This PR was opened by the [Changesets release](https://github.com/changesets/action) GitHub action. When you're ready to do a release, you can merge this and the packages will be published to npm automatically. If you're not ready to do a release yet, that's fine, whenever you add more changesets to main, this PR will be updated. # Releases ## deepagents@1.12.0 ### Minor Changes - [#703](https://github.com/langchain-ai/deepagentsjs/pull/703) [`d25097f`](https://github.com/langchain-ai/deepagentsjs/commit/d25097f78d0e66741da34e1d74551f3c19991126) Thanks [@hntrl](https://github.com/hntrl)! - feat(deepagents): adopt more minimal prompting We've observed that current models don't need as verbose of prompting guidance, so we're reducing the amount of perscriptive guidance that deepagents has. This is reflected in the generic system prompt (which is now blank), and in the tool descriptions (which have been simplified). - [#708](https://github.com/langchain-ai/deepagentsjs/pull/708) [`1225a7f`](https://github.com/langchain-ai/deepagentsjs/commit/1225a7ff8673686c2a3c0411636a9511b7d8d0d0) Thanks [@hntrl](https://github.com/hntrl)! - feat(deepagents): make todo middleware opt-in - [#674](https://github.com/langchain-ai/deepagentsjs/pull/674) [`dd142fe`](https://github.com/langchain-ai/deepagentsjs/commit/dd142fe4fc54c986d5bcf51211d9a839a427e931) Thanks [@hntrl](https://github.com/hntrl)! - feat(filesystem): allow `write_file` to create missing files or completely replace existing files ## deepagents-acp@0.1.21 ### Patch Changes - Updated dependencies [[`d25097f`](https://github.com/langchain-ai/deepagentsjs/commit/d25097f78d0e66741da34e1d74551f3c19991126), [`1225a7f`](https://github.com/langchain-ai/deepagentsjs/commit/1225a7ff8673686c2a3c0411636a9511b7d8d0d0), [`dd142fe`](https://github.com/langchain-ai/deepagentsjs/commit/dd142fe4fc54c986d5bcf51211d9a839a427e931)]: - deepagents@1.12.0 ## @langchain/node-vfs@1.0.0 ### Patch Changes - [#674](https://github.com/langchain-ai/deepagentsjs/pull/674) [`dd142fe`](https://github.com/langchain-ai/deepagentsjs/commit/dd142fe4fc54c986d5bcf51211d9a839a427e931) Thanks [@hntrl](https://github.com/hntrl)! - fix: allow `VfsBackend.write` to overwrite existing files while continuing to reject symlinks ## @langchain/daytona@1.0.0 ## @langchain/deno@1.0.0 ## @langchain/modal@1.0.0 ## @langchain/quickjs@1.0.0 ## @langchain/sandbox-standard-tests@2.0.0 ## @deepagents/evals@0.0.20 ### Patch Changes - Updated dependencies [[`d25097f`](https://github.com/langchain-ai/deepagentsjs/commit/d25097f78d0e66741da34e1d74551f3c19991126), [`1225a7f`](https://github.com/langchain-ai/deepagentsjs/commit/1225a7ff8673686c2a3c0411636a9511b7d8d0d0), [`dd142fe`](https://github.com/langchain-ai/deepagentsjs/commit/dd142fe4fc54c986d5bcf51211d9a839a427e931)]: - deepagents@1.12.0 Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
@langchain/quickjs
Sandboxed JavaScript/TypeScript REPL for deepagents, powered by QuickJS-NG through QuickJS-Emscripten
Installation
npm install @langchain/quickjs deepagents
Quick Start
import { createDeepAgent } from "deepagents";
import { createQuickJSMiddleware } from "@langchain/quickjs";
const agent = createDeepAgent({
model: "claude-sonnet-4-5",
middleware: [createQuickJSMiddleware()],
});
const result = await agent.invoke({
messages: [
{ role: "user", content: "Calculate the first 20 Fibonacci numbers" },
],
});
The agent now has a js_eval tool. It can write and execute JavaScript/TypeScript in a sandboxed REPL where variables persist across calls:
// Call 1: the agent writes
var fibs = [0, 1];
for (let i = 2; i < 20; i++) fibs.push(fibs[i - 1] + fibs[i - 2]);
console.log(fibs);
// Call 2: state persists — `fibs` is still available
console.log(`Sum: ${fibs.reduce((a, b) => a + b, 0)}`);
Features
WASM Sandbox
All code runs inside a QuickJS WASM interpreter. There is no require, no import, no fetch, no filesystem access — only the explicitly bridged helpers (readFile, writeFile, and optionally tools.*).
