---
description: In this blog, we will cover everything you need to know about AI Agent observability tools and the best ones to add to your stack.
title: Top 5 Tools for AI Agent Observability in 2025
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2025/10/ChatGPT-Image-Oct-4--2025--04_07_30-AM--1-.png
---
Try Bifrost Enterprise free for 14 days. [Request access](https://www.getmaxim.ai/#enterprise-trial)
**TL;DR**
[****Maxim AI****](https://www.getmaxim.ai/): End-to-end platform for simulations, evals, and observability; built for cross-functional teams
****LangSmith****: Tracing, evals, and prompt iteration; works with or without LangChain
****Arize****: Enterprise-grade evaluation and OTEL-powered tracing with online evals and dashboards
****Langfuse****: Open-source LLM observability with multi-modal tracing and cost tracking.
****Comet Opik****: Open-source platform for logging, viewing, and evaluating LLM traces during dev and production.
**What AI Observability Is and Why It Matters in 2025**
AI observability provides end-to-end visibility into agent behavior, spanning prompts, tool calls, retrievals, and multi-turn sessions. In 2025, teams rely on observability to maintain AI reliability across complex stacks and non-deterministic workflows. Platforms that support distributed tracing, online evaluations, and cross-team collaboration help catch regressions early and ship trustworthy AI faster.
**Why AI observability is needed**
•**Non‑determinism:** LLMs vary run‑to‑run. Distributed tracing across **traces, spans, generations, tool calls, retrievals, and sessions** turns opaque behavior into explainable execution paths.
• **Production reliability:** Observability catches regressions early via **online evaluations**, alerts, and dashboards tracking latency, error rate, and quality scores. Weekly reports and saved views help teams see trends.
• **Cost and performance control:** Token usage and per‑trace cost attribution surface expensive prompts, slow tools, and inefficient RAG. Optimizing with this visibility reduces spend without sacrificing quality.
• **Tooling and integrations:** OTEL/OTLP support routes the same traces to Maxim and existing collectors (Snowflake/New Relic) for unified ops, without dual instrumentation.
• **Human feedback loops:** Structured user ratings complement automated evals to align agents with real user preferences and drive prompt/version decisions.
• **Governance and safety:** Subjective metrics, guardrails, and alerts help detect toxicity, jailbreaks, or policy violations before users are impacted.
• **Team velocity:** Shared **saved views**, annotations, and eval dashboards shorten MTTR, speed prompt iteration, and align PMs, engineers, and reviewers on evidence.
• **Enterprise readiness:** RBAC, SSO, **In‑VPC** deployment, and SOC 2 ensure trace data stays compliant while enabling deep analysis.
**Key Features for AI Agent Observability**
- **Distributed tracing**: Capture traces, spans, generations, tool calls, and retrievals to debug complex flows.
- **Drift and quality metrics**: Track mean scores, pass rates, latency, and error rates over time via dashboards and reports. [Tracing concepts](https://www.getmaxim.ai/docs/tracing/concepts)
- **Cost and latency tracking**: Attribute tokens, cost, and timing at trace and span levels for optimization.
- **Online evaluations**: Score real-world interactions continuously, trigger alerts, and gate deployments. [ Online evaluations overview](https://www.getmaxim.ai/docs/online-evals/overview)
- **User feedback**: Collect structured ratings and comments to align agents with human preference. [User feedback](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/user-feedback)
- **Real-time alerts**: Notify Slack/PagerDuty/OpsGenie on thresholds for latency, cost, or evaluation regressions. [Set up alerts](https://www.getmaxim.ai/docs/online-evals/set-up-alerts-and-notifications)
- **Collaboration and saved views**: Share filters and views for faster debugging across product and engineering.
- **Flexible evals and datasets**: Combine AI-as-judge, [programmatic](https://www.getmaxim.ai/docs/library/evaluators/pre-built-evaluators/programmatic-evaluators/contains-special-characters), and [human evaluators](https://www.getmaxim.ai/docs/offline-evals/via-ui/prompts/human-annotation) at session/trace/span granularity.
