7 Best LLM Observability Tools for Debugging and Tracing in 2026

Your production agent processed 50,000 customer requests. Somewhere in that batch, a multi-step workflow started returning corrupted recommendations—but your logs show nothing but successful completions. 

Traditional debugging fails here because LLM applications operate probabilistically: identical inputs produce different outputs, errors compound silently across chains, and failures manifest as semantically wrong answers rather than exceptions.

Without proper observability, you're debugging blind—hours disappear isolating issues, regressions appear after prompt changes with no way to trace causality, and cost spikes hit before anyone notices.

LLM observability tools solve these challenges through structured tracing, step-level inspection, replay capabilities, and integrated evaluation. This overview covers platforms giving engineering teams deep visibility into LLM application behavior.

TLDR:

• LLM observability requires capturing prompts, completions, and token-level metadata—not just request/response timing

• Hierarchical tracing across sessions, traces, and spans enables root-cause analysis for multi-step workflows

• Evaluation-integrated platforms connect debugging insights directly to quality improvement workflows

• Gateway-based tools offer minimal integration effort while SDK-native tools provide deeper visibility

• OpenTelemetry-based instrumentation is emerging as the vendor-neutral standard

• Open-source options provide data sovereignty while commercial platforms reduce operational burden

What is an LLM observability tool for debugging and tracing

LLM observability tools capture, structure, and visualize the full execution path of LLM applications. They enable engineers to inspect, debug, and optimize every step from initial request through final response.

These tools differ fundamentally from traditional APM. Conventional monitoring tracks HTTP status codes and infrastructure metrics. LLM observability must capture complete prompt and completion bodies, token-level cost attribution, and semantic quality scores. Non-deterministic outputs mean identical inputs can yield different results, making traditional reproduction-based debugging ineffective.

Core capabilities include distributed tracing across chains and agents, prompt/completion logging with full metadata, step-level latency and cost breakdowns, and session threading for multi-turn conversations.

Session and conversation threading groups related interactions, enabling teams to trace issues across multi-turn exchanges. Search and filtering capabilities let engineers query traces by metadata, timestamps, or error patterns. For engineering leaders, these tools translate to reduced debugging time, faster incident resolution, and quantifiable visibility into AI system reliability.

1. Galileo

Galileo unifies tracing, evaluation, and runtime protection into one eval engineering platform. The Agent Graph visualization provides interactive exploration of multi-step decision paths and tool interactions.

The platform implements three-layer hierarchical tracing: Sessions (entire workflows), Traces (individual operations), and Spans (granular steps). Telemetry flows through OpenTelemetry collectors into log streams with configurable metric evaluation.

What distinguishes Galileo is the closed-loop integration of experiments, monitoring, and runtime protection. Luna-2 models—fine-tuned Llama 3B and 8B variants—attach quality assessments to every trace span at sub-200ms latency and 97% lower cost than GPT-4-based evaluation. 

CLHF improves metric accuracy from human feedback over time. The runtime protection engine uses configurable rules, rulesets, and stages to block unsafe outputs before reaching users.

Key features

• Agent Graph visualization for multi-agent workflow debugging with interactive node exploration

• Luna-2 small language models (fine-tuned Llama 3B/8B) attaching real-time quality scores to trace spans with CLHF

• Hierarchical tracing across sessions, traces, and spans

• Runtime protection with configurable rules, rulesets, and stages

• Out-of-the-box metrics across five categories including agentic performance, response quality, and safety

• Signals surfacing failure patterns and clustering similar issues

Strengths and weaknesses

Strengths:

• Closed-loop integration between tracing, evaluation, and runtime protection

• Agent-specific visualization with dedicated agentic metrics (action advancement, tool selection quality, agent efficiency)

• Cost-efficient evaluation at scale through Luna-2 with CLHF

• Framework-agnostic with OpenTelemetry plus LangChain, LangGraph, CrewAI, OpenAI Agents SDK, and Google ADK

Weaknesses:

• Luna-2 and runtime protection available only on Enterprise tier

• Enterprise pricing requires direct engagement

• Deepest capabilities require platform commitment versus lightweight integration

Use cases

Teams building complex agent workflows use Galileo to trace hallucination root causes across multi-step reasoning chains. When an agent selects the wrong tool or processes incorrect context, the Agent Graph reveals where decisions diverged. Production teams identify latency bottlenecks through span-level timing. Evaluation-annotated traces drive systematic quality improvement across thousands of agents.

