Context7Agent

Overview

The Context7Agent is a pre-configured Vercel AI SDK agent that automatically handles the multi-step workflow for documentation lookup. It extends ToolLoopAgent and comes pre-configured with:

resolveLibraryId and queryDocs tools
Optimized system prompt for documentation retrieval
Sensible defaults for step count and workflow

Class Signature

class Context7Agent extends ToolLoopAgent<never, ToolSet>

Configuration

interface Context7AgentConfig extends ToolLoopAgentSettings<never, ToolSet> {
  /**
   * Context7 API key. If not provided, uses the CONTEXT7_API_KEY environment variable.
   */
  apiKey?: string;
}

Inherited Settings from ToolLoopAgentSettings

model - The AI model to use (required)
instructions - Custom system prompt (optional, defaults to optimized prompt)
stopWhen - Condition to stop agent execution (optional, defaults to stepCountIs(5))
tools - Additional tools to include alongside Context7 tools (optional)
maxSteps - Maximum number of steps (deprecated, use stopWhen instead)
onStepFinish - Callback after each step

Basic Usage

Simple Query

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
});

const result = await agent.generate({
  prompt: 'How do I use React Server Components?',
});

console.log(result.text);

With Custom API Key

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { openai } from '@ai-sdk/openai';

const agent = new Context7Agent({
  model: openai('gpt-4o'),
  apiKey: 'ctx7sk-...',
});

const result = await agent.generate({
  prompt: 'How do I set up routing in Next.js?',
});

console.log(result.text);

Advanced Configuration

Custom Stop Condition

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';
import { stepCountIs } from 'ai';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
  stopWhen: stepCountIs(3), // Limit to 3 steps
});

const result = await agent.generate({
  prompt: 'Explain Next.js data fetching',
});

Custom Instructions

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { openai } from '@ai-sdk/openai';

const customInstructions = `
You are a specialized Next.js documentation assistant.

Always:
1. Search for the latest Next.js documentation
2. Provide App Router examples when applicable
3. Include TypeScript code samples
4. Cite the documentation version used
`;

const agent = new Context7Agent({
  model: openai('gpt-4o'),
  instructions: customInstructions,
});

const result = await agent.generate({
  prompt: 'How do I create API routes?',
});

Adding Custom Tools

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';
import { tool } from 'ai';
import { z } from 'zod';

const searchWebTool = tool({
  description: 'Search the web for additional context',
  inputSchema: z.object({
    query: z.string().describe('Search query'),
  }),
  execute: async ({ query }) => {
    // Your implementation
    return { results: [] };
  },
});

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
  tools: {
    searchWeb: searchWebTool,
  },
});

// Agent now has access to: resolveLibraryId, queryDocs, and searchWeb
const result = await agent.generate({
  prompt: 'Find React docs and latest community discussions',
});

Agent Workflow

The agent follows this multi-step workflow:

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
});

const result = await agent.generate({
  prompt: 'How do I implement server actions in Next.js?',
});

// Internal workflow:
// Step 1: Extract library name ("Next.js") from prompt
// Step 2: Call resolveLibraryId({ query: "...", libraryName: "Next.js" })
// Step 3: Analyze results and select best library ID (e.g., /vercel/next.js)
// Step 4: Call queryDocs({ libraryId: "/vercel/next.js", query: "server actions" })
// Step 5: Synthesize answer with code examples from documentation

console.log(result.text);
console.log('Steps taken:', result.steps.length);

Streaming Responses

Text Streaming

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { openai } from '@ai-sdk/openai';

const agent = new Context7Agent({
  model: openai('gpt-4o'),
});

const result = await agent.stream({
  prompt: 'Explain React hooks with examples',
});

for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

Full Stream with Tool Calls

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
});

const result = await agent.stream({
  prompt: 'How do I use Vue composables?',
});

for await (const part of result.fullStream) {
  switch (part.type) {
    case 'text-delta':
      process.stdout.write(part.textDelta);
      break;
    case 'tool-call':
      console.log('Calling tool:', part.toolName);
      break;
    case 'tool-result':
      console.log('Tool result received');
      break;
  }
}

Default System Prompt

The agent uses this optimized prompt by default:

You are a documentation search assistant powered by Context7.