**AI Observability Tools - At A Glance.**
| Tool | Category | Key Strengths | Best For | Hosting / Openness |
| --- | --- | --- | --- | --- |
| **Maxim AI** | End-to-end platform for simulation, evals, and observability | • Full distributed tracing (LLM calls, spans, tools, retrievals)
• Online evals + alerts
• Large-scale agent simulation
• Data curation engine
• OTLP ingestion + connectors
• Dashboards & saved views | Teams needing a unified platform to **simulate → evaluate → observe → improve** | SaaS + In-VPC; OTEL-compatible |
| **LangSmith** | Tracing & evals (LangChain ecosystem) | • Deep tracing for LangChain/LangGraph
• Prompt versioning & comparison
• LLM-as-Judge + human feedback
• OTEL-compliant logging
• Dataset creation from production traces | Teams using **LangChain** needing tracing + prompt iteration | Cloud + self-host; partially open |
| **Arize** | Enterprise ML + LLM observability | • OTEL tracing
• Online evals & dashboards
• Real-time drift detection
• Data quality monitoring
• Enterprise analytics | Enterprises needing unified **ML + LLM** monitoring | SaaS with enterprise integrations |
| **Langfuse** | Open-source LLM observability | • Multi-modal tracing
• Cost & latency tracking
• Custom evaluators
• Human annotation queues
• Fully self-hostable | Teams needing open-source control & custom pipelines | Fully open-source; self-host |
| **Comet Opik** | Open-source eval + experiment tracking | • Experiment tracking for prompts, RAG, agents
• LLM-as-Judge + heuristics
• Custom metrics & dashboards
• Production monitoring | Data science teams needing unified ML + LLM experiment tracking | OSS + cloud |
**The Best Tools for Agent Observability**
1. [**Maxim AI**](https://www.getmaxim.ai/)
Maxim AI is an end-to-end evaluation and observability platform focused on agent quality across development and production. It combines **Playground (prompt engineering)**, **AI-powered simulations**, **unified evals (LLM-as-judge, programmatic, human)**, and **real-time observability** into one system. Teams ship agents reliably and more than **5x faster** with cross-functional workflows spanning engineering and product.
- Key features:
- **Comprehensive distributed tracing** : for LLM apps with traces, spans, generations, retrieval, tool calls, events, sessions, tags, metadata, and errors for easy anomaly/drift detection, RCS and quick debugging.
- **Online evaluations:** with alerting and reporting to monitor production quality.
- **Data engine** : for curation, multi-modal datasets, and continuous improvement from production logs.
- **OTLP ingestion + connectors:** to forward traces to Snowflake/New Relic/OTEL collectors with enriched AI context.
- **Saved views and** [**custom dashboards**](https://www.getmaxim.ai/docs/dashboards/custom-logs-dashboard)**:** to accelerate debugging and share insights.
- **Agent Simulation**: to simulate at scale across thousands of real-world scenarios and personas and capture detailed traces across tools, LLM calls, state transitions, etc and identify failure modes before releasing to production.
- **Flexi evals**: while SDKs allow evals to be run at any level of granularity for multi-agent systems, as shown here, from the UI teams could configure evaluations with fine-grained flexibility
- **User experience and cross-functional collaboration**: delivers highly performant SDKs in Python, TypeScript, Java, and Go. At the same time, the user experience is designed so that product teams can manage the AI lifecycle without writing code, reducing dependence on engineering.
- Best for:
- Teams needing a single platform for production grade end-to-end **simulation, evals, and observability** with **enterprise-grade tracing**, online evals, and data curation.
- Additional sources:
- Product pages: [Agent observability](https://www.getmaxim.ai/products/agent-observability), [Agent simulation & evaluation](https://www.getmaxim.ai/products/agent-simulation-evaluation), [Experimentation](https://www.getmaxim.ai/products/experimentation)
- Docs: [Tracing overview](https://www.getmaxim.ai/docs/tracing/overview), [Generations](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/generations), [Tool calls](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/tool-calls)
2. **LangSmith**
LangSmith provides unified observability and evals for AI applications built with LangChain or LangGraph. It offers detailed tracing to debug non-deterministic agent behavior, dashboards for cost/latency/quality, and workflows for turning production traces into datasets for evals. It supports **OTEL-compliant logging**, hybrid or self-hosted deployments, and collaboration on prompts.
