Skip to content

Latest commit

 

History

History
491 lines (360 loc) · 19.1 KB

README.md

File metadata and controls

491 lines (360 loc) · 19.1 KB
Raw
Observability and DevTool platform for AI Agents

🐦 Twitter   •   📢 Discord   •   🖇️ Dashboard   •   📙 Documentation

Dashboard Banner

AgentOps helps developers build, evaluate, and monitor AI 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 Native Integrations with CrewAI, AutoGen, & LangChain

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

Agent Debugging Agent Debugging
Session Replays Session Replays
Summary Analytics Summary Analytics

First class Developer Experience

Add powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.
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]'

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()

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.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!

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')

Anthropic ﹨

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

Installation 
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()

LiteLLM 🚅

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

Installation 
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.

Installation 
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 🔮

Time Travel Banner

Try it out!

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? 🤔

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:

Logo

Popular projects using AgentOps

Repository Stars
  geekan / MetaGPT 42787
  run-llama / llama_index 34446
  crewAIInc / crewAI 18287
  camel-ai / camel 5166
  superagent-ai / superagent 5050
  iyaja / llama-fs 4713
  BasedHardware / Omi 2723
  MervinPraison / PraisonAI 2007
  AgentOps-AI / Jaiqu 272
  strnad / CrewAI-Studio 134
  alejandro-ao / exa-crewai 55
  tonykipkemboi / youtube_yapper_trapper 47
  sethcoast / cover-letter-builder 27
  bhancockio / chatgpt4o-analysis 19
  breakstring / Agentic_Story_Book_Workflow 14
  MULTI-ON / multion-python 13

Generated using github-dependents-info, by Nicolas Vuillamy