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---
og_image: "/images/guides/best-npm-packages-ai-agents-2026.webp"
title: "Top npm Packages for AI Agents in 2026 (Ranked)"
description: "Mastra, OpenAI Agents SDK, LangGraph.js, and Vercel AI SDK compared. We rank the top npm packages for building AI agents in JS/TS by DX and capability in 2026."
date: "2026-03-08"
authors: ["team"]
tier: 1
tags: ["ai-agents", "mastra", "langchain", "openai", "javascript", "typescript", "2026"]
---

TypeScript officially surpassed Python in GitHub's 2025 language report — and the JavaScript AI agent ecosystem has responded. In 2024, building a production AI agent in JavaScript meant stitching together low-level API calls. In 2026, there are at least five mature frameworks specifically designed for this use case, each with distinct architectural philosophies.

## TL;DR

For most teams building AI agents in JavaScript or TypeScript, **Mastra** or **Vercel AI SDK with tools** is the right starting point in 2026. For complex multi-step agent orchestration with observability, **LangGraph.js** remains the most powerful option. **@openai/agents** is excellent if you're committed to the OpenAI ecosystem.

## Key Takeaways

- Mastra (YC W25, Jan 2025) is the fastest-growing TypeScript-native agent framework with built-in RAG, tools, and observability
- Vercel AI SDK's `generateText` with `tools` option handles 80% of agent use cases with minimal setup
- LangGraph.js powers enterprise-scale multi-agent workflows with persistent state and human-in-the-loop
- @openai/agents (OpenAI's official TypeScript SDK) supports Swarm-style handoffs, guardrails, and voice agents
- Microsoft retired AutoGen in favor of the unified Microsoft Agent Framework (GA Q1 2026)
- LangGraph handles 34.5M monthly downloads across its ecosystem
- TypeScript-first frameworks offer full type inference from tool definitions to return types

## The Agent Framework Landscape in 2026

The definition of an "AI agent" has standardized: it's an LLM that can use tools, maintain state across steps, call other agents, and (optionally) pause for human review. The npm ecosystem now has mature solutions for each part of this definition.

What's changed since 2024: the frameworks have converged on a common pattern (define tools, define agent, run loop), but diverged sharply on orchestration complexity, observability, and framework integration.

## 1. Vercel AI SDK — Tools and Agents

**Package**: `ai` + `@ai-sdk/openai` (or any provider)
**Weekly downloads**: ~2.8M
**GitHub stars**: ~38K
**Best for**: Agents that need tight React/Next.js integration

AI SDK doesn't market itself as an "agent framework" but its `generateText` with the `tools` option is genuinely capable of agentic behavior:

```typescript
import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';
import { tool } from 'ai';

const result = await generateText({
  model: openai('gpt-4o'),
  tools: {
    getWeather: tool({
      description: 'Get weather for a location',
      parameters: z.object({ location: z.string() }),
      execute: async ({ location }) => {
        return await fetchWeather(location);
      },
    }),
    searchWeb: tool({
      description: 'Search the web',
      parameters: z.object({ query: z.string() }),
      execute: async ({ query }) => searchWeb(query),
    }),
  },
  maxSteps: 10, // Allow multi-step reasoning
  prompt: 'What is the weather in NYC and find me 3 recent news articles about it?',
});
```

`maxSteps` is the key: it lets the model call tools, observe results, and decide next steps — a basic agent loop with no extra framework needed.

**Limitations**: No persistent state between calls, no built-in observability, no multi-agent handoffs.

## 2. Mastra — TypeScript-Native Agent Framework

**Package**: `@mastra/core`
**GitHub stars**: ~12K
**Backed by**: YC W25 (from the team behind Gatsby)
**Best for**: Full production agent systems with observability

Mastra is the most exciting newcomer. It launched in January 2025 and rapidly became the go-to for teams that want a batteries-included TypeScript agent framework:

```typescript
import { Mastra, Agent } from '@mastra/core';
import { OpenAI } from '@mastra/openai';

const mastra = new Mastra({
  llm: new OpenAI({ model: 'gpt-4o' }),
});

const agent = mastra.createAgent({
  name: 'Research Assistant',
  instructions: 'You are a thorough research assistant.',
  tools: {
    webSearch: webSearchTool,
    readFile: readFileTool,
    saveNote: saveNoteTool,
  },
});

const result = await agent.generate('Research the current state of quantum computing');
```

Mastra includes:
- **Built-in RAG**: Document ingestion, chunking, embedding, and retrieval out of the box
- **Workflows**: Durable, resumable multi-step workflows with branching
- **Memory**: Persistent agent memory across sessions
- **Observability**: OpenTelemetry tracing built in
- **Evals**: Automated evaluation of agent outputs

This is close to a full agent platform, not just a library.