- Key features:
- **OTEL-compliant:** to integrate with existing monitoring solutions.
- **Evals with LLM-as-Judge** and **human feedback**
- **Prompt playground and versioning:** to iterate and compare outputs.
- Best for:
- Teams already using LangChain or seeking flexible tracing and evals with prompt iteration capabilities.
3. **Arize**
Arize is an AI engineering platform for development, observability, and evaluation. It provides ML observability, drift detection, and evaluation tools for model monitoring in production. It offers strong visualization tools and integrates with various MLOps pipelines.
- Features:
- **Open standard tracing (OTEL)** and **online evals:** to catch issues instantly.
- **Monitoring and dashboards:** with custom analytics and cost tracking.
- **LLM-as-a-Judge:** and CI/CD experiments.
- **Real-time model drift**
- **data quality monitoring**
- **Integration:** with major cloud and data platforms
- Best for:
- Enterprises with ML infrastructure seeking for ML monitoring.
4. **Langfuse**
Langfuse is an open-source platform for observability and tracing of LLM applications. It captures inputs, outputs, tool usage, retries, latencies, and costs across multi-modal and multi-model stacks. It is framework and language agnostic, supporting Python and JavaScript SDKs.
- Features:
- **Comprehensive tracing:** Visualize and debug LLM calls, prompt chains, and tool usage.
- **Open-source and self-hostable:** Full control over deployment, data, and integrations.
- **Evaluation framework:** Supports custom evaluators and prompt management.
- **Human annotation queues:** Built-in support for human review.
- Best for:
- Teams prioritizing open-source, customizability, and self-hosting, with strong developer resources. Langfuse is particularly popular with organizations building their own LLMOps pipelines and needing full-stack control.
5. **Comet Opik**
Opik (by Comet) is an open-source platform to log, view, and evaluate LLM traces in development and production. It supports LLM-as-a-Judge and heuristic evaluators, datasets for experiments, and production monitoring dashboards.
- Features:
- **Experiment tracking:** Log, compare, and reproduce LLM experiments at scale.
- **Integrated evaluation:** Supports RAG, prompt, and agentic workflows.
- **Custom metrics and dashboards:** Build your own evaluation pipelines.
- **Collaboration:** Share results, annotations, and insights across teams.
- **Production monitoring** with online evaluation metrics and dashboards.
- Best for:
- Data science teams that want to unify LLM evaluation with broader ML experiment tracking and governance.
- Additional sources:
- Self-hosting and Kubernetes deployment options.
**Why Maxim Stands Out for AI Observability**
Maxim is built for the entire AI lifecycle experiment, evaluate, observe, and curate data, so teams can scale AI **reliability** from pre-production and production stages. Its **stateless SDKs** and **OpenTelemetry compatibility** ensure robust tracing across services and micro-services. With **online evals**, multi-turn evaluations, unified metric, **saved views**, **alerts**, cross-functional collaboration, and **data curation**, Maxim ensures agent quality, including tools to convert logs into datasets for iterative improvement. See product pages and docs for details: [Agent observability](https://www.getmaxim.ai/products/agent-observability), [Tracing overview](https://www.getmaxim.ai/docs/tracing/overview), [Forwarding via data connectors](https://www.getmaxim.ai/docs/tracing/opentelemetry/forwarding-via-data-connectors), [Ingesting via OTLP](https://www.getmaxim.ai/docs/tracing/opentelemetry/ingesting-via-otlp).
For enterprise use cases, Maxim supports **In-VPC deployment**, **SSO**, **RBAC**, and **SOC 2 Type 2**.
**Which AI Observability Tool Should You Use?**
- Choose **Maxim** if you need an integrated platform that spans simulations, evals, and observability with powerful **agent tracing**, **online evaluations**, and **data curation**.
