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AgentOps

An observability & ops platform for LLM agents, giving dev teams tracing, debugging, session replay and live dashboards to ship and scale agent apps without surprises.
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llm observabilityagent debugging platformsession replay tooltrace visualizationtoken cost trackingmulti model compatible

Features of AgentOps

Granular Trace/Span/Tag capture with session overview and drill-down
Session replay to reproduce execution paths and debug decision chains
Visualize parallel & multi-agent calls to spot collaboration issues
Live dashboards for metrics, alerts and quota management
Token & API cost tracking with attribution analytics
400+ LLMs and major agent frameworks supported (LangChain, AutoGen, etc.)
Python SDK + Jupyter notebooks; install in one line: pip install agentops
Account/org/project & API-key management; dashboard works in Playground mode before login

Use Cases of AgentOps

Replay sessions during dev to find prompt, tool or model bugs
Runtime monitoring & alerting on latency, throughput and error rates
Cost analysis: track token burn and API usage to optimize spend
Visualize multi-agent workflows and debug cross-agent failures
Add observability in minutes with minimal instrumentation or callbacks
Continuous QA: measure agent stability and performance across versions
Observe staged rollouts across dev, test and prod environments

FAQ about AgentOps

QWhat is AgentOps?

AgentOps is an observability and operations platform for LLM-powered agents. It traces, debugs and monitors your agents so your team can ship reliable AI services faster.

QHow do I get started quickly?

Install the SDK: pip install agentops, set your AGENTOPS_API_KEY and OPENAI_API_KEY, then add a single callback or decorator to start sending traces to the dashboard.

QWhich models and frameworks are supported?

The platform claims 400+ LLMs and popular agent frameworks such as LangChain and AutoGen. Check the docs for the latest integration examples.

QIs there a free plan?

Yes—sign up and click “Start for Free” to begin. Paid tiers with higher volume and team features are listed on the pricing page.

QHow do I handle API keys and initialization?

Export OPENAI_API_KEY and AGENTOPS_API_KEY as environment variables, or pass them directly to agentops.init(...). Sample notebooks walk you through the setup.

QWhat exactly does AgentOps observe?

Full traces, spans, tags, session replays, decision chains, plus token and cost attribution—everything you need to see why an agent did what it did.

QWhat about privacy and security?

AgentOps ingests conversation data. Use built-in redaction, access controls and retention policies to keep sensitive information out of logs. Configure per your compliance needs.

QIs AgentOps open source and is there a community?

The SDK is open source on GitHub and there’s an active Discord community where you can ask questions, share examples and contribute code.

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