CRITICAL WORKFLOW - YOU MUST FOLLOW THESE STEPS:

Step 1: ALWAYS start by calling 'resolveLibraryId' with the library name from the user's query
   - Extract the main library/framework name (e.g., "React", "Next.js", "Vue")
   - Call resolveLibraryId with just the library name
   - Review ALL the search results returned

Step 2: Analyze the results from resolveLibraryId and select the BEST library ID based on:
   - Official sources (e.g., /reactjs/react.dev for React, /vercel/next.js for Next.js)
   - Name similarity to what the user is looking for
   - Description relevance
   - Source reputation (High/Medium is better)
   - Code snippet coverage (higher is better)
   - Benchmark score (higher is better)

Step 3: Call 'queryDocs' with the selected library ID and the user's query
   - Use the exact library ID from the resolveLibraryId results
   - Include the user's original question as the query parameter

Step 4: Provide a clear answer with code examples from the documentation

IMPORTANT:
- You MUST call resolveLibraryId first before calling queryDocs
- Do NOT skip resolveLibraryId - it helps you find the correct official documentation
- Do not call either tool more than 3 times per question
- Always cite which library ID you used

Step Tracking

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';
import { stepCountIs } from 'ai';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
  stopWhen: stepCountIs(5),
});

const result = await agent.generate({
  prompt: 'How do I use React hooks?',
});

// Inspect all steps
for (const [index, step] of result.steps.entries()) {
  console.log(`Step ${index + 1}:`);
  console.log('Tool calls:', step.toolCalls.map(tc => tc.toolName));
  console.log('Text:', step.text);
}

// Get all tool calls across all steps
const allToolCalls = result.steps.flatMap(step => step.toolCalls);
console.log('Total tools called:', allToolCalls.length);

Step Callbacks

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { openai } from '@ai-sdk/openai';

const agent = new Context7Agent({
  model: openai('gpt-4o'),
  onStepFinish: ({ text, toolCalls, toolResults }) => {
    console.log('Step completed');
    console.log('Tools called:', toolCalls.map(tc => tc.toolName));
    console.log('Generated text:', text);
  },
});

const result = await agent.generate({
  prompt: 'Explain Next.js middleware',
});

Error Handling

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';

const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
  apiKey: 'ctx7sk-...',
});

try {
  const result = await agent.generate({
    prompt: 'How do I use an obscure-library?',
  });
  console.log(result.text);
} catch (error) {
  if (error instanceof Error) {
    console.error('Agent error:', error.message);
  }
}

Complete Example

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { anthropic } from '@ai-sdk/anthropic';
import { stepCountIs, tool } from 'ai';
import { z } from 'zod';

// Custom tool for saving code snippets
const saveSnippet = tool({
  description: 'Save code snippet to database',
  inputSchema: z.object({
    code: z.string(),
    language: z.string(),
    description: z.string(),
  }),
  execute: async ({ code, language, description }) => {
    // Save to your database
    console.log('Saving snippet:', description);
    return { saved: true, id: 'snippet-123' };
  },
});

// Create agent with custom configuration
const agent = new Context7Agent({
  model: anthropic('claude-sonnet-4-20250514'),
  apiKey: process.env.CONTEXT7_API_KEY,
  stopWhen: stepCountIs(6),
  tools: {
    saveSnippet,
  },
  onStepFinish: ({ toolCalls, text }) => {
    console.log('\n--- Step Complete ---');
    console.log('Tools:', toolCalls.map(tc => tc.toolName).join(', '));
    if (text) console.log('Response:', text.slice(0, 100) + '...');
  },
});

// Generate response
const result = await agent.generate({
  prompt: 'Find React useEffect documentation and save an example',
});

console.log('\n=== Final Result ===');
console.log(result.text);
console.log('\nSteps taken:', result.steps.length);
console.log('Tools used:', 
  result.steps
    .flatMap(s => s.toolCalls)
    .map(tc => tc.toolName)
    .join(' → ')
);