- Choose **LangSmith** if your stack centers on LangChain and you want **prompt iteration** with unified tracing/evals.
- Consider Arize for OTEL-based tracing, online evaluations, and comprehensive dashboards across AI/ML/CV workloads.
- Choose **Langfuse** for **open-source** observability with flexible tracing and strong **cost/latency** tracking.
- Choose **Comet Opik** for OSS-first teams needing **tracing, evaluation, and production monitoring**.
**Conclusion**
AI agent observability in 2025 is about unifying tracing, evaluations, and monitoring to build **trustworthy AI**. AI observability has become essential. With LLMs, agentic workflows, and voice AI driving business processes, strong observability platforms are key to maintaining performance and user trust. Maxim AI offers the comprehensive depth, flexible tooling, and proven reliability that modern AI teams need. To deploy with confidence and accelerate iteration, consider [**Maxim**](https://www.getmaxim.ai/) for an end-to-end approach across the AI lifecycle.
Ready to evaluate and observe your agents with confidence? [Book a demo](https://getmaxim.ai/demo) or [Sign up](https://app.getmaxim.ai/sign-up).
**FAQs**
- What is AI agent observability?
- Visibility into agent behavior across prompts, tool calls, retrievals, multi-turn sessions, and production performance, enabled by **distributed tracing** and **online evaluations**.
- How does distributed tracing help with agent debugging?
- Traces, spans, generations, and tool calls reveal execution paths, timing, errors, and results to diagnose issues quickly.
- Can I use OpenTelemetry with Maxim?
- Yes. Maxim supports OTLP ingestion and forwarding to external collectors (Snowflake, New Relic, OTEL) with AI-specific semantic conventions. [OTLP endpoint](https://www.getmaxim.ai/docs/tracing/opentelemetry/ingesting-via-otlp), [Data connectors](https://www.getmaxim.ai/docs/tracing/opentelemetry/forwarding-via-data-connectors)
- How do online evaluations improve AI reliability?
- Continuous scoring on real user interactions surfaces regressions early, enabling alerting and targeted remediation. [Online evaluations](https://www.getmaxim.ai/docs/online-evals/overview)
- Does Maxim support human-in-the-loop evaluation?
- Yes. Teams can configure [**human evaluations**](https://www.getmaxim.ai/docs/offline-evals/via-ui/prompts/human-annotation) for last-mile quality checks alongside LLM-as-a-Judge and programmatic evaluators.
- What KPIs should we track for agent observability?
- Latency, cost per trace, token usage, mean score, pass rate, error rate, and user feedback trends via dashboards and reports. [Dashboard](https://www.getmaxim.ai/docs/tracing/dashboard), [Reporting](https://www.getmaxim.ai/docs/tracing/reporting)
- How do saved views help teams collaborate?
- [Saved filters](https://www.getmaxim.ai/docs/tracing/overview#saved-views) enable repeatable debugging workflows across teams, speeding up issue resolution.
- Can I export logs and eval data?
- Yes. Maxim supports **CSV exports** and APIs to download logs and associated evaluation data with filters and time ranges.
- Is Maxim suitable for multi-agent and multimodal systems?
- Yes. Maxim’s tracing entities ([sessions](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/sessions), [traces](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/traces), [spans](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/spans), [generations](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/generations), [tool calls](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/tool-calls), [retrievals](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/retrieval), [events](https://www.getmaxim.ai/docs/tracing/tracing-via-sdk/events)) and **attachments** support complex multi-agent, multimodal workflows.
