AgentOps

AgentOps is an AI agent observability and development platform that lets you monitor, debug, and evaluate AI agents in production. It records and replays entire agent sessions — every step, tool call, and LLM interaction — while tracking cost and performance. With one-line integrations for the major agent frameworks, it's the observability layer for the agentic era.

Key Features

• Session Recording & Replay: Watch every agent step, tool call, and LLM interaction in a replayable session.
• Cost Tracking: Per-session, per-model, and per-agent cost breakdowns.
• LLM Call Tracing: Full traces of every model call with latency and token usage.
• Execution Graphs: Visualize agent execution paths and decision flows.
• Evaluations: Test suites and LLM-as-judge scoring for agent quality.
• Alerts: Proactive notifications when agents misbehave or costs spike.
• One-Line Integration: Add monitoring with a single line to OpenAI, Anthropic, CrewAI, AutoGen, LangChain, LangGraph, LlamaIndex, and OpenAI Agents SDK.
• Privacy Options: Self-hosted or cloud deployment.

Use Cases

Who Should Use This Tool?

• Agent Framework Users: Teams building with CrewAI, AutoGen, or LangGraph who need visibility.
• Production Agent Teams: Anyone shipping agents who must debug and monitor them.
• Cost-Conscious Builders: Teams tracking agent spend per session and model.

Problems It Solves

1. Black-box agent behavior: Session replay shows exactly what the agent did.
2. Debugging multi-agent systems: Execution graphs make complex flows inspectable.
3. Runaway costs: Per-session cost tracking catches expensive agent runs early.

Pricing

Advantages & Unique Selling Points

Compared to Competitors:

1. Agent-specific replay: Whole-session replay, not just per-call traces.
2. Framework-native: One-line integration for CrewAI, AutoGen, LangGraph, and more.
3. Execution graphs: See how agents branch, delegate, and decide.

What Makes It Stand Out:

• Built for multi-agent debugging — the hardest observability problem.
• 5,000+ GitHub stars with an active open-source community.
• Both self-hosted and cloud options.

Getting Started

Quick Start Guide

1. Install: pip install agentops.
2. Get an API key: Create an account at agentops.ai.
3. Integrate: Add AgentOps.init() to your agent code — one line.
4. Observe: Watch session replays, traces, and costs in the dashboard.

Integration

Integrates with:

• CrewAI, AutoGen, LangChain, LangGraph, LlamaIndex, OpenAI Agents SDK
• OpenAI, Anthropic, and other model providers
• Python and TypeScript

Frequently Asked Questions

Does it work with CrewAI?

Yes — AgentOps has a one-line integration for CrewAI and other major frameworks.

Can I self-host it?

Yes — self-hosted and cloud options are both available.

Does it track costs?

Yes — per-session, per-model, and per-agent cost tracking is built in.

Alternatives

If AgentOps isn't the right fit, consider these alternatives:

• Langfuse: Open-source observability with 40+ integrations.
• LangSmith: LangChain's platform with deployment built in.
• Helicone: LLM observability with gateway caching.

Tips & Best Practices

1. Integrate before you ship: One line at the start beats retrofitting later.
2. Watch execution graphs: Spot delegation loops and runaway branches early.
3. Set cost alerts: Catch expensive agent runs before they spike your bill.

Conclusion

AgentOps brings production-grade observability to the agentic era — session replay, cost tracking, and execution graphs with one-line framework integrations. If you're building agents with CrewAI, AutoGen, or LangGraph and need to see what they're doing, AgentOps is built for exactly that.