2. LangSmith

Deep tracing within the LangChain ecosystem comes from LangSmith's native observability capabilities. The platform implements hierarchical run-based tracing where each operation becomes a structured span with full parent-child relationships.

LangSmith Studio delivers a free local visual interface for LangGraph agent development. Engineers see DAG renderings of multi-node workflows with step-by-step inspection. Hot reloading through langgraph dev reflects prompt changes immediately without restart.

Key features

• SDK-based automatic tracing via environment variables for Python and TypeScript

• LangSmith Studio providing free local visual interface with DAG rendering and hot reloading

• Five streaming modes (values, updates, messages, custom, debug) for real-time development

• Dataset management with automatic versioning and trace-to-dataset export

• Offline and online evaluation with LLM-as-judge, code-based rules, and human feedback

• Annotation queues for human feedback integration

Strengths and weaknesses

Strengths:

• Native integration with LangChain/LangGraph provides zero-friction adoption

• Comprehensive evaluation framework with multiple evaluator types

• Strong dataset management for systematic testing workflows

Weaknesses:

• 400-day maximum trace retention requires external archival for compliance

• Ecosystem-centric design optimized for LangChain/LangGraph may limit flexibility for teams using other frameworks

Use cases

Teams building conversational agents and RAG applications within the LangChain ecosystem benefit from LangSmith's comprehensive platform. Development workflows leverage Studio for visual debugging with step-by-step inspection. The hierarchical tracing architecture captures complete execution flows including LLM calls, tool invocations, and retrieval operations.

3. Arize AI and Phoenix

Phoenix serves as an open-source tracing tool built on the OpenInference standard. Arize AX provides the commercial enterprise layer on the same technical foundation. Both leverage OpenInference built on OpenTelemetry Protocol for standardized capture of LLM-specific events.

Phoenix offers comprehensive auto-instrumentation for LlamaIndex, LangChain, DSPy, and major LLM providers. The Span Replay feature enables developers to replay LLM calls with different inputs for side-by-side comparison.

Key features

• OpenInference/OTLP-based tracing ensuring cross-platform compatibility

• Span Replay for debugging prompt variations without full pipeline execution

• Session grouping for multi-turn conversation analysis

• External evaluator integration with Ragas, Deepeval, and Cleanlab

• Arize AX enterprise capabilities: Alyx Copilot IDE integration and AI Agent Search with natural language queries

Strengths and weaknesses

Strengths:

• Open-source Phoenix provides full data ownership with zero licensing costs

• Migration guidance provided for transitioning between Phoenix and Arize AX

• Strong evaluation framework with external evaluator integrations

Weaknesses:

• Phoenix optimized for less than 1TB data volume; larger deployments require Arize AX

• AI-assisted debugging features exclusive to Arize AX commercial tier

• Enterprise pricing not publicly disclosed

Use cases

Teams prioritizing data sovereignty deploy self-hosted Phoenix for development, graduating to Arize AX for production monitoring at scale. The OpenInference standard ensures traces collected with Phoenix migrate to Arize AX with minimal code changes. Engineers use Span Replay to debug and compare LLM outputs without re-running entire pipelines.

4. Langfuse

Open-source LLM observability with production-ready self-hosting defines Langfuse's approach. The platform implements hierarchical observability through observations (spans, events, generations), traces (complete workflows), and sessions (grouped trace collections).

Session management groups multiple traces into meaningful collections representing complete user interactions. Self-hosted deployments leverage Kubernetes orchestration with PostgreSQL, Clickhouse, and Redis components.

Key features

• Three observation types: Spans for execution steps, events for discrete occurrences, and generations for LLM completions

• Session-based grouping for multi-turn conversation debugging

• Production-ready self-hosting with comprehensive deployment guidance across Docker Compose and Kubernetes

• Native framework integrations for LangChain, LlamaIndex, OpenAI SDK, and Haystack

• Full-featured open-source tier with unlimited core tracing capabilities at $0 cost

Strengths and weaknesses

Strengths:

• Core observability features fully available in self-hosted open-source version

• Strong community health with 21.3k GitHub stars and active development

• Framework-native callbacks minimize integration complexity

Weaknesses:

• 1MB trace size limit with automatic truncation affects long-context applications

• Rate limiting in evaluation pipelines can extend execution times

• Community-based support without enterprise SLAs

Use cases

Engineering teams with existing infrastructure capabilities choose Langfuse for complete data ownership and cost predictability. Session management enables debugging multi-turn interactions by grouping related traces. Self-hosting requires operational expertise for managing database components but eliminates licensing fees.

