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PostHog/posthog

Product analytics and observability platform

+125 stars in 7 days

Developers can unify product data and AI agents to autonomously identify and fix issues

By consolidating user errors, click patterns, and the reasoning behind AI agent decisions in one place, developers can fix products based on facts rather than guesses. PostHog is a platform that collects and stores data essential for product operations, including analytics, session replay, error tracking, and feature flags. Based on this data, it provides a 'self-driving' product development environment where AI diagnoses problems and proposes fixes.

Role as a Product Context Layer

PostHog functions not just as an analytics tool but as a layer that stores all product context. Analytics events, error logs, session recordings, and business data are stored together, allowing both humans and AI agents to query this data and act on it. It focuses on capturing the context needed for AI agents to diagnose problems, discover opportunities, and deploy fixes. You can control all of this via Slack, web, desktop, or MCP.

From Data Collection to AI Diagnosis

Understand user behavior through product analytics, web analytics, and group analytics, and verify actual usage scenarios with session replay and heatmaps.

  • AI Observability Records traces, generations, latency, and costs for LLM-based apps and catches regressions through LLM-as-a-judge evaluations.
  • MCP Analytics (Beta) Shows how agents actually use MCP servers.
  • PostHog AI Allows you to ask anything about product data, create insights, write SQL, and find replays.
  • Self-Driving Converts signals from product data (errors, rage clicks, failed queries, etc.) into investigated reports and pull requests that can be reviewed and merged in the inbox.

Installation and Getting Started

Using PostHog Cloud is the fastest and most stable method. The open-source hobby deployment can be deployed with a single line using Docker on Linux, with a recommended memory of 4GB. No customer support or warranty is provided for the open-source deployment. You can automate installation for your app using the setup wizard (npx -y @posthog/wizard@latest).

License and Cost Structure

This repository is provided under the MIT expat license, while the ee directory applies a separate license. If you want 100% FOSS, you can use the posthog-foss repository. All products have a monthly free tier and do not require a credit card. According to the README, 97% of users pay $0. Pricing policies for paid plans are publicly disclosed.

Written by AI from this repository's README on October 7, 2026. For the facts, the original on GitHub is the reference.

By the numbers

GitHub trending history
12 days between Jun 19, when Trend began recording, and Nov 8, 2026 · first on Jul 16 · best daily rank #2 (Jul 17)

Read from GitHub, as of November 8, 2026.

Language
Python
Topics
ab-testing · ai-analytics · analytics · cdp · data-warehouse · experiments · feature-flags · javascript
Stars
40,701
Stars gained in 7 days
+125
Contributors
597
Forks
3,606
Commits
73,823
Last commit
November 8, 2026
Open issues
3,346
Open pull requests
1,202

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trend.ondo.sh · GitHub daily trending #2 · July 17, 2026

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