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By Latitude · March 23, 2026

Key Takeaways

  • Agent observability requires session-level trace capture — multi-turn failures trace back to step 3 of 22, invisible in call-level APM tools that see individual spans rather than causal chains.

  • Of 15 platforms compared, only Latitude closes the loop from issue → opened PR: it connects your coding agent (Claude Code, Cursor, and similar) via its MCP server, so detected failures can be driven toward a fix — on top of Signal-based issue tracking and evals auto-generated from real production failures.

  • Purpose-built tools (Latitude, Langfuse, LangSmith, Braintrust) integrate in minutes with AI-native workflows; embedded enterprise tools (Datadog, New Relic) consolidate vendors but lack multi-turn causal analysis.

  • Open-source foundations (Arize Phoenix, DeepEval, MLflow) offer full self-hosted deployment for teams with data residency requirements — with more operational investment required.

  • Braintrust has the most generous free tier (1M spans/month, 10K evals) for teams starting systematic evaluation without production budget.

  • The highest-leverage investment regardless of platform: treat evals as production code — convert every observed failure into a regression test before the next deployment.

AI agent observability is a fundamentally different problem than LLM observability. A single user query can trigger dozens of tool calls, spawn sub-agents, loop through memory retrievals, and branch across conditional logic paths — all before producing a single output. When something goes wrong, the failure might trace back to step 3 of 22, buried inside a context window that was already corrupted by step 1.

Most observability tools were built for a simpler world: one prompt in, one completion out. Applying them to multi-turn agent systems means retrofitting agent tracing onto architectures that were never designed for it. The result is partial visibility — you can see that something failed, but not why, not how to reproduce it, and not how to prevent it from happening again.

This guide compares 15 AI observability platforms specifically on their ability to handle production AI agents in 2026. We cover multi-turn conversation tracing, evaluation depth, pricing, and which platform fits which team.

What to Look for in an AI Agent Observability Platform

Before comparing tools, it helps to define what “good” observability looks like for agents specifically. We evaluated each platform across five dimensions:

1. Multi-Turn Conversation Tracing

Can the tool trace a full agent session — not just individual LLM calls — as a single, coherent unit? This means linking tool invocations, sub-agent spawns, memory reads, and intermediate reasoning steps into a single trace thread. Without this, you’re looking at individual events in isolation, with no way to understand how they relate.

2. Agent-Specific Failure Detection

Does the platform surface agent-specific failure patterns: hallucinations in intermediate steps, tool call loops, context overflow, compounding errors across turns? Generic error monitoring (latency spikes, exception rates) misses most agent failures, which are semantic, not operational.

3. Evaluation Quality and Coverage

Can you run evals against production traces? Are those evals connected to real failure patterns — not just synthetic benchmarks? The best platforms let you convert observed failures directly into regression tests.

4. Human-in-the-Loop Workflow

Agents behave differently across domains. A legal document agent and a customer support agent have completely different definitions of “correct.” Platforms that let domain experts define quality — not just run automated scoring — produce more accurate and defensible evals.

5. Integration Complexity and Cost Model

Enterprise APM tools (Datadog, New Relic) are deeply embedded in existing infrastructure. LLM-native tools (Langfuse, LangSmith) plug in with a few lines of Python. The right choice depends on what’s already deployed, how fast you’re moving, and how unpredictably your traces might scale.

