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bboynton97 merged 1 commit into from
May 14, 2025
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Updated examples in docs #973

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45 changes: 29 additions & 16 deletions docs/v2/integrations/anthropic.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -129,9 +129,14 @@ client = anthropic.Anthropic(api_key="<YOUR_ANTHROPIC_API_KEY>")
# Define tools
tools = [
{
"type": "custom",
"name": "get_current_time",
"description": "Get the current date and time",
"input_schema": {},
"input_schema": {
"type": "object",
"properties": {},
"required": []
}
}
]

Expand All @@ -149,9 +154,9 @@ def get_current_time():
)

# Handle tool use
if message.content[0].type == "tool_use":
tool_use = message.content[0].tool_use
tool_name = tool_use.name
if message.content[0].type == "tool_calls":
tool_call = message.content[0].tool_calls[0]
tool_name = tool_call.name

if tool_name == "get_current_time":
tool_response = get_current_time()
Expand All @@ -162,18 +167,26 @@ if message.content[0].type == "tool_use":
max_tokens=1000,
messages=[
{"role": "user", "content": "What time is it now?"},
{"role": "assistant", "content": [
{"type": "tool_use", "tool_use": {
"name": "get_current_time",
"input": {}
}}
]},
{"role": "user", "content": [
{"type": "tool_result", "tool_result": {
"tool_name": "get_current_time",
"content": json.dumps(tool_response)
}}
]}
{
"role": "assistant",
"content": [
{
"type": "tool_calls",
"tool_calls": [
{
"type": "custom",
"name": "get_current_time",
"input": {}
}
]
}
]
},
{
"role": "tool",
"content": json.dumps(tool_response),
"tool_call_id": tool_call.id
}
]
)

Expand Down
113 changes: 64 additions & 49 deletions docs/v2/integrations/crewai.mdx
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Expand Up @@ -21,55 +21,65 @@ import EnvTooltip from '/snippets/add-env-tooltip.mdx'
</Step>
<Step title="Add AgentOps to your code">
<CodeTooltip/>
<CodeGroup>
```python python
import agentops
from crewai import Agent, Task, Crew, LLM
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or "YOUR_OPENAI_API_KEY"
AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") or "YOUR_AGENTOPS_API_KEY"

# Initialize AgentOps at the start of your application
agentops.init(AGENTOPS_API_KEY)

# Set up your CrewAI elements as you normally would
# AgentOps will automatically track all interactions
llm = LLM(model="openai/gpt-4o", api_key=OPENAI_API_KEY)

researcher = Agent(
role="Researcher",
goal="Find accurate information about a topic",
backstory="You're an expert researcher with keen attention to detail",
llm=llm
)

research_task = Task(
description="Research the latest developments in quantum computing",
expected_output="A comprehensive report on the latest breakthroughs in quantum computing, including advancements in quantum algorithms, hardware, and potential applications.",
agent=researcher
)

crew = Crew(
agents=[researcher],
tasks=[research_task],
verbose=True
)

# Run the crew - AgentOps will track everything
result = crew.kickoff()
```
</CodeGroup>
<CodeGroup>
```python python
import os
from dotenv import load_dotenv
import agentops
from crewai import Agent, Task, Crew
from langchain_openai import ChatOpenAI

# Load environment variables
load_dotenv()

# Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"))

# Create LLM
llm = ChatOpenAI(
model="gpt-4",
temperature=0.7,
api_key=os.getenv("OPENAI_API_KEY")
)

# Create an agent
researcher = Agent(
role='Researcher',
goal='Research and provide accurate information about cities and their history',
backstory='You are an expert researcher with vast knowledge of world geography and history.',
llm=llm,
verbose=True
)

# Create a task
research_task = Task(
description='What is the capital of France? Provide a detailed answer about its history, culture, and significance.',
expected_output='A comprehensive response about Paris, including its status as the capital of France, historical significance, cultural importance, and key landmarks.',
agent=researcher
)

# Create a crew with the researcher
crew = Crew(
agents=[researcher],
tasks=[research_task],
verbose=True
)

# Execute the task - AgentOps will automatically track all LLM calls
result = crew.kickoff()

print("\nCrew Research Results:")
print(result)
```
</CodeGroup>
<EnvTooltip />
<CodeGroup>
```python .env
AGENTOPS_API_KEY=<YOUR API KEY>
```
</CodeGroup>
Read more about environment variables in [Advanced Configuration](/v2/usage/advanced-configuration)
<CodeGroup>
```python .env
AGENTOPS_API_KEY=<YOUR API KEY>
OPENAI_API_KEY=<YOUR OPENAI API KEY>
```
</CodeGroup>
Read more about environment variables in [Advanced Configuration](/v2/usage/advanced-configuration)
</Step>
<Step title="Run your crew">
Execute your program and visit [app.agentops.ai/traces](https://app.agentops.ai/traces) to observe your Crew! 🕵️
Expand All @@ -83,6 +93,12 @@ import EnvTooltip from '/snippets/add-env-tooltip.mdx'
</Step>
</Steps>

