The 5 Best Agent Debugging Platforms in 2026

TL;DR

Debugging AI agents is fundamentally different from debugging traditional software. As agentic systems grow in complexity, teams need specialized platforms to trace multi-step workflows, evaluate agent behavior, and identify failure patterns. This guide compares the five leading agent debugging platforms in 2026: Maxim AI (comprehensive end-to-end platform with simulation and cross-functional collaboration), LangSmith (deep LangChain integration with AI-powered debugging), Arize (enterprise-grade ML monitoring with agent support), Langfuse (open-source observability with prompt management), and Comet Opik (unified LLM evaluation and experiment tracking). Each platform offers unique strengths depending on your stack, team structure, and production requirements.

Table of Contents

1. Why Agent Debugging is Different
2. What to Look For in an Agent Debugging Platform
3. Platform Comparison Table
4. The 5 Best Agent Debugging Platforms
   • 1. Maxim AI
   • 2. LangSmith
   • 3. Arize
   • 4. Langfuse
   • 5. Comet Opik
5. Choosing the Right Platform
6. Further Reading

Why Agent Debugging is Different

AI agents present debugging challenges that traditional software never faced. Unlike deterministic programs that execute the same steps every time, agents make autonomous decisions based on context, previous interactions, and real-time tool outputs. A single user query can trigger dozens or hundreds of intermediate steps, each involving LLM calls, tool executions, retrievals, and decision points.

Key challenges in agent debugging:

• Non-determinism: The same input can produce different execution paths depending on model responses, making bugs difficult to reproduce
• Multi-step complexity: Agents run for minutes across hundreds of operations, generating massive trace volumes that are impossible to parse manually
• Multi-turn conversations: Agent interactions span multiple turns with human-in-the-loop feedback, requiring session-level visibility
• Distributed failures: Errors can originate anywhere in the agent graph, from prompt engineering issues to tool call failures to context window limits
• Performance bottlenecks: Identifying which specific step causes latency or cost spikes requires granular span-level metrics

According to recent research, agentic AI systems now generate millions of traces daily in production environments. Without proper instrumentation and tooling, debugging these systems becomes guesswork rather than systematic analysis.

What to Look For in an Agent Debugging Platform

Effective agent debugging requires specialized capabilities beyond traditional APM or logging tools:

Essential features:

• Distributed tracing: Capture every LLM call, tool execution, retrieval, and decision point across multi-agent workflows
• Session tracking: Group traces into conversational threads to understand multi-turn agent behavior
• Span-level evaluation: Assess quality at individual steps, not just final outputs, to pinpoint where agents fail
• Agent graph visualization: See the execution flow visually to understand routing decisions and tool selection
• Cost and latency tracking: Monitor token usage and timing for each component to optimize performance
• Online evaluations: Run continuous quality checks on production data using LLM-as-a-judge and heuristic metrics
• Dataset management: Curate test datasets from production traces for systematic offline evaluation

Advanced capabilities:

• AI-powered debugging assistants: Analyze complex traces using LLMs to surface insights and suggest prompt improvements
• Simulation and replay: Re-run agent interactions from any step to reproduce issues and test fixes
• Cross-functional collaboration: Enable product managers and QA teams to review agent behavior without requiring code access
• Integration ecosystem: Support for major frameworks like LangChain, LangGraph, OpenAI Agents, CrewAI, and more

Platform Comparison Table

The 5 Best Agent Debugging Platforms

1. Maxim AI

Platform Overview

Maxim AI is an end-to-end platform for simulation, evaluation, and observability built specifically for cross-functional teams deploying AI agents at scale. Unlike point solutions focused solely on tracing or evaluation, Maxim unifies the entire AI lifecycle from pre-production testing through production monitoring in a single interface designed for both technical and non-technical stakeholders.

What sets Maxim apart is its emphasis on proactive quality assurance rather than reactive debugging. Teams use Maxim to simulate hundreds of agent scenarios before deployment, catching issues that would otherwise surface in production. This simulation-first approach, combined with comprehensive observability, enables teams to ship agents 5x faster with higher confidence.