TypeScript Support
LLMs naturally produce TypeScript. An AST-based transform pipeline strips type annotations, interfaces, and generics before evaluation — the model doesn't need to write pure JavaScript.
Virtual Filesystem
The REPL has readFile(path) and writeFile(path, content) functions that read from and write to the agent's backend (LangGraph state by default):
const raw = await readFile("/data.json");
const data = JSON.parse(raw);
const summary = { total: data.items.length };
await writeFile("/summary.json", JSON.stringify(summary, null, 2));
Programmatic Tool Calling (PTC)
Any agent tool can be exposed inside the REPL as a typed async function. Instead of the LLM emitting tool calls one at a time, it writes code that calls tools directly — loops, conditionals, parallel execution, and result transformation all happen in code:
const agent = createDeepAgent({
model: "claude-sonnet-4-5-20250929",
middleware: [
createQuickJSMiddleware({
ptc: true, // expose all agent tools inside the REPL
}),
],
});
Inside the REPL, the agent can then write:
const urls = ["/users", "/orders", "/products"];
const results = await Promise.all(
urls.map((u) => tools.httpRequest({ url: "https://api.example.com" + u })),
);
const parsed = results.map((r) => JSON.parse(r));
console.log(`Users: ${parsed[0].length}, Orders: ${parsed[1].length}`);
PTC configuration is progressive:
| Value | Behavior |
|---|---|
false |
Disabled (default) |
true |
All agent tools except VFS builtins |
string[] |
Only these tools |
{ include: string[] } |
Only these tools |
{ exclude: string[] } |
All tools except these |
Recursive Language Model (RLM)
When the agent has subagents configured, a task() global is available inside
the REPL (no PTC needed), so the agent can spawn sub-agents in parallel from
within the REPL:
const agent = createDeepAgent({
model: "claude-sonnet-4-5-20250929",
subagents: [
{
name: "general-purpose",
description: "Research agent",
systemPrompt: "...",
},
],
middleware: [createQuickJSMiddleware()],
});
The agent then writes code like:
const topics = ["quantum computing", "fusion energy", "CRISPR"];
const results = await Promise.all(
topics.map((topic) =>
task({
description: `Research ${topic} in depth`,
subagentType: "general-purpose",
}),
),
);
// Aggregate and return the report from the eval; the agent then writes it to a
// file with its own write_file tool.
const report = topics.map((t, i) => `## ${t}\n${results[i]}`).join("\n\n");
report;
taskcannot be exposed viaptc— it is reserved for thetask()global, so passingptc: ["task"]throws.
API
createQuickJSMiddleware(options?)
Creates a middleware that adds the js_eval tool to your agent.
interface QuickJSMiddlewareOptions {
backend?: BackendProtocol | BackendFactory; // File I/O backend (default: StateBackend)
ptc?: boolean | string[] | { include: string[] } | { exclude: string[] }; // PTC config
memoryLimitBytes?: number; // Default: 50MB
maxStackSizeBytes?: number; // Default: 320KB
executionTimeoutMs?: number; // Default: 30s (-1 to disable)
systemPrompt?: string | null; // Override the built-in REPL system prompt
}
ReplSession
The underlying session class. Usually you don't interact with this directly — the middleware manages sessions per thread.
ReplSession.getOrCreate(id, options?) // Get or create a session
ReplSession.get(id) // Look up existing session
session.eval(code, timeoutMs) // Execute code
session.flushWrites(backend) // Persist buffered file writes
session.toJSON() / ReplSession.fromJSON(data) // Serialization
License
MIT — see LICENSE.