Why Migrate OpenAI Agents to Claude?

Switching from OpenAI's SDK to Anthropic's Claude TypeScript SDK isn't just a drop-in replacement—it's an upgrade for many agent-based workflows. Claude models like Claude 3.5 Sonnet excel in complex reasoning, tool use, and long-context handling, often at 2-5x lower cost per token compared to GPT-4o. Developers report fewer hallucinations, more reliable function calling, and faster inference.

Key Benefits:

• Cost Savings: Claude 3.5 Sonnet: $3/$15 per million input/output tokens vs. GPT-4o-mini at $0.15/$0.60 (but Sonnet outperforms).
• Performance: Native support for parallel tool calls and XML-structured outputs reduces parsing errors.
• Agent Reliability: Better adherence to ReAct patterns and multi-step reasoning.

If your OpenAI agents handle tools like calculators, APIs, or databases, this migration guide will save you weeks of debugging.

Prerequisites and Setup

Ensure Node.js 18+ and TypeScript 5+. We'll compare a simple ReAct-style agent that uses a math tool and a weather API.

Install Dependencies

OpenAI (Baseline):

npm install openai
npm install -D typescript @types/node

Claude SDK:

npm install @anthropic-ai/sdk
npm install -D @types/node

Set API keys:

// .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=anthropic-...

Basic tsconfig.json:

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ESNext",
    "strict": true
  }
}

Migrating Basic Chat Completions

OpenAI and Claude both use a messages API, but syntax and defaults differ slightly.

OpenAI Example

import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await openai.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [{ role: 'user', content: 'What is 15 * 23?' }],
});

console.log(response.choices[0].message.content);

Claude Equivalent

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

const response = await client.messages.create({
  model: 'claude-3-5-sonnet-20241022',
  max_tokens: 1024,
  messages: [{ role: 'user', content: 'What is 15 * 23?' }],
});

console.log(response.content[0].text);

Key Diffs:

• No system role in messages; use a leading user message with <system> tags for prompts.
• Response: choices[0].message.content → content[0].text.
• Always specify max_tokens; Claude doesn't auto-truncate as aggressively.

Agent Architecture: ReAct Pattern

Agents loop: Observe → Think → Act (call tools) → Observe results.

We'll refactor a math/weather agent:

• Tools: calculator (JS eval), get_weather (mock API).
• Goal: "What's the temperature in NYC during a heatwave? Calculate wind chill if 85°F and 10mph wind."

Claude shines here with structured XML tool outputs and fewer iterations.

Defining Tools

Tools use JSON Schema in both, but Claude requires input_schema as JSONSchemaDraft07.

OpenAI Tools

const tools = [
  {
    type: 'function',
    function: {
      name: 'calculator',
      description: 'Solve math problems',
      parameters: {
        type: 'object',
        properties: { expression: { type: 'string' } },
        required: ['expression'],
      },
    },
  },
  // Similar for get_weather
];

Claude Tools (Near-Identical)

import { z } from 'zod'; // Optional for schema gen

const tools = [
  {
    name: 'calculator',
    description: 'Solve math problems',
    input_schema: {
      type: 'object',
      properties: { expression: { type: 'string' } },
      required: ['expression'],
      additionalProperties: false,
    },
  },
  {
    name: 'get_weather',
    description: 'Get current weather',
    input_schema: {
      type: 'object',
      properties: {
        city: { type: 'string' },
        unit: { type: 'string', enum: ['F', 'C'] },
      },
      required: ['city'],
    },
  },
];

Diff: OpenAI wraps in function; Claude uses flat name, input_schema. Add additionalProperties: false for safety.

Tool Execution Handlers

Shared logic:

function executeTool(toolName: string, args: any) {
  switch (toolName) {
    case 'calculator':
      return { result: eval(args.expression) }; // WARNING: Use safe eval in prod!
    case 'get_weather':
      return { temperature: 85, unit: 'F', wind_speed: 10, feels_like: 82 };
    default:
      throw new Error('Unknown tool');
  }
}

Full Agent Loop: OpenAI vs Claude

Here's the refactor with code diffs. The loop is ~90% similar, but Claude parses tools more reliably.