Key Features

Distributed Tracing & Session Management

Maxim provides complete visibility into agent execution with hierarchical tracing that captures:

• Multi-level entities: Sessions, traces, spans, generations, tool calls, retrievals, and events
• Multimodal support: Handle text, images, audio, and structured data across complex workflows
• Saved filters: Create reusable queries to quickly surface specific types of failures across teams
• Custom attributes: Enrich traces with user_id, session_id, environment tags, and business metadata

The platform's tracing architecture is designed specifically for agentic systems, allowing teams to debug distributed workflows where multiple agents, tools, and data sources interact.

Agent Simulation & Scenario Testing

Maxim's simulation capabilities let teams test agents against hundreds of scenarios and user personas before production:

• AI-powered simulations: Generate realistic customer interactions across diverse use cases
• Conversational evaluation: Assess whether agents complete tasks successfully and identify failure points
• Step-by-step replay: Re-run simulations from any step to reproduce issues and test fixes
• Scenario libraries: Build and version test scenarios aligned with product requirements

This approach transforms debugging from "finding production issues" to "preventing them systematically through pre-release testing."

Flexible Evaluation Framework

Maxim's evaluation system supports quality assessment at every granularity level:

• Session-level evaluations: Did the agent accomplish the user's overall goal across multiple turns?
• Trace-level evaluations: Was this single interaction successful and appropriate?
• Span-level evaluations: Did individual tools execute correctly? Were retrievals relevant?

Teams can choose from:

• Off-the-shelf evaluators: Pre-built metrics for common patterns like hallucination detection, answer relevance, and safety
• Custom evaluators: Deterministic, statistical, or LLM-as-a-judge evaluators tailored to specific use cases
• Human-in-the-loop: Annotation queues for subjective quality assessment and edge case review

The platform's evaluator store provides a growing library of community-contributed evaluators that teams can adapt to their needs.

Production Observability with Automated Quality Checks

Once agents are deployed, Maxim's observability suite monitors production behavior:

• Real-time alerting: Get notified immediately when quality, latency, or cost metrics breach thresholds
• Automated evaluations: Run periodic quality checks on production data using custom rules
• Custom dashboards: Build team-specific views that slice data across user segments, agent types, or business KPIs
• Cost analytics: Track token usage and costs per user, session, or feature

Maxim's approach to AI reliability emphasizes catching regressions early through continuous monitoring rather than discovering issues through user complaints.

Data Engine for Continuous Improvement

The platform includes comprehensive data management capabilities:

• Dataset curation: Build evaluation datasets from production traces, simulations, or manual imports
• Multimodal support: Handle images, audio, and structured data alongside text
• Data enrichment: Add human annotations and feedback loops to improve dataset quality
• Data splits: Create targeted subsets for specific evaluation scenarios or A/B testing

This data-centric approach enables teams to continuously evolve their quality benchmarks as product requirements change.

Cross-Functional Collaboration

A defining feature of Maxim is its UX designed for non-technical stakeholders:

• No-code evaluation configuration: Product managers can define and run evaluations without engineering support
• Annotation workflows: QA teams can review agent behavior and provide structured feedback
• Shared dashboards: Cross-functional visibility into agent performance without requiring code access
• Comment threads: Collaborate directly on specific traces or evaluation results

Organizations like Clinc, Thoughtful AI, and Atomicwork have used Maxim to accelerate shipping by breaking down silos between engineering, product, and QA teams.

Integration Ecosystem

Maxim integrates with the full AI development stack:

• Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Haystack
• LLM providers: OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure OpenAI
• Tools: SDKs in Python, TypeScript, Java, and Go for flexible instrumentation
• Gateway: Bifrost, Maxim's AI gateway, provides unified access to 12+ providers with automatic failover and semantic caching

Best For

Maxim is ideal for:

• Product-led teams that need to move fast while maintaining quality standards
• Cross-functional organizations where engineering, product, QA, and support teams collaborate on AI quality
• Enterprises requiring comprehensive pre-production testing before deploying customer-facing agents
• Teams building complex agentic systems with multi-agent coordination, tool use, and multimodal interactions

Companies choose Maxim when they need more than just observability and want a complete solution that prevents issues through simulation, catches them through evaluation, and resolves them through production monitoring.

