Awesome

AgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.

Quick Start ⌨️

pip install agentops

Session replays in 2 lines of code

Initialize the AgentOps client and automatically get analytics on all your LLM calls.

Get an API key

import agentops

# Beginning of your program (i.e. main.py, __init__.py)
agentops.init( < INSERT YOUR API KEY HERE >)

...

# End of program
agentops.end_session('Success')

All your sessions can be viewed on the AgentOps dashboard <br/>

First class Developer Experience

Add powerful observability to your agents, tools, and functions with as little code as possible: one line at a time. <br/> Refer to our documentation

# Automatically associate all Events with the agent that originated them
from agentops import track_agent

@track_agent(name='SomeCustomName')
class MyAgent:
  ...

# Automatically create ToolEvents for tools that agents will use
from agentops import record_tool

@record_tool('SampleToolName')
def sample_tool(...):
  ...

# Automatically create ActionEvents for other functions.
from agentops import record_action

@agentops.record_action('sample function being record')
def sample_function(...):
  ...

# Manually record any other Events
from agentops import record, ActionEvent

record(ActionEvent("received_user_input"))

Integrations 🦾

CrewAI 🛶

Build Crew agents with observability with only 2 lines of code. Simply set an AGENTOPS_API_KEY in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.

pip install 'crewai[agentops]'

• AgentOps integration example
• Official CrewAI documentation

AutoGen 🤖

With only two lines of code, add full observability and monitoring to Autogen agents. Set an AGENTOPS_API_KEY in your environment and call agentops.init()

• Autogen Observability Example
• Autogen - AgentOps Documentation

Langchain 🦜🔗

AgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:

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.partners.langchain_callback_handler 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.

Cohere ⌨️

First class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!

• AgentOps integration example
• Official Cohere documentation

pip install cohere

import cohere
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)
co = cohere.Client()

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

print(chat)

agentops.end_session('Success')

import cohere
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)

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')

Anthropic ﹨

Track agents built with the Anthropic Python SDK (>=0.32.0).

• AgentOps integration guide
• Official Anthropic documentation

pip install anthropic

import anthropic
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)

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

import anthropic
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)

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

import asyncio
from anthropic import AsyncAnthropic

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()

Mistral 〽️

Track agents built with the Anthropic Python SDK (>=0.32.0).

• AgentOps integration example
• Official Mistral documentation

pip install mistralai

Sync

from mistralai import Mistral
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)

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

from mistralai import Mistral
import agentops

# Beginning of program's code (i.e. main.py, __init__.py)
agentops.init(<INSERT YOUR API KEY HERE>)

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

import asyncio
from mistralai import Mistral

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

import asyncio
from mistralai import Mistral

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()

LiteLLM 🚅

AgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format.

• AgentOps integration example
• Official LiteLLM documentation

pip install litellm

# Do not use LiteLLM like this
# from litellm import completion
# ...
# response = completion(model="claude-3", messages=messages)

# Use LiteLLM like this
import litellm
...
response = litellm.completion(model="claude-3", messages=messages)
# or
response = await litellm.acompletion(model="claude-3", messages=messages)

LlamaIndex 🦙

AgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.

pip install llama-index-instrumentation-agentops

To use the handler, import and set

from llama_index.core import set_global_handler

# 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.

Time travel debugging 🔮

Try it out!

Agent Arena 🥊

(coming soon!)

Evaluations Roadmap 🧭

Debugging Roadmap 🧭

Why AgentOps? 🤔

Without the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here's why AgentOps stands out:

• Comprehensive Observability: Track your AI agents' performance, user interactions, and API usage.
• Real-Time Monitoring: Get instant insights with session replays, metrics, and live monitoring tools.
• Cost Control: Monitor and manage your spend on LLM and API calls.
• Failure Detection: Quickly identify and respond to agent failures and multi-agent interaction issues.
• Tool Usage Statistics: Understand how your agents utilize external tools with detailed analytics.
• Session-Wide Metrics: Gain a holistic view of your agents' sessions with comprehensive statistics.

AgentOps is designed to make agent observability, testing, and monitoring easy.

Star History

Check out our growth in the community:

Popular projects using AgentOps

Generated using github-dependents-info, by Nicolas Vuillamy