posthog-agent-toolkit

⚠️ DEPRECATION NOTICE ⚠️

This package has been deprecated and will no longer receive updates.

We recommend using the PostHog MCP server directly with your preferred MCP client instead. See the PostHog MCP documentation for integration options.

Tools to give agents access to your PostHog data, manage feature flags, create insights, and more.

This is a Python wrapper around the PostHog MCP (Model Context Protocol) server, providing easy integration with AI frameworks like LangChain.

Installation

pip install posthog-agent-toolkit

Quick Start

The toolkit provides integrations for popular AI frameworks:

Using with LangChain

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from posthog_agent_toolkit.integrations.langchain.toolkit import PostHogAgentToolkit

# Initialize the PostHog toolkit
toolkit = PostHogAgentToolkit(
    personal_api_key="your_posthog_personal_api_key",
    url="https://mcp.posthog.com/mcp"  # or your own, if you are self hosting the MCP server
)

# Get the tools
tools = await toolkit.get_tools()

# Initialize the LLM
llm = ChatOpenAI(model="gpt-5-mini")

# Create a prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a data analyst with access to PostHog analytics"),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

# Create and run the agent
agent = create_tool_calling_agent(llm=llm, tools=tools, prompt=prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = await executor.ainvoke({
    "input": "Analyze our product usage by getting the top 5 most interesting insights and summarising the data from them."
})

→ See full LangChain example

Available Tools

For a list of all available tools, please see the docs.