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docs/docs/tutorials/get-started/1-build-basic-chatbot.md
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# Build a basic chatbot
In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let’s dive in! 🌟
In this tutorial, you will build a basic chatbot. This chatbot is the basis for the following series of tutorials where you will progressively add more sophisticated capabilities, and be introduced to key LangGraph concepts along the way. Let's dive in! 🌟
## Prerequisites
@@ -13,13 +13,25 @@ tool-calling features, such as [OpenAI](https://platform.openai.com/api-keys),
Install the required packages:
:::python
```bash
pip install -U langgraph langsmith
```
:::
:::js
```bash
npm install @langchain/langgraph @langchain/core
```
:::
!!! tip
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph. For more information on how to get started, see [LangSmith docs](https://docs.smith.langchain.com).
## 2. Create a `StateGraph`
@@ -27,6 +39,8 @@ Now you can create a basic chatbot using LangGraph. This chatbot will respond di
Start by creating a `StateGraph`. A `StateGraph` object defines the structure of our chatbot as a "state machine". We'll add `nodes` to represent the llm and functions our chatbot can call and `edges` to specify how the bot should transition between these functions.
:::python
```python
from typing import Annotated
@@ -46,24 +60,47 @@ class State(TypedDict):
graph_builder = StateGraph(State)
```
:::
:::js
```typescript
import { Annotation } from "@langchain/langgraph";
import { StateGraph, START, END } from "@langchain/langgraph";
import { BaseMessage } from "@langchain/core/messages";
const State = Annotation.Root({
// Messages have the type "BaseMessage[]". The reducer function
// defines how this state key should be updated
// (in this case, it appends messages to the list, rather than overwriting them)
messages: Annotation<BaseMessage[]>({
reducer: (x, y) => x.concat(y),
}),
});
const graphBuilder = new StateGraph(State);
```
:::
Our graph can now handle two key tasks:
1. Each `node` can receive the current `State` as input and output an update to the state.
2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/?h=add+messages#add_messages) function used with the `Annotated` syntax.
2. Updates to `messages` will be appended to the existing list rather than overwriting it, thanks to the prebuilt reducer function.
------
---
!!! tip "Concept"
When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a `TypedDict` with one key: `messages`. The [`add_messages`](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages) reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
When defining a graph, the first step is to define its `State`. The `State` includes the graph's schema and [reducer functions](https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers) that handle state updates. In our example, `State` is a schema with one key: `messages`. The reducer function is used to append new messages to the list instead of overwriting it. Keys without a reducer annotation will overwrite previous values. To learn more about state, reducers, and related concepts, see [LangGraph reference docs](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.message.add_messages).
## 3. Add a node
Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular Python functions.
Next, add a "`chatbot`" node. **Nodes** represent units of work and are typically regular functions.
Let's first select a chat model:
{% include-markdown "../../../snippets/chat_model_tabs.md" %}
{!snippets/chat_model_tabs.md!}
<!---
```python
@@ -73,9 +110,26 @@ llm = init_chat_model("anthropic:claude-3-5-sonnet-latest")
```
-->
:::
:::js
```typescript
import { ChatOpenAI } from "@langchain/openai";
// or import { ChatAnthropic } from "@langchain/anthropic";
const llm = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
});
```
:::
We can now incorporate the chat model into a simple node:
:::python
```python
def chatbot(state: State):
@@ -88,38 +142,99 @@ def chatbot(state: State):
graph_builder.add_node("chatbot", chatbot)
```
:::
:::js
```typescript
const chatbot = async (state: typeof State.State) => {
return { messages: [await llm.invoke(state.messages)] };
};
// The first argument is the unique node name
// The second argument is the function or object that will be called whenever
// the node is used.
graphBuilder.addNode("chatbot", chatbot);
```
:::
**Notice** how the `chatbot` node function takes the current `State` as input and returns a dictionary containing an updated `messages` list under the key "messages". This is the basic pattern for all LangGraph node functions.
:::python
The `add_messages` function in our `State` will append the LLM's response messages to whatever messages are already in the state.
:::
:::js
The reducer function in our `State` will append the LLM's response messages to whatever messages are already in the state.
:::
## 4. Add an `entry` point
Add an `entry` point to tell the graph **where to start its work** each time it is run:
:::python
```python
graph_builder.add_edge(START, "chatbot")
```
:::
:::js
```typescript
graphBuilder.addEdge(START, "chatbot");
```
:::
## 5. Add an `exit` point
Add an `exit` point to indicate **where the graph should finish execution**. This is helpful for more complex flows, but even in a simple graph like this, adding an end node improves clarity.
