Tool calling with LiteLLM and thinking models fail

[gal-checksum]

Describe the bug

When running the agents SDK with tool calling and a thinking model through LITELLM (e.g. sonnet 4) getting this error

litellm.exceptions.BadRequestError: litellm.BadRequestError: AnthropicException - {"type":"error","error":{"type":"invalid_request_error","message":"messages.1.content.0.type: Expected `thinking` or `redacted_thinking`, but found `text`. When `thinking` is enabled, a final `assistant` message must start with a thinking block (preceeding the lastmost set of `tool_use` and `tool_result` blocks). We recommend you include thinking blocks from previous turns. To avoid this requirement, disable `thinking`. Please consult our documentation at https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking"}}

Debug information

• Agents SDK version: 0.0.16
• Python version 3.13

Repro steps

1. Run the agents sdk with sonnet 4
2. Produce a scenario that requires 2 tool calls or more
3. Get the failure above

Expected behavior

Everything works :)

[rm-openai]

Can you please provide a full working script? Happy to take a look!

[gal-checksum]

@rm-openai see below

from agents import (
    Agent,
    function_tool,
    RunContextWrapper,
    Runner,
    ModelSettings,
)
from dataclasses import dataclass
import asyncio
from agents.extensions.models.litellm_model import LitellmModel
import os
from openai.types import Reasoning


from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()


@dataclass
class Count:
    count: int


@function_tool
def count(ctx: RunContextWrapper[Count]) -> str:
    """
    Increments the count by 1 and returns the count

    Returns:
        A string with the count
    """
    ctx.context.count += 1
    return f"Counted to {ctx.context.count}"


count_ctx = Count(count=0)

agent = Agent[Count](
    name="Counter Agent",
    instructions="Count until the number the user tells you to stop using count tool",
    tools=[count],
    model=LitellmModel(
        model="anthropic/claude-sonnet-4-20250514",
        api_key=os.getenv("ANTHROPIC_API_KEY"),
    ),
    model_settings=ModelSettings(
        reasoning=Reasoning(effort="high", summary="detailed")
    ),
)


async def main():
    results = await Runner.run(
        agent, input="Count to 10", context=count_ctx, max_turns=30
    )
    print(results)


if __name__ == "__main__":
    asyncio.run(main())

[knowsuchagency]

Root Cause Analysis and Current Status

TLDR: Thinking with tool calling for Anthropic is broken in LiteLLM.

---

I've investigated this issue thoroughly and determined the root cause is in LiteLLM, not the openai-agents-python SDK.

What's Actually Happening

1. LiteLLM doesn't preserve thinking blocks when reconstructing conversation history for Anthropic's API
2. When reasoning is enabled, Anthropic requires assistant messages to start with thinking blocks
3. LiteLLM converts tool_calls to tool_use content blocks but loses the thinking context

Current Workarounds

Until LiteLLM fixes this upstream:

1. Use OpenAI o4-mini - Full thinking + tools support works perfectly
2. Disable reasoning for Anthropic when using tools
3. Use reasoning without tools for Anthropic

Related Issues

• LiteLLM Issue: [Bug]: reasoning content is not returned from anthropic when using aresponses BerriAI/litellm#11302
• My analysis: See my detailed comment on that issue with implementation guidance

Why No Fix in This SDK

I initially created a PR with a workaround, but decided against it because:

• It's a dependency issue that should be fixed upstream
• The workaround only partially works (single tool call only)
• The agents SDK shouldn't need to know about provider-specific API quirks

[gal-checksum]

Thanks! Another workaorund could also be use Anthropic through the OpenAI Responses API Compatibility no?

Haven't tried it but should work