AI applications used to rely on a handful of straightforward LLM calls. Now agents make hundreds of decisions in response to a single user input, calling tools, retrieving context, and compounding outputs. When something goes wrong, the failure can be six steps deep and invisible from the outside.

Most AI observability tools were designed to monitor LLM calls, then later extended to cover agents. That’s why so many of them still feel like log viewers with charts on top. They show you what happened, but the work of testing, fixing, and iterating happens elsewhere — back in your IDE, in a separate evaluation framework, in a Notion doc of test cases someone updates when they remember, or in scattered Slack messages.

Every handoff is a place where context gets lost and iteration slows down.

The teams shipping reliable agents in 2026 are reaching for tools that do more than report. They want platforms that help them test agents the same way they test software, debug those agents quickly, and iterate without breaking things. This guide compares the leading AI observability platforms through that lens, and will help you figure out which option is right for your team.

AI Observability Tools at a Glance

• AI observability tools trace, evaluate, and monitor LLM-powered agents across development and production. They capture execution paths, score output quality at each step, and track cost, latency, and reliability.
• The category has shifted from logging individual LLM calls to observing complex agentic systems. Agent observability tools now need multi-step trace visualization, span-level evaluation, and capabilities for fixing problems, not just spotting them.
• The leading AI observability platforms in 2026 include Opik by Comet, Langfuse, LangSmith, Arize Phoenix and Arize AX, Braintrust, Datadog LLM Observability, MLflow, Galileo, Fiddler, and Raindrop.
• Tools fall into five shapes: full-lifecycle platforms, evaluation-first tools, production monitoring layers, enterprise control planes, and extensions of broader platforms. The right choice depends more on workflow fit than feature count.
• The most comprehensive open-source options are Opik (Apache 2.0), Langfuse (MIT), Arize Phoenix (Elastic License 2.0), and MLflow (Apache 2.0). Among them, Opik is the most complete for agent development because it adds assertion-based testing, AI-assisted debugging, and automated optimization on top of tracing and evaluation.

What Is AI Observability?

AI observability is the practice of monitoring, tracing, and evaluating everything that happens inside an AI application, including the prompts going into the agent decisions, tool calls, reasoning, and output. It’s built on three pillars:

• LLM Tracing captures the full execution path of a run, including every tool call, retrieval step, and intermediate reasoning step the agent takes. A trace typically includes timing, inputs, outputs, token counts, cost per step, and metadata you attach (user ID, session, prompt version).
• LLM Evaluation measures whether outputs are actually good. This can happen offline (run a dataset of test cases against your agent and score the results) or online (score production traffic as it flows through). Common approaches include heuristic metrics, LLM-as-a-judge, and human-in-the-loop review through annotation workflows.
• LLM Monitoring tracks cost, latency, error rates, and quality metrics in production. Good monitoring layers slice these by user, feature, model, prompt version, or session so you can answer specific questions instead of staring at aggregate dashboards.

Why AI observability is even more important for agentic systems

The shift to agentic systems compounds every problem that already existed with LLM observability. One bad tool selection upstream can cascade through ten downstream steps before anything visibly fails. Traditional APM tools can confirm the service is up, but they can’t confirm the agent picked the right tool, passed the right arguments, retrieved the right context, or stayed on the original plan. That’s what AI observability platforms solve.

How to Choose an AI Observability Platform

In addition to looking at pricing and feature comparisons, here are nine important questions to ask yourself when evaluating AI observability solutions.

Does it actually handle agentic workflows, or just LLM calls?

Look at the trace visualization. Can you see the full execution graph, with every tool call, retrieval, and reasoning step, or are you mostly looking at prompt-and-response pairs? Most platforms started as LLM call loggers and added agent support later. Some did it well, but some feel bolted-on.

What to ask: Can I see a demo with a real multi-agent workflow, not a single-prompt example?

Can you evaluate quality at every level, not just the final output?

