Top 10 Best Agent Monitoring Software of 2026

LangSmith is a powerful observability platform from LangChain designed specifically for monitoring, debugging, testing, and evaluating LLM applications, with a strong focus on AI agents. It offers end-to-end tracing of agent executions, including tool calls, reasoning steps, and outputs, enabling developers to pinpoint failures, measure latency, and optimize performance. Additional features like datasets, custom evaluators, and collaborative projects make it ideal for iterating on production-grade agents.

Langfuse is an open-source observability platform tailored for LLM applications and AI agents, offering end-to-end tracing of LLM calls, tool executions, and agent interactions. It provides detailed analytics on latency, costs, token usage, and performance metrics, enabling developers to debug, evaluate, and optimize agent behavior. With support for evaluations via human feedback or LLM-as-judge, prompt management, and integrations with frameworks like LangChain and LlamaIndex, it stands out for production-grade monitoring.

Helicone is an open-source observability platform focused on monitoring LLM requests in AI applications, including agent workflows. It acts as a proxy to track metrics like latency, token usage, costs, and errors across providers such as OpenAI, Anthropic, and others. Key capabilities include real-time dashboards, caching for cost optimization, and experimentation tools, making it suitable for agent monitoring by providing granular insights into LLM interactions within multi-step processes.

Phoenix (phoenix.arize.com) is an open-source observability platform from Arize AI, specialized in tracing, evaluating, and debugging LLM applications, with strong support for agentic workflows. It captures detailed spans for LLM calls, tool invocations, and agent reasoning steps, presenting them in an interactive UI for exploration and analysis. Users can evaluate outputs using custom metrics and datasets, making it ideal for iterative development of AI agents.

AgentOps is an observability platform tailored for monitoring AI agents and LLM applications, providing session tracking, performance metrics, and cost analysis. It captures traces of agent runs, including tool calls, LLM interactions, and errors, with features like session replay for debugging. Developers can gain insights into latency, token usage, and overall agent behavior through intuitive dashboards.

Lunary.ai is an open-source observability platform tailored for monitoring LLM-powered applications and AI agents, offering detailed tracing of requests, tool calls, and multi-step interactions. It tracks key metrics like latency, costs, errors, and token usage across providers such as OpenAI, Anthropic, and Grok. Additionally, it includes evaluation tools, session replays, and experiment tracking to debug and optimize agent performance.

TruLens is an open-source Python framework designed for instrumenting, evaluating, and monitoring LLM applications and AI agents. It captures detailed traces of agent interactions, including inputs, outputs, latency, costs, and custom metrics via feedback functions for aspects like relevance, groundedness, and toxicity. Developers can visualize experiments in a dashboard, compare runs, and persist data to databases for iterative improvement of agent performance.

PromptLayer is an observability platform focused on tracking, debugging, and evaluating LLM prompts and responses in applications. It logs detailed traces including latency, token usage, costs, and custom metadata, with support for frameworks like LangChain and LlamaIndex used in AI agents. Developers can perform searches, A/B testing, and automated evaluations to optimize agent performance and identify issues in multi-step interactions.

Weights & Biases (W&B) is a leading MLOps platform for experiment tracking, visualization, and collaboration in machine learning workflows, adaptable for monitoring AI agent training and evaluation. It logs metrics, hyperparameters, model artifacts, and system resources in real-time, with interactive dashboards for comparing runs and identifying performance issues in agent behaviors. While not exclusively for runtime agent inference tracing, it supports LLM integrations and custom logging for agent trajectories via SDKs and Weave for tracing.

Humanloop is a comprehensive platform for developing, evaluating, and monitoring AI agents and LLM-powered applications. It offers tools for prompt iteration, human and LLM-based evaluations, production logging, and analytics to track metrics like latency, cost, and feedback. Designed for teams building reliable agentic systems, it emphasizes continuous improvement through data-driven insights.