Comparison Table: 15 AI Observability Tools at a Glance

Platform Multi-Turn Tracing Eval Depth Agent Failure Detection Human Annotation Free Tier Best For
Latitude ✓ Native Deep (evals auto-generated from real failures) ✓ Signals + closed loop (issue → PR via MCP) ✓ Annotation queues Free (20K credits/mo); self-host free (MIT) Production AI teams needing quality control
Langfuse ✓ Session threading Deep (LLM-as-judge + custom) Moderate ✓ Manual annotation Yes (self-hosted free) Teams needing open-source + self-hosting
LangSmith ✓ LangChain-native Deep (within LangChain) Moderate ✓ Human review queues Yes (14-day) LangChain-heavy teams
Arize Phoenix ✓ Span-based Deep (RAG + agent evals) Strong (embedding drift) Moderate Yes (open-source) ML-focused teams, RAG apps
Braintrust ✓ Session grouping Deep (eval experiments) Moderate ✓ Review interface Yes (hobby tier) Eval-first engineering teams
LangWatch ✓ Thread-based Deep (LLM-as-judge, DSPy) Strong (simulations) ✓ Collaborative annotation Yes (50K logs) AI-native dev teams, multi-turn testing
Comet / Opik ✓ threadId grouping Deep (heuristic + LLM-judge) Moderate Limited Yes ($0 plan) ML teams bridging classical + LLM workflows
Honeycomb ✓ Distributed tracing Shallow (APM-focused) Moderate (BubbleUp ML) No Yes (20M events/mo) DevOps/SRE teams extending existing observability
Datadog LLM Obs. ✓ Agent decision graphs Moderate (LLM Experiments) Strong (loop detection) No No Enterprise teams on existing Datadog stack
New Relic ✓ Waterfall view Moderate (real-time feedback) Moderate No Yes (100 GB/mo) Enterprise teams extending existing APM
Galileo ✓ Session-based Deep (Guardrail metrics) Strong (hallucination scoring) ✓ Human review Limited trial Teams focused on safety and hallucination prevention
Helicone Partial (request groups) Moderate Limited Limited Yes (generous free) Early-stage teams, cost monitoring focus
Maxim AI ✓ Native Deep (visual eval builder) Moderate ✓ Review workflows Yes Teams wanting no-code eval design
MLflow Partial (experiment tracking) Deep (within ML experiments) Limited Limited Yes (open-source) Data science teams with existing MLflow investment
Confident AI Partial Deep (DeepEval framework) Strong (G-Eval, RAG metrics) Limited Yes (open-source DeepEval) Teams running automated eval suites at scale

Platform Deep Dives

Latitude

Latitude is built specifically for production AI agents, not retrofitted from an LLM logging tool. It’s organized as a loop — Observe → Understand → Refine — rather than a dashboard, and it’s open source (MIT) and self-hostable.

Its sharpest differentiator is closing the loop from issue → opened PR. Latitude’s MCP server connects your coding agent (Claude Code, Cursor, and similar) directly to your workspace, so a detected issue can move toward a fix and an opened PR from inside the agent rather than staying a line item on a dashboard. The MCP-to-coding-agent connection is real today; the direction is to make reliability work actually close instead of stopping at the observability layer.

On top of that sits an intelligence layer, not just an observability layer. Behaviours cluster your agent’s real sessions by meaning, surfacing patterns you didn’t know to look for. Signals turn recurring failures into named, tracked problems (with example traces, affected-user counts, and a lifecycle), fed by human annotations, flaggers (auto-detected frustration, refusal, jailbreaking, tool errors, empty responses), and scores. Evals are auto-generated from those Signals so you catch regressions after you ship a fix. Semantic + exact-text search runs across 100% of traces (no sampling), and Monitors watch a Signal, saved search, or raw traffic and alert in Slack, email, or webhook.

For multi-turn conversations, Latitude traces the full agent session — tool calls, sub-agent spawns, memory retrievals, and reasoning steps — as a single linked unit. GEPA (an eval-optimization technique) and MCC-based alignment scoring are supported for teams that want them, but they’re supporting details, not the core pitch.

Best for: Engineering teams running agents in production who need to close the loop between observability and quality — not just see failures, but drive them toward shipped fixes.

Pricing: Free Starter (20K credits/mo, 30-day retention, unlimited seats) → $99/mo Pro (100K credits/mo, 90-day retention, unlimited seats, SOC 2 & ISO 27001, extra credits $20/10K) → custom Enterprise. Latitude meters usage in credits; self-hosting is free and MIT-licensed.

Langfuse

Langfuse is a popular open-source LLM observability platform that acquired Clickhouse in January 2026 to strengthen its data infrastructure. It supports multi-turn agent tracing through session IDs that group related traces, and offers a complete evaluation stack: LLM-as-judge, rule-based evals, and manual annotation workflows. Its self-hosting option makes it attractive for teams with strict data residency requirements. The platform integrates with essentially every major framework (OpenAI, LangChain, LlamaIndex, Anthropic, AWS Bedrock) through simple SDK wrapping.

