tiennm99/litellm
1
0
Fork 0
mirror of https://github.com/tiennm99/litellm.git synced 2026-08-05 12:23:28 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: initial doc cleanup

Browse Source
This commit is contained in:
Krrish Dholakia
2025-11-25 11:57:51 -08:00
parent 67622fb040
commit db2c8e3631
2 changed files with 194 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/my-website/blog/anthropic_advanced_features/index.md → docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md
+193 -12
View File
@@ -1,7 +1,7 @@
---
slug: anthropic_advanced_features
title: "Advanced Anthropic Features in LiteLLM: Tool Search, Programmatic Tool Calling, Input Examples, and Effort Control"
date: 2025-01-25T10:00:00
title: "Day 0 Support: Claude 4.5 Opus (+Advanced Features)"
date: 2025-11-25T10:00:00
authors:
- name: Sameer Kankute
title: SWE @ LiteLLM (LLM Translation)
@@ -22,24 +22,205 @@ hide_table_of_contents: false
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
This guide covers Anthropic's latest model (Claude Opus 4.5) and its advanced features now available in LiteLLM: Tool Search, Programmatic Tool Calling, Tool Input Examples, and the Effort Parameter.
---
## Usage
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
```python
import os
from litellm import completion
# set env - [OPTIONAL] replace with your anthropic key
os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
messages = [{"role": "user", "content": "Hey! how's it going?"}]
## OPENAI /chat/completions API format
response = completion(model="claude-opus-4-5-20251101", messages=messages)
print(response)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-4 ### RECEIVED MODEL NAME ###
litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
model: claude-opus-4-5-20251101 ### MODEL NAME sent to `litellm.completion()` ###
api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY")
```
**2. Start the proxy**
```bash
litellm --config /path/to/config.yaml
```
**3. Test it!**
<Tabs>
<TabItem value="curl" label="OpenAI Chat Completions">
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
```
</TabItem>
<TabItem value="anthropic" label="Anthropic /v1/messages API">
```bash
curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
```
</TabItem>
</Tabs>
</TabItem>
</Tabs>
## Usage - Bedrock
:::info
This guide covers Anthropic's latest advanced features now available in LiteLLM: Tool Search, Programmatic Tool Calling, Tool Input Examples, and the Effort Parameter.
LiteLLM uses the boto3 library to authenticate with Bedrock.
For more ways to authenticate with Bedrock, see the [Bedrock documentation](../../docs/providers/bedrock#authentication).
:::
We're excited to announce support for Anthropic's latest advanced features in LiteLLM! These powerful capabilities enable you to build more efficient, scalable, and cost-effective AI applications with Claude.
<Tabs>
<TabItem value="sdk" label="LiteLLM Python SDK">
## Table of Contents
1. [Tool Search](#tool-search)
2. [Programmatic Tool Calling](#programmatic-tool-calling)
3. [Tool Input Examples](#tool-input-examples)
4. [Effort Parameter: Control Token Usage](#effort-parameter)
5. [Cost Tracking: Monitor Tool Search Usage](#cost-tracking)
6. [Combining Features](#combining-features)
```python
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
## OPENAI /chat/completions API format
response = completion(
model="bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0",
messages=[{ "content": "Hello, how are you?","role": "user"}]
)
```
</TabItem>
<TabItem value="proxy" label="LiteLLM Proxy">
**1. Setup config.yaml**
```yaml
model_list:
- model_name: claude-4 ### RECEIVED MODEL NAME ###
litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
model: bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0 ### MODEL NAME sent to `litellm.completion()` ###
aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
aws_region_name: os.environ/AWS_REGION_NAME
```
**2. Start the proxy**
```bash
litellm --config /path/to/config.yaml
```
**3. Test it!**
<Tabs>
<TabItem value="curl" label="OpenAI Chat Completions">
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
```
</TabItem>
<TabItem value="anthropic" label="Anthropic /v1/messages API">
```bash
curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"model": "claude-4",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}
'
```
</TabItem>
<TabItem value="invoke" label="Bedrock /invoke API">
```bash
curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/invoke' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"max_tokens": 1024,
"messages": [{"role": "user", "content": "Hello, how are you?"}]
}'
```
</TabItem>
<TabItem value="converse" label="Bedrock /converse API">
```bash
curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/converse' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data ' {
"messages": [{"role": "user", "content": "Hello, how are you?"}]
}'
```
</TabItem>
</Tabs>
</TabItem>
</Tabs>
---
## Tool Search {#tool-search}
docs/my-website/docs/providers/anthropic_tool_search.md
+1 -1
View File
@@ -380,7 +380,7 @@ If Claude references a tool that isn't in your deferred tools list, you'll get a
**When traditional tool calling is better:**
- Less than 10 tools total
- All tools are frequently used
- Very small tool definitions (<100 tokens total)
- Very small tool definitions (\<100 tokens total)
## Limitations