AgentOps-AI/agentops

pip install "camel-ai[all]==0.2.11"
pip install agentops

import os
import agentops
from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"), tags=["CAMEL Example"])
Import toolkits after AgentOps init for tracking
from camel.toolkits import SearchToolkit
Set up the agent with search tools
sys_msg = BaseMessage.make_assistant_message(
    role_name='Tools calling operator',
    content='You are a helpful assistant'
)
Configure tools and model
tools = [*SearchToolkit().get_tools()]
model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)
Create and run the agent
camel_agent = ChatAgent(
    system_message=sys_msg,
    model=model,
    tools=tools,
)
response = camel_agent.step("What is AgentOps?")
print(response)
agentops.end_session("Success")

Check out our Camel integration guide for more examples including multi-agent scenarios.

pip install agentops[langchain]

To use the handler, import and set

import os
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from agentops.integration.callbacks.langchain import LangchainCallbackHandler
AGENTOPS_API_KEY = os.environ['AGENTOPS_API_KEY']
handler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=['Langchain Example'])
llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,
                 callbacks=[handler],
                 model='gpt-3.5-turbo')
agent = initialize_agent(tools,
                         llm,
                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
                         verbose=True,
                         callbacks=[handler], # You must pass in a callback handler to record your agent
                         handle_parsing_errors=True)

Check out the Langchain Examples Notebook for more details including Async handlers.

pip install cohere

```python python import cohere import agentops

Beginning of program's code (i.e. main.py, __init__.py)

chat = co.chat( message="Is it pronounced ceaux-hear or co-hehray?" )

print(chat)

agentops.end_session('Success')

Beginning of program's code (i.e. main.py, __init__.py)

co = cohere.Client()

stream = co.chat_stream( message="Write me a haiku about the synergies between Cohere and AgentOps" )

for event in stream: if event.event_type == "text-generation": print(event.text, end='')

agentops.end_session('Success')

Beginning of program's code (i.e. main.py, __init__.py)

client = anthropic.Anthropic( # This is the default and can be omitted api_key=os.environ.get("ANTHROPIC_API_KEY"), )

message = client.messages.create( max_tokens=1024, messages=[ { "role": "user", "content": "Tell me a cool fact about AgentOps", } ], model="claude-3-opus-20240229", ) print(message.content)

agentops.end_session('Success')

Streaming

Beginning of program's code (i.e. main.py, __init__.py)

client = anthropic.Anthropic( # This is the default and can be omitted api_key=os.environ.get("ANTHROPIC_API_KEY"), )

stream = client.messages.create( max_tokens=1024, model="claude-3-opus-20240229", messages=[ { "role": "user", "content": "Tell me something cool about streaming agents", } ], stream=True, )

response = "" for event in stream: if event.type == "content_block_delta": response += event.delta.text elif event.type == "message_stop": print("\n") print(response) print("\n")

Async

client = AsyncAnthropic( # This is the default and can be omitted api_key=os.environ.get("ANTHROPIC_API_KEY"), )

async def main() -> None: message = await client.messages.create( max_tokens=1024, messages=[ { "role": "user", "content": "Tell me something interesting about async agents", } ], model="claude-3-opus-20240229", ) print(message.content)

await main()

Sync

Beginning of program's code (i.e. main.py, __init__.py)

client = Mistral( # This is the default and can be omitted api_key=os.environ.get("MISTRAL_API_KEY"), )

message = client.chat.complete( messages=[ { "role": "user", "content": "Tell me a cool fact about AgentOps", } ], model="open-mistral-nemo", ) print(message.choices[0].message.content)

agentops.end_session('Success')

Streaming

Beginning of program's code (i.e. main.py, __init__.py)

client = Mistral( # This is the default and can be omitted api_key=os.environ.get("MISTRAL_API_KEY"), )

message = client.chat.stream( messages=[ { "role": "user", "content": "Tell me something cool about streaming agents", } ], model="open-mistral-nemo", )

response = "" for event in message: if event.data.choices[0].finish_reason == "stop": print("\n") print(response) print("\n") else: response += event.text

agentops.end_session('Success')

Async

client = Mistral( # This is the default and can be omitted api_key=os.environ.get("MISTRAL_API_KEY"), )

async def main() -> None: message = await client.chat.complete_async( messages=[ { "role": "user", "content": "Tell me something interesting about async agents", } ], model="open-mistral-nemo", ) print(message.choices[0].message.content)

await main()

Async Streaming

client = Mistral( # This is the default and can be omitted api_key=os.environ.get("MISTRAL_API_KEY"), )

async def main() -> None: message = await client.chat.stream_async( messages=[ { "role": "user", "content": "Tell me something interesting about async streaming agents", } ], model="open-mistral-nemo", )

response = "" async for event in message: if event.data.choices[0].finish_reason == "stop": print("\n") print(response) print("\n") else: response += event.text

await main()

Import Dependencies

Set Keys

Do not use LiteLLM like this

from litellm import completion

...

response = completion(model="claude-3", messages=messages)

Use LiteLLM like this

or

To use the handler, import and set

NOTE: Feel free to set your AgentOps environment variables (e.g., 'AGENTOPS_API_KEY')

as outlined in the AgentOps documentation, or pass the equivalent keyword arguments

anticipated by AgentOps' AOClient as **eval_params in set_global_handler.

set_global_handler("agentops")

Check out the LlamaIndex docs for more details.

bash pip install swarmzero pip install agentops

import agentops agentops.init()

from swarmzero import Agent, Swarm

...