AI agents suck. We’re fixing that.

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AgentOps 🖇️

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

📊 Replay Analytics and Debugging	Step-by-step agent execution graphs
💸 LLM Cost Management	Track spend with LLM foundation model providers
🧪 Agent Benchmarking	Test your agents against 1,000+ evals
🔐 Compliance and Security	Detect common prompt injection and data exfiltration exploits
🤝 Framework Integrations	Easily plugs in with frameworks like CrewAI and LangChain

Quick Start ⌨️

pip install agentops

Session replays in 3 lines of code

Initialize the AgentOps client and automatically get analytics on every LLM call.

import agentops

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

...
# (optional: record specific functions)
@agentops.record_function('sample function being record')
def sample_function(...):
    ...

# End of program
agentops.end_session('Success')
# Woohoo You're done 🎉

All your sessions are available on the AgentOps dashboard. Refer to our API documentation for detailed instructions.

Agent Dashboard

Session Analytics

Session Replays

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.

AgentOps is integrated with CrewAI on a pre-release fork. Install crew with

pip install git+https://github.com/AgentOps-AI/crewAI.git@main

• AgentOps integration example
• Official CrewAI documentation

Langchain 🦜🔗

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

Installation

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.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

Installation

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')

LlamaIndex 🦙

(Coming Soon)

Time travel debugging 🔮

(coming soon!)

Agent Arena 🥊

(coming soon!)

Evaluations Roadmap 🧭

Platform	Dashboard	Evals
✅ Python SDK	✅ Multi-session and Cross-session metrics	✅ Custom eval metrics
🚧 Evaluation builder API	✅ Custom event tag tracking	🔜 Agent scorecards
✅ Javascript/Typescript SDK	✅ Session replays	🔜 Evaluation playground + leaderboard

Debugging Roadmap 🧭

Performance testing	Environments	LLM Testing	Reasoning and execution testing
✅ Event latency analysis	🔜 Non-stationary environment testing	🔜 LLM non-deterministic function detection	🚧 Infinite loops and recursive thought detection
✅ Agent workflow execution pricing	🔜 Multi-modal environments	🚧 Token limit overflow flags	🔜 Faulty reasoning detection
🚧 Success validators (external)	🔜 Execution containers	🔜 Context limit overflow flags	🔜 Generative code validators
🔜 Agent controllers/skill tests	✅ Honeypot and prompt injection detection (PromptArmor)	🔜 API bill tracking	🔜 Error breakpoint analysis
🔜 Information context constraint testing	🔜 Anti-agent roadblocks (i.e. Captchas)	🔜 CI/CD integration checks
🔜 Regression testing	🔜 Multi-agent framework visualization

Why AgentOps? 🤔

Our mission is to bring your agent from prototype to production.

Agent developers often work with little to no visibility into agent testing performance. This means their agents never leave the lab. We're changing that.

AgentOps is the easiest way to evaluate, grade, and test agents. Is there a feature you'd like to see AgentOps cover? Just raise it in the issues tab, and we'll work on adding it to the roadmap.