> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rebuno.io/llms.txt
> Use this file to discover all available pages before exploring further.

# LangChain.js

> Durable execution, policy, and approvals for LangChain.js agents

A LangChain.js agent runs on Rebuno unchanged apart from two seams. The chat
model takes `rebunoFetch`, so every model call is recorded as an `llm_call`
step, and tools run through `defineTool`, so every tool call is recorded as a
`tool_call` step. On a re-dispatch the agent runs from the top and recorded
steps replay instead of calling the model or the tool again.

## Install

```bash theme={"theme":{"light":"min-light","dark":"material-theme-ocean"}}
npm install rebuno langchain @langchain/core @langchain/openai zod
```

## Model calls

`ChatOpenAI` passes `configuration` through to the OpenAI client, which accepts
a custom `fetch`. Pass `rebunoFetch`:

```ts theme={"theme":{"light":"min-light","dark":"material-theme-ocean"}}
import { ChatOpenAI } from "@langchain/openai";
import { rebunoFetch } from "rebuno";

const model = new ChatOpenAI({ model: "gpt-5.5", configuration: { fetch: rebunoFetch } });
```

See [LLM calls](/sdk/typescript/llm-calls).

## Tools

Declare the Rebuno side with `defineTool`, and call it from a LangChain `tool`:

```ts theme={"theme":{"light":"min-light","dark":"material-theme-ocean"}}
import { tool } from "@langchain/core/tools";
import { defineTool } from "rebuno";
import { z } from "zod";

const sendEmail = defineTool({
  name: "send_email",
  idempotency: "at_most_once",
  execute: async ({ body }: { body: string }) => mail.send("ops@acme.com", body),
});

const sendEmailTool = tool((args) => sendEmail(args), {
  name: "send_email",
  description: "Email the support summary.",
  schema: z.object({ body: z.string() }),
});
```

Mark anything with a side effect, such as sending an email or creating a
ticket, `at_most_once`. See [idempotency](/sdk/typescript/tools#idempotency).

## Stopping on approval

When a tool is held for approval, it throws. The agent loop passes a tool error
back to the model and keeps looping, and every model call after that is refused.
Abort the loop on the first tool error instead, by catching it in the tool and
aborting a signal passed to `invoke`. The agent below does this.

## The agent

```ts theme={"theme":{"light":"min-light","dark":"material-theme-ocean"}}
import { createAgent } from "langchain";
import { Agent } from "rebuno";

async function process(input: { query: string }) {
  const model = new ChatOpenAI({ model: "gpt-5.5", configuration: { fetch: rebunoFetch } });
  const halt = new AbortController();
  const tools = [
    tool(
      async (args) => {
        try { return await sendEmail(args); }
        catch (error) { halt.abort(error); throw error; }
      },
      { name: "send_email", description: "Email the support summary.", schema: z.object({ body: z.string() }) },
    ),
  ];

  const graph = createAgent({ model, tools });
  const result = await graph.invoke(
    { messages: [{ role: "user", content: input.query }] },
    { signal: halt.signal },
  );
  return { answer: result.messages.at(-1)?.content };
}

const agent = new Agent("support");
await agent.serve({ port: 5000 }, process);
```

## What Rebuno adds

* **Policy.** Every model and tool call is checked against [policy](/policy)
  before it runs. A denied tool returns the rule's reason to the model as the
  tool result, so the agent can take a different path.
* **Approvals.** A tool that requires approval parks the execution. Once it's
  approved, the agent is dispatched again, the earlier steps replay, and the
  approved call runs.
* **Recovery.** If the worker dies partway through, the next dispatch replays
  every completed step and continues from the first one that didn't finish.
  Model calls that already ran are not paid for twice.

## Full example

[`examples/frameworks/typescript/langchain_agent.ts`](https://github.com/rebuno/rebuno/blob/main/examples/frameworks/typescript/langchain_agent.ts)
is a support agent that investigates a customer issue, creates a ticket, and
emails a summary after approval.