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merged 4 commits into from
Oct 27, 2025
+217 −72
Merged

feat: integrate anthropic with tool call block #20100

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c391681
feat: integrate anthropic with tool call block
AstraBert Oct 16, 2025
b748ee5
fix: fix anthropic tool call storage logic
AstraBert Oct 23, 2025
5e6decb
fix: go back to parse_partial_json
AstraBert Oct 23, 2025
7af8a2d
Merge branch 'main' into clelia/anthropic-tool-call-block
AstraBert Oct 23, 2025
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111 changes: 67 additions & 44 deletions llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,9 @@
Set,
Tuple,
Union,
cast,
)

from llama_index.core.llms.utils import parse_partial_json
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
Expand All @@ -23,6 +24,7 @@
LLMMetadata,
MessageRole,
ContentBlock,
ToolCallBlock,
)
from llama_index.core.base.llms.types import TextBlock as LITextBlock
from llama_index.core.base.llms.types import CitationBlock as LICitationBlock
Expand All @@ -35,7 +37,6 @@
llm_completion_callback,
)
from llama_index.core.llms.function_calling import FunctionCallingLLM, ToolSelection
from llama_index.core.llms.utils import parse_partial_json
from llama_index.core.types import BaseOutputParser, PydanticProgramMode
from llama_index.core.utils import Tokenizer
from llama_index.llms.anthropic.utils import (
Expand All @@ -44,6 +45,7 @@
is_anthropic_prompt_caching_supported_model,
is_function_calling_model,
messages_to_anthropic_messages,
update_tool_calls,
)

import anthropic
Expand Down Expand Up @@ -351,8 +353,7 @@ def _completion_response_from_chat_response(

def _get_blocks_and_tool_calls_and_thinking(
self, response: Any
) -> Tuple[List[ContentBlock], List[Dict[str, Any]], List[Dict[str, Any]]]:
tool_calls = []
) -> Tuple[List[ContentBlock], List[Dict[str, Any]]]:
blocks: List[ContentBlock] = []
citations: List[TextCitation] = []
tracked_citations: Set[str] = set()
Expand Down Expand Up @@ -392,9 +393,15 @@ def _get_blocks_and_tool_calls_and_thinking(
)
)
elif isinstance(content_block, ToolUseBlock):
tool_calls.append(content_block.model_dump())
blocks.append(
ToolCallBlock(
tool_call_id=content_block.id,
tool_kwargs=cast(Dict[str, Any] | str, content_block.input),
tool_name=content_block.name,
)
)

return blocks, tool_calls, [x.model_dump() for x in citations]
return blocks, [x.model_dump() for x in citations]

@llm_chat_callback()
def chat(
Expand All @@ -412,17 +419,12 @@ def chat(
**all_kwargs,
)

blocks, tool_calls, citations = self._get_blocks_and_tool_calls_and_thinking(
response
)
blocks, citations = self._get_blocks_and_tool_calls_and_thinking(response)

return AnthropicChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
blocks=blocks,
additional_kwargs={
"tool_calls": tool_calls,
},
),
citations=citations,
raw=dict(response),
Expand Down Expand Up @@ -536,13 +538,18 @@ def gen() -> Generator[AnthropicChatResponse, None, None]:
else:
tool_calls_to_send = cur_tool_calls

for tool_call in tool_calls_to_send:
tc = ToolCallBlock(
tool_call_id=tool_call.id,
tool_name=tool_call.name,
tool_kwargs=cast(Dict[str, Any] | str, tool_call.input),
)
update_tool_calls(content, tc)

yield AnthropicChatResponse(
message=ChatMessage(
role=role,
blocks=content,
additional_kwargs={
"tool_calls": [t.dict() for t in tool_calls_to_send]
},
),
citations=cur_citations,
delta=content_delta,
Expand All @@ -560,13 +567,23 @@ def gen() -> Generator[AnthropicChatResponse, None, None]:
content.append(cur_block)
cur_block = None

if cur_tool_call is not None:
tool_calls_to_send = [*cur_tool_calls, cur_tool_call]
else:
tool_calls_to_send = cur_tool_calls

for tool_call in tool_calls_to_send:
tc = ToolCallBlock(
tool_call_id=tool_call.id,
tool_name=tool_call.name,
tool_kwargs=cast(Dict[str, Any] | str, tool_call.input),
)
update_tool_calls(content, tc)

