The Best AI Observability Tools in 2025: Maxim AI, LangSmith, Arize, Helicone, and Comet Opik

TL;DR
Maxim AI: End-to-end platform for simulations, evaluations, and observability built for cross-functional teams shipping reliable AI agents 5x faster.
LangSmith: Tracing, evaluations, and prompt iteration designed for teams building with LangChain.
Arize: Enterprise-grade evaluation platform with OTEL-powered tracing and comprehensive ML monitoring dashboards.
Helicone: Open-source LLM observability focused on cost tracking, caching, and lightweight integration.
Comet Opik: Open-source platform for logging, viewing, and evaluating LLM traces during development and production.

AI Observability Platforms: Quick Comparison

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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 Critical

Non-determinism: LLMs vary run-to-run, making reproducibility challenging. Distributed tracing across traces, spans, generations, tool calls, retrievals, and sessions turns opaque behavior into explainable execution paths that engineering teams can debug systematically.

Production reliability: Observability catches regressions early through online evaluations, alerts, and dashboards that track latency, error rate, and quality scores. Weekly reports and saved views help teams identify trends before they impact end users.

Cost and performance control: Token usage and per-trace cost attribution surface expensive prompts, slow tools, and inefficient RAG implementations. Optimizing with this visibility reduces spend without sacrificing quality, a critical consideration as AI applications scale.

Tooling and integrations: OTEL/OTLP support enables teams to route the same traces to Maxim's observability platform and existing collectors like Snowflake or New Relic for unified operations, eliminating dual instrumentation overhead.

Human feedback loops: Structured user ratings complement automated evaluations to align agents with real user preferences and drive prompt versioning decisions based on actual production feedback.

Governance and safety: Subjective metrics, guardrails, and alerts help detect toxicity, jailbreaks, or policy violations before users are impacted, ensuring compliance with organizational standards.

Team velocity: Shared saved views, annotations, and evaluation dashboards shorten mean time to resolution, speed prompt iteration, and align product managers, engineers, and reviewers on evidence-based decisions.

Enterprise readiness: Role-based access control, single sign-on, in-VPC deployment, and SOC 2 Type 2 compliance ensure trace data stays secure while enabling deep analysis across distributed teams.

Key Features for AI Agent Observability

Distributed Tracing

Capture traces, spans, generations, tool calls, and retrievals to debug complex flows and understand execution paths through comprehensive distributed tracing.

Drift and Quality Metrics

Track mean scores, pass rates, latency, and error rates over time using dashboards and reports that provide actionable insights into agent performance trends.

Cost and Latency Tracking

Attribute tokens, cost, and timing at trace and span levels for optimization, enabling teams to identify and eliminate performance bottlenecks through detailed cost tracking.

Online Evaluations

Score real-world interactions continuously, trigger alerts, and gate deployments through online evaluations that monitor production quality in real time.

User Feedback

Collect structured ratings and comments to align agents with human preference, creating feedback loops that improve agent behavior based on actual user interactions.

Real-Time Alerts

Notify Slack, PagerDuty, or OpsGenie on thresholds for latency, cost, or evaluation regressions through configurable alerting systems.

Collaboration and Saved Views

Share filters and views for faster debugging across product and engineering teams using saved views that capture repeatable debugging workflows.

Flexible Evaluations and Datasets

Combine AI-as-judge, programmatic, and human evaluators at session, trace, or span granularity through flexible evaluation frameworks that adapt to diverse use cases.

The Best Tools for Agent Observability in 2025

Maxim AI

Maxim AI is an end-to-end evaluation and observability platform focused on agent quality across development and production. It combines Playground++ for prompt engineering, AI-powered simulations, unified evaluations including LLM-as-judge, programmatic, and human evaluators, and real-time observability into one integrated system. Teams ship agents reliably and more than 5x faster with cross-functional workflows spanning engineering and product teams.

Key Features

Comprehensive distributed tracing: Full support for LLM applications with traces, spans, generations, retrieval, tool calls, events, sessions, tags, metadata, and errors for easy anomaly detection, root cause analysis, and quick debugging.

Online evaluations: Real-time monitoring with alerting and reporting to maintain production quality and catch regressions before they impact users.

Data engine: Advanced capabilities for curation, multi-modal datasets, and continuous improvement from production logs and evaluation data.