:::python
```python
graph_builder.add_edge("chatbot", END)
```
:::
:::js
```typescript
graphBuilder.addEdge("chatbot", END);
```
:::
This tells the graph to terminate after running the chatbot node.
## 6. Compile the graph
Before running the graph, we'll need to compile it. We can do so by calling `compile()`
on the graph builder. This creates a `CompiledStateGraph` we can invoke on our state.
on the graph builder. This creates a `CompiledGraph` we can invoke on our state.
:::python
```python
graph = graph_builder.compile()
```
:::
:::js
```typescript
const graph = graphBuilder.compile();
```
:::
## 7. Visualize the graph (optional)
:::python
You can visualize the graph using the `get_graph` method and one of the "draw" methods, like `draw_ascii` or `draw_png`. The `draw` methods each require additional dependencies.
```python
@@ -132,17 +247,36 @@ except Exception:
pass
```
![basic chatbot diagram](basic-chatbot.png)
:::
:::js
You can visualize the graph using the `getGraph` method and one of the "draw" methods, like `drawMermaidPng`. The `draw` methods each require additional dependencies.
```typescript
// Note: tslab only works inside a jupyter notebook. Don't worry about running this code yourself!
import * as tslab from "tslab";
const drawableGraph = graph.getGraph();
const image = await drawableGraph.drawMermaidPng();
const arrayBuffer = await image.arrayBuffer();
await tslab.display.png(new Uint8Array(arrayBuffer));
```
:::
![basic chatbot diagram](basic-chatbot.png)
## 8. Run the chatbot
Now run the chatbot!
Now run the chatbot!
!!! tip
You can exit the chat loop at any time by typing `quit`, `exit`, or `q`.
:::python
```python
def stream_graph_updates(user_input: str):
for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
@@ -165,15 +299,63 @@ while True:
break
```
:::
:::js
```typescript
import { HumanMessage } from "@langchain/core/messages";
async function streamGraphUpdates(userInput: string) {
const stream = await graph.stream({
messages: [new HumanMessage(userInput)],
});
for await (const event of stream) {
for (const value of Object.values(event)) {
console.log(
"Assistant:",
value.messages[value.messages.length - 1].content
);
}
}
}
// Example usage (in a real application, you'd implement input handling differently)
async function runChatbot() {
try {
const userInput = "What do you know about LangGraph?";
console.log("User: " + userInput);
await streamGraphUpdates(userInput);
} catch (error) {
console.error("Error:", error);
}
}
// Run the example
await runChatbot();
```
:::
```
Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models. It provides tools for creating workflows and state machines to coordinate multiple AI agents or language model interactions. LangGraph is built on top of LangChain, leveraging its components while adding graph-based coordination capabilities. It's particularly useful for developing more complex, stateful AI applications that go beyond simple query-response interactions.
```
:::python
```
Goodbye!
```
:::
**Congratulations!** You've built your first chatbot using LangGraph. This bot can engage in basic conversation by taking user input and generating responses using an LLM. You can inspect a [LangSmith Trace](https://smith.langchain.com/public/7527e308-9502-4894-b347-f34385740d5a/r) for the call above.
Below is the full code for this tutorial:
:::python
```python
from typing import Annotated
@@ -207,8 +389,44 @@ graph_builder.add_edge("chatbot", END)
graph = graph_builder.compile()
```
:::
:::js
```typescript
import { Annotation } from "@langchain/langgraph";
import { StateGraph, START, END } from "@langchain/langgraph";
import { BaseMessage, HumanMessage } from "@langchain/core/messages";
import { ChatOpenAI } from "@langchain/openai";
const State = Annotation.Root({
messages: Annotation<BaseMessage[]>({
reducer: (x, y) => x.concat(y),
}),
});
const graphBuilder = new StateGraph(State);
const llm = new ChatOpenAI({
model: "gpt-4o",
temperature: 0,
});
const chatbot = async (state: typeof State.State) => {
return { messages: [await llm.invoke(state.messages)] };
};
// The first argument is the unique node name
// The second argument is the function or object that will be called whenever
// the node is used.
graphBuilder.addNode("chatbot", chatbot);
graphBuilder.addEdge(START, "chatbot");
graphBuilder.addEdge("chatbot", END);
const graph = graphBuilder.compile();
```
:::
## Next steps
You may have noticed that the bot's knowledge is limited to what's in its training data. In the next part, we'll [add a web search tool](./2-add-tools.md) to expand the bot's knowledge and make it more capable.