swarmauri_llm_anthropic 0.11.0.dev1

Provider-specific Swarmauri imports for Anthropic Claude Messages API chat, streaming, async, batch, and tool-use workflows.

Swarmauri Anthropic LLM

swarmauri_llm_anthropic provides provider-specific imports for Anthropic Claude models in Swarmauri. It re-exports AnthropicModel for standard Messages API chat workflows and AnthropicToolModel for tool-assisted conversations, both backed by the maintained implementations in swarmauri_standard.

Why Swarmauri Anthropic LLM?

Use this package when a Swarmauri application should depend explicitly on Anthropic Claude support while keeping the shared Swarmauri conversation and tool abstractions. The adapters send requests to Anthropic's /v1/messages endpoint, handle system-message extraction, support sync and async prediction, stream server-sent events, map token usage into Swarmauri UsageData, and connect Swarmauri toolkits to Anthropic tool schemas.

FAQ

Q: Which Anthropic API does this package use?

A: Both adapters use the Anthropic Messages API at https://api.anthropic.com/v1/messages with the anthropic-version header set to 2023-06-01.

Q: Which classes are exported?

A: The package exports AnthropicModel through the swarmauri.llms entry point and AnthropicToolModel through the swarmauri.tool_llms entry point.

Q: Which Claude model names are configured locally?

A: AnthropicModel and AnthropicToolModel include configured model IDs such as claude-opus-4-1, claude-opus-4-0, claude-sonnet-4-0, claude-3-7-sonnet-latest, and claude-3-5-haiku-latest. Set name explicitly in production.

Q: Does the tool model execute Swarmauri tools?

A: Yes. AnthropicToolModel converts Swarmauri toolkit tool schemas, sends them in the Anthropic tools payload, invokes matching Swarmauri tools when a tool_use block is returned, and adds the result to the conversation.

Features

• Provider-specific imports for AnthropicModel and AnthropicToolModel.
• Swarmauri Conversation, message, toolkit, and usage-data integration.
• Sync predict() and async apredict() message generation.
• Sync stream() and async astream() event streaming.
• Sync batch() and async abatch() helpers for multiple conversations.
• System-message extraction into Anthropic's top-level system field for AnthropicModel.
• Toolkit schema conversion for AnthropicToolModel.
• Retry handling for rate-limit and overloaded-provider status codes on the standard model.
• Python 3.10, 3.11, 3.12, 3.13, and 3.14 support.

Prerequisites

• Anthropic API key from the Anthropic Console.
• Network access to api.anthropic.com.
• Swarmauri conversations and messages.
• Swarmauri Toolkit objects when using AnthropicToolModel.

Installation

Install with uv:

uv add swarmauri_llm_anthropic

Install with pip:

pip install swarmauri_llm_anthropic

Usage

Run a standard Claude Messages API request:

import os

from swarmauri_llm_anthropic import AnthropicModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage
from swarmauri_standard.messages.SystemMessage import SystemMessage

conversation = Conversation()
conversation.add_message(SystemMessage(content="Answer in concise technical prose."))
conversation.add_message(HumanMessage(content="What is Swarmauri?"))

model = AnthropicModel(
    api_key=os.environ["ANTHROPIC_API_KEY"],
    name="claude-sonnet-4-0",
)
result = model.predict(conversation=conversation, max_tokens=256, temperature=0.2)

print(result.get_last().content)

Stream a Claude response:

import os

from swarmauri_llm_anthropic import AnthropicModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage

conversation = Conversation()
conversation.add_message(HumanMessage(content="Draft a short API changelog entry."))

model = AnthropicModel(api_key=os.environ["ANTHROPIC_API_KEY"])

for token in model.stream(conversation=conversation, max_tokens=200):
    print(token, end="")

Use the tool model with a Swarmauri toolkit:

import os

from swarmauri_llm_anthropic import AnthropicToolModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage
from swarmauri_standard.toolkits.Toolkit import Toolkit

toolkit = Toolkit()
# toolkit.add_tool(...)

conversation = Conversation()
conversation.add_message(HumanMessage(content="Use an available tool if needed."))

model = AnthropicToolModel(api_key=os.environ["ANTHROPIC_API_KEY"])
result = model.predict(conversation=conversation, toolkit=toolkit)

print(result.get_last().content)

Related Packages

LLM provider packages:

• swarmauri_llm_ai21
• swarmauri_llm_cerebras
• swarmauri_llm_cohere
• swarmauri_llm_gemini
• swarmauri_llm_groq
• swarmauri_llm_mistral
• swarmauri_llm_openai

Foundational packages:

• swarmauri_core defines core interfaces.
• swarmauri_base provides base component classes.
• swarmauri_standard contains the shared Anthropic implementations.
• swarmauri provides namespace imports and plugin discovery.

Best Practices

• Store ANTHROPIC_API_KEY in a secret manager or environment variable.
• Set name explicitly for reproducible model behavior.
• Use async methods for high-concurrency service workloads.
• Keep system instructions in SystemMessage so the standard model can map them to Anthropic's system field.
• Test tool schemas before production use so Anthropic tool calls map cleanly to Swarmauri toolkit functions.

License

Apache-2.0