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Add doc and tests for agent skils

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This commit is contained in:
Sameer Kankute 2025-12-16 10:20:21 +05:30
parent bcfc77f683
commit 1222d9e376
3 changed files with 278 additions and 2 deletions
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84
docs/my-website/docs/providers/anthropic.md
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@ -1936,3 +1936,87 @@ curl http://0.0.0.0:4000/v1/chat/completions \
</TabItem>
</Tabs>
## Usage - Agent Skills
LiteLLM supports using Agent Skills with the API
<Tabs>
<TabItem value="sdk" label="SDK">
```python
response = completion(
model="claude-sonnet-4-5-20250929",
messages=messages,
tools= [
{
"type": "code_execution_20250825",
"name": "code_execution"
}
],
container= {
"skills": [
{
"type": "anthropic",
"skill_id": "pptx",
"version": "latest"
}
]
}
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
1. Setup config.yaml
```yaml
model_list:
- model_name: claude-sonnet-4-5-20250929
litellm_params:
model: anthropic/claude-sonnet-4-5-20250929
api_key: os.environ/ANTHROPIC_API_KEY
```
2. Start Proxy
```
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://localhost:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <YOUR-LITELLM-KEY>' \
--data '{
"model": "claude-sonnet-4-5-20250929",
"messages": [
{
"role": "user",
"content": "Hi"
}
],
"tools": [
{
"type": "code_execution_20250825",
"name": "code_execution"
}
],
"container": {
"skills": [
{
"type": "anthropic",
"skill_id": "pptx",
"version": "latest"
}
]
}
}'
```
</TabItem>
</Tabs>
The container and its "id" will be present in "provider_specific_fields" in streaming/non-streaming response

2
litellm/model_prices_and_context_window_backup.json
View file

@ -30628,7 +30628,7 @@
"litellm_provider": "fireworks_ai",
"mode": "embedding"
},
"fireworks_ai/accounts/fireworks/models/qwen3-embedding-8b": {
"fireworks_ai/accounts/fireworks/models/": {
"max_tokens": 40960,
"max_input_tokens": 40960,
"max_output_tokens": 40960,

194
tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
View file

@ -1,11 +1,11 @@
from unittest.mock import MagicMock
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
from litellm.llms.anthropic.chat.handler import ModelResponseIterator
from litellm.types.llms.openai import (
ChatCompletionToolCallChunk,
ChatCompletionToolCallFunctionChunk,
)
from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
def test_redacted_thinking_content_block_delta():
@ -779,3 +779,195 @@ def test_web_search_tool_result_captured_in_provider_specific_fields():
assert (
web_search_results[0]["content"][0]["title"] == "Fun Otter Facts"
), "First result title should match"
def test_container_in_provider_specific_fields_streaming():
"""
Test that container is captured in provider_specific_fields for streaming responses.
When container with skills is used, the container field should be present in
the provider_specific_fields of the message_delta chunk.
"""
iterator = ModelResponseIterator(
streaming_response=MagicMock(), sync_stream=True, json_mode=False
)
# Simulate streaming chunks
chunks = [
# 1. message_start
{
"type": "message_start",
"message": {
"id": "msg_123",
"type": "message",
"role": "assistant",
"content": [],
"usage": {"input_tokens": 98976, "output_tokens": 1},
},
},
# 2. content_block_start for text
{
"type": "content_block_start",
"index": 0,
"content_block": {
"type": "text",
"text": "",
},
},
# 3. content_block_delta with text
{
"type": "content_block_delta",
"index": 0,
"delta": {"type": "text_delta", "text": "Hello, this is a response"},
},
# 4. content_block_stop for text
{"type": "content_block_stop", "index": 0},
# 5. message_delta with container - THIS IS WHAT WE'RE TESTING
{
"type": "message_delta",
"delta": {
"stop_reason": "end_turn",
"stop_sequence": None,
"container": {
"id": "container_011CW9hA9zpZ8xD3bjjShy4p",
"expires_at": "2025-12-16T04:57:16.913181Z",
"skills": [
{
"type": "anthropic",
"skill_id": "pptx",
"version": "20251013",
}
],
},
},
"usage": {
"input_tokens": 98976,
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0,
"output_tokens": 931,
"server_tool_use": {"web_search_requests": 0},
},
},
]
container_field = None
for chunk in chunks:
parsed = iterator.chunk_parser(chunk)
if (
parsed.choices
and parsed.choices[0].delta.provider_specific_fields
and "container" in parsed.choices[0].delta.provider_specific_fields
):
container_field = parsed.choices[0].delta.provider_specific_fields[
"container"
]
# Verify container was captured
assert container_field is not None, "container should be captured in provider_specific_fields"
assert (
container_field["id"] == "container_011CW9hA9zpZ8xD3bjjShy4p"
), "container id should match"
assert (
container_field["expires_at"] == "2025-12-16T04:57:16.913181Z"
), "expires_at should match"
assert len(container_field["skills"]) == 1, "Should have 1 skill"
assert (
container_field["skills"][0]["skill_id"] == "pptx"
), "skill_id should be pptx"
assert (
container_field["skills"][0]["version"] == "20251013"
), "version should match"
def test_container_in_provider_specific_fields_non_streaming():
"""
Test that container is captured in provider_specific_fields for non-streaming responses.
When container with skills is used in non-streaming, the container field should be
present in the provider_specific_fields of the response.
"""
iterator = ModelResponseIterator(
streaming_response=MagicMock(), sync_stream=False, json_mode=False
)
# Simulate a message_delta chunk with container (as it would appear in non-streaming)
message_delta_chunk = {
"type": "message_delta",
"delta": {
"stop_reason": "end_turn",
"stop_sequence": None,
"container": {
"id": "container_abc123xyz",
"expires_at": "2025-12-20T10:30:00.000000Z",
"skills": [
{
"type": "anthropic",
"skill_id": "code_execution",
"version": "latest",
},
{
"type": "anthropic",
"skill_id": "pptx",
"version": "20251013",
},
],
},
},
"usage": {
"input_tokens": 1000,
"output_tokens": 200,
},
}
model_response = iterator.chunk_parser(message_delta_chunk)
# Verify container is in provider_specific_fields
assert model_response.choices[0].delta.provider_specific_fields is not None
assert "container" in model_response.choices[0].delta.provider_specific_fields
container_field = model_response.choices[0].delta.provider_specific_fields[
"container"
]
assert container_field["id"] == "container_abc123xyz", "container id should match"
assert (
container_field["expires_at"] == "2025-12-20T10:30:00.000000Z"
), "expires_at should match"
assert len(container_field["skills"]) == 2, "Should have 2 skills"
assert (
container_field["skills"][0]["skill_id"] == "code_execution"
), "First skill_id should be code_execution"
assert (
container_field["skills"][1]["skill_id"] == "pptx"
), "Second skill_id should be pptx"
def test_container_absent_when_not_provided():
"""
Test that container is not added to provider_specific_fields when not provided.
This ensures we don't add empty or None container fields.
"""
iterator = ModelResponseIterator(
streaming_response=MagicMock(), sync_stream=False, json_mode=False
)
# message_delta without container
message_delta_chunk = {
"type": "message_delta",
"delta": {
"stop_reason": "end_turn",
"stop_sequence": None,
},
"usage": {
"input_tokens": 1000,
"output_tokens": 200,
},
}
model_response = iterator.chunk_parser(message_delta_chunk)
# Verify container is NOT in provider_specific_fields when not provided
if model_response.choices[0].delta.provider_specific_fields:
assert (
"container" not in model_response.choices[0].delta.provider_specific_fields
), "container should not be present when not provided in delta"