AgentOps
Observability, session replay, and cost tracking for AI-agent applications.
Per-dimension scores and reasoning
Evidence: SDK requires API key for initialization, but least privilege principle not explicitly stated; user confirmation mechanism absent; data flow transparency partial, README mentions data sent to AgentOps dashboard but not detailed; sensitive data handling not specified; dependency security has version ranges but no security audit mentioned; external effects include sending data to external service but impact not detailed; rollback not mentioned; source attribution lists authors in pyproject.toml but publisher unverified. Deductions: lack of user confirmation, rollback, and insufficient data flow and sensitive data handling documentation.
Evidence: README and pyproject.toml descriptions consistent; dependencies available on PyPI but not verified; failure messages not documented. Deduction: insufficient failure message documentation.
Evidence: Target audience clearly AI agent developers, scenarios include monitoring, cost tracking; capability boundaries described in README but not explicit limits; trigger precision clear with init and decorators; environment fit supports Python 3.9+ but OS limitations not mentioned. Deductions: capability boundaries and trigger precision not detailed enough.
Evidence: Information architecture clear with README and docs links; install notes simple; naming stability with version numbers; examples abundant but FAQ missing; known limitations not listed; license MIT complete; changelog not provided; maintenance responsibility with multiple authors and GitHub repo. Deductions: missing FAQ and changelog.
Evidence: Output usability with dashboard and session replays; marginal value with multiple integrations and monitoring features; cost-benefit open source free but requires API key. Deduction: no cost-benefit analysis.
Evidence: README claims have doc links but no specific evidence; cross-source verification with multiple integration doc links but not independently verified; fact vs inference separation not explicit. Deductions: lack of concrete evidence and independent verification.
- The SDK sends data to external services; ensure you understand data flow and privacy implications.
- No user confirmation mechanism; data collection may start automatically after initialization.
- Dependency version ranges are broad; consider pinning versions for security.
What does this agent do, and when should you use it?
AgentOps is an observability and developer-tool platform for AI agents, with a Python SDK and a JavaScript/TypeScript SDK listed as Alpha on its roadmap. Developers instrument sessions, agents, operations, tasks, and workflows with agentops.init(), agentops.end_session(), and decorators. The platform presents recorded execution data as session replays, step-by-step execution graphs, summary analytics, latency analysis, and LLM/API bill tracking in the AgentOps Dashboard. Documented integration paths include the OpenAI Agents SDK, CrewAI, AG2, CAMEL, LangChain, Cohere, Anthropic, Mistral, LiteLLM, LlamaIndex, Llama Stack, and SwarmZero. The full Dashboard and API backend have a self-hosting path, with setup material located in app/README.md.
An application calls agentops.init(<API_KEY>) at startup and agentops.end_session('Success') when the session ends. The @session, @agent, @operation, @task, and @workflow decorators mark functions and classes as hierarchical spans; they support input/output recording, exception handling, async/await functions, generators, and custom names and attributes. The SDK provides automatic analytics for LLM calls and sends session data to the Dashboard for replays, execution graphs, session-level metrics, and cost tracking. For LangChain, an application creates LangchainCallbackHandler(api_key=..., tags=...) and passes it to model and agent callbacks; the CrewAI, AG2, and CAMEL paths use AGENTOPS_API_KEY and initialization.
- A team building a Python or TypeScript multi-agent system with the OpenAI Agents SDK needs visibility into tools, handoffs, and guardrails.
- A CrewAI developer has set AGENTOPS_API_KEY and wants crew executions automatically monitored.
- A LangChain team needs to record both model and agent callbacks through LangchainCallbackHandler.
- An application using the Anthropic, Cohere, or Mistral Python SDK needs to track synchronous, streaming, or asynchronous calls.
- A team that needs the Dashboard and API backend in its own environment can use the documented self-hosting path for the app.
What are this agent's strengths and limitations?
- A two-call session entry point—agentops.init() and agentops.end_session()—with automatic analytics for LLM calls.
- @session, @agent, @operation, @task, and @workflow support nested spans plus async, generators, exceptions, and input/output recording.
- It combines session replay, execution graphs, latency analysis, session-wide metrics, and LLM/API cost tracking.
- It documents paths for multiple agent frameworks and provider SDKs, alongside self-hosting for the complete Dashboard and API backend.
- The quick start requires an AgentOps API key, and the default viewing surface is the AgentOps Dashboard.
- Integrations carry their own SDK and version requirements; for example, Cohere requires >=5.4.0 and LiteLLM requires >=1.3.1.
- LangChain requires agentops[langchain] and passing LangchainCallbackHandler to both model and agent callbacks.
- The JavaScript/TypeScript SDK is marked Alpha on the roadmap, and the supplied material does not provide complete setup and execution details for it.
How do you install or deploy this agent?
Python quick start:
pip install agentopsObtain an API key from AgentOps project settings. For LangChain:
pip install agentops[langchain]Other integrations require their respective framework or provider SDKs, such as openai-agents, crewai[agentops], anthropic, cohere, or mistralai. The supplied material does not include commands for self-hosting the full Dashboard and API backend; it points to app/README.md for those details.
How do you use this agent?
Minimal Python invocation:
import agentopsagentops.init(<INSERT YOUR API KEY HERE>)
# Run your agent or LLM calls
agentops.end_session('Success')For explicit spans, import session from agentops.sdk.decorators and decorate a workflow with @session. The CrewAI, AG2, and CAMEL examples use the AGENTOPS_API_KEY environment variable; LangChain uses a LangchainCallbackHandler passed into callbacks.