Final-output scoring tells you the agent failed, not where. Make sure the platform supports trace-level, span-level, and thread-level evaluation so you can score retrievals, tool calls, and reasoning steps independently. This matters most for RAG pipelines and long-running conversations, where the failure is rarely in the last step.

Can the platform help you fix problems, or only show them to you?

Most observability tools stop at detection. The platforms worth considering go further. Look for:

• Automated prompt and agent optimization, i.e. can the platform improve your prompts based on evaluation results without you doing it by hand?
• Structured testing that fits into existing CI/CD workflows
• AI-assisted debugging tools that have full context on your traces and code
• Sandbox environments where you can safely iterate on prompts and models without breaking your agent

A platform that only flags problems leaves the hard work on you. A platform that helps you fix them changes how fast your team can ship.

Is your evaluation approach built for agents, or borrowed from model benchmarking?

Many platforms still use the dataset-and-score model: build a reference dataset, run evals, read a score like “0.6 helpfulness.” That tells you something is off but rarely what to do about it. As more and more teams need to test agents the way they test software, newer platforms support assertion-based testing. Pass/fail rules like “the agent should always cite a source when providing pricing” will help you identify specific fixes. Ask which approach a platform supports before committing.

Is the open-source version actually the same product as the enterprise version?

Some platforms publish an open-source version that’s strong for experimentation but missing important production capabilities such as monitoring, alerting, online evaluation, or deployment-tier features. The enterprise version is essentially a different product. If you pilot on the open-source side and try to scale, you’re facing a migration instead of an upgrade.

What to ask: Are the open-source and enterprise versions the same codebase with different deployment options, or different products?

How will it integrate with your stack?

Check compatibility with your LLM providers, agent frameworks (LangGraph, CrewAI, AutoGen, LlamaIndex, OpenAI Agents SDK), and existing observability infrastructure. Consider the integration approach too. SDK-based platforms give you deep instrumentation but require code changes. Proxy-based tools are faster to set up but capture less. OpenTelemetry-native tools fit into existing observability infrastructure without lock-in.

A claim of “supporting” a framework can mean anything from full auto-instrumentation to a bare-bones wrapper. Ask for specifics.

Will it scale to production volume?

Each agent run can generate dozens of spans, which compounds quickly. Some platforms use purpose-built databases for AI trace data, while some bolt LLM observability onto general-purpose backends. Performance differences can be an order of magnitude or more.

What to ask: What are your trace ingestion and query performance numbers? If the answer is vague, that’s an answer.

What’s the long-term story?

AI observability is a hot category, which means consolidation and acquisitions. A platform that’s great today might be on a roadmap freeze in a year if it gets acquired by a larger company with different priorities.

None of the following are conclusive on their own, but together these signs will tell you whether the platform you’re betting on will be around in three years. Check:

• Funding status and ownership changes
• Public roadmap and recent release cadence
• Open-source repo activity (still active, or slowing?)
• Customer churn and retention signals

Does it fit your security and deployment requirements?

Verify SOC 2 compliance, encryption, RBAC, and self-hosting options. Match the platform to your industry’s compliance requirements (e.g. HIPAA, SR 11-7, GDPR, EU AI Act) and confirm whether self-hosting is fully air-gapped or requires a connection back to the vendor for evaluation or analytics. That’s an important detail for sensitive data.

The Best AI Observability Tools and Platforms of 2026

Not every observability solution is trying to do the same job. There are a few types of AI observability tools to keep in mind:

• Full-lifecycle platforms support development, testing, evaluation, and production monitoring in one tool (Opik, Langfuse, LangSmith)
• Evaluation-first platforms prioritize scoring, dataset management, and quality measurement (Braintrust, Galileo)
• Production monitoring layers focus on detecting issues in live agents (Raindrop)
• Enterprise control planes prioritize governance, compliance, and unified ML/AI observability (Fiddler)
• Extensions of broader platforms add LLM features to existing tools (Datadog LLM Observability, MLflow GenAI, Arize Phoenix for development)

