feat: update docs for python package

Author: joshsny

examples/langchain/posthog_agent_example.py

@@ -33,7 +33,7 @@ async def analyze_product_usage():
 
     # Initialize the LLM
     llm = ChatOpenAI(
-        model="gpt-4o-mini",
+        model="gpt-5-mini",
         temperature=0,
         api_key=os.getenv("OPENAI_API_KEY")
     )
@@ -59,7 +59,7 @@ async def analyze_product_usage():
         agent=agent,
         tools=tools,
         verbose=False,
-        max_iterations=5,
+        max_iterations=30,
     )
 
     # Invoke the agent with an analysis request

examples/langchain/pyproject.toml

@@ -5,20 +5,17 @@ description = "PostHog LangChain integration example"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-    "posthog-agent-toolkit",
     "langchain>=0.3.0",
     "langchain-openai>=0.2.0",
     "langchain-core>=0.3.0",
     "python-dotenv>=1.0.0",
+    "posthog-agent-toolkit>=0.1.0",
 ]
 
-[tool.uv.sources]
-posthog-agent-toolkit = { path = "../../python", editable = true }
-
 [tool.ruff]
 line-length = 120
 target-version = "py311"
 
 [tool.black]
 line-length = 120
-target-version = ['py311']
+target-version = ['py311']

examples/langchain/uv.lock

@@ -1,73 +1,57 @@
-# PostHog MCP Server - Python Implementation
+# posthog-agent-toolkit
 
-Python wrapper of the MCP server tools to make using PostHog easier in agents.
+Tools to give agents access to your PostHog data, manage feature flags, create insights, and more.
 
-## Setup
+This is a Python wrapper around the PostHog MCP (Model Context Protocol) server, providing easy integration with AI frameworks like LangChain.
 
-### 1. Install Dependencies with uv
-
-For production:
-
-```bash
-uv sync
-```
-
-For development (includes schema generation tools):
+## Installation
 
 ```bash
-uv sync --dev
+pip install posthog-agent-toolkit
 ```
 
-### 2. Alternative: Create Virtual Environment Manually
+## Quick Start
 
-If you prefer to use pip:
+The toolkit provides integrations for popular AI frameworks:
 
-```bash
-python3 -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-pip install -e .
-```
+### Using with LangChain
 
-## Development
+```python
+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
 
-### Schema Generation
+# 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
+)
 
-The Python implementation uses Pydantic models generated from the TypeScript Zod schemas:
+# Get the tools
+tools = await toolkit.get_tools()
 
-```bash
-# From the root directory
-pnpm run schema:build          # Generate both JSON schema and Python models
-```
+# Initialize the LLM
+llm = ChatOpenAI(model="gpt-5-mini")
 
-This generates type-safe Pydantic models in `schema/tool_inputs.py` based on the shared JSON schema.
+# Create a prompt
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a data analyst with access to PostHog analytics"),
+    ("human", "{input}"),
+    MessagesPlaceholder("agent_scratchpad"),
+])
 
-### Project Structure
+# 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."
+})
 ```
-python/
-├── pyproject.toml           # Project configuration and dependencies
-├── uv.lock                 # Locked dependency versions (generated by uv)
-├── schema/
-│   └── tool_inputs.py      # Generated Pydantic models
-└── scripts/
-    └── generate-pydantic-models.sh  # Schema generation script
-```
-
-### Running Commands
-
-All Python commands should be run through uv:
 
-```bash
-# Run tests
-uv run pytest
-
-# Format code
-uv run ruff format .
-
-# Lint code
-uv run ruff check --fix .
-```
+**[→ See full LangChain example](https://github.com/posthog/mcp/tree/main/examples/langchain)**
 
-### Dependencies
+## Available Tools
 
-Dependencies are managed in `pyproject.toml`.
+For a list of all available tools, please see the [docs](https://posthog.com/docs/model-context-protocol).