Best for: Teams that need open-source, self-hosted deployment with full evaluation capabilities.

Pricing: Free for self-hosted; cloud plans start at ~$49/month; enterprise custom.

LangSmith

LangSmith is LangChain’s native observability platform, deeply integrated with the LangChain ecosystem. If your agents are built on LangChain or LangGraph, LangSmith provides out-of-the-box full tracing with zero additional instrumentation. Its evaluation workflows include dataset management, human review queues, and automated scoring. Outside the LangChain ecosystem, LangSmith requires more manual setup and loses some of its native advantages. The platform has recently added more standalone agent tracing capabilities, but its competitive moat remains its LangChain integration depth.

Best for: Teams primarily using LangChain or LangGraph for agent development.

Pricing: Developer plan free (limited usage); Plus at $39/month; enterprise custom.

Arize Phoenix

Arize Phoenix is the open-source product from Arize AI, optimized for ML teams that care about model quality at the data layer. It handles multi-turn tracing through OpenTelemetry-compatible spans, supports RAG-specific evaluation metrics (context relevance, faithfulness, completeness), and provides embedding drift detection that can catch model degradation before it appears in user-facing metrics. The paid Arize platform extends Phoenix with enterprise-grade monitoring and SLA-backed support.

Best for: ML-focused teams, RAG applications, and teams that want open-source tracing with enterprise upgrade path.

Pricing: Phoenix is open-source/free; Arize cloud pricing on request.

Braintrust

Braintrust takes an eval-first philosophy: instead of observability as the primary entry point, it centers evaluation experiments as the core workflow. Teams define eval datasets, run scored experiments across model versions or prompt changes, and review diffs to make shipping decisions. Its logging and tracing features are solid but secondary to the evaluation experience. Braintrust is particularly strong for teams that treat their eval suite as a first-class software artifact and want tight integration between evaluation and CI/CD pipelines.

Best for: Engineering teams that have already built eval culture and want a dedicated platform for running and comparing eval experiments.

Pricing: Hobby tier free (limited logs); Teams at $200/month; enterprise custom.

LangWatch

LangWatch is an open-source LLMOps platform (2,500+ GitHub stars) that supports the full agent lifecycle — from multi-turn conversation tracing through automated eval generation to pre-release testing with synthetic simulations. Its thread-based tracing links every step of an agent session (tool calls, memory reads, delegations) into a coherent view. The simulation capability is notable: teams can generate thousands of synthetic conversations across edge cases before deploying, then monitor for regressions in production. DSPy optimization support makes it appealing for teams using prompt optimization workflows.

Best for: AI-native development teams who want strong multi-turn tracing plus pre-release simulation testing.

Pricing: Free starter (50K logs/month, 14-day retention); paid plans from ~€59/month; Growth at ~€499/month; enterprise custom.

Comet / Opik

Comet offers two distinct product lines: its original MLOps platform (experiment tracking, model versioning for classical ML) and Opik, its open-source LLM observability tool. Opik handles multi-turn tracing through explicit thread ID grouping across spans and supports hallucination scoring, context recall metrics, and LLM-as-judge evaluations. The platform scales to 40M+ traces/day. The dual product structure is a source of occasional confusion — teams focused purely on LLM/agent workflows typically engage only with Opik rather than the broader Comet platform.

Best for: ML teams that want to bridge classical model tracking and LLM/agent observability in one vendor relationship.

Pricing: Opik free plan ($0, unlimited team members); Pro at $39/month; enterprise custom.

Honeycomb

Honeycomb is a general-purpose observability platform that has expanded into AI monitoring through distributed tracing capabilities that naturally accommodate agent decision paths. Its March 2026 feature releases include MCP server integrations, automated infrastructure investigations, and BubbleUp ML-powered anomaly detection applied to LLM traces. Where Honeycomb excels is correlating AI behavior with broader system health — if an agent’s latency spike correlates with a database query pattern, Honeycomb surfaces that connection. What it lacks is purpose-built LLM evaluation workflows; there’s no built-in LLM-as-judge or offline eval management.