## 3. LangGraph.js — Stateful Multi-Agent Orchestration

**Package**: `@langchain/langgraph`
**Monthly downloads (ecosystem)**: 34.5M
**GitHub stars**: 8K+ (JS repo)
**Best for**: Complex, stateful, multi-agent workflows

LangGraph is the most powerful option for sophisticated agent architectures. It models agents as state machines — nodes are functions, edges are transitions, and the graph can cycle (unlike a simple chain):

```typescript
import { StateGraph, END } from '@langchain/langgraph';
import { Annotation } from '@langchain/langgraph';

const AgentState = Annotation.Root({
  messages: Annotation<BaseMessage[]>({
    reducer: (x, y) => x.concat(y),
  }),
  plan: Annotation<string | null>(),
  documents: Annotation<Document[]>(),
});

const workflow = new StateGraph(AgentState)
  .addNode('planner', plannerNode)
  .addNode('researcher', researcherNode)
  .addNode('writer', writerNode)
  .addNode('reviewer', reviewerNode)
  .addConditionalEdges('reviewer', shouldRevise, {
    revise: 'writer',
    done: END,
  })
  .setEntryPoint('planner');

// Persistent checkpointing
const app = workflow.compile({
  checkpointer: new SqliteSaver(db),
});
```

Key LangGraph features:
- **Cycles**: Agents can loop until a condition is met
- **Parallel branches**: Multiple agents run simultaneously
- **Checkpointing**: Pause and resume workflows
- **Human-in-the-loop**: Interrupt for human approval mid-workflow
- **Multi-agent**: Supervisor agents that delegate to subagents

LangSmith (LangChain's observability platform) integrates natively.

## 4. @openai/agents — OpenAI's Official TypeScript Agent SDK

**Package**: `@openai/agents`
**GitHub**: openai/openai-agents-js
**Best for**: OpenAI-committed teams, voice agents, Swarm-style handoffs

OpenAI released their official TypeScript Agents SDK (the production successor to the Swarm experiment) in 2025:

```typescript
import { Agent, run } from '@openai/agents';

const triageAgent = new Agent({
  name: 'Triage Agent',
  instructions: 'Determine which specialist should handle the request',
  handoffs: [billingAgent, technicalAgent, generalAgent],
});

const result = await run(triageAgent, 'My invoice shows a wrong amount');
// Automatically hands off to billingAgent based on context
```

Notable features:
- **Handoffs**: Agents delegate to other agents with full context transfer
- **Guardrails**: Input/output validation as first-class primitives
- **Voice agents**: Built-in support for real-time voice with interruption detection
- **Tracing**: OpenAI's built-in tracing dashboard

The SDK is deliberately minimal — less magic, more control. It's a good fit for teams already using the OpenAI SDK and wanting structured multi-agent patterns without a full framework.

## 5. Google GenAI SDK — Gemini Agents

**Package**: `@google/genai`
**GitHub stars**: 5K+
**Best for**: Gemini models, Google Cloud workloads

Google's unified GenAI SDK (released 2025, replacing `@google/generative-ai`) supports agent patterns with Gemini:

```typescript
import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

const response = await ai.models.generateContent({
  model: 'gemini-2.0-flash',
  contents: 'What is the weather in London?',
  tools: [{ googleSearch: {} }, { codeExecution: {} }],
});
```

Gemini's built-in Google Search grounding is unique — no custom search tool setup required.

## 6. Microsoft Agent Framework (formerly AutoGen)

**Package**: `@microsoft/agents` (preview)
**Status**: GA targeted Q1 2026
**Note**: AutoGen is now maintenance-only

Microsoft merged AutoGen and Semantic Kernel into a unified framework in October 2025. The new Microsoft Agent Framework emphasizes enterprise concerns: Azure integration, compliance, and multi-model support. Still in preview as of March 2026.