OTLP ingestion and connectors: Forward traces to Snowflake, New Relic, or OTEL collectors with enriched AI context through OTLP ingestion and data connectors.

Saved views and custom dashboards: Accelerate debugging and share insights across teams with customizable dashboards and reusable view configurations.

Agent simulation: Simulate at scale across thousands of real-world scenarios and personas through AI-powered simulations that capture detailed traces across tools, LLM calls, state transitions, and identify failure modes before releasing to production.

Flexible evaluations: SDKs enable evaluations at any level of granularity for multi-agent systems, while the UI allows product teams to configure evaluations with fine-grained flexibility without writing code.

User experience and cross-functional collaboration: Highly performant SDKs in Python, TypeScript, Java, and Go combined with a user experience designed so product teams can manage the AI lifecycle without writing code, reducing dependence on engineering resources.

Best For

Teams needing a single platform for production-grade end-to-end simulation, evaluations, and observability with enterprise-grade tracing, online evaluations, and data curation capabilities.

Additional Resources

• Product pages: Agent observability, Agent simulation & evaluation, Experimentation
• Documentation: Tracing overview, Generations, Tool calls

LangSmith

LangSmith provides unified observability and evaluations for AI applications built with LangChain or LangGraph. It offers detailed tracing to debug non-deterministic agent behavior, dashboards for cost, latency, and quality metrics, and workflows for turning production traces into datasets for evaluations. The platform supports OTEL-compliant logging, hybrid or self-hosted deployments, and collaboration on prompts.

Key Features

OTEL-compliant tracing: Integrate seamlessly with existing monitoring solutions through OpenTelemetry standards.

Evaluations with LLM-as-Judge and human feedback: Combine automated and human evaluation approaches for comprehensive quality assessment.

Prompt playground and versioning: Iterate and compare outputs through interactive prompt development environments.

Best For

Teams already using LangChain or seeking flexible tracing and evaluations with prompt iteration capabilities tightly integrated with their existing framework.

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. The platform offers strong visualization tools and integrates with various MLOps pipelines for comprehensive machine learning operations.

Key Features

Open standard tracing and online evaluations: Catch issues instantly through OTEL-based tracing and continuous evaluation in production environments.

Monitoring and dashboards: Custom analytics and cost tracking through comprehensive visualization tools.

LLM-as-a-Judge and CI/CD experiments: Automated evaluation pipelines integrated with continuous integration workflows.

Real-time model drift detection: Monitor model performance degradation and data quality issues as they occur.

Cloud and data platform integration: Connect seamlessly with major cloud providers and data infrastructure.

Best For

Enterprises with existing ML infrastructure seeking comprehensive ML monitoring across both traditional machine learning and LLM workloads.

Helicone

Helicone is an open-source LLM observability platform focused on lightweight integration, cost optimization, and caching. It provides straightforward logging of LLM requests with minimal overhead, making it accessible for teams that want to start monitoring without complex instrumentation. The platform emphasizes cost tracking and caching capabilities to reduce API expenses.

Key Features

Lightweight integration: Simple drop-in proxy or SDK integration that requires minimal code changes to start logging LLM requests.

Cost tracking and optimization: Detailed cost analytics per request, user, or prompt to identify expensive patterns and optimize spending.

Semantic caching: Intelligent response caching based on semantic similarity to reduce costs and latency for similar queries.

Open-source and self-hostable: Full control over deployment and data with transparent, community-driven development.

Request logging and analytics: Comprehensive logging of inputs, outputs, latencies, and metadata for all LLM calls.

Best For

Teams prioritizing simplicity, cost optimization, and open-source flexibility who want to add observability without heavy infrastructure investment.

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 that unify LLM evaluation with broader ML experiment tracking.

Key Features

Experiment tracking: Log, compare, and reproduce LLM experiments at scale with comprehensive versioning.

Integrated evaluation: Support for RAG, prompt, and agentic workflows with built-in evaluation frameworks.

Custom metrics and dashboards: Build evaluation pipelines tailored to specific application needs.

Collaboration: Share results, annotations, and insights across teams through centralized dashboards.

Production monitoring: Online evaluation metrics and dashboards for continuous quality monitoring.

Best For

Data science teams that want to unify LLM evaluation with broader ML experiment tracking and governance workflows.

Why Maxim Stands Out for AI Observability

Maxim is built for the entire AI lifecycle: experiment, evaluate, observe, and curate data. This enables teams to scale AI reliability from pre-production through production stages with a unified platform. The stateless SDKs and OpenTelemetry compatibility ensure robust tracing across services and microservices architectures.