## Important Notes

1. The `Task` class requires an `expected_output` field that describes what the task should produce
2. Setting `verbose=True` on both the agent and crew provides better visibility into the execution process
3. Tasks should have clear, detailed descriptions to guide the agent effectively

## Crew + AgentOps Examples

<CardGroup cols={3}>
Expand All @@ -95,4 +111,3 @@ import EnvTooltip from '/snippets/add-env-tooltip.mdx'
<script type="module" src="/scripts/scroll-img-fadein-animation.js"></script>
<script type="module" src="/scripts/button_heartbeat_animation.js"></script>
<script type="css" src="/styles/styles.css"></script>
<script type="module" src="/scripts/adjust_api_dynamically.js"></script>
28 changes: 14 additions & 14 deletions docs/v2/integrations/gemini.mdx
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Expand Up @@ -48,19 +48,23 @@ print(response.text)

## Alternative Client Setup

You can also set up the client using environment variables or with Vertex AI:
You can also set up the client using Vertex AI or with environment variables:

<CodeGroup>
```python Using Environment Variables

```python Vertex AI
import agentops
from google import genai

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)

# Set GOOGLE_API_KEY environment variable before running
# export GOOGLE_API_KEY='your-api-key'
client = genai.Client()
# Using Vertex AI
client = genai.Client(
vertexai=True,
project='your-project-id',
location='us-central1'
)

# Generate content
response = client.models.generate_content(
Expand All @@ -71,19 +75,16 @@ response = client.models.generate_content(
print(response.text)
```

```python Vertex AI
```python Using Environment Variables
import agentops
from google import genai

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)

# Using Vertex AI
client = genai.Client(
vertexai=True,
project='your-project-id',
location='us-central1'
)
# Set GOOGLE_API_KEY environment variable before running
# export GOOGLE_API_KEY='your-api-key'
client = genai.Client()

# Generate content
response = client.models.generate_content(
Expand Down Expand Up @@ -142,10 +143,9 @@ agentops.init(<INSERT YOUR API KEY HERE>)
client = genai.Client(api_key="YOUR_GEMINI_API_KEY")

# Generate streaming content
for chunk in client.models.generate_content(
for chunk in client.models.generate_content_stream(
model='gemini-2.0-flash-001',
contents='Explain quantum computing in simple terms.',
stream=True
):
print(chunk.text, end="", flush=True)

Expand Down
79 changes: 27 additions & 52 deletions docs/v2/integrations/litellm.mdx
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Expand Up @@ -25,32 +25,18 @@ The simplest way to integrate AgentOps with LiteLLM is to set up the success_cal

<CodeGroup>
```python Simple Integration
import os
from dotenv import load_dotenv
import litellm

# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]

# Make completion requests with LiteLLM
response = litellm.completion(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "Hello, how are you?"}]
)

print(response.choices[0].message.content)

# All LiteLLM API calls are automatically tracked by AgentOps
```
</CodeGroup>

You can also initialize AgentOps separately for additional configuration:

<CodeGroup>
```python With AgentOps Init
import agentops
from litellm import completion

# Initialize AgentOps
agentops.init(<INSERT YOUR API KEY HERE>)
# Load environment variables
load_dotenv()

# Set API keys for different providers
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")

# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]
Expand All @@ -67,40 +53,22 @@ print(response.choices[0].message.content)
```
</CodeGroup>

## Using the LiteLLM Client

<CodeGroup>
```python Python
import litellm
from litellm import LiteLLM

# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]

# Create a LiteLLM client
client = LiteLLM()

# Make a completion request
response = client.completion(
model="anthropic/claude-3-opus-20240229",
messages=[{"role": "user", "content": "What are the benefits of using LiteLLM?"}]
)

print(response.choices[0].message.content)

# All client requests are automatically tracked by AgentOps
```
</CodeGroup>

## Streaming Example

AgentOps also tracks streaming requests with LiteLLM:

<CodeGroup>
```python Python
```python Streaming
import os
from dotenv import load_dotenv
import litellm
from litellm import completion

# Load environment variables and set API keys
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")

# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]

Expand All @@ -115,8 +83,7 @@ response = completion(
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)

# All streaming requests are automatically tracked by AgentOps
print() # Add a newline at the end
```
</CodeGroup>

Expand All @@ -125,10 +92,18 @@ for chunk in response:
One of LiteLLM's key features is the ability to switch between providers easily:

<CodeGroup>
```python Python
```python Multi-Provider
import os
from dotenv import load_dotenv
import litellm
from litellm import completion

# Load environment variables and set API keys
load_dotenv()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["ANTHROPIC_API_KEY"] = os.getenv("ANTHROPIC_API_KEY")
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")

# Configure LiteLLM to use AgentOps
litellm.success_callback = ["agentops"]

Expand Down
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