## Comparison Table

| Framework | Focus | Complexity | Observability | Edge Support | Persistent State |
|-----------|-------|-----------|---------------|--------------|-----------------|
| Vercel AI SDK | UI-first | Low | None | Yes | No |
| Mastra | Full platform | Medium | Built-in | Partial | Yes |
| LangGraph.js | Orchestration | High | LangSmith | No | Yes |
| @openai/agents | Handoffs | Low-Medium | OpenAI Traces | No | No |
| Google GenAI | Gemini-first | Low | Google Cloud | Partial | No |
| Microsoft Agent | Enterprise | High | Azure Monitor | Partial | Yes |

## Architectural Patterns

### Pattern 1: ReAct Loop (Reason + Act)
Best implemented with: Vercel AI SDK (`maxSteps`), @openai/agents

```
Think → Act (call tool) → Observe → Think → ... → Respond
```

### Pattern 2: Plan and Execute
Best implemented with: Mastra workflows, LangGraph

```
Plan → Step 1 → Step 2 → Step 3 → Synthesize → Respond
```

### Pattern 3: Multi-Agent Supervisor
Best implemented with: LangGraph (supervisor pattern), @openai/agents (handoffs)

```
Supervisor → Route to Specialist A or B → Collect results → Synthesize
```

### Pattern 4: Retrieval-Augmented Generation (RAG)
Best implemented with: Mastra (built-in), LangChain.js

```
Query → Embed → Retrieve → Augment prompt → Generate
```

## Choosing the Right Package

**Choose Vercel AI SDK tools if:**
- You're in a React/Next.js app
- Your agent logic is relatively simple (< 5 tools, < 10 steps)
- You don't need persistent state between calls
- Edge runtime support is required

**Choose Mastra if:**
- You want a batteries-included TypeScript agent platform
- You need built-in RAG, memory, and observability
- You're building a production system and don't want to assemble pieces yourself
- Your team is new to agent development

**Choose LangGraph.js if:**
- You need complex state machines with cycles
- Human-in-the-loop is a hard requirement
- Multi-agent coordination with parallel branches is needed
- You want LangSmith observability

**Choose @openai/agents if:**
- You're already on the OpenAI SDK
- Swarm-style agent handoffs match your architecture
- Voice agent support is needed
- You want minimal abstraction

## Essential Supporting Packages

No matter which framework you choose, these packages are almost always needed:

```bash
# Vector stores for RAG
npm install @pinecone-database/pinecone   # Cloud vector store
npm install chromadb                      # Local vector store

# Embeddings
npm install @xenova/transformers          # Local embeddings

# Document processing
npm install @langchain/community          # 50+ document loaders

# Observability
npm install @opentelemetry/sdk-node       # OpenTelemetry
npm install langsmith                     # LangChain observability
```

## The Future of JavaScript Agents

The trend in 2026 is toward **model-native tools** (models that can search, execute code, browse the web without external tool setup) and **durable workflows** (agents that can pause, sleep, and resume). Mastra and LangGraph are ahead on durability; Google Gemini and OpenAI GPT-4o are ahead on built-in tools.

Watch for: TypeScript-first evaluation frameworks, standardized agent protocols (A2A — Agent-to-Agent), and tighter MCP (Model Context Protocol) integration across all frameworks.

## Explore More on PkgPulse

Compare download trends, bundle sizes, and release frequency for all these packages on [PkgPulse](https://pkgpulse.com).

The JavaScript agent ecosystem is converging on a clearer separation of concerns. The Vercel AI SDK and `@openai/agents` occupy the low-complexity end — they are the right starting point for teams that want agentic behavior without adopting a full framework. Mastra and LangGraph.js occupy the full-platform end — they add persistence, observability, and multi-agent coordination at the cost of higher setup complexity. The trajectory of the space suggests that model-native capabilities (built-in search, code execution, and web browsing available without external tool setup) will continue to close the gap between simple and complex frameworks, making the complexity-versus-capability trade-off easier for more teams over time. Starting with the simplest option and adding framework complexity only when you hit its limits is the right default strategy.

*See also: [AVA vs Jest](/compare/ava-vs-jest) and [AI SDK vs LangChain: Which to Use in 2026](/guides/ai-sdk-vs-langchain-2026), [AI Development Stack for JavaScript 2026](/guides/ai-development-stack-javascript-2026).*