yield AnthropicChatResponse(
message=ChatMessage(
role=role,
blocks=content,
additional_kwargs={
"tool_calls": [t.dict() for t in tool_calls_to_send]
},
),
citations=cur_citations,
delta="",
Expand Down Expand Up @@ -604,17 +621,12 @@ async def achat(
**all_kwargs,
)

blocks, tool_calls, citations = self._get_blocks_and_tool_calls_and_thinking(
response
)
blocks, citations = self._get_blocks_and_tool_calls_and_thinking(response)

return AnthropicChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
blocks=blocks,
additional_kwargs={
"tool_calls": tool_calls,
},
),
citations=citations,
raw=dict(response),
Expand Down Expand Up @@ -728,13 +740,18 @@ async def gen() -> ChatResponseAsyncGen:
else:
tool_calls_to_send = cur_tool_calls

for tool_call in tool_calls_to_send:
tc = ToolCallBlock(
tool_call_id=tool_call.id,
tool_name=tool_call.name,
tool_kwargs=cast(Dict[str, Any] | str, tool_call.input),
)
update_tool_calls(content, tc)

yield AnthropicChatResponse(
message=ChatMessage(
role=role,
blocks=content,
additional_kwargs={
"tool_calls": [t.dict() for t in tool_calls_to_send]
},
),
citations=cur_citations,
delta=content_delta,
Expand All @@ -752,13 +769,23 @@ async def gen() -> ChatResponseAsyncGen:
content.append(cur_block)
cur_block = None

if cur_tool_call is not None:
tool_calls_to_send = [*cur_tool_calls, cur_tool_call]
else:
tool_calls_to_send = cur_tool_calls

for tool_call in tool_calls_to_send:
tc = ToolCallBlock(
tool_call_id=tool_call.id,
tool_name=tool_call.name,
tool_kwargs=cast(Dict[str, Any] | str, tool_call.input),
)
update_tool_calls(content, tc)

yield AnthropicChatResponse(
message=ChatMessage(
role=role,
blocks=content,
additional_kwargs={
"tool_calls": [t.dict() for t in tool_calls_to_send]
},
),
citations=cur_citations,
delta="",
Expand Down Expand Up @@ -867,7 +894,11 @@ def get_tool_calls_from_response(
**kwargs: Any,
) -> List[ToolSelection]:
"""Predict and call the tool."""
tool_calls = response.message.additional_kwargs.get("tool_calls", [])
tool_calls = [
block
for block in response.message.blocks
if isinstance(block, ToolCallBlock)
]

if len(tool_calls) < 1:
if error_on_no_tool_call:
Expand All @@ -879,24 +910,16 @@ def get_tool_calls_from_response(

tool_selections = []
for tool_call in tool_calls:
if (
"input" not in tool_call
or "id" not in tool_call
or "name" not in tool_call
):
raise ValueError("Invalid tool call.")
if tool_call["type"] != "tool_use":
raise ValueError("Invalid tool type. Unsupported by Anthropic")
argument_dict = (
json.loads(tool_call["input"])
if isinstance(tool_call["input"], str)
else tool_call["input"]
json.loads(tool_call.tool_kwargs)
if isinstance(tool_call.tool_kwargs, str)
else tool_call.tool_kwargs
)

tool_selections.append(
ToolSelection(
tool_id=tool_call["id"],
tool_name=tool_call["name"],
tool_id=tool_call.tool_call_id or "",
tool_name=tool_call.tool_name,
tool_kwargs=argument_dict,
)
)
Expand Down
82 changes: 68 additions & 14 deletions llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/utils.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
CitationBlock,
ThinkingBlock,
ContentBlock,
ToolCallBlock,
)

from anthropic.types import (
Expand All @@ -24,6 +25,7 @@
DocumentBlockParam,
ThinkingBlockParam,
ImageBlockParam,
ToolUseBlockParam,
CacheControlEphemeralParam,
Base64PDFSourceParam,
)
Expand Down Expand Up @@ -207,6 +209,7 @@ def blocks_to_anthropic_blocks(
) -> List[AnthropicContentBlock]:
anthropic_blocks: List[AnthropicContentBlock] = []
global_cache_control: Optional[CacheControlEphemeralParam] = None
unique_tool_calls = []

if kwargs.get("cache_control"):
global_cache_control = CacheControlEphemeralParam(**kwargs["cache_control"])
Expand Down Expand Up @@ -269,6 +272,19 @@ def blocks_to_anthropic_blocks(
if global_cache_control:
anthropic_blocks[-1]["cache_control"] = global_cache_control

