Vendor: AgentOps-AI
AgentOps is the premier observability and session replay platform built specifically for autonomous AI agents, offering one-line tracking for tool calls, token spend, infinite loop detection, and graphical replays.
| Repository | AgentOps-AI/agentOps |
|---|---|
| GitHub Stars | ★ 5.8k |
| Forks | 616 forks |
| Primary Language | Python |
| License | MIT |
| Technical Domain | TOOLING |
$ bash
pip install agentops
AgentOps (AgentOps-AI/agentOps) is the premier open-source observability, debugging, and evaluation platform built specifically for autonomous AI agents and multi-agent systems. While traditional LLM monitoring tools focus on isolated request-response pairs, autonomous agents exhibit complex, non-linear behaviors: multi-turn reasoning loops, external tool executions, cross-agent task delegation, and potential infinite recursive loops. AgentOps is purpose-built to observe and govern these dynamic agentic execution graphs.
From an integration perspective, AgentOps offers an effortless two-line setup. Calling agentops.init() at runtime automatically instruments frameworks including CrewAI, LangChain, AutoGen, and native model SDKs. It transparently captures full ReAct execution loops, tool calling parameters, return values, token expenditures, and provider latencies without requiring manual tracing spans.
Functionally, AgentOps provides a visual Session Replay dashboard where engineers can step through multi-agent communication chronologies, inspect tool dependency graphs, and diagnose failure cascades. Crucially, AgentOps includes autonomous infinite loop detection and token circuit breakers, alerting maintainers and halting runaway agent executions before unexpected billing spikes occur.
AgentOps arose to resolve the 'black-box crisis' that emerged as AI agents migrated into mission-critical production. In simple single-turn generation, standard request logging suffices; however, in dynamic workflows where multiple autonomous agents execute 30+ iterative ReAct steps across dozens of external APIs, pinpointing root causes of failures becomes virtually impossible without specialized tracing.
The AgentOps team adapted the principles of distributed microservice tracing (APM) for non-linear agentic lifecycles. By structuring an agent's 'Thought', 'Action', 'Observation', and 'Inter-Agent Delegation' steps into a unified timeline graph, AgentOps renders non-deterministic multi-agent executions completely transparent, replayable, and measurable.
To counter infinite looping and billing spikes, AgentOps introduced pattern-matching execution circuit breakers. When an agent loops repeatedly over failing tools, AgentOps intervenes automatically, halting execution and saving full diagnostic dumps.
In multi-agent framework debugging (CrewAI, AutoGen), teams step through session replays to inspect handoff payloads and resolve communication deadlocks.
In agent tool performance profiling, engineers analyze latency bottlenecks and error distributions across database tools, sandboxes, and browser automations.
In enterprise budget governance, administrators enforce token caps and time budgets per session, triggering automated circuit breakers on runaway infinite loops.
In continuous evaluation pipelines, developers compare agent trajectory graphs across releases to guarantee that prompt optimizations do not cause regression.
Install the AgentOps package via pip:
pip install agentopsInitialize AgentOps at the entry point of your agent application (compatible with CrewAI, LangChain, and vanilla SDKs):
import agentops
from crewai import Agent, Task, Crew
# 1. Initialize AgentOps tracing
agentops.init(api_key="your-agentops-api-key", default_tags=["production", "v1.0"])
# 2. Build and run your agent workflow normally
researcher = Agent(
role="Tech Analyst",
goal="Research cutting-edge AI Agent Prompt frameworks in 2026",
backstory="You are an expert technical intelligence researcher."
)
task = Task(
description="Summarize top 5 agent frameworks.",
expected_output="Markdown report with architectural highlights.",
agent=researcher
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
# 3. End session and print the replay dashboard link
agentops.end_session("Success")Instrument custom Python tools with @agentops.record_tool to trace execution payloads and latencies:
@agentops.record_tool("execute_sql_query")
def execute_sql_query(query: str) -> dict:
return {"rows_count": 42, "status": "success"}In runtime overhead and ergonomics, AgentOps utilizes asynchronous background batching, ensuring telemetry collection adds less than 1% latency to active model and tool execution loops.