With online evaluations, multi-turn evaluations, unified metrics, saved views, alerts, cross-functional collaboration, and data curation, Maxim ensures agent quality at every stage. The platform includes tools to convert logs into datasets for iterative improvement, creating a continuous feedback loop that drives agent performance.

For enterprise use cases, Maxim supports in-VPC deployment, single sign-on, role-based access control, and SOC 2 Type 2 compliance as detailed in the platform overview.

Full-Stack Offering for Multimodal Agents

Maxim takes an end-to-end approach to AI quality that spans the entire development lifecycle. While observability may be the immediate need, pre-release experimentation, evaluations, and simulation become critical as applications mature. The integrated platform helps cross-functional teams move faster across both pre-release and production stages.

User Experience and Cross-Functional Collaboration

Maxim delivers highly performant SDKs in Python, TypeScript, Java, and Go while maintaining a user experience designed for product teams to drive the AI lifecycle without writing code. This reduces engineering dependencies and accelerates iteration:

Flexible evaluations: SDKs allow evaluations to run at any level of granularity for multi-agent systems, while the UI enables teams to configure evaluations with fine-grained flexibility through visual interfaces.

Custom dashboards: Teams need deep insights across agent behavior that cut across custom dimensions. Custom dashboards provide the control to create these insights with minimal configuration.

Data Curation and Flexible Evaluators

Deep support for human review collection, custom evaluators including deterministic, statistical, and LLM-as-a-judge approaches, and pre-built evaluators configurable at session, trace, or span level. Human and LLM-in-the-loop evaluations ensure continuous alignment of agents to human preferences.

Synthetic data generation and data curation workflows help teams curate high-quality, multi-modal datasets and continuously evolve them using logs, evaluation data, and human-in-the-loop workflows.

Enterprise Support and Partnership

Beyond technology capabilities, Maxim provides hands-on support for enterprise deployments with robust service level agreements for managed deployments and self-serve customer accounts. This partnership approach has consistently been highlighted by customers as a key differentiator.

Maxim's Evaluation and Data Management Stack

Experimentation

The Playground++ for prompt engineering enables rapid iteration, deployment, and experimentation with advanced features:

• Organize and version prompts directly from the UI for iterative improvement
• Deploy prompts with different deployment variables and experimentation strategies without code changes
• Connect with databases, RAG pipelines, and prompt tools seamlessly
• Simplify decision-making by comparing output quality, cost, and latency across various combinations of prompts, models, and parameters

Simulation

Use AI-powered simulations to test and improve AI agents across hundreds of scenarios and user personas:

• Simulate customer interactions across real-world scenarios and user personas, monitoring how agents respond at every step
• Evaluate agents at a conversational level by analyzing the trajectory agents choose, assessing task completion, and identifying failure points
• Re-run simulations from any step to reproduce issues, identify root causes, and apply learnings to debug and improve agent performance

Evaluation

The unified framework for machine and human evaluations allows teams to quantify improvements or regressions and deploy with confidence:

• Access off-the-shelf evaluators through the evaluator store or create custom evaluators suited to specific application needs
• Measure prompt or workflow quality quantitatively using AI, programmatic, or statistical evaluators
• Visualize evaluation runs on large test suites across multiple versions of prompts or workflows
• Define and conduct human evaluations for last-mile quality checks and nuanced assessments

Observability

The observability suite empowers teams to monitor real-time production logs and run them through periodic quality checks:

• Track, debug, and resolve live quality issues with real-time alerts that minimize user impact
• Create multiple repositories for multiple applications with production data logged and analyzed through distributed tracing
• Measure in-production quality using automated evaluations based on custom rules
• Curate datasets with ease for evaluation and fine-tuning needs from production logs

Data Engine

Seamless data management for AI applications allows users to curate and enrich multi-modal datasets easily:

• Import datasets, including images, with minimal configuration
• Continuously curate and evolve datasets from production data
• Enrich data using in-house or Maxim-managed data labeling and feedback
• Create data splits for targeted evaluations and experiments

Bifrost: LLM Gateway by Maxim AI

Bifrost is a high-performance AI gateway that unifies access to 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, and more through a single OpenAI-compatible API. Deploy in seconds with zero configuration and get automatic failover, load balancing, semantic caching, and enterprise-grade features.

