AssessWhy
AgentOps is a Python SDK and platform for observing AI agents in production — tracking LLM calls, tool use, multi-agent interactions, and session state with session replay and point-in-time rewind. It's a niche alternative to LangSmith optimized specifically for multi-agent debugging rather than general LLM tracing.
What Makes It Agent-Specific
Standard LLM tracing tools capture request/response pairs well but struggle with multi-agent dynamics: who called whom, in what order, what state was shared between agents. AgentOps models these explicitly:
- Session replay: Rewind and replay agent runs with point-in-time precision to reproduce failures
- Multi-agent graphs: Visualize inter-agent handoffs, shared memory reads, and concurrent tool calls
- Reasoning traces: Capture the full chain of thought alongside tool calls and API requests
Framework Support
Integrates with CrewAI, OpenAI Agents SDK, LangChain, AutoGen, AG2, Agno, CamelAI, and Google ADK. Tracks 400+ LLMs.
Why assess and Not trial
AgentOps sits in assess because:
- The use case (multi-agent-specific debugging) is genuinely useful but narrower than general LLM tracing
- Langfuse and Arize Phoenix both cover multi-agent tracing and have larger communities
- Less documentation and fewer reported production deployments than the Trial-ring alternatives
- Evaluate it when you're running complex multi-agent systems and need session-level replay that Langfuse's trace viewer doesn't provide
Key Characteristics
| Property | Value |
|---|---|
| License | MIT |
| Provider | AgentOps AI |
| GitHub | AgentOps-AI/agentops |
| Website | agentops.ai |