Best for: DevOps and SRE teams who want to bring AI monitoring into existing Honeycomb infrastructure without adopting a separate tool.

Pricing: Free tier (20M events/month); paid plans volume-based; metrics add-on available.

Datadog LLM Observability

Datadog’s LLM Observability product (launched in 2024, expanded in 2025–2026) provides interactive agent decision-path graphs, infinite loop detection, and full span capture (inputs, outputs, latency, token usage, cost estimates) across OpenAI, Anthropic, LangChain, and AWS Bedrock. The AI Agents Console and LLM Experiments feature (structured quality testing) were added in recent releases. Datadog’s primary advantage is for enterprises already deeply invested in the platform — AI monitoring integrates directly with existing dashboards, alerts, and incident workflows. The pricing model is a significant consideration: billing per LLM span with an automatic daily premium when LLM spans are detected creates unpredictable costs at scale.

Best for: Enterprise teams already running Datadog for infrastructure and application monitoring who want AI observability without a separate vendor.

Pricing: Per LLM span; no meaningful free tier for LLM Observability. Cost can spike unexpectedly at high trace volumes.

New Relic

New Relic positions itself as “the industry’s first APM for AI,” and its February 2026 Agentic Platform launch adds multi-agent system visualization, waterfall views of full LLM request lifecycles, and 50+ integrations across popular LLMs, vector databases, and frameworks. A no-code agentic deployment layer enables observability agents to be deployed without instrumentation changes. Like Datadog, New Relic’s strengths are clearest for teams already on the platform — it’s a powerful extension of existing APM investment, not a standalone LLM observability tool.

Best for: Enterprise teams extending existing New Relic APM into AI workloads.

Pricing: Free tier (100 GB/month data ingest + 1 full-platform user); paid usage-based; AI monitoring included in platform pricing.

Galileo

Galileo specializes in responsible AI development with a particular emphasis on hallucination detection, groundedness scoring, and safety evaluation. Its Guardrail Metrics suite (ChainPoll, uncertainty estimation, context adherence) provides quantitative hallucination risk scores at the span level. Session-based tracing groups multi-turn conversations for review, and human annotation workflows let teams label outputs before running automated scoring. Galileo is frequently cited in enterprise AI governance contexts, where teams need auditable evaluation records.

Best for: Teams where hallucination prevention and responsible AI compliance are primary concerns.

Pricing: Limited trial available; enterprise pricing on request.

Helicone

Helicone is a lightweight, proxy-based LLM observability tool — you route your API calls through Helicone’s endpoint, and logging happens automatically without SDK changes. It’s fast to deploy (minutes, not days), free for early-stage usage, and covers cost monitoring, latency tracking, and request grouping. Multi-turn tracing is partial — sessions can be grouped, but agent decision trees and sub-agent interactions are not natively visualized. Evaluation capabilities are limited compared to dedicated eval platforms.

Best for: Early-stage teams who need immediate cost visibility and basic logging without a complex integration.

Pricing: Generous free tier; paid plans based on volume.

Maxim AI

Maxim AI is a relatively newer entrant focused on making evaluation design accessible through a visual, no-code interface. Teams can build custom eval criteria using drag-and-drop workflows, run evals against production traces, and review results through structured human review queues. Native multi-turn conversation support handles agent traces as first-class objects. Maxim targets teams that want powerful eval capabilities without writing custom scoring code, though its ecosystem integrations are less mature than older platforms.

Best for: Product-focused teams who want to design and run evals without deep engineering involvement.

Pricing: Free tier available; paid plans based on usage.

MLflow

MLflow is the widely-adopted open-source experiment tracking platform from Databricks. Its LLM-specific features (MLflow Tracing, LLM-as-judge evaluators, prompt engineering UI) were added in versions 2.x as part of its evolution toward modern AI workflows. Multi-turn tracing is partial — MLflow captures experiments and runs well, but full agent session threading is not its native paradigm. For teams with existing MLflow investment and Databricks infrastructure, it’s a pragmatic choice that avoids another vendor. For greenfield agent development, purpose-built tools offer more depth.

Best for: Data science and ML engineering teams with existing MLflow and Databricks investment.

