chore(deps): bump langchain from 0.3.36 to 1.0.2

[dependabot]

Bumps langchain from 0.3.36 to 1.0.2.

Release notes

Sourced from langchain's releases.

langchain==1.0.0

🎉 LangChain v1.0 is here! This release provides a focused, production-ready foundation for building agents. We've streamlined the framework around three core improvements: createAgent, standard content blocks, and a simplified package structure. See the release notes for complete details.

✨ Major Features

createAgent - A new standard for building agents

createAgent is the new standard way to build agents in LangChain 1.0. It provides a simpler interface than createReactAgent from LangGraph while offering greater customization potential through middleware.

Key features:

• Clean, intuitive API: Build agents with minimal boilerplate
• Built on LangGraph: Get persistence, streaming, human-in-the-loop, and time travel out of the box
• Middleware-first design: Highly customizable through composable middleware
• Improved structured output: Generate structured outputs in the main agent loop without additional LLM calls

Example:

import { createAgent } from "langchain";
const agent = createAgent({
model: "anthropic:claude-sonnet-4-5-20250929",
tools: [getWeather],
systemPrompt: "You are a helpful assistant.",
});
const result = await agent.invoke({
messages: [{ role: "user", content: "What is the weather in Tokyo?" }],
});
console.log(result.content);

Under the hood, createAgent is built on the basic agent loop—calling a model using LangGraph, letting it choose tools to execute, and then finishing when it calls no more tools.

Built on LangGraph features (work out of the box):

• Persistence: Conversations automatically persist across sessions with built-in checkpointing
• Streaming: Stream tokens, tool calls, and reasoning traces in real-time
• Human-in-the-loop: Pause agent execution for human approval before sensitive actions
• Time travel: Rewind conversations to any point and explore alternate paths

Structured output improvements:

• Generate structured outputs in the main loop instead of requiring an additional LLM call
• Models can choose between calling tools or using provider-side structured output generation
• Significant cost reduction by eliminating extra LLM calls

Example:

... (truncated)

Commits

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