At-a-glance comparison

Tool	Type	Open Source	Pricing Start	Key Differentiator
Opik by Comet	Full-lifecycle platform	Yes (Apache 2.0)	Free (25k spans/mo), Pro $19/mo	Test Suites, Ollie coding agent, automated optimization, true OSS/enterprise parity
Langfuse	Tracing + prompt management	Yes (MIT)	Free (50k events/mo), Cloud $29/mo	Comprehensive open-source tracing and prompt management
LangSmith	LangChain-native observability	No	Free (5k traces/mo), Plus $39/seat/mo	Deepest LangChain and LangGraph integration
Arize Phoenix/ AX	OSS dev tool / enterprise platform	Phoenix: Yes (ELv2); AX: No	Phoenix free, AX free (25k spans/mo), AX Pro $50/mo	OpenTelemetry-native; embedding clustering
Braintrust	Evaluation-centric platform	No	Free (1GB/mo, 10k scores), Pro $249/mo	Polished playground and Brainstore database
Datadog LLM Observability	LLM extension of APM platform	No	Free (40k spans/mo), Pro $160/mo	Unified infrastructure + LLM monitoring
MLflow	ML lifecycle with GenAI support	Yes (Apache 2.0)	Free	Same instrumentation for ML and GenAI
Galileo	Evaluation + guardrails	No	Free (5k traces/mo), Pro $100/mo	Luna-2 small models for cheap evaluation at scale
Fiddler	Enterprise control plane	No	Custom enterprise	Native Trust Models, deep compliance support
Raindrop	Production agent monitoring	No	$65/mo + per-interaction	Real-time agent error tracking and alerting

Opik by Comet

Opik is an open-source, framework-agnostic full-lifecycle platform built to develop agents the way software gets developed. Where most observability tools focus on monitoring LLM calls, Opik adds testing, debugging, and iteration tooling that closes the loop from detection to fix. It runs locally, on Opik Cloud, or with flexible deployment options for the enterprise. It’s the same product across all three, with no features hidden behind tiers and no migration when you scale.

Strengths:

• Test Suites turn agent evaluation into something closer to unit and regression testing. Define plain-English assertions about what your agent should and shouldn’t do, and Opik runs them like a regression suite with pass/fail results tied to specific failure modes.
• Ollie is a coding agent built into Opik with full context on your traces, tests, and source code. It can diagnose problems, implement fixes, and generate test cases directly.
• Agent Playground provides a sandbox for iterating on prompts, models, and parameters across your full agent without destructive edits to source code.
• Agent Optimizer runs seven optimization algorithms to automatically improve prompts and tool definitions based on LLM evaluation metrics.
• Strong performance: published benchmarks show Opik completes trace logging and evaluation in ~23 seconds vs. Arize Phoenix at ~170s and Langfuse at ~327s.
• Native integrations with 12+ agent frameworks including LangGraph, CrewAI, AutoGen, LlamaIndex, and OpenAI Agents SDK.

Integration: Works with any LLM provider and all major agent frameworks, Python and TypeScript/JavaScript SDKs, native OpenTelemetry support.

Pricing: Truly open-source and self-hostable with full features in the codebase. Free hosted plan includes 25k spans per month, with up to 10 team members, and 60-day data retention. Pro plan is $19/month for 100k spans with up to 50 team members.

Best for: Teams that want observability paired with an actual development workflow — testing, debugging, automated optimization, and safe iteration — without giving up features on the open-source side or facing a hard migration to enterprise.

Langfuse

Langfuse is an open-source LLM engineering platform focused on tracing, prompt management, and evaluations. The MIT-licensed core has strong community traction (over 20,000 GitHub stars), and self-hosting deploys cleanly via Docker Compose which is attractive for teams that want to keep traces inside their infrastructure.