Pricing: Open-source (free self-hosted); managed on Databricks at platform rates.

Confident AI / DeepEval

Confident AI is the commercial platform built on DeepEval, the popular open-source LLM evaluation framework (10,000+ GitHub stars). DeepEval provides a comprehensive library of evaluation metrics — G-Eval, RAG faithfulness, contextual recall, hallucination detection, custom LLM-as-judge — that teams can run as automated test suites. Confident AI wraps this into a hosted platform with regression tracking and CI/CD integration. Evaluation depth is a genuine strength; production trace coverage is less mature than dedicated observability platforms.

Best for: Engineering teams running automated eval suites at scale, especially those already using the DeepEval library.

Pricing: DeepEval open-source (free); Confident AI cloud plans available; enterprise custom.

How to Choose: Recommendation Matrix

If you need… Best fit Why
Full agent observability + eval loop in one platform Latitude Signals + evals auto-generated from real failures, plus the closed loop (issue → PR) via the MCP server that connects your coding agent
Open-source, self-hosted with strong eval support Langfuse Clickhouse-backed, active community, full eval stack, GDPR-friendly
Best tracing for LangChain / LangGraph agents LangSmith Native integration, zero-instrumentation setup, deep framework support
Multi-turn simulation testing before deploy LangWatch Thousands of synthetic conversation simulations across edge cases
Eval-first workflow with CI/CD integration Braintrust Experiment-centered design, strong eval dataset management
Automated eval suites via open-source framework Confident AI / DeepEval Most comprehensive eval metric library, strong community
Hallucination monitoring and responsible AI compliance Galileo Purpose-built Guardrail Metrics, auditable annotation records
Enterprise AI monitoring on existing Datadog stack Datadog LLM Observability Integrated with existing dashboards and alerting; agent loop detection
Enterprise AI monitoring on existing New Relic stack New Relic Agentic Platform Extends APM investment; multi-agent visualization; no-code deployment
Fastest path to production logging with no code changes Helicone Proxy-based setup, minutes to deploy, generous free tier
AI monitoring inside existing DevOps observability Honeycomb Distributed tracing + BubbleUp anomaly detection across AI + infra
ML + LLM observability in one vendor Comet / Opik Bridges classical ML experiment tracking with LLM/agent observability
Existing MLflow / Databricks investment MLflow Extend existing experiment tracking infrastructure without new vendors
RAG-specific evaluation and embedding drift detection Arize Phoenix Open-source, strong RAG metrics, embedding drift visualization
No-code eval design for non-engineering stakeholders Maxim AI Visual eval builder, structured human review workflows

The Core Trade-off: Purpose-Built vs. Embedded

The fifteen tools in this comparison fall into two broad categories, and the right choice depends as much on organizational context as on feature sets.

Purpose-built AI observability tools — Latitude, Langfuse, LangSmith, Braintrust, LangWatch, Galileo — were designed from the ground up for LLM and agent workflows. Their architectures reflect the specific requirements of multi-turn conversations: session threading, semantic evaluation, human annotation, and eval generation from production data. They integrate quickly, often in minutes, and their default workflows match how AI teams actually work. The trade-off is that they’re another vendor alongside your existing monitoring stack.

Embedded enterprise tools — Datadog, New Relic, Honeycomb — bring AI monitoring inside platforms teams are already operating. There’s no new login, no new incident workflow, no new alert routing to set up. For organizations with mature Datadog or New Relic deployments, this consolidation has real value. The trade-off is that AI-specific capabilities are secondary to the platform’s core APM story, and pricing models designed for infrastructure monitoring can produce unexpected costs when applied to LLM span volumes.

A third category — open-source foundations (MLflow, Arize Phoenix, DeepEval, LangWatch) — offers full self-hosted deployment with no vendor lock-in. These are compelling for teams with strong infrastructure capabilities and data residency requirements, but they require more operational investment to maintain and extend.

The Multi-Turn Problem: Why Standard Tracing Falls Short

To understand why agent observability is hard, consider what happens in a typical multi-turn agent interaction. A user asks a customer support agent to resolve a billing issue. The agent calls a billing API (tool call 1), retrieves account history (tool call 2), queries its reasoning memory to check for similar past cases (tool call 3), drafts a response, determines it needs escalation criteria confirmed (tool call 4), and finally generates a response — all within a single conversation turn.

