Crawler Summary
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI <div align="center"> <a href="https://agentops.ai?ref=gh"> <img src="docs/images/external/logo/github-banner.png" alt="Logo"> </a> </div> <div align="center"> <em>Observability and DevTool platform for AI Agents</em> </div> <br /> <div align="center"> <a href="https://pepy.tech/project/agentops"> <img src="https://static.pepy.tech/badge/agentops/month" alt="Downloads"> </a> <a href="https://github.com/agentops-ai/age Capability contract not published. No trust telemetry is available yet. 5.5K GitHub stars reported by the source. Last updated 4/15/2026.
Freshness
Last checked 4/15/2026
Best For
agentops is best for crewai, multi-agent workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI <div align="center"> <a href="https://agentops.ai?ref=gh"> <img src="docs/images/external/logo/github-banner.png" alt="Logo"> </a> </div> <div align="center"> <em>Observability and DevTool platform for AI Agents</em> </div> <br /> <div align="center"> <a href="https://pepy.tech/project/agentops"> <img src="https://static.pepy.tech/badge/agentops/month" alt="Downloads"> </a> <a href="https://github.com/agentops-ai/age
Public facts
5
Change events
1
Artifacts
0
Freshness
Apr 15, 2026
Capability contract not published. No trust telemetry is available yet. 5.5K GitHub stars reported by the source. Last updated 4/15/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 15, 2026
Vendor
Agentops Ai
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. 5.5K GitHub stars reported by the source. Last updated 4/15/2026.
Setup snapshot
git clone https://github.com/AgentOps-AI/agentops.gitSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Agentops Ai
Protocol compatibility
OpenClaw
Adoption signal
5.5K GitHub stars
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
python
bash
pip install agentops
python
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')python
# Create a session span (root for all other spans)
from agentops.sdk.decorators import session
@session
def my_workflow():
# Your session code here
return resultpython
# Create an agent span for tracking agent operations
from agentops.sdk.decorators import agent
@agent
class MyAgent:
def __init__(self, name):
self.name = name
# Agent methods herepython
# Create operation/task spans for tracking specific operations
from agentops.sdk.decorators import operation, task
@operation # or @task
def process_data(data):
# Process the data
return resultpython
# Create workflow spans for tracking multi-operation workflows
from agentops.sdk.decorators import workflow
@workflow
def my_workflow(data):
# Workflow implementation
return resultFull documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI <div align="center"> <a href="https://agentops.ai?ref=gh"> <img src="docs/images/external/logo/github-banner.png" alt="Logo"> </a> </div> <div align="center"> <em>Observability and DevTool platform for AI Agents</em> </div> <br /> <div align="center"> <a href="https://pepy.tech/project/agentops"> <img src="https://static.pepy.tech/badge/agentops/month" alt="Downloads"> </a> <a href="https://github.com/agentops-ai/age
AgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.
The AgentOps app is open source under the MIT license. Explore the code in our app directory.
| | | | ------------------------------------- | ------------------------------------------------------------- | | ๐ Replay Analytics and Debugging | Step-by-step agent execution graphs | | ๐ธ LLM Cost Management | Track spend with LLM foundation model providers | | ๐ค Framework Integrations | Native Integrations with CrewAI, AG2 (AutoGen), Agno, LangGraph, & more | | โ๏ธ Self-Host | Want to run AgentOps on your own cloud? You're covered |
pip install agentops
Initialize the AgentOps client and automatically get analytics on all your LLM calls.
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/>
Looking to run the full AgentOps app (Dashboard + API backend) on your machine? Follow the setup guide in app/README.md:
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
# Create a session span (root for all other spans)
from agentops.sdk.decorators import session
@session
def my_workflow():
# Your session code here
return result
# Create an agent span for tracking agent operations
from agentops.sdk.decorators import agent
@agent
class MyAgent:
def __init__(self, name):
self.name = name
# Agent methods here
# Create operation/task spans for tracking specific operations
from agentops.sdk.decorators import operation, task
@operation # or @task
def process_data(data):
# Process the data
return result
# Create workflow spans for tracking multi-operation workflows
from agentops.sdk.decorators import workflow
@workflow
def my_workflow(data):
# Workflow implementation
return result
# Nest decorators for proper span hierarchy
from agentops.sdk.decorators import session, agent, operation
@agent
class MyAgent:
@operation
def nested_operation(self, message):
return f"Processed: {message}"
@operation
def main_operation(self):
result = self.nested_operation("test message")
return result
@session
def my_session():
agent = MyAgent()
return agent.main_operation()
All decorators support:
Build multi-agent systems with tools, handoffs, and guardrails. AgentOps natively integrates with the OpenAI Agents SDKs for both Python and TypeScript.
pip install openai-agents
npm install agentops @openai/agents
Build Crew agents with observability in just 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]'
With only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an AGENTOPS_API_KEY in your environment and call agentops.init()
Track and analyze CAMEL agents with full observability. Set an AGENTOPS_API_KEY in your environment and initialize AgentOps to get started.
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.
</details>AgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:
<details> <summary>Installation</summary>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.
</details>First class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!
<details> <summary>Installation</summary>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')
Track agents built with the Anthropic Python SDK (>=0.32.0).
<details> <summary>Installation</summary>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()
Track agents built with the Mistral Python SDK (>=0.32.0).
<details> <summary>Installation</summary>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()
Track agents built with the CamelAI Python SDK (>=0.32.0).
<details> <summary>Installation</summary>pip install camel-ai[all]
pip install agentops
#Import Dependencies
import agentops
import os
from getpass import getpass
from dotenv import load_dotenv
#Set Keys
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY") or "<your openai key here>"
agentops_api_key = os.getenv("AGENTOPS_API_KEY") or "<your agentops key here>"
You can find usage examples here!.
AgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format.
<details> <summary>Installation</summary>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)
AgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.
<details> <summary>Installation</summary>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.
</details>AgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications.
Track and analyze SwarmZero agents with full observability. Set an AGENTOPS_API_KEY in your environment and initialize AgentOps to get started.
pip install swarmzero
pip install agentops
from dotenv import load_dotenv
load_dotenv()
import agentops
agentops.init(<INSERT YOUR API KEY HERE>)
from swarmzero import Agent, Swarm
# ...
| Platform | Dashboard | Evals | | ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- | | โ Python SDK | โ Multi-session and Cross-session metrics | โ Custom eval metrics | | ๐ง Evaluation builder API | โ Custom event tag tracking | ๐ Agent scorecards | | ๐ง Javascript/Typescript SDK (Alpha) | โ Session replays | ๐ Evaluation playground + leaderboard |
| Performance testing | Environments | LLM Testing | Reasoning and execution testing | | ----------------------------------------- | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- | | โ Event latency analysis | ๐ Non-stationary environment testing | ๐ LLM non-deterministic function detection | ๐ง Infinite loops and recursive thought detection | | โ Agent workflow execution pricing | ๐ Multi-modal environments | ๐ง Token limit overflow flags | ๐ Faulty reasoning detection | | ๐ง Success validators (external) | ๐ Execution containers | ๐ Context limit overflow flags | ๐ Generative code validators | | ๐ Agent controllers/skill tests | โ Honeypot and prompt injection detection (PromptArmor) | โ API bill tracking | ๐ Error breakpoint analysis | | ๐ Information context constraint testing | ๐ Anti-agent roadblocks (i.e. Captchas) | ๐ CI/CD integration checks | | | ๐ Regression testing | โ Multi-agent framework visualization | | |
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:
AgentOps is designed to make agent observability, testing, and monitoring easy.
Check out our growth in the community:
<img src="https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date" style="max-width: 500px" width="50%" alt="Logo">| Repository | Stars | | :-------- | -----: | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/2707039?s=40&v=4" width="20" height="20" alt=""> ย geekan / MetaGPT | 42787 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/130722866?s=40&v=4" width="20" height="20" alt=""> ย run-llama / llama_index | 34446 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/170677839?s=40&v=4" width="20" height="20" alt=""> ย crewAIInc / crewAI | 18287 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/134388954?s=40&v=4" width="20" height="20" alt=""> ย camel-ai / camel | 5166 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/152537519?s=40&v=4" width="20" height="20" alt=""> ย superagent-ai / superagent | 5050 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/30197649?s=40&v=4" width="20" height="20" alt=""> ย iyaja / llama-fs | 4713 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/188122941?s=40&v=4" width="20" height="20" alt=""> ย ag2ai / ag2 | 4240 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/162546372?s=40&v=4" width="20" height="20" alt=""> ย BasedHardware / Omi | 2723 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/454862?s=40&v=4" width="20" height="20" alt=""> ย MervinPraison / PraisonAI | 2007 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/140554352?s=40&v=4" width="20" height="20" alt=""> ย AgentOps-AI / Jaiqu | 272 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/173542722?s=48&v=4" width="20" height="20" alt=""> ย swarmzero / swarmzero | 195 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/3074263?s=40&v=4" width="20" height="20" alt=""> ย strnad / CrewAI-Studio | 134 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/18406448?s=40&v=4" width="20" height="20" alt=""> ย alejandro-ao / exa-crewai | 55 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/64493665?s=40&v=4" width="20" height="20" alt=""> ย tonykipkemboi / youtube_yapper_trapper | 47 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/17598928?s=40&v=4" width="20" height="20" alt=""> ย sethcoast / cover-letter-builder | 27 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/109994880?s=40&v=4" width="20" height="20" alt=""> ย bhancockio / chatgpt4o-analysis | 19 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/14105911?s=40&v=4" width="20" height="20" alt=""> ย breakstring / Agentic_Story_Book_Workflow | 14 | |<img class="avatar mr-2" src="https://avatars.githubusercontent.com/u/124134656?s=40&v=4" width="20" height="20" alt=""> ย MULTI-ON / multion-python | 13 |
Generated using github-dependents-info, by Nicolas Vuillamy
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/snapshot"
curl -s "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/contract"
curl -s "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
Rank
70
AI Agents & MCPs & AI Workflow Automation โข (~400 MCP servers for AI agents) โข AI Automation / AI Agent with MCPs โข AI Workflows & AI Agents โข MCPs for AI Agents
Traction
No public download signal
Freshness
Updated 2d ago
Rank
70
AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | ๐ Star if you like it!
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
The Frontend for Agents & Generative UI. React + Angular
Traction
No public download signal
Freshness
Updated 23d ago
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-04-17T05:36:01.901Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "OPENCLEW",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
},
{
"key": "crewai",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
},
{
"key": "multi-agent",
"type": "capability",
"support": "supported",
"confidenceSource": "profile",
"notes": "Declared in agent profile metadata"
}
],
"flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}Facts JSON
[
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Agentops Ai",
"href": "https://github.com/AgentOps-AI/agentops",
"sourceUrl": "https://github.com/AgentOps-AI/agentops",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:28.906Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:28.906Z",
"isPublic": true
},
{
"factKey": "traction",
"category": "adoption",
"label": "Adoption signal",
"value": "5.5K GitHub stars",
"href": "https://github.com/AgentOps-AI/agentops",
"sourceUrl": "https://github.com/AgentOps-AI/agentops",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-15T06:04:28.906Z",
"isPublic": true
},
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/crewai-agentops-ai-agentops/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub ยท GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]Sponsored
Ads related to agentops and adjacent AI workflows.