Evaluation follows the dataset-and-score pattern rather than assertion-based testing, and there’s no automated agent or prompt optimization, so Langfuse is best understood as a visibility and prompt management tool rather than a full development workflow. Native SDK support is limited to Python and TypeScript.

Note that Langfuse was acquired by ClickHouse in late 2025. The product is still active, but long-term roadmap direction is worth factoring into a multi-year commitment.

Strengths:

• Deep tracing with detailed visibility into complex multi-step workflows
• Robust prompt management with versioning, folder organization, and environment labels
• Customizable dashboards with a drag-and-drop builder
• Public-link sharing for stakeholder reviews
• SOC 2 compliance
• Clean self-hosting via Docker Compose

Integration: SDK-based with callback handlers for LangChain and LlamaIndex, native OpenTelemetry support.

Pricing: Free for self-hosting. Cloud Hobby tier covers 50k units/month and 2 users with 30-day retention. Core is $29/month for 100k units with 90-day retention and unlimited users. Pro is $199/month with 3-year retention. Enterprise is $2,499/month. Additional usage is $8 per 100k units, lower with volume.

Best for: Teams that prioritize self-hosting with comprehensive tracing and prompt management, and that don’t mind doing their own work for testing and optimization workflows.

LangSmith by LangChain

LangSmith is LangChain’s observability and evaluation platform, and unsurprisingly, it’s where LangChain and LangGraph applications get the smoothest experience. It’s closed source, with self-hosting restricted to the Enterprise tier. Outside the LangChain ecosystem, the value proposition narrows considerably. Evaluation uses the dataset-and-score approach, and when fixes are needed, you’re back in your IDE; there’s no in-platform code editing or test generation.

Strengths:

• Deepest integration with LangChain and LangGraph
• Automatic instrumentation of graph state, intermediate steps, and tool calls for LangChain apps with no explicit configuration required
• Visual LangGraph Studio integration for building and testing agents
• Strong annotation queues for domain experts to review production traces
• Works with non-LangChain stacks through a traceable wrapper

Integration: Native LangChain/LangGraph, plus framework-agnostic SDKs for Python and TypeScript.

Pricing: Free tier for 1 user and 5k traces/month. Plus is $39 per user per month for 10k traces, then volume-based.

Best for: Teams deeply committed to the LangChain or LangGraph ecosystem, building primarily with those frameworks, and wanting the smoothest possible observability path.

Arize Phoenix vs. Arize AX

Phoenix is Arize’s open-source product and Arize AX is their commercial enterprise platform, but these aren’t a free and paid tier of the same product. They’re effectively separate products with different capabilities. Teams that pilot on Phoenix and try to scale to AX face a migration, not an upgrade. Phoenix itself lacks production monitoring, online evaluation, alerting, and annotation queues. Those capabilities live in Arize AX. Phoenix is excellent for development and debugging, but it’s not a complete production observability solution on its own.

Phoenix strengths:

• Strong open-source tracing with OpenInference (OpenTelemetry-based) instrumentation
• Notebook-friendly experience that runs locally with zero external dependencies
• Embedding clustering to identify failure patterns
• Shortens experimentation cycles: just spin up in Jupyter, debug RAG, and never leave your dev environment
• Integrations with LangChain, LlamaIndex, Haystack, DSPy, and other frameworks

AX strengths:

• Adds production monitoring, online evaluation, alerting, and annotation queues that Phoenix lacks
• Alyx AI debugging assistant with full context on your traces
• Prompt IDE for designing and comparing prompts with live evaluation results
• Real-time alerts via PagerDuty and Slack with custom dashboards and metrics
• Unified observability across LLMs and traditional ML/CV with drift detection, embedding analysis, and model performance monitoring from Arize’s ML monitoring heritage
• Enterprise security (SOC 2, GDPR, HIPAA, RBAC, SAML), proven at petabyte scale with customers including DoorDash, Instacart, Reddit, Uber, and Booking

Integration: Both products use OpenInference/OpenTelemetry instrumentation. Phoenix runs locally or self-hosted, AX deploys on AWS or Azure with marketplace listings.