5. Helicone

Proxy-based LLM observability through a gateway architecture defines Helicone's approach. Teams change their API base URL to point to Helicone's gateway and add their API key. No SDK installation or code modifications needed.

The platform automatically captures comprehensive metadata for each request including timestamps, model versions, token usage, latency measurements, and cost calculations. Session-based tracing groups related requests for visualizing complex multi-step workflows.

Key features

• Proxy-based gateway architecture requiring only base URL change and API key—no SDK installation or code modifications

• Automatic metadata capture including timestamps, model versions, token usage, latency, cost calculations, and error details

• Session-based tracing grouping related LLM requests for multi-step workflow visualization

• Universal provider support with compatibility across 100+ LLM providers

• Cost tracking with automatic token-level cost attribution via Model Registry v2

Strengths and weaknesses

Strengths:

• Provider-agnostic with support for OpenAI-compatible API syntax

• Multiple integration approaches: SDK-native, proxy-based, and direct API instrumentation

• Open-source availability with hosted service options for flexible deployment

Weaknesses:

• All traffic routes through Helicone infrastructure, creating operational dependency

• Limited visibility into internal application logic compared to SDK-based approaches

• Missing granular insight into multi-step reasoning chains within agent workflows

Use cases

Teams implementing rapid LLM observability deployment choose Helicone's gateway architecture for universal compatibility. The platform suits organizations evaluating observability before committing to deeper SDK integration. Production teams use Helicone for cost tracking and anomaly detection at the API call level.

6. Braintrust

Braintrust combines tracing with evaluation workflows through Brainstore—a purpose-built database for AI data at scale deployed within customer cloud infrastructure. The hybrid deployment model keeps the data plane (logs, traces, prompts) in customer infrastructure while the control plane remains managed.

This architecture ensures sensitive AI application data never leaves customer infrastructure while enabling complete reconstruction of decision paths across multi-step workflows.

Key features

• Brainstore database optimized for large, complex AI trace data

• Hybrid deployment keeping data in customer AWS, GCP, or Azure environments

• Trace-to-test conversion with one-click creation of test cases from production traces

• Side-by-side diff comparison of prompt versions and model outputs

• Temporal integration for durable execution across workflow restarts

• SOC 2 Type II and HIPAA compliance certifications

Strengths and weaknesses

Strengths:

• Data sovereignty through hybrid architecture without full self-hosting operational burden

• Closed-loop quality improvement connecting production traces to regression test suites

• Strong compliance posture for regulated industries

Weaknesses:

• Technical implementation details for Brainstore not publicly documented

• Pricing structures not covered in publicly available documentation

• Fewer framework-specific integrations compared to ecosystem-native tools

Use cases

Teams in regulated industries requiring data sovereignty without full self-hosting complexity choose Braintrust's hybrid model. The trace-to-test conversion workflow suits organizations building systematic regression testing. Engineers debugging long-running agent workflows benefit from Temporal integration for maintaining trace continuity.

7. Portkey

Portkey implements an AI gateway combining observability with active operational control across 1,600+ LLMs. Rather than passive monitoring, the gateway enables weighted load balancing, sticky routing for conversation context, and automatic failover.

The unified telemetry model standardizes logs, metrics, and traces from gateway operations, capturing 40+ metadata attributes for every request.