elif isinstance(block, ToolCallBlock):
unique_tool_calls.append((block.tool_call_id, block.tool_name))
anthropic_blocks.append(
ToolUseBlockParam(
id=block.tool_call_id or "",
input=block.tool_kwargs,
name=block.tool_name,
type="tool_use",
)
)
if global_cache_control:
anthropic_blocks[-1]["cache_control"] = global_cache_control

elif isinstance(block, CachePoint):
if len(anthropic_blocks) > 0:
anthropic_blocks[-1]["cache_control"] = CacheControlEphemeralParam(
Expand All @@ -282,20 +298,25 @@ def blocks_to_anthropic_blocks(
else:
raise ValueError(f"Unsupported block type: {type(block)}")

# keep this code for compatibility with older chat histories
tool_calls = kwargs.get("tool_calls", [])
for tool_call in tool_calls:
assert "id" in tool_call
assert "input" in tool_call
assert "name" in tool_call

anthropic_blocks.append(
ToolUseBlockParam(
id=tool_call["id"],
input=tool_call["input"],
name=tool_call["name"],
type="tool_use",
)
)
try:
assert "id" in tool_call
assert "input" in tool_call
assert "name" in tool_call

if (tool_call["id"], tool_call["name"]) not in unique_tool_calls:
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Added this check here to avoid duplicates

anthropic_blocks.append(
ToolUseBlockParam(
id=tool_call["id"],
input=tool_call["input"],
name=tool_call["name"],
type="tool_use",
)
)
except AssertionError:
continue

return anthropic_blocks

Expand Down Expand Up @@ -359,9 +380,15 @@ def messages_to_anthropic_messages(


def force_single_tool_call(response: ChatResponse) -> None:
tool_calls = response.message.additional_kwargs.get("tool_calls", [])
tool_calls = [
block for block in response.message.blocks if isinstance(block, ToolCallBlock)
]
if len(tool_calls) > 1:
response.message.additional_kwargs["tool_calls"] = [tool_calls[0]]
response.message.blocks = [
block
for block in response.message.blocks
if not isinstance(block, ToolCallBlock)
] + [tool_calls[0]]


# Anthropic models that support prompt caching
Expand Down Expand Up @@ -400,6 +427,33 @@ def force_single_tool_call(response: ChatResponse) -> None:
)


def update_tool_calls(blocks: list[ContentBlock], tool_call: ToolCallBlock) -> None:
if len([block for block in blocks if isinstance(block, ToolCallBlock)]) == 0:
blocks.append(tool_call)
return
elif not any(
block.tool_call_id == tool_call.tool_call_id
for block in blocks
if isinstance(block, ToolCallBlock)
):
blocks.append(tool_call)
return
elif any(
block.tool_call_id == tool_call.tool_call_id
and block.tool_kwargs == tool_call.tool_kwargs
for block in blocks
if isinstance(block, ToolCallBlock)
):
return
else:
for i, block in enumerate(blocks):
if isinstance(block, ToolCallBlock):
if block.tool_call_id == tool_call.tool_call_id:
blocks[i] = tool_call
break
return
Comment on lines +430 to +454
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Added this logic because, since Anthropic streams partial JSON, tool calls with the same ID are streamed, but they might have different tool_kwargs. So:

  • We append the tool call if no other tool call is present/there is not tool call with the same ID
  • We skip if there is already a tool call with the same ID and arguments
  • We update the tool call if there is one with the same ID but it has different arguments

I added a test for this and I also tested end-to-end with the agent script, and now everything goes smooth (no duplicate calls, correct arguments parsing)



def is_anthropic_prompt_caching_supported_model(model: str) -> bool:
"""
Check if the given Anthropic model supports prompt caching.
Expand Down
4 changes: 2 additions & 2 deletions llama-index-integrations/llms/llama-index-llms-anthropic/pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -27,15 +27,15 @@ dev = [

[project]
name = "llama-index-llms-anthropic"
version = "0.9.7"
version = "0.10.0"
description = "llama-index llms anthropic integration"
authors = [{name = "Your Name", email = "you@example.com"}]
requires-python = ">=3.9,<4.0"
readme = "README.md"
license = "MIT"
dependencies = [
"anthropic[bedrock, vertex]>=0.69.0",
"llama-index-core>=0.14.3,<0.15",
"llama-index-core>=0.14.5,<0.15",
]

[tool.codespell]
Expand Down
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