mirror of
https://github.com/langchain-ai/deepagentsjs.git
synced 2026-07-20 19:40:28 -04:00
Colin Francis
254895457f
### Summary - Rename `createQuickJSMiddleware` → `createREPLMiddleware` and `QuickJSMiddlewareOptions` → `REPLMiddlewareOptions` to match the Python `REPLMiddleware` naming - Rename middleware registration from `"QuickJSMiddleware"` → `"REPLMiddleware"` - Rename default tool name from `"js_eval"` → `"eval"`, now configurable via `toolName` option - Add `captureConsole` option (default `true`) to toggle console output capture - Align default memory limit to 64 MiB (was 50 MiB) and timeout to 5s (was 30s) to match Python ### Tests - All 156 existing unit tests pass with updated references - Updated `middleware.test.ts`, `middleware.int.test.ts`, and example files to use new names --------- Co-authored-by: Hunter Lovell <40191806+hntrl@users.noreply.github.com>
50 lines
1.6 KiB
TypeScript
50 lines
1.6 KiB
TypeScript
/**
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* Data Analysis Agent Example
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*
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* Demonstrates the QuickJS REPL as a computational scratch pad.
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* The agent reads data from the VFS, processes it in sandboxed JavaScript,
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* and writes results back — all without network access or Node.js APIs.
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*
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* This is useful for tasks where LLMs typically hallucinate:
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* - Arithmetic and statistical calculations
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* - Sorting, filtering, grouping data
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* - JSON transformation and restructuring
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* - Multi-step logic with intermediate state
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*/
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import "dotenv/config";
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import dedent from "dedent";
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import { HumanMessage } from "@langchain/core/messages";
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import { ChatAnthropic } from "@langchain/anthropic";
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import { createDeepAgent } from "deepagents";
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import { createREPLMiddleware } from "@langchain/quickjs";
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const model = new ChatAnthropic({
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model: "claude-sonnet-4-5",
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temperature: 0,
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});
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const agent = createDeepAgent({
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model,
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systemPrompt: dedent`
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You are a data analyst. Use the eval REPL to perform calculations
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and data transformations. Always show your work in code — never guess
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at arithmetic or statistics.
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`,
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middleware: [createREPLMiddleware()],
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});
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const result = await agent.invoke({
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messages: [
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new HumanMessage(dedent`
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I have sales data in /data/sales.json. Parse it, calculate the total
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revenue per region, find the top-performing region, and write a
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summary report to /reports/sales-summary.md.
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`),
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],
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});
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const last = result.messages[result.messages.length - 1];
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console.log(
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typeof last.content === "string" ? last.content.slice(0, 500) : last.content,
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);
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