failed queries into researched reports and pull requests that engineers review and merge.

The product suite includes product analytics with autocapture or manual instrumentation and SQL analysis, web analytics with a dashboard resembling Google Analytics, session replays for web and mobile, feature flags, experiments with statistical measurement, surveys, error tracking, logs, a data warehouse and a customer data platform. The back end is Python and the front end is React and TypeScript.

PostHog's repository license is listed as 'Other' on GitHub, so check which parts are open source and which are enterprise-only. It can be self-hosted, and the company also runs a managed cloud service. It suits product and engineering teams that prefer a single tool over separate analytics, flagging and replay vendors.

OpenReplay is an open-source experience platform that you host on your own infrastructure. It combines session replay, DevTools-level debugging context, product analytics and live cobrowsing, so teams can see how real users navigate, where they struggle and what errors they hit without sending sessions to a third party.

Because everything stays in your own cloud, it is pitched at teams that cannot hand customer data to an external vendor. Data can be masked, obscured or skipped at capture time before it reaches your servers, and a private mode redacts everything by default. A lightweight tracker sends data asynchronously to limit the impact on page performance, and SDKs cover frameworks such as React, Angular, Vue, Svelte, Next.js and React Native.

The README states it can be self-hosted for free on AWS, GCP, Azure and other platforms. The repository lists its licence as Other and its topics also reference feature flags, web vitals and real-user monitoring, so it spans debugging, product and support use cases.

HyperDX is an open-source observability platform for working out why production is broken. It brings logs, metrics, traces, errors and session replays into one place, and works as a search and visualization layer on top of ClickHouse and OpenTelemetry, which the authors compare to Kibana for ClickHouse. It is a core component of ClickStack.

It is schema agnostic and can sit on top of an existing ClickHouse schema. Searching uses full-text and property syntax such as level:err, with SQL as an option, events can be compared with event deltas, high-cardinality events can be charted on dashboards, and alerts can be set up in a few clicks. Live tail shows the newest logs and traces, JSON strings can be queried natively, OpenTelemetry works out of the box, and APM views cover HTTP requests down to database queries.

One deployment option is ClickStack, a bundle of ClickHouse, HyperDX, an OpenTelemetry Collector and MongoDB, and HyperDX can instead be pointed at your own ClickHouse instance. It can also be used with ClickHouse Cloud. The project is MIT licensed and written in TypeScript.

Highlight, at highlight.io, is an open-source monitoring platform for developers. Its aim is to give teams one cohesive tool covering the front end and back end, instead of separate products for errors, logs and user sessions.

The core features are session replay, error monitoring and logging, with tracing and metrics also in scope. Console logs from the front end can be analyzed, and errors are embedded alongside the session in which they happened, so you can see what the user was doing beforehand. Session comments let teams discuss user frustration or bugs, there are integrations with other tools, and client SDKs install with a few lines of code.

You can sign up for the free-to-start hosted version at app.highlight.io, or deploy a hobby instance on Linux with Docker, sized at roughly 8 GB of memory, four CPUs and 64 GB of storage. The code is written in TypeScript and Go, and the repository lists the license as Other, so check the licence file for terms.

Clarity is a behavioral analytics library written in TypeScript with two main goals: privacy and performance. It watches DOM mutations and user interactions in the browser to generate aggregated insights about how visitors view and use a website, across modern devices and browsers. It is released under the MIT license.

The data it captures supports session replay and heatmaps, which help product and design teams see where features succeed or fail. The README says the same code powers Microsoft's hosted behavioral analytics service at clarity.microsoft.com, where a live demo is also available, so the repository represents the client-side collection library rather than a full self-contained analytics backend.

The community is invited to help build a privacy-first, high-performance behavioral analytics library. It suits web teams who want to understand user behavior, improve conversion and retention, and review the code that runs on their pages before adding it to a site.

Talivia is an analytics platform that ties website traffic to revenue. This repository holds the focused open-source edition of the product at talivia.com: a self-hosted version that combines core web analytics, session replay, website collaborators, shared analytics, import and export, and customer revenue from Stripe, LemonSqueezy, Polar, Dodo, Yolfi, or a manual payment API. It is released under the MIT license and positions itself as an alternative to Datafast.

The open-source edition is a subset of the complete product. Talivia Cloud offers managed hosting and extra integrations, including Google Search Console, Bing Webmaster Tools, GitHub activity, and social mentions from X, Reddit, TikTok, and others. Documentation covers product and integration guides.

It is written in TypeScript and was created in 2026, so it is a young project that reached version 1.0.0. Talivia suits founders and small SaaS teams who want to see which traffic sources and pages lead to paying customers, while keeping analytics data on their own infrastructure.