Why it matters
- Agent debugging without observability is guesswork — AgentOps makes every LLM call, tool use, and decision visible and replayable.
- Session replay is uniquely valuable for agents: unlike traditional apps, agent failures often happen deep in multi-step execution chains that logs can't fully capture.
- Multi-framework support (LangChain, CrewAI, AutoGen, custom) means one observability tool covers the entire agentic stack.
- Cost tracking at the session level reveals which agent runs are most expensive — critical for optimizing prompts and reducing token spend.
Key capabilities
- Session recording: Every LLM call, tool use, and agent action recorded with full input/output and timing.
- Session replay: Step through any agent run chronologically to understand decisions and failures.
- Cost tracking: Per-session and per-run token costs across all LLM providers.
- Error detection: Automatic flagging of agent failures, infinite loops, and unexpected behaviors.
- Framework integrations: LangChain, CrewAI, AutoGen, OpenAI Agents SDK, LlamaIndex, Haystack.
- Dashboard: Web UI for browsing, filtering, and analyzing agent sessions.
- Alerts: Notify on agent failures, high cost runs, or behavioral anomalies.
- Tags and metadata: Tag agent runs with custom metadata for filtering and analysis.
Technical notes
- SDK: Python (primary);
pip install agentops - Integration:
agentops.init(api_key)+ framework-specific decorators - Frameworks: LangChain, CrewAI, AutoGen, OpenAI Agents SDK, LlamaIndex, Haystack
- GitHub: github.com/AgentOps-AI/agentops (3.5K stars; MIT license)
- Pricing: Free (10K events/mo); Pro ~$50/mo; Enterprise custom
- Founded: 2023; San Francisco
Ideal for
- Teams building AI agents in production who need to debug failures and understand why agents make wrong decisions.
- Organizations deploying agents at scale who need cost visibility and anomaly detection.
- Research teams iterating on agent architectures who want to compare behavior across different agent versions.
Not ideal for
- Simple LLM API calls without agent logic — LangSmith or Helicone are simpler for non-agentic observability.
- Real-time agent interventions or human-in-the-loop systems — AgentOps is observability, not control.
- Teams primarily using LangSmith — LangChain's native tracing overlaps significantly with AgentOps for LangChain agents.
See also
- LangSmith — LangChain's native observability with stronger LangChain-specific trace visualization.
- Portkey — LLM gateway with logging; less agent-specific but covers routing + observability.
- Braintrust — Enterprise eval platform; better for structured evaluation, less for agent debugging.