Core Infrastructure

• Unified interface providing a single OpenAI-compatible API for all providers
• Multi-provider support across OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, Ollama, and Groq
• Automatic fallbacks for seamless failover between providers and models with zero downtime
• Load balancing with intelligent request distribution across multiple API keys and providers

Advanced Features

• Model Context Protocol support enabling AI models to use external tools including filesystem, web search, and databases
• Semantic caching with intelligent response caching based on semantic similarity to reduce costs and latency
• Multimodal support for text, images, audio, and streaming behind a common interface
• Custom plugins through an extensible middleware architecture for analytics, monitoring, and custom logic
• Governance features including usage tracking, rate limiting, and fine-grained access control

Enterprise and Security

• Budget management with hierarchical cost control across virtual keys, teams, and customer budgets
• SSO integration supporting Google and GitHub authentication
• Observability with native Prometheus metrics, distributed tracing, and comprehensive logging
• Vault support for secure API key management with HashiCorp Vault integration

Developer Experience

• Zero-config startup to begin immediately with dynamic provider configuration
• Drop-in replacement for OpenAI, Anthropic, or GenAI APIs with one line of code
• SDK integrations with native support for popular AI SDKs requiring zero code changes
• Configuration flexibility through web UI, API-driven, or file-based configuration options

Which AI Observability Tool Should You Use?

Choose Maxim if you need an integrated platform that spans simulations, evaluations, and observability with powerful agent tracing, online evaluations, and data curation for cross-functional teams.

Choose LangSmith if your stack centers on LangChain and you want prompt iteration with unified tracing and evaluations tightly integrated with the framework.

Consider Arize for OTEL-based tracing, online evaluations, and comprehensive dashboards across AI, ML, and computer vision workloads with existing MLOps infrastructure.

Choose Helicone for lightweight, open-source observability with strong cost tracking, semantic caching, and simple integration when infrastructure investment needs to be minimal.

Choose Comet Opik for open-source teams needing tracing, evaluation, and production monitoring unified with broader ML experiment tracking workflows.

Conclusion

AI agent observability in 2025 requires unifying tracing, evaluations, and monitoring to build trustworthy AI systems. With LLMs, agentic workflows, and voice AI driving business processes, robust observability platforms maintain performance and user trust. Maxim AI offers comprehensive depth, flexible tooling, and proven reliability that modern AI teams need to deploy with confidence and accelerate iteration across the entire AI lifecycle.

Ready to evaluate and observe your agents with confidence? Book a demo or sign up to get started.

Frequently Asked Questions

What is AI agent observability?

AI agent observability provides 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 and understand agent behavior systematically.

Can I use OpenTelemetry with Maxim?

Yes. Maxim supports OTLP ingestion and forwarding to external collectors including Snowflake, New Relic, and OTEL with AI-specific semantic conventions through data connectors.

How do online evaluations improve AI reliability?

Continuous scoring on real user interactions surfaces regressions early, enabling alerting and targeted remediation before issues impact end users at scale.

Does Maxim support human-in-the-loop evaluation?

Yes. Teams can configure human evaluations for last-mile quality checks alongside LLM-as-a-Judge and programmatic evaluators through the agent simulation and evaluation platform.

What KPIs should we track for agent observability?

Track latency, cost per trace, token usage, mean score, pass rate, error rate, and user feedback trends through dashboards and reporting features.

How do saved views help teams collaborate?

Saved filters enable repeatable debugging workflows across teams, speeding up issue resolution by capturing and sharing effective investigation patterns.

Can I export logs and evaluation data?

Yes. Maxim supports CSV exports and APIs to download logs and associated evaluation data with filters and time ranges for custom analysis workflows.

Is Maxim suitable for multi-agent and multimodal systems?

Yes. Maxim's tracing entities including sessions, traces, spans, generations, tool calls, retrievals, and events along with attachment support handle complex multi-agent, multimodal workflows.

How do alerts work in production?

Configure threshold-based alerts on latency, cost, or evaluator scores and route notifications to Slack, PagerDuty, or OpsGenie for immediate incident response.

Further Reading and Resources

• How to Implement Observability in Multi-Step Agentic Workflows: A Technical Guide with Code Examples
• Session-Level Observability: Tracking Multi-Turn Conversations at Scale
• Why You Should Care About Observability as an AI Product Manager in 2025