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docs: add LangGraph notebook example
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‎examples/tracing/langgraph/langgraph_tracing.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2722b419",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openlayer-ai/openlayer-python/blob/main/examples/tracing/langgraph/langgraph_tracing.ipynb)\n",
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"\n",
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"\n",
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"# <a id=\"top\">LangGraph tracing</a>\n",
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"\n",
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"This notebook illustrates how use Openlayer's callback handler to monitor LangGraph workflows."
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]
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},
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{
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"cell_type": "markdown",
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"id": "75c2a473",
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"metadata": {},
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"source": [
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"## 1. Set the environment variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f3f4fa13",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# OpenAI env variables\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"YOUR_OPENAI_API_KEY_HERE\"\n",
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"\n",
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"# Openlayer env variables\n",
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"os.environ[\"OPENLAYER_API_KEY\"] = \"YOUR_OPENLAYER_API_KEY_HERE\"\n",
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"os.environ[\"OPENLAYER_INFERENCE_PIPELINE_ID\"] = \"YOUR_OPENLAYER_INFERENCE_PIPELINE_ID_HERE\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "9758533f",
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"metadata": {},
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"source": [
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"## 2. Instantiate the `OpenlayerHandler`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e60584fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openlayer.lib.integrations import langchain_callback\n",
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"\n",
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"openlayer_handler = langchain_callback.OpenlayerHandler()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72a6b954",
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"metadata": {},
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"source": [
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"## 3. Use LangGraph \n",
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"\n",
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"### 3.1 Simple chatbot example"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76a350b4",
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"metadata": {},
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"source": [
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"We can start with a simple chatbot example similar to the one in the [LangGraph quickstart](https://langchain-ai.github.io/langgraph/tutorials/get-started/1-build-basic-chatbot/).\n",
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"\n",
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"The idea is passing the `openlayer_handler` as a callback to the LangGraph graph. After running the graph,\n",
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"you'll be able to see the traces in the Openlayer platform."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc351618",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Annotated\n",
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"from typing_extensions import TypedDict\n",
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"\n",
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"from langgraph.graph import StateGraph\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langchain_core.messages import HumanMessage\n",
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"from langgraph.graph.message import add_messages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4595c63b",
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"metadata": {},
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"outputs": [],
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"source": [
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"class State(TypedDict):\n",
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" # Messages have the type \"list\". The `add_messages` function in the annotation defines how this state key should be updated\n",
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" # (in this case, it appends messages to the list, rather than overwriting them)\n",
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" messages: Annotated[list, add_messages]\n",
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"\n",
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"graph_builder = StateGraph(State)\n",
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"\n",
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"llm = ChatOpenAI(model = \"gpt-4o\", temperature = 0.2)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00a6fa80",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The chatbot node function takes the current State as input and returns an updated messages list. This is the basic pattern for all LangGraph node functions.\n",
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"def chatbot(state: State):\n",
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" return {\"messages\": [llm.invoke(state[\"messages\"])]}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a36e5160",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Add a \"chatbot\" node. Nodes represent units of work. They are typically regular python functions.\n",
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"graph_builder.add_node(\"chatbot\", chatbot)\n",
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"\n",
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"# Add an entry point. This tells our graph where to start its work each time we run it.\n",
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"graph_builder.set_entry_point(\"chatbot\")\n",
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"\n",
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"# Set a finish point. This instructs the graph \"any time this node is run, you can exit.\"\n",
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"graph_builder.set_finish_point(\"chatbot\")\n",
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"\n",
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"# To be able to run our graph, call \"compile()\" on the graph builder. This creates a \"CompiledGraph\" we can use invoke on our state.\n",
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"graph = graph_builder.compile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "deef517e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Pass the openlayer_handler as a callback to the LangGraph graph. After running the graph,\n",
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"# you'll be able to see the traces in the Openlayer platform.\n",
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"for s in graph.stream({\"messages\": [HumanMessage(content = \"What is the meaning of life?\")]},\n",
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" config={\"callbacks\": [openlayer_handler]}):\n",
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" print(s)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c049c8fa",
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"metadata": {},
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"source": [
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"### 3.2 Multi-agent example\n",
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"\n",
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"Now, we're going to use a more complex example. The principle, however, remains the same: passing the `openlayer_handler` as a callback to the LangGraph graph."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "213fc402",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Annotated\n",
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"from datetime import datetime\n",
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"\n",
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"from langchain.tools import Tool\n",
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"from langchain_community.tools import WikipediaQueryRun\n",
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"from langchain_community.utilities import WikipediaAPIWrapper\n",
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"\n",
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"# Define a tools that searches Wikipedia\n",
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"wikipedia_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())\n",
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"\n",
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"# Define a new tool that returns the current datetime\n",
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"datetime_tool = Tool(\n",
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" name=\"Datetime\",\n",
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" func = lambda x: datetime.now().isoformat(),\n",
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" description=\"Returns the current datetime\",\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c76c8935",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langchain_core.messages import BaseMessage, HumanMessage\n",
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"\n",
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"\n",
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"def create_agent(llm: ChatOpenAI, system_prompt: str, tools: list):\n",
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" # Each worker node will be given a name and some tools.\n",
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" prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" system_prompt,\n",
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" ),\n",
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" MessagesPlaceholder(variable_name=\"messages\"),\n",