Pricing: Phoenix is fully open-source and self-hostable. Arize AX Free covers 25k spans/month and 1 GB ingestion with 15-day retention. AX Pro is $50/month for 50k spans, 10 GB ingestion, and 30-day retention. AX Enterprise has custom pricing for unlimited usage and self-hosting options.

Best for: Phoenix is best for ML engineers working primarily in notebooks, teams that need OpenTelemetry-based tracing during development, and privacy-focused teams that want fully local observability. Arize AX is best for organizations already invested in Arize’s ML monitoring ecosystem who want to extend the same platform to LLMs and agents, and enterprises that need unified observability across traditional ML and generative AI.

Braintrust

Braintrust is a closed-source, evaluation-centric AI observability platform with a polished UI and strong collaboration features. The playground experience is genuinely well-designed for prompt iteration. You can load a trace, modify the prompt, rerun, and see a side-by-side comparison without writing code. Evaluation follows the dataset-and-score pattern, so there’s no assertion-based testing approach, and no automated agent or prompt optimization.

Strengths:

• Iterative evaluation workflows with structured datasets, scorers, and prompt versioning
• AI proxy for instant integration
• Brainstore database designed for AI trace patterns with fast query performance
• Integrated AI assistant (Loop) that generates scorers from natural language
• Bidirectional code/UI syncing for cross-functional teams

Integration: SDK integrations for Python and TypeScript, OpenTelemetry support, AI Proxy for quick setup.

Pricing: Starter is free with 1 GB processed data and 10k evaluation scores per month, 14-day retention. Pro is $249/month for 5 GB processed data and 50k scores with 30-day retention. Enterprise pricing is custom.

Best for: Teams that prioritize a polished evaluation workflow with intuitive collaboration features, especially when product managers and domain experts need to participate in quality reviews.

Datadog LLM Observability

Datadog extended its enterprise monitoring platform to cover LLM applications, which makes it a logical fit for teams already standardized on Datadog. The LLM Observability product has matured significantly. There’s now a free tier with 40k LLM spans/month, plus out-of-the-box and custom evaluators, annotation workflows, datasets, and experiments. Where it still trails purpose-built platforms: no assertion-based testing for regression, no automated prompt or agent optimization, and no AI-assisted debugging tied to your codebase. Span-based metering also means a complex agent run can rack up costs quickly.

Strengths:

• Correlation between LLM spans and standard APM traces
• Agentless deployment mode for serverless environments
• Unified view of LLM and infrastructure metrics
• Mature alerting and dashboard tooling
• LLM monitoring rolls into existing on-call workflows — no second rotation needed

Integration: SDKs for major LLM providers and frameworks via standard Datadog instrumentation.

Pricing: Free plan includes 40k LLM spans/month with 15-day retention and full feature access. Pro starts at $160/month for 100k LLM spans, with annual and month-to-month contract discounts available.

Best for: Large enterprises already invested in Datadog for infrastructure monitoring who want LLM visibility in their existing dashboard.

MLflow

MLflow is the mature open-source platform for the ML lifecycle, with GenAI support added through prompt tracking and tracing extensions. GenAI was layered on top of an ML experiment tracking platform, and it shows. There’s no token and cost tracking, no built-in alerting, partial agent evaluation, no annotation queues, and no automated optimization. Workable for tracing if you’re already in the MLflow ecosystem, but not purpose-built for agent development.

Strengths:

• 1-line-of-code integrations for 20+ frameworks
• Same instrumentation works for development and production
• Full OpenTelemetry compatibility
• Established platform with broad adoption across ML teams
• For existing MLflow users, adding LLM tracing is essentially free — no new infrastructure to stand up

Integration: Auto-instrumentation for major frameworks, fully OpenTelemetry-compatible.

Pricing: Free and open-source. Self-hosted or managed cloud through providers like Databricks.

