AI platform

What is the AI platform?

The PostHog AI platform is our infrastructure for building and delivering AI-powered features across all PostHog products. Instead of each team building isolated AI capabilities, we provide shared architecture, reusable components, and a consistent framework that lets everyone contribute toward our AI capabilities while maintaining quality and consistency.

Think of it like HogQL: rather than having every team write their own query engines, we built one shared system that everyone can use and extend. The AI platform follows the same philosophy — avoid reinventing AI infrastructure and prevent "death by random AI widgets."

Why we built it

Almost every team at PostHog either is building or needs to build AI features. Without a platform approach, we'd face:

• Fragmented user experience: Different AI interactions across products with inconsistent quality and UX patterns
• Duplicated effort: Multiple teams solving the same problems (authentication, error handling, rate limiting, tool calling)
• Maintenance burden: Each team maintaining their own AI infrastructure, models, and prompt engineering
• Limited capabilities: Teams constrained to simple AI features because building advanced functionality (like multi-step reasoning or agentic workflows) from scratch is too expensive

The AI platform solves these problems by providing:

1. Shared architecture: A single-loop agent system that any product can extend with domain-specific tools and expertise
2. Reusable components: Common tools (search, data access, taxonomy reading) that work across all AI features
3. Consistent UX: Standard patterns for AI interactions, loading states, error handling, and result presentation
4. Platform-level improvements: When we improve the core agent (better reasoning, faster responses, cheaper inference), all products benefit automatically

Architecture at a glance

The AI platform has three main layers:

1. User-facing products

These are the AI features users interact with directly:

• PostHog AI: In-app conversational agent for interacting with PostHog
• Deep research: Automated investigative research for complex, open-ended problems
• Session summaries: Batch analysis of session recordings to find patterns
• Array: Desktop app that turns PostHog signals into shipped code
• Wizard: CLI tool for automated PostHog installation and setup
• MCP Server: Protocol integration for third-party AI tools like Claude Code

2. Core infrastructure

The shared components that power all products:

• Single-loop agent: An agent architecture that maintains full context and can dynamically load domain expertise
• Agent modes: Pluggable modules that give the agent specialized knowledge and tools (SQL, Analytics, CDP, etc.)
• Core tools: Universal features like search, data reading, and task tracking
• MCP integration: Exposes agent features to external tools via Model Context Protocol

3. Integration points

How everything connects together:

• Products share the same agent features through the MCP server
• Task generation systems (from Deep Research, Session Summaries, PostHog signals) feed Array
• The Wizard and Array consume MCP tools to interact with PostHog

For a detailed technical overview, see AI platform architecture.

Products overview

PostHog AI [Beta]

Your primary interface for working with PostHog. Instead of clicking through forms and menus, describe what you want in natural language. PostHog AI can create dashboards, write SQL queries, set up surveys, and answer questions about your data — all through conversation.

Best for: Quick answers, creating resources, learning PostHog, iterative exploration Status: Beta | Pricing: Paid with free tier

Learn more →

Deep research [Under development]

When you need to investigate complex, open-ended problems, Deep research digs deep. It systematically explores your data — session recordings, analytics, error logs — and produces comprehensive research reports that would take a human analyst hours to create.

Best for: Understanding why metrics changed, investigating user behavior patterns, root cause analysis Status: Under development | Pricing: Paid with free tier

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Session summaries [Alpha]

Analyze hundreds of session recordings in minutes instead of hours. Session summaries finds patterns, clusters similar issues, and shows you what's actually happening across your user sessions — not just what you caught in the first few recordings you watched.

Best for: Understanding UX issues, debugging problems affecting multiple users, finding edge cases Status: Alpha | Pricing: Paid with free tier

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Array [Under development]

A desktop agent that watches PostHog for signals (errors, frustration patterns, user feedback) and automatically turns them into shipped code. Array handles the entire workflow - from signal detection to task creation to code generation to PR creation - with human oversight at key decision points.

Best for: Product engineers who want to automate repetitive fixes and focus on building features Status: Under development | Pricing: TBD

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Wizard [General availability]

Get PostHog set up in minutes instead of hours. The Wizard detects your tech stack, generates integration code, verifies the installation, and gets you collecting data with minimal manual work.

Best for: New PostHog users, setting up new projects, quick integration Status: General availability | Pricing: Free

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MCP server [General availability]

Bring PostHog into your development environment. The MCP server makes PostHog AI's features available to Claude Code, VS Code, and other MCP-compatible tools, so you never have to leave your editor to check analytics or create insights.

Best for: Engineers who prefer editor-based workflows, combining PostHog with other data sources Status: General availability | Pricing: Free

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Key concepts

For a list of key concepts definitions, see the Glossary.

Getting started

For users

• Want to try PostHog AI? Open the chat interface in PostHog and start asking questions. See user documentation.
• Need deep investigation? Toggle to Deep research feature in PostHog AI.
• Prefer working in your editor? Set up the MCP server in Claude Code or VS Code.

For engineers building AI features

• Adding AI to your product? Start with Team structure and collaboration to understand the process.
• Want to add a new agent mode? See Architecture for technical details.
• Need implementation guidance? Check Implementation guide for best practices and patterns.

For product managers

• Planning an AI feature? Read Pricing and product positioning to understand our approach.
• Want to understand capabilities? See Products for detailed breakdowns of each product.

What's next?

The AI platform is actively evolving. Major initiatives include:

• Third-party context integration: Connect PostHog AI to Slack, Zendesk, and other tools for richer context
• Array expansion: Moving from alpha dogfooding to broader availability
• Deep research refinement: Improving research strategies and denoising algorithms
• Mode expansion: Adding more specialized agent modes as product teams identify needs

For details on upcoming work, see Future directions.

Documentation navigation

• Products: Detailed information about each user-facing product
• Architecture: Technical deep dive on agent systems and infrastructure
• Team Structure: How teams collaborate on AI features
• Implementation Guide: Best practices, pricing, and implementation patterns

Contact

For questions about working with the AI platform:

• Slack: #team-posthog-ai
• Team page:
  
  PostHog AI Team
  
  PostHog AI Team
• Objectives: Current goals and initiatives