Compare Maxim with LangSmith | Compare Maxim with Arize | Schedule a demo

2. LangSmith

Platform Overview

LangSmith is the observability and debugging platform built by the team behind LangChain, one of the most widely adopted frameworks for building AI agents. If your agents are built with LangChain or LangGraph, LangSmith provides native integration with minimal setup, capturing every step of your agent's execution automatically.

The platform introduced major debugging enhancements in December 2025 specifically for "deep agents" (complex, multi-step autonomous systems), including an AI assistant named Polly that analyzes traces and suggests improvements, and LangSmith Fetch, a CLI tool for debugging directly from your terminal.

Key Features

• Native LangChain integration: One environment variable enables automatic tracing for all LangChain/LangGraph applications
• Polly AI assistant: Chat with an AI that understands agent architectures to analyze trace data and suggest prompt improvements
• LangSmith Fetch: CLI tool for pulling trace data directly into coding agents like Claude Code or Cursor for AI-powered debugging
• Prompt playground: Test and iterate on prompts with different models, parameters, and contexts
• Insights Agent: Automatically cluster production traces to discover usage patterns and common failure modes
• Multi-turn evaluations: Score complete agent conversations rather than just individual turns
• Thread tracking: Group traces into conversation threads for session-level analysis

Best For

LangSmith is best suited for teams that:

• Build agents using the LangChain or LangGraph frameworks
• Want tightly integrated tracing with minimal instrumentation overhead
• Need AI-powered analysis of complex agent traces
• Prefer working with CLI tools and coding agents for debugging
• Value prompt iteration and experimentation workflows

The platform's deep understanding of LangChain's abstractions makes it the natural choice for teams already invested in that ecosystem. However, teams using other frameworks may find integration more complex.

3. Arize

Platform Overview

Arize AI brings enterprise-grade ML monitoring capabilities to LLM applications and AI agents. Originally known for traditional model observability and drift detection, Arize expanded its platform in 2025 with Phoenix (open-source) and AX (enterprise) product lines specifically for agent debugging and evaluation.

Arize's strength lies in its comprehensive monitoring infrastructure built on OpenTelemetry standards, making it highly flexible and compatible with existing observability stacks. The platform is particularly strong for organizations with mature MLOps practices looking to extend their monitoring to agentic systems.

Key Features

• OTEL-based tracing: Built on OpenTelemetry for vendor-agnostic, framework-independent instrumentation
• Phoenix open-source: Self-hosted observability platform for LLM applications with comprehensive evaluation tooling
• AX enterprise platform: Advanced monitoring with automated evaluations, drift detection, and production analytics
• Agent graph visualization: Visual representation of agent execution flows to understand decision paths
• Comprehensive evaluation templates: Pre-built evaluators for tool calling, path convergence, and planning
• Embeddings analysis: Track semantic drift in model outputs over time
• Multi-environment support: Monitor across development, staging, and production with unified dashboards

Best For

Arize is ideal for:

• Enterprises with existing ML infrastructure and monitoring practices
• Teams requiring OTEL-compliant tracing for integration with existing systems
• Organizations prioritizing model drift detection and long-term performance tracking
• Teams building with Amazon Bedrock, Google Vertex, or other cloud-native AI services

The platform's enterprise focus means it may have higher overhead for smaller teams or startups compared to lighter-weight alternatives.

4. Langfuse

Platform Overview

Langfuse is an open-source observability platform for LLM applications and agents that emphasizes prompt management and collaborative debugging. The platform gained significant traction in 2025 for its flexible tracing, comprehensive evaluation framework, and strong integration ecosystem supporting 50+ libraries and frameworks.