- How do alerts work in production? Further reading and resources:
- Configure threshold-based alerts on latency, cost, or evaluator scores; route notifications to [**Slack**](https://www.getmaxim.ai/docs/integrations/create-a-slack-integration#create-a-slack-integration)or [**PagerDuty**](https://www.getmaxim.ai/docs/integrations/create-a-pagerduty-integration)
**Further Reading and Resources:**
- [How to Implement Observability in Multi-Step Agentic Workflows: A Technical Guide with Code Examples](https://www.getmaxim.ai/articles/how-to-implement-observability-in-multi-step-agentic-workflows-a-technical-guide-with-code-examples/)
- [Session‑Level Observability: Tracking Multi‑Turn Conversations at Scale](https://www.getmaxim.ai/articles/session-level-observability/)
- [Why You Should Care About Observability as an AI Product Manager in 2025](https://www.getmaxim.ai/articles/why-you-should-care-about-observability-as-an-ai-product-manager-in-2025/)
## Read next
[ Compare the top AI observability tools for LLM monitoring in 2026 on latency and error SLOs, cost alerts, failover visibility, and on-call alert routing.](https://www.getmaxim.ai/articles/top-ai-observability-tools-for-llm-monitoring-in-2026/)
[ Compare the top LLM observability tools in 2026 by capture layer: gateway capture, SDK tracing, and OpenTelemetry libraries, with open-source options.](https://www.getmaxim.ai/articles/top-7-llm-observability-tools-in-2026/)
[ An AI observability platform captures traces, metrics, and cost for every LLM request and delivers them to the tools teams already run. This guide compares Bifrost, Datadog LLM Observability, New Relic AI Monitoring, Dynatrace, and Grafana Cloud on fit with an existing telemetry stack.](https://www.getmaxim.ai/articles/top-5-enterprise-ai-observability-platforms-in-2026/)
## More in Observability
[ TL;DR AI agent observability is critical for production success, yet 46% of AI proof-of-concepts fail before production, representing $30 billion in lost value. Traditional monitoring tools fall short because AI agents are non-deterministic, autonomous systems that require purpose-built observability. Effective observability must track four core](https://www.getmaxim.ai/articles/the-role-of-observability-in-maintaining-ai-agent-performance/)
[ The deployment of large language model applications in production environments has created an urgent need for comprehensive monitoring and observability solutions. As organizations scale their AI systems, the ability to track performance metrics, detect quality issues, and maintain reliability has become critical for delivering consistent user experiences. This article examines](https://www.getmaxim.ai/articles/top-3-tools-for-monitoring-llm-powered-applications-in-2025/)
```json
{
"@context": "https://schema.org",
"@type": "Article",
"publisher": {
"@type": "Organization",
"name": "Maxim Articles",
"url": "https://www.getmaxim.ai/articles/",
"logo": {
"@type": "ImageObject",
"url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w256h256/2025/08/thumbnail.png",
"width": 60,
"height": 60
}
},
"author": {
"@type": "Person",
"name": "Kamya Shah",
"image": {
"@type": "ImageObject",
"url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/2025/09/WhatsApp-Image-2025-08-29-at-17.40.40-1.jpeg",
"width": 1200,
"height": 1600
},
"url": "https://www.getmaxim.ai/articles/author/kamya/",
"sameAs": []
},
"headline": "Top 5 Tools for AI Agent Observability in 2025",
"url": "https://www.getmaxim.ai/articles/top-5-tools-for-ai-agent-observability-in-2025/",
"datePublished": "2025-11-27T15:00:00.000Z",
"dateModified": "2026-06-18T08:15:47.000Z",
"image": {
"@type": "ImageObject",
"url": "https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2025/10/ChatGPT-Image-Oct-4--2025--04_07_30-AM--1-.png",
"width": 1200,
"height": 800
},
"keywords": "Observability",
"description": "TL;DR\n\nMaxim AI: End-to-end platform for simulations, evals, and observability; built for cross-functional teams\nLangSmith: Tracing, evals, and prompt iteration; works with or without LangChain\nArize: Enterprise-grade evaluation and OTEL-powered tracing with online evals and dashboards\nLangfuse: Open-source LLM observability with multi-modal tracing and cost tracking.\nComet Opik: Open-source platform for logging, viewing, and evaluating LLM traces during dev and production.\n\nWhat AI Observabilit",
"mainEntityOfPage": "https://www.getmaxim.ai/articles/top-5-tools-for-ai-agent-observability-in-2025/"
}
```