Standard distributed tracing captures each of these as separate spans. But the failure that produces a wrong response might be in span 3: the memory retrieval returned context from a different account type. Without tracing that links all six spans into a single agent session, and without evaluation logic that can assess whether the retrieved context was appropriate for the query, you see a completed trace with no obvious errors — and a user who got the wrong answer.

The platforms that handle this well share a common architectural pattern: they capture agent sessions as first-class objects, not as collections of individual LLM calls. They link spans through session IDs or trace hierarchies that reflect agent intent, not just execution order. And they provide evaluation capabilities that can assess multi-step reasoning paths, not just final outputs.

Conclusion

AI agent observability in 2026 is not a solved problem, and no single platform wins across every dimension. The right tool depends on your agent architecture, your team’s existing infrastructure, your evaluation maturity, and how you define quality for your specific domain.

For teams starting fresh with production agents, the purpose-built LLM-native platforms (Latitude, Langfuse, LangSmith, Braintrust) offer the fastest path to meaningful observability. For teams embedded in enterprise monitoring infrastructure, extending Datadog or New Relic may be the pragmatic choice. For teams with specific requirements — open-source self-hosting, hallucination prevention, RAG evaluation depth, or no-code eval design — there are specialized tools that address each of those needs directly.

The one investment worth making regardless of platform: treat your evals as production code. The teams that get the most value from agent observability tools are the ones that have built disciplined workflows for converting observed failures into regression tests. The best platform is the one that makes that workflow as easy as possible for your team.

Frequently Asked Questions

Which AI observability platform is best for multi-turn agents in 2026?

For multi-turn agents, the best platforms are those built natively for agent sessions rather than retrofitted from LLM monitoring. Latitude is the strongest for production agents needing Signal-based issue tracking, evals auto-generated from real failures, and the closed loop from issue → opened PR via its MCP server connecting your coding agent. Braintrust has the most generous free tier (1M spans/month, 10K evals) and CI/CD eval gates. LangSmith is best for LangChain/LangGraph stacks with near-zero setup. Langfuse is best for self-hosted requirements. Enterprise tools like Datadog and New Relic handle basic LLM monitoring but lack multi-turn causal analysis for complex agents.

Why do standard APM tools like Datadog fall short for AI agent observability?

Standard APM tools like Datadog capture each agent step as a separate span, but agent failures typically appear in how steps interact — not at individual span level. A memory retrieval in span 3 that returns context from the wrong account type produces a wrong answer in span 6, but every span shows a 200 status. Datadog’s LLM monitoring module tracks call-level latency and cost; it cannot model agent sessions as causal traces, surface failure patterns, or support evaluation workflows. It’s the wrong abstraction for agents, not a deficiency in Datadog’s core capabilities.

What is the difference between purpose-built AI observability tools and embedded enterprise tools?

Purpose-built AI observability tools (Latitude, Langfuse, LangSmith, Braintrust) were designed from the ground up for LLM and agent workflows — session threading, semantic evaluation, human annotation, and eval generation from production data. They integrate quickly and their default workflows match AI team practices. Embedded enterprise tools (Datadog, New Relic, Honeycomb) bring AI monitoring inside platforms teams already operate, eliminating a new vendor. The trade-off: AI-specific capabilities are secondary to the core APM story, and pricing designed for infrastructure monitoring can produce unexpected costs at LLM span volumes.

Can Latitude fix issues automatically, not just find them?

This is Latitude’s sharpest differentiator. Its MCP server connects your coding agent (Claude Code, Cursor, and similar) directly to your workspace, so the loop from detected issue → evaluator → fix → opened PR runs from inside the agent rather than as manual steps across separate tools. The MCP-to-coding-agent connection is real today; the direction is to make reliability work actually close instead of stopping at the observability layer. Most tools in this comparison surface traces and scores, but writing the fix and opening the PR stays manual and outside the platform.

Latitude’s free Starter plan (20K credits/month, unlimited seats) and free MIT-licensed self-hosting let you evaluate it with your own production agent data. Start free →