mirror of
https://github.com/langchain-ai/deepagentsjs.git
synced 2026-07-22 04:15:30 -04:00
Hunter Lovell
454fa26804
* feat(quickjs): add @langchain/quickjs — sandboxed JS/TS REPL with PTC, env isolation, and RLM support * chore: split changesets into quickjs minor + deepagents patch * refactor(quickjs): remove env/secrets, lazy runtime, buffered writes, fix thread_id propagation - Remove entire env/secrets system (EnvVarConfig, EnvConfig, secretRef, injectEnv, blockEnvLeaks, checkEnvAccess, generateEnvPrompt) - Lazy-load QuickJS WASM runtime on first eval via ensureStarted() - Buffer file writes in pendingWrites[], flush after eval via flushWrites(backend) - Make ReplSession.getOrCreate() synchronous (runtime init deferred) - Add toJSON()/fromJSON() for session serialization - Fix thread_id propagation: move session creation from wrapModelCall (which lacks configurable) to tool handler (which has config.configurable.thread_id) - Remove dead starting map from ReplSession - Add integration test verifying REPL state persists across js_eval calls - Add thread_id propagation unit tests - Remove secret-env-agent example - 68 unit tests + 1 integration test passing, types clean * cr * cr * docs(quickjs): add README and LICENSE, fix lint errors * cr * cr
51 lines
1.6 KiB
TypeScript
51 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 { createQuickJSMiddleware } 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 js_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: [createQuickJSMiddleware()],
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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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// eslint-disable-next-line no-console
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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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