For enterprise compliance, AgentOps supports self-hosted private deployments and client-side PII masking, allowing organizations to maintain full governance over sensitive customer prompts and tool payloads.
Alerting integrates natively with Slack, PagerDuty, Discord, and Webhooks, routing instant replay links to on-call engineers when failure spikes occur.
AgentOps is the official premier observability partner for CrewAI and is deployed across thousands of production systems built on LangChain and AutoGen. From automated customer support to financial analysis bots, AgentOps monitors millions of agent actions daily.
In an enterprise autonomous DevOps deployment, AgentOps intercepted a recursive retry storm caused by a transient network glitch, saving the company thousands of dollars in wasted token spend.
As multi-agent orchestration expands across enterprise operations, AgentOps continues to pioneer integrated evaluations (Agent Evals) and real-time guardrails, solidifying its place as the standard APM platform for autonomous AI.
AgentOps - Full-Lifecycle Observability, Replay & Evaluation for AI Agents is an open-source AI project developed primarily in Python under the MIT license. AgentOps is the premier observability and session replay platform built specifically for autonomous AI agents, offering one-line tracking for tool calls, token spend, infinite loop detection, and graphical replays.. AgentOps arose to resolve the 'black-box crisis' that emerged as AI agents migrated into mission-critical production. In simple single-turn generation, standard request logging suffices; however, in dynamic workflows where multiple autonomous agents execute 30+ iterative ReAct steps across dozens of external APIs, pinpointing root causes of failures becomes virtually impossible without specialized tracing. The AgentOps team adapted the principles of distributed microservice tracing (APM) for non-linear agentic lifecycles. By structuring an agent's 'Thought', 'Action', 'Observation', and 'Inter-Agent Delegation' steps into a unified timeline graph, AgentOps renders non-deterministic multi-agent executions completely transparent, replayable, and measurable. To counter infinite looping and billing spikes, AgentOps introduced pattern-matching execution circuit breakers. When an agent loops repeatedly over failing tools, AgentOps intervenes automatically, halting execution and saving full diagnostic dumps.
Install the AgentOps package via pip:
pip install agentopsInitialize AgentOps at the entry point of your agent application (compatible with CrewAI, LangChain, and vanilla SDKs):
import agentops
from crewai import Agent, Task, Crew
# 1. Initialize AgentOps tracing
agentops.init(api_key="your-agentops-api-key", default_tags=["production", "v1.0"])
# 2. Build and run your agent workflow normally
researcher = Agent(
role="Tech Analyst",
goal="Research cutting-edge AI Agent Prompt frameworks in 2026",
backstory="You are an expert technical intelligence researcher."
)
task = Task(
description="Summarize top 5 agent frameworks.",
expected_output="Markdown report with architectural highlights.",
agent=researcher
)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
# 3. End session and print the replay dashboard link
agentops.end_session("Success")Instrument custom Python tools with @agentops.record_tool to trace execution payloads and latencies:
@agentops.record_tool("execute_sql_query")
def execute_sql_query(query: str) -> dict:
return {"rows_count": 42, "status": "success"}AgentOps - Full-Lifecycle Observability, Replay & Evaluation for AI Agents is well-suited for Multi-Agent System Debugging, Agent Skill & Tool Execution Diagnostics, Enterprise Agent Cost Auditing, Automated Agent Evaluation & Regression Testing. With an overall rating of 4.8/5, it offers strong community activity, reliable performance, and easy integration with existing AI pipelines.
In runtime overhead and ergonomics, AgentOps utilizes asynchronous background batching, ensuring telemetry collection adds less than 1% latency to active model and tool execution loops. For enterprise compliance, AgentOps supports self-hosted private deployments and client-side PII masking, allowing organizations to maintain full governance over sensitive customer prompts and tool payloads. Alerting integrates natively with Slack, PagerDuty, Discord, and Webhooks, routing instant replay links to on-call engineers when failure spikes occur.
Minimal tool for running large language models locally