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docs[patch]: Fix typos in Agents tutorial #6675

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Sep 3, 2024
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docs[patch]: Fix typos in Agents tutorial #6675

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101c4a9
fix tiny tiny typos
ayatnkw Sep 2, 2024
0b37032
fix typo
ayatnkw Sep 2, 2024
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10 changes: 5 additions & 5 deletions docs/core_docs/docs/tutorials/agents.mdx
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Expand Up @@ -26,7 +26,7 @@ In this tutorial we will build an agent that can interact with multiple differen
By definition, agents take a self-determined, input-dependent sequence of steps before returning a user-facing output. This makes debugging these systems particularly tricky, and observability particularly important.
[LangSmith](https://smith.langchain.com) is especially useful for such cases.

When building with LangChain, all steps will automatically be traced in LangSmith. To set up LangSmith we just need set the following environment variables:
When building with LangChain, all steps will automatically be traced in LangSmith. To set up LangSmith we just need to set the following environment variables:

```bash
export LANGCHAIN_TRACING_V2="true"
Expand All @@ -42,7 +42,7 @@ We first need to create the tools we want to use. We will use two tools: [Tavily

### [Tavily](https://app.tavily.com)

We have a built-in tool in LangChain to easily use Tavily search engine as tool.
We have a built-in tool in LangChain to easily use Tavily search engine as a tool.
Note that this requires a Tavily API key set as an environment variable named `TAVILY_API_KEY` - they have a free tier, but if you don’t have one or don’t want to create one, you can always ignore this step.

```typescript
Expand Down Expand Up @@ -100,7 +100,7 @@ console.log(retrieverResult[0]);
*/
```

Now that we have populated our index that we will do doing retrieval over, we can easily turn it into a tool (the format needed for an agent to properly use it):
Now that we have populated our index that we will be doing retrieval over, we can easily turn it into a tool (the format needed for an agent to properly use it):

```typescript
import { createRetrieverTool } from "langchain/tools/retriever";
Expand Down Expand Up @@ -150,7 +150,7 @@ const prompt = await pull<ChatPromptTemplate>(
```

Now, we can initalize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take.
Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to thing about these components, see our [conceptual guide](/docs/concepts#agents).
Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts#agents).

```typescript
import { createOpenAIFunctionsAgent } from "langchain/agents";
Expand All @@ -163,7 +163,7 @@ const agent = await createOpenAIFunctionsAgent({
```

Finally, we combine the agent (the brains) with the tools inside the AgentExecutor (which will repeatedly call the agent and execute tools).
For more information about how to thing about these components, see our [conceptual guide](/docs/concepts#agents).
For more information about how to think about these components, see our [conceptual guide](/docs/concepts#agents).

```typescript
import { AgentExecutor } from "langchain/agents";
Expand Down
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