Compare AgentOps with top alternatives in the ai observability category. Find detailed side-by-side comparisons to help you choose the best tool for your needs.
These tools are commonly compared with AgentOps and offer similar functionality.
AI Observability
LangSmith is LangChain's commercial observability, evaluation and prompt management platform for LLM apps and agents in production.
LLM Observability
Open-source LLM observability, gateway, and cost analytics platform — proxy your OpenAI, Anthropic, or Bedrock calls through Helicone and get traces, caching, retries, rate limiting, and cost tracking in one line of code.
MLOps
End-to-end MLOps and AI developer platform — Models (experiment tracking, sweeps, model registry) plus Weave (LLM/agent observability and evals) — used by frontier labs and enterprise ML teams.
ML & LLM Observability
ML and LLM observability platform with production tracing, evals, drift detection, and the open-source Phoenix project for local LLM debugging.
Other tools in the ai observability category that you might want to compare with AgentOps.
AI Observability
Arize Phoenix is an open-source AI observability and evaluation platform for tracing, testing, and improving LLM applications and agents.
AI Observability
Latitude AI observability review covering agent traces, issue detection, human-aligned evaluations, verified pricing, integrations, benefits, and limitations.
AI Observability
Open-source AI agent engineering platform for tracing, evaluating, and debugging agent runs across any framework and LLM provider.
AI Observability
An observability platform and open-source instrumentation toolkit for tracing language-model applications and agents using OpenTelemetry-compatible conventions.
AI Observability
An observability and evaluation toolkit for tracing generative-AI applications, comparing outputs, managing evaluation datasets, and inspecting model behavior.
💡 Pro tip: Most tools offer free trials or free tiers. Test 2-3 options side-by-side to see which fits your workflow best.
Just two lines: import the AgentOps SDK and call the init function with your API key. The SDK automatically instruments supported frameworks.
AgentOps supports a wide range of frameworks including CrewAI, AutoGen, LangChain, OpenAI Agents SDK, Cohere, Mistral, LiteLLM, and AG2. Custom agents can be instrumented via the Python or TypeScript SDK.
Yes. The entire AgentOps platform is open source under the MIT license and can be self-hosted for teams that need full data control or air-gapped deployments.
AgentOps is framework-agnostic and open source, while LangSmith is tightly integrated with the LangChain ecosystem. AgentOps emphasizes agent-level observability with time travel debugging, whereas LangSmith focuses on LLM chain tracing and evaluation.
Time travel debugging lets you replay an agent session step by step, examining every LLM call, tool invocation, and decision point in sequence to diagnose exactly where and why a workflow succeeded or failed.
Yes, the free tier includes up to 5,000 events per month with core observability features. The Pro plan starts at $40/month for higher volumes, and Enterprise pricing is available by contacting sales.
Compare features, test the interface, and see if it fits your workflow.