Anthropic Client
The Anthropic client provides access to Claude models through the native Messages API. It is a standalone client (not OpenAI-compatible).
Configuration
from padwan_ai.anthropic import AnthropicClient
client = AnthropicClient(
api_key="...", # or set ANTHROPIC_API_KEY env var
model="claude-opus-4-8", # default model
max_tokens=4096, # required by the Messages API, per-response cap
)
Sampling parameters
Current Claude models reject non-default sampling parameters, so the
inherited temperature field is never sent — steer behavior via the
prompt instead.
Usage
Basic Chat
from padwan_ai.conversation import Message
async with AnthropicClient() as client:
response, usage = await client.complete_chat(
[Message(role="user", content="Hello!")]
)
print(response["content"])
Streaming
from padwan_ai.conversation import Message
async with AnthropicClient() as client:
stream = client.stream_chat([Message(role="user", content="Tell me a story")])
async for chunk in stream:
print(chunk, end="")
With System Prompt
System messages are translated to the Messages API's top-level system field.
from padwan_ai import ConversationState
state = ConversationState(system="You are a helpful assistant.")
state.add_user_message("Hello!")
async with AnthropicClient() as client:
response, usage = await client.complete_chat(state.messages)
state.add_assistant_message(response["content"])
state.accumulate_usage(usage)
Tool Calling
tool_use blocks map to the shared ToolCall shape; send results back as
ToolResultMessages.
from padwan_ai.models import ToolDefinition
WEATHER_TOOL: ToolDefinition = {
"name": "get_weather",
"description": "Get the current weather for a given city.",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
}
async with AnthropicClient() as client:
response, _ = await client.complete_chat(
[{"role": "user", "content": "Weather in Paris?"}],
tools=[WEATHER_TOOL],
)
call = response["tool_calls"][0]
response2, _ = await client.complete_chat(
[
{"role": "user", "content": "Weather in Paris?"},
{"role": "assistant", "content": response["content"], "tool_calls": [call]},
{
"role": "tool",
"tool_call_id": call["id"],
"name": "get_weather",
"content": "18°C",
},
],
tools=[WEATHER_TOOL],
)
Thinking
Claude models think adaptively by default. Summarized thinking (when exposed
by the model) is forwarded to the on_thought callback and never leaks into
the answer text.
async with AnthropicClient(
model="claude-opus-4-8",
on_thought=lambda t: print(f"[thinking] {t}"),
) as client:
response, _ = await client.complete_chat(
[{"role": "user", "content": "What is 7 * 8?"}]
)