Key features

• AI gateway supporting 1,600+ LLMs through unified API endpoint with standardized telemetry

• Intelligent routing with weighted load balancing, sticky routing for conversation context, and conditional routing based on task complexity

• Automatic failover detecting rate limits, timeouts, and outages across providers

• Unified telemetry model capturing 40+ metadata attributes including token usage, latency, retry counts, and routing decisions

• Smart caching reducing costs and latency for repeated queries

• A/B testing capabilities for model version comparison through configurable routing strategies

Strengths and weaknesses

Strengths:

• Active operational control through routing, fallback, and load balancing across 1,600+ providers

• Provider-agnostic architecture eliminates vendor lock-in

• Intelligent caching reduces costs and latency for repeated queries

• Minimal integration effort—no SDK required, just configuration changes

Weaknesses:

• Gateway dependency requires all traffic to route through Portkey infrastructure

• Routing logic lives in Portkey configuration rather than application code

• Less visibility into application-level logic compared to SDK-native approaches

Use cases

Teams managing multi-provider LLM deployments use Portkey for unified observability with standardized telemetry. The platform enables intelligent routing through weighted load balancing and conditional routing. Production teams leverage automatic failover, cost optimization through caching, and A/B testing—all without custom implementation.

Building an LLM observability and debugging strategy

You cannot evaluate, monitor, or intervene on what you cannot see. LLM observability forms the foundation for your AI quality stack. Without it, debugging remains reactive, incident response stays slow, and systematic quality improvement becomes impossible.

Consider a layered approach: a primary observability platform with integrated evaluation and intervention capabilities, lightweight proxy tools for quick request logging, and open-source options for self-hosted environments. Start instrumentation early rather than retrofitting after production issues emerge.

Galileo delivers comprehensive LLM observability purpose-built for agent reliability:

• Agent Graph visualization: Interactive exploration of multi-step decision paths, tool interactions, and agent reasoning

• Luna-2 evaluation models: Fine-tuned Llama 3B/8B variants attaching real-time quality scores to trace spans at 97% lower cost than GPT-4-based evaluation

• Hierarchical tracing: Sessions, traces, and spans provide visibility from high-level workflows down to individual API calls

• Runtime protection: Configurable rules, rulesets, and stages blocking unsafe outputs before user impact

• Signals: Automated failure pattern detection and clustering surfaces root causes without manual analysis

• OpenTelemetry compatibility: Standards-based instrumentation integrates with existing observability stacks

Book a demo to see how Galileo's agent observability platform can transform hours of investigation into minutes.

Frequently asked questions

How is LLM tracing different from traditional application tracing?

Traditional APM traces capture request/response timing and infrastructure metrics for deterministic systems. LLM tracing must capture fundamentally different signals due to probabilistic outputs. According to research from Vellum AI and Comet, LLM tracing requires complete prompt and completion bodies, token-level cost attribution, semantic quality scores, and intermediate reasoning steps. Since identical inputs can produce different results, comprehensive context capture is essential for reproduction and debugging.

How do I know when to invest in dedicated LLM observability?

Invest in dedicated observability when moving beyond prototypes to production, when debugging time exceeds acceptable thresholds, or when cost attribution becomes critical. Generic logging captures HTTP requests as opaque operations—it cannot provide semantic understanding for quality assessment or token-level cost tracking in multi-step agent workflows.

How do I choose between open-source and commercial observability tools?

Open-source solutions like Phoenix or Langfuse require substantial infrastructure expertise and lack production-grade evaluation. Commercial platforms like LangSmith ($39/seat/month) remain locked to specific ecosystems with limited evaluation depth.

Galileo stands apart as the clear leader with a unified observability, evaluation, and intervention platform purpose-built for production AI. Luna-2 slashes evaluation costs by up to 97% while attaching real-time quality scores to every trace span. Agent Graph visualization makes multi-agent workflows intuitive and debuggable. Runtime Protection guardrails actively prevent harmful outputs before they reach users.

For teams serious about agent reliability at scale, Galileo transforms debugging from hours to minutes—the only solution delivering complete, closed-loop quality improvement out of the box.

What debugging workflows can I enable with LLM observability?

LLM observability platforms enable root-cause analysis through distributed tracing across multi-step workflows. Regression identification comes through prompt version control and comparative analysis. Latency analysis through span-level timing reveals bottlenecks. Cost spike investigation uses token-level attribution. Session management enables debugging context-dependent failures in multi-turn conversations.

How does integrated evaluation improve debugging efficiency?

Integrated evaluation attaches real-time quality scores directly to trace spans. Instead of manually reviewing outputs, engineers filter traces by quality scores to surface problematic patterns immediately. Low-latency evaluation architecture enables production-scale assessment. The integrated workflow connects identified issues directly to intervention policies through guardrails that trigger protective actions before problematic outputs reach users.