OpenAI Agent

async function runOpenAIAgent(prompt: string) {
  let messages: OpenAI.Chat.ChatCompletionMessageParam[] = [
    { role: 'system', content: 'You are a helpful agent. Use tools via function calls. Think step-by-step.' },
    { role: 'user', content: prompt },
  ];

  while (true) {
    const response = await openai.chat.completions.create({
      model: 'gpt-4o-mini',
      messages,
      tools,
      tool_choice: 'auto',
    });

    const message = response.choices[0].message;
    messages.push(message);

    if (message.tool_calls) {
      for (const toolCall of message.tool_calls) {
        const result = executeTool(toolCall.function.name, JSON.parse(toolCall.function.arguments));
        messages.push({
          role: 'tool',
          tool_call_id: toolCall.id,
          content: JSON.stringify(result),
        });
      }
    } else {
      return message.content;
    }
  }
}

Claude Agent (Refactored)

async function runClaudeAgent(prompt: string) {
  let messages: Anthropic.MessageParam[] = [
-   { role: 'user', content: `<system>You are a helpful agent. Use &lt;tool_call&gt; XML. Think step-by-step.` </system>\
\
Human: ${prompt}` },
+   { role: 'user', content: `You are a helpful agent. Use tools via &lt;tool_call&gt; tags. Think step-by-step.\
\
${prompt}` },
  ];

  while (true) {
    const response = await client.messages.create({
      model: 'claude-3-5-sonnet-20241022',
+     max_tokens: 4096,
      messages,
-     tools,
+     tools,  // Claude supports up to 100 tools
      tool_choice: { type: 'auto' },
    });

-   const message = response.choices[0].message;
+   const message = { role: 'assistant' as const, content: response.content };
    messages.push(message);

-   if (message.tool_calls) {
+   const toolCalls = message.content
+     .filter((c): c is Anthropic.AnthropicToolUseBlock => c.type === 'tool_use')
+     .map(tc => ({
+       id: tc.id,
+       name: tc.name,
+       args: tc.input,
+     }));
+
+   if (toolCalls.length > 0) {
      for (const toolCall of toolCalls) {
-       const result = executeTool(toolCall.function.name, JSON.parse(toolCall.function.arguments));
+       const result = executeTool(toolCall.name, toolCall.args);
        messages.push({
-         role: 'tool',
-         tool_call_id: toolCall.id,
-         content: JSON.stringify(result),
+         role: 'user' as const,
+         content: [
+           {
+             type: 'tool_result',
+             tool_use_id: toolCall.id,
+             content: JSON.stringify(result),
+           },
+         ],
        });
      }
    } else {
-     return message.content;
+     return message.content[0]?.text ?? 'No response';
    }
  }
}

Major Changes:

• System Prompt: Embed as XML-like in user message: "<thinking>Thought</thinking><tool_call...>". Claude prefers this for agents.
• Tool Response Parsing: Claude uses content array with tool_use blocks—no JSON parsing needed; args are pre-parsed objects.
• Tool Results: Push tool_result block with tool_use_id, not role: 'tool'.
• Streaming: Claude's stream: true yields tool uses incrementally (add later).

Run it:

// Usage
const result = await runClaudeAgent('NYC weather and wind chill at 85F 10mph?');
console.log(result); // "Temperature: 85°F, wind chill: 82°F..."

Streaming for Real-Time Agents

Enhance UX with streams. Claude streams tool calls natively.

// Claude Streaming Snippet
const stream = await client.messages.stream({
  model: 'claude-3-5-sonnet-20241022',
  messages: [...],
  tools,
  stream: true,
});

for await (const chunk of stream) {
  if (chunk.type === 'content_block_delta' && chunk.delta.type === 'tool_use') {
    console.log('Tool:', chunk.delta.name); // Incremental!
  }
}

OpenAI similar, but Claude's deltas are more predictable for agents.

Optimizations for Production

• Caching: Use Anthropic's prompt caching (beta): Add cache_control to messages.

{ role: 'user', content: 'Static context...', cache_control: { type: 'ephemeral' } }

Saves 50-90% on repeated prefixes.

• Parallel Tools: Claude auto-calls multiple tools in one turn.

• Error Handling: Claude rarely invents tools; validate name exists.

• Cost Tracking: Log usage.input_tokens from response.

Perf Comparison (100 Agents Runs):

Common Pitfalls and Fixes

• XML Tags: Claude expects <tool_call name="calc"><input><expression>2+2</expression></input></tool_call>—enforce in system prompt.
• Max Tokens: Set high (4096+) for agents.
• Model Choice: Sonnet for agents; Haiku for simple tools.
• Type Safety: Use Zod for args validation.

const calcSchema = z.object({ expression: z.string() });

Scaling to Complex Agents

For production:

• Integrate MCP servers for external tools (e.g., Claude Desktop).
• Use n8n/Zapier for no-code agent orchestration.
• Build with Claude Code CLI for local dev.

Example: Multi-agent debate—Claude handles 200k context effortlessly vs. OpenAI's limits.

Conclusion

Migrating agents to Claude SDK takes ~2 hours for basics, yields immediate ROI. Fork this repo [link placeholder], test your codebase, and watch reliability soar. Questions? Join Claude Directory Discord.

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