Comparison with Direct Tool Usage

Using Tools Directly

import { resolveLibraryId, queryDocs } from '@upstash/context7-tools-ai-sdk';
import { generateText, stepCountIs } from 'ai';
import { openai } from '@ai-sdk/openai';

// You manage the workflow
const { text } = await generateText({
  model: openai('gpt-4o'),
  prompt: 'How do I use React hooks?',
  tools: {
    resolveLibraryId: resolveLibraryId(),
    queryDocs: queryDocs(),
  },
  stopWhen: stepCountIs(5),
});

Using Context7Agent

import { Context7Agent } from '@upstash/context7-tools-ai-sdk';
import { openai } from '@ai-sdk/openai';

// Agent handles the workflow with optimized prompt
const agent = new Context7Agent({
  model: openai('gpt-4o'),
});

const { text } = await agent.generate({
  prompt: 'How do I use React hooks?',
});

Use the agent when:

You want optimized documentation retrieval out-of-the-box
You need consistent behavior across multiple queries
You want to extend with additional tools while keeping Context7 workflow

Use tools directly when:

You need full control over the system prompt
You’re integrating into an existing agent architecture
You want to customize the tool selection logic

Type Exports

import type { Context7AgentConfig } from '@upstash/context7-tools-ai-sdk';

// Use for type-safe configuration
const config: Context7AgentConfig = {
  model: openai('gpt-4o'),
  apiKey: 'ctx7sk-...',
  stopWhen: stepCountIs(5),
};

const agent = new Context7Agent(config);

Best Practices

Set reasonable step limits: Use stopWhen: stepCountIs(5) to prevent excessive API calls
Use streaming for better UX: Stream responses to show progress to users
Add custom tools carefully: Don’t overwhelm the agent with too many tools
Monitor steps: Use onStepFinish to track agent behavior and debug issues
Handle errors gracefully: Wrap generate() calls in try-catch blocks
Reuse agent instances: Create one agent and use it for multiple queries

Next Steps

Tools Reference

Learn more about resolveLibraryId and queryDocs tools

Get Started

MCP Server

SDK

AI SDK Integration

Guides

Overview

Class Signature

Configuration

Inherited Settings from ToolLoopAgentSettings

Basic Usage

Simple Query

With Custom API Key

Advanced Configuration

Custom Stop Condition

Custom Instructions

Adding Custom Tools

Agent Workflow

Streaming Responses

Text Streaming

Full Stream with Tool Calls

Default System Prompt

Step Tracking

Step Callbacks

Error Handling

Complete Example

Comparison with Direct Tool Usage

Using Tools Directly

Using Context7Agent

Type Exports

Best Practices

Next Steps

Tools Reference

Build docs developers (and LLMs) love

Get Started

MCP Server

SDK

AI SDK Integration

Guides

​Overview

​Class Signature

​Configuration

​Inherited Settings from ToolLoopAgentSettings

​Basic Usage

​Simple Query

​With Custom API Key

​Advanced Configuration

​Custom Stop Condition

​Custom Instructions

​Adding Custom Tools

​Agent Workflow

​Streaming Responses

​Text Streaming

​Full Stream with Tool Calls

​Default System Prompt

​Step Tracking

​Step Callbacks

​Error Handling

​Complete Example

​Comparison with Direct Tool Usage

​Using Tools Directly

​Using Context7Agent

​Type Exports

​Best Practices

​Next Steps

Tools Reference

Build docs developers (and LLMs) love

Overview

Class Signature

Configuration

Inherited Settings from ToolLoopAgentSettings

Basic Usage

Simple Query

With Custom API Key

Advanced Configuration

Custom Stop Condition

Custom Instructions

Adding Custom Tools

Agent Workflow

Streaming Responses

Text Streaming

Full Stream with Tool Calls

Default System Prompt

Step Tracking

Step Callbacks

Error Handling

Complete Example

Comparison with Direct Tool Usage

Using Tools Directly

Using Context7Agent

Type Exports

Best Practices

Next Steps