Best for: ML teams already using MLflow for traditional ML workflows who want basic LLM tracing in the same interface, accepting that more sophisticated agent observability needs will require a complementary tool.

Galileo

Galileo is an evaluation-centric AI reliability platform built around Luna-2, which is a family of small language models specifically tuned for evaluation tasks. The pitch is fast, cheap evaluation at scale that can run on 100% of production traffic instead of sampling. It’s a newer platform with a smaller community than the open-source incumbents and isn’t open source itself. There’s no automated agent or prompt optimization, and evaluation still uses the dataset-and-score pattern rather than assertion-based testing.

Strengths:

• 20+ pre-built evaluators for RAG, agents, safety, and security
• Luna-2 SLMs run at sub-200ms latency and ~$0.02 per million tokens
• Continuous Learning with Human Feedback auto-tunes evaluators based on annotations
• Eval-to-guardrail lifecycle converts evaluations into real-time production safety checks
• Multiple deployment options (SaaS, VPC, on-prem)
• Luna-2 economics make 100% production traffic evaluation viable

Integration: Python and TypeScript SDKs, integrations with major LLM providers and frameworks.

Pricing: Free tier with 5k traces/month. Pro is $100/month for 50k traces. Custom Enterprise.

Best for: Production teams running agents at high volume who need cost-effective evaluation across all production traffic and real-time guardrails to block risky outputs before they reach users.

Fiddler

Fiddler positions itself as a “control plane for AI agents,” focused heavily on enterprise governance, compliance, and unified observability across traditional ML and generative AI. It’s enterprise-only with no public free tier or self-serve onboarding. It’s not open source or developer-first, with a steeper learning curve given the breadth of features. The customer list (Mastercard, US Navy, American Family Insurance, AIG, Ally) tells you who Fiddler is built for: large, regulated organizations buying through procurement.

Strengths:

• Native Trust Models that run in your environment for evaluation and guardrails (no external LLM-as-a-judge API costs)
• Sub-100ms guardrails for hallucinations, toxicity, PII, and prompt injection
• Hierarchical agent visibility (application → session → agent → trace → span)
• Automated root cause analysis
• Deep compliance support (GDPR, HIPAA, NAIC, SR 11-7)
• Proven at 30 million+ traces/day in production at a Fortune 20 customer

Integration: Major cloud and ML platform integrations including AWS SageMaker, Google Vertex AI, Databricks, NVIDIA NIM, and Datadog APM

Pricing: Custom enterprise pricing.

Best for: Regulated industries (e.g. finance, healthcare, defense, insurance) and large enterprises with strict governance, audit, and compliance requirements.

Raindrop

Raindrop is a real-time monitoring and error tracking platform built specifically for AI agents in production. Customers compare it to “Sentry for AI,” and the platform leans into detecting failures you didn’t know to look for. It isn’t open source and isn’t a full-lifecycle platform. Raindrop actively positions itself as complementary to eval-based tools rather than a replacement, and there’s no real development-phase story (i.e. no automated agent or prompt optimization). It’s best understood as an alerting and incident response layer on top of whatever platform you use during development.

Strengths:

• Auto-detection of silent hallucinations, infinite loops, tool failures, refusal spikes, and abnormal trajectories
• Slack alerts in real time with linked traces
• Trajectory visualization purpose-built for agent debugging
• Plain-language behavior monitoring (define what to watch in English, no code changes)
• Strong customer base including Replit, Framer, Clay, AngelList, and Tolan
• SOC 2 Type II with PII Guard

Integration: SDKs for Vercel AI SDK, TypeScript, Python, Go, Claude Agent SDK, LangChain, AWS Bedrock, OpenAI Agents, Vertex AI, Pydantic AI, and Mastra.

Pricing: 14-day free trial. Starter is $65/month plus $0.001 per interaction. Pro is $350/month plus $0.0007 per interaction. Enterprise is custom.