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" MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
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" ]\n",
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" )\n",
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" agent = create_openai_tools_agent(llm, tools, prompt)\n",
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" executor = AgentExecutor(agent=agent, tools=tools)\n",
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" return executor\n",
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"\n",
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"def agent_node(state, agent, name):\n",
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" result = agent.invoke(state)\n",
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" return {\"messages\": [HumanMessage(content=result[\"output\"], name=name)]}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f626e7f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
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"\n",
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"members = [\"Researcher\", \"CurrentTime\"]\n",
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"system_prompt = (\n",
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" \"You are a supervisor tasked with managing a conversation between the\"\n",
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" \" following workers: {members}. Given the following user request,\"\n",
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" \" respond with the worker to act next. Each worker will perform a\"\n",
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" \" task and respond with their results and status. When finished,\"\n",
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" \" respond with FINISH.\"\n",
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")\n",
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"# Our team supervisor is an LLM node. It just picks the next agent to process and decides when the work is completed\n",
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"options = [\"FINISH\"] + members\n",
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"\n",
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"# Using openai function calling can make output parsing easier for us\n",
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"function_def = {\n",
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" \"name\": \"route\",\n",
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" \"description\": \"Select the next role.\",\n",
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" \"parameters\": {\n",
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" \"title\": \"routeSchema\",\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"next\": {\n",
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" \"title\": \"Next\",\n",
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" \"anyOf\": [\n",
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" {\"enum\": options},\n",
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" ],\n",
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" }\n",
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" },\n",
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" \"required\": [\"next\"],\n",
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" },\n",
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"}\n",
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"\n",
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"# Create the prompt using ChatPromptTemplate\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system_prompt),\n",
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" MessagesPlaceholder(variable_name=\"messages\"),\n",
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" (\n",
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" \"system\",\n",
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" \"Given the conversation above, who should act next?\"\n",
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" \" Or should we FINISH? Select one of: {options}\",\n",
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" ),\n",
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" ]\n",
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").partial(options=str(options), members=\", \".join(members))\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-4o\")\n",
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"\n",
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"# Construction of the chain for the supervisor agent\n",
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"supervisor_chain = (\n",
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" prompt\n",
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" | llm.bind_functions(functions=[function_def], function_call=\"route\")\n",
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" | JsonOutputFunctionsParser()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec307b80",
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"metadata": {},
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"outputs": [],
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"source": [
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"import operator\n",
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"import functools\n",
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"from typing import Sequence, TypedDict\n",
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"\n",
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"from langgraph.graph import END, START, StateGraph\n",
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"\n",
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"\n",
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"# The agent state is the input to each node in the graph\n",
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"class AgentState(TypedDict):\n",
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" # The annotation tells the graph that new messages will always be added to the current states\n",
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" messages: Annotated[Sequence[BaseMessage], operator.add]\n",
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" # The 'next' field indicates where to route to next\n",
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" next: str\n",
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"\n",
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"# Add the research agent using the create_agent helper function\n",
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"research_agent = create_agent(llm, \"You are a web researcher.\", [wikipedia_tool])\n",
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"research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n",
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"\n",
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"# Add the time agent using the create_agent helper function\n",
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"currenttime_agent = create_agent(llm, \"You can tell the current time at\", [datetime_tool])\n",
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"currenttime_node = functools.partial(agent_node, agent=currenttime_agent, name = \"CurrentTime\")\n",
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"\n",
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"workflow = StateGraph(AgentState)\n",
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"\n",
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"# Add a \"chatbot\" node. Nodes represent units of work. They are typically regular python functions.\n",
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"workflow.add_node(\"Researcher\", research_node)\n",
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"workflow.add_node(\"CurrentTime\", currenttime_node)\n",
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"workflow.add_node(\"supervisor\", supervisor_chain)\n",
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"\n",
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"# We want our workers to ALWAYS \"report back\" to the supervisor when done\n",
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"for member in members:\n",
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" workflow.add_edge(member, \"supervisor\")\n",
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"\n",
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"# Conditional edges usually contain \"if\" statements to route to different nodes depending on the current graph state.\n",
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"# These functions receive the current graph state and return a string or list of strings indicating which node(s) to call next.\n",
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"conditional_map = {k: k for k in members}\n",
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"conditional_map[\"FINISH\"] = END\n",
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"workflow.add_conditional_edges(\"supervisor\", lambda x: x[\"next\"], conditional_map)\n",
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"\n",
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"# Add an entry point. This tells our graph where to start its work each time we run it.\n",
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"workflow.add_edge(START, \"supervisor\")\n",
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"\n",
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"# To be able to run our graph, call \"compile()\" on the graph builder. This creates a \"CompiledGraph\" we can use invoke on our state.\n",
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"graph_2 = workflow.compile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "08e35ae9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Pass the openlayer_handler as a callback to the LangGraph graph. After running the graph,\n",
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"# you'll be able to see the traces in the Openlayer platform.\n",
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"for s in graph_2.stream({\"messages\": [HumanMessage(content = \"How does photosynthesis work?\")]},\n",
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" config={\"callbacks\": [openlayer_handler]}):\n",
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" print(s)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "16acecc2",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "callback-improvements",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.19"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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