Langfuse distinguishes itself by treating prompts as first-class entities with version control, deployment tracking, and A/B testing capabilities built directly into the platform. This makes it particularly valuable for teams that iterate frequently on prompt engineering.

Key Features

• Open-source core: Self-host the entire platform or use the managed cloud service
• Observation types: Semantic labeling for agents, tools, chains, retrievers, embeddings, and guardrails
• Prompt management: Version control, deployment labels, and change tracking for prompts
• Playground side-by-side comparison: Test multiple prompts, models, or configurations in parallel
• Dataset folders: Organize evaluation datasets hierarchically as agent capabilities expand
• Annotation queues: Human-in-the-loop evaluation workflows for quality assessment
• Webhooks and Slack integration: Real-time notifications for prompt changes and production issues
• Cost tracking: Automatic token usage and cost calculation per trace, session, or user

Best For

Langfuse works well for teams that:

• Prefer open-source solutions with full control over their data
• Iterate frequently on prompts and need robust version management
• Want flexibility to self-host while retaining access to managed cloud features
• Need deep integration with specific frameworks like LlamaIndex, DSPy, or Haystack
• Value collaborative workflows between engineers and prompt engineers

The open-source nature provides transparency and customizability but may require more DevOps investment for production deployments compared to fully managed alternatives.

5. Comet Opik

Platform Overview

Comet Opik is an open-source platform for logging, evaluating, and monitoring LLM applications that extends Comet's established ML experiment tracking capabilities to agentic systems. Opik differentiates itself by unifying LLM observability with broader ML workflows, making it attractive for data science teams already using Comet for traditional machine learning.

Released as open-source in 2025, Opik includes comprehensive tracing, evaluation frameworks, and production monitoring dashboards that work across development and deployment phases.

Key Features

• Open-source and self-hostable: Full platform available for local deployment via Docker or Kubernetes
• 40+ framework integrations: Support for OpenAI Agents, Google ADK, AutoGen, CrewAI, LlamaIndex, and more
• Span-level metrics: Evaluate quality of individual steps within agent workflows, not just final outputs
• Agent optimizer: Automated agent improvement algorithms to maximize performance
• Online evaluation rules: Continuous quality monitoring with LLM-as-a-judge metrics in production
• Guardrails and anonymizers: Built-in safety mechanisms and PII protection for production systems
• Experiment tracking integration: Unified view of LLM experiments alongside traditional ML pipelines
• Dataset management: Training/validation splits and versioning for evaluation workflows

Best For

Comet Opik is well-suited for:

• Data science teams already using Comet for ML experiment tracking
• Organizations wanting to unify LLM and traditional ML monitoring in one platform
• Teams requiring comprehensive evaluation frameworks with custom metrics
• Open-source advocates who want full control over their observability stack
• Teams building with recently released frameworks like OpenAI Agents or Google ADK

The tight integration with Comet's ML platform makes it particularly appealing for teams that want consistency across their AI/ML tooling rather than separate systems for different model types.

Further Reading

Agent Evaluation & Quality

• AI Agent Quality Evaluation: A Comprehensive Guide
• AI Agent Evaluation Metrics: 12 Essential Metrics for 2025
• Agent Evaluation vs Model Evaluation: What's the Difference?
• What Are AI Evals? A Complete Guide

Agent Debugging & Observability

• Agent Tracing for Debugging Multi-Agent AI Systems
• LLM Observability: How to Monitor Large Language Models in Production
• Why AI Model Monitoring is Key to Reliable AI in 2025
• AI Reliability: How to Build Trustworthy AI Systems

Workflows & Best Practices

• Evaluation Workflows for AI Agents
• Prompt Management in 2025: How to Organize, Test, and Optimize
• How to Ensure Reliability of AI Applications

Ready to debug your AI agents with confidence? Schedule a demo with Maxim AI to see how end-to-end simulation, evaluation, and observability can help your team ship production-ready agents 5x faster.