Best for: Production teams running agents at scale who need fast incident detection and want a dedicated alerting layer, ideally paired with a separate development-phase platform.

Where Agent Observability Goes from Here

The category is shifting. Five years ago, observability for AI mostly meant logging LLM calls and watching for anomalies. Today, it’s closer to development infrastructure and the toolchain teams use to build, test, debug, and ship agents reliably.

There are three key things to keep in mind as you evaluate agent observability tools:

1. Fit the workflow, not the feature list. The right tool is the one your team will actually use. A platform that’s technically superior but doesn’t match how your team thinks about agents will be a waste of resources.
2. Pilot with production-shaped data. Demo environments hide everything that matters. Run a real workload through the tool for two weeks before committing to anything.
3. Plan for the migration you might not want to make. Open-source/enterprise parity, framework lock-in, and acquisition risk all become migration projects later. Account for them now.

The platforms gaining traction are treating agents like the software they are. That means structured testing instead of fuzzy scores, AI-assisted debugging with full context, safe iteration without code round-trips, and automated optimization where it makes sense.

Ready to build agents the way you’d build any other piece of software? Get started with Opik free or self-host the open-source version via GitHub.

Frequently Asked Questions About Agent Observability Tools

What are AI observability tools?

AI observability tools are platforms that monitor, trace, and evaluate AI applications across development and production. They capture the full execution path of agent runs, measure output quality at each step, and track cost, latency, and reliability metrics so teams can ship and maintain trustworthy AI software.

What’s the difference between LLM observability and AI observability?

LLM observability typically focuses on monitoring individual LLM calls, including inputs, outputs, latency, and cost. AI observability is broader, covering the full behavior of agentic systems including multi-step reasoning, tool calls, retrieval, and multi-agent communication. As AI applications have shifted from single LLM calls to complex agents, the terminology has shifted with them.

What is agent observability?

Agent observability is a subset of AI observability focused on agentic systems, or AI applications that make autonomous decisions across multiple steps, call tools, retrieve context, and branch on intermediate outputs. Agent observability tools provide trace visualization of full execution paths, span-level evaluation of intermediate steps, and debugging features purpose-built for multi-step complexity.

What are the top AI observability tools in 2026?

The top AI observability tools in 2026 are Opik by Comet, Langfuse, LangSmith, Arize Phoenix and Arize AX, Braintrust, Datadog LLM Observability, MLflow, Galileo, Fiddler, and Raindrop. Opik leads on full-lifecycle agent development with built-in testing and AI-assisted debugging. The others specialize in evaluation, enterprise compliance, framework-specific workflows, or production monitoring.

Which AI observability tools are open source?

The leading open-source AI observability tools are Opik by Comet (Apache 2.0), Langfuse (MIT), Arize Phoenix (Elastic License 2.0), and MLflow (Apache 2.0). Opik is the most comprehensive for agent development, with full feature parity across self-hosted, cloud, and enterprise versions. Langfuse leads on prompt management depth, Phoenix on notebook-based experimentation, and MLflow on integration with existing ML lifecycle workflows.

How do AI observability tools differ from APM tools like Datadog?

Traditional APM tools track infrastructure health such as uptime, response codes, and latency, but can’t tell you whether an AI agent gave a correct answer or chose the right tool. AI observability tools add evaluation (was the output actually good?), trace-level visibility into agent reasoning, and specialized workflows for prompt iteration and quality testing. Datadog offers an LLM observability extension, but purpose-built tools generally provide deeper agent-specific capabilities.

How do I choose the right AI observability platform?

The right AI observability platform depends on whether you need full-lifecycle development support, evaluation-focused workflows, production monitoring, or enterprise compliance. Key criteria to evaluate include native agent (not just LLM call) support, multi-level evaluation, whether the platform helps you fix problems or only shows them, assertion-based testing, open-source/enterprise parity, framework integrations, performance at scale, and security and compliance fit.