Anthropic Codex API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Codex Agent SDK. Use when building applications with the Codex API or Anthropic SDKs.
Build applications with the Anthropic Codex API and SDKs.
anthropic (Python) or @anthropic-ai/sdk (TypeScript)| Model | ID | Best For |
|---|---|---|
| Opus 4.1 | Codex-opus-4-1 | Complex reasoning, architecture, research |
| Sonnet 4 | Codex-sonnet-4-0 | Balanced coding, most development tasks |
| Haiku 3.5 | Codex-3-5-haiku-latest | Fast responses, high-volume, cost-sensitive |
Default to Sonnet 4 unless the task requires deep reasoning (Opus) or speed/cost optimization (Haiku). For production, prefer pinned snapshot IDs over aliases.
pip install anthropic
import anthropic
client = anthropic.Anthropic() # reads ANTHROPIC_API_KEY from env
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain async/await in Python"}
]
)
print(message.content[0].text)
with client.messages.stream(
model="Codex-sonnet-4-0",
max_tokens=1024,
messages=[{"role": "user", "content": "Write a haiku about coding"}]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
system="You are a senior Python developer. Be concise.",
messages=[{"role": "user", "content": "Review this function"}]
)
npm install @anthropic-ai/sdk
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic(); // reads ANTHROPIC_API_KEY from env
const message = await client.messages.create({
model: "Codex-sonnet-4-0",
max_tokens: 1024,
messages: [
{ role: "user", content: "Explain async/await in TypeScript" }
],
});
console.log(message.content[0].text);
const stream = client.messages.stream({
model: "Codex-sonnet-4-0",
max_tokens: 1024,
messages: [{ role: "user", content: "Write a haiku" }],
});
for await (const event of stream) {
if (event.type === "content_block_delta" && event.delta.type === "text_delta") {
process.stdout.write(event.delta.text);
}
}
Define tools and let Codex call them:
tools = [
{
"name": "get_weather",
"description": "Get current weather for a location",
"input_schema": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
]
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
tools=tools,
messages=[{"role": "user", "content": "What's the weather in SF?"}]
)
# Handle tool use response
for block in message.content:
if block.type == "tool_use":
# Execute the tool with block.input
result = get_weather(**block.input)
# Send result back
follow_up = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
tools=tools,
messages=[
{"role": "user", "content": "What's the weather in SF?"},
{"role": "assistant", "content": message.content},
{"role": "user", "content": [
{"type": "tool_result", "tool_use_id": block.id, "content": str(result)}
]}
]
)
Send images for analysis:
import base64
with open("diagram.png", "rb") as f:
image_data = base64.standard_b64encode(f.read()).decode("utf-8")
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_data}},
{"type": "text", "text": "Describe this diagram"}
]
}]
)
For complex reasoning tasks:
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=16000,
thinking={
"type": "enabled",
"budget_tokens": 10000
},
messages=[{"role": "user", "content": "Solve this math problem step by step..."}]
)
for block in message.content:
if block.type == "thinking":
print(f"Thinking: {block.thinking}")
elif block.type == "text":
print(f"Answer: {block.text}")
Cache large system prompts or context to reduce costs:
message = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=1024,
system=[
{"type": "text", "text": large_system_prompt, "cache_control": {"type": "ephemeral"}}
],
messages=[{"role": "user", "content": "Question about the cached context"}]
)
# Check cache usage
print(f"Cache read: {message.usage.cache_read_input_tokens}")
print(f"Cache creation: {message.usage.cache_creation_input_tokens}")
Process large volumes asynchronously at 50% cost reduction:
import time
batch = client.messages.batches.create(
requests=[
{
"custom_id": f"request-{i}",
"params": {
"model": "Codex-sonnet-4-0",
"max_tokens": 1024,
"messages": [{"role": "user", "content": prompt}]
}
}
for i, prompt in enumerate(prompts)
]
)
# Poll for completion
while True:
status = client.messages.batches.retrieve(batch.id)
if status.processing_status == "ended":
break
time.sleep(30)
# Get results
for result in client.messages.batches.results(batch.id):
print(result.result.message.content[0].text)
Build multi-step agents:
# Note: Agent SDK API surface may change — check official docs
import anthropic
# Define tools as functions
tools = [{
"name": "search_codebase",
"description": "Search the codebase for relevant code",
"input_schema": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"]
}
}]
# Run an agentic loop with tool use
client = anthropic.Anthropic()
messages = [{"role": "user", "content": "Review the auth module for security issues"}]
while True:
response = client.messages.create(
model="Codex-sonnet-4-0",
max_tokens=4096,
tools=tools,
messages=messages,
)
if response.stop_reason == "end_turn":
break
# Handle tool calls and continue the loop
messages.append({"role": "assistant", "content": response.content})
# ... execute tools and append tool_result messages
| Strategy | Savings | When to Use |
|---|---|---|
| Prompt caching | Up to 90% on cached tokens | Repeated system prompts or context |
| Batches API | 50% | Non-time-sensitive bulk processing |
| Haiku instead of Sonnet | ~75% | Simple tasks, classification, extraction |
| Shorter max_tokens | Variable | When you know output will be short |
| Streaming | None (same cost) | Better UX, same price |
import time
from anthropic import APIError, RateLimitError, APIConnectionError