What are the best observability tools for AI agents?

The “best” observability tool for AI agents depends heavily on your stack and what you need to observe. Agent observability is broader than traditional logging: you usually need **multi-step traces, tool calls, retrieval steps, memory/state changes, token cost, latency, failures, and quality evaluations**. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/?utm_source=chatgpt.com) Here are the strongest options today: | Tool | Best for | Strengths | Trade-offs | |---|---|---|---| | Langfuse | Open-source, self-hosted teams | Great tracing, prompts, datasets, evaluations, OpenTelemetry support | You operate infrastructure if self-hosting | | LangSmith | LangChain/LangGraph agents | Excellent agent traces, debugging UX, evaluation workflows | Most compelling inside the LangChain ecosystem | | Arize AI / Phoenix | Evaluation-heavy production AI | Strong tracing + evaluation + ML observability; Phoenix is open source | Enterprise features are in the commercial platform | | Weights & Biases Weave | ML teams already using W&B | Connects experiments, evaluations, and production traces | Less focused purely on agent workflows | | Datadog LLM Observability | Companies already on Datadog | Unified infra + application + AI monitoring | Can be heavier than AI-native tools | | Helicone | Quick LLM request visibility | Easy setup, cost/token tracking, gateway approach | Less agent-native than full tracing platforms | | Braintrust | Eval-driven development | Strong datasets, regression testing, scoring workflows | More evaluation-centric than general monitoring | [Agent Observability: The Monitoring Stack for Production AI Workflows - Stackcurve Advisory Brief](https://stackcurve.net/blog/agent-observability?utm_source=chatgpt.com) ## My practical picks by scenario ### Best default choice for a new agent startup **Langfuse** Good balance of: - open source - self-hosting option - traces for complex agent workflows - prompt/version tracking - evaluations It is a strong “first observability layer” before you know exactly how your architecture will evolve. [Top 5 LLM Observability Platforms 2026: Langfuse vs LangSmith vs Helicone vs Arize vs Weights & Biases | Deepak Gupta](https://guptadeepak.com/tools/top-5-llm-observability-platforms-2026/?utm_source=chatgpt.com) ### Best if you use LangChain or LangGraph **LangSmith** Choose this if your agents are already built around the LangChain ecosystem. The tight integration reduces instrumentation work and gives you a polished debugging workflow. [AI Agent Observability 2026: LangSmith vs Langfuse vs Helicone vs Arize](https://aiagentrank.io/blog/ai-agent-observability-2026?utm_source=chatgpt.com) ### Best for serious production evaluation **Arize Phoenix / Arize** Use when you care about questions like: - “Did this RAG answer become less grounded?” - “Which agent version performs better?” - “Which traces fail quality checks?” Phoenix is particularly attractive if you want an open-source evaluation stack. [Top 5 LLM Observability Platforms 2026: Langfuse vs LangSmith vs Helicone vs Arize vs Weights & Biases | Deepak Gupta](https://guptadeepak.com/tools/top-5-llm-observability-platforms-2026/?utm_source=chatgpt.com) ### Best enterprise option if you already have monitoring infrastructure **Datadog** If your company already relies on Datadog for logs, metrics, alerts, and incidents, adding AI observability there can simplify operations. [AI Agent Observability Tools: 2026 Buyer's Guide for Production Teams | Latitude](https://latitude.so/blog/ai-agent-observability-tools-comparison-2026?utm_source=chatgpt.com) ## What I would instrument from day one Regardless of tool, capture: 1. **Agent trace** - user request - planning steps - model calls - tool calls - retries - final answer 2. **Cost signals** - input/output tokens - model used - latency - tool/API costs 3. **Quality signals** - human feedback - task success/failure - hallucination checks - retrieval quality - evaluator scores 4. **Safety signals** - policy violations - prompt injection attempts - unsafe tool usage ## A common production stack A lot of mature teams end up combining layers: - **OpenTelemetry/OpenInference instrumentation** → collect traces - **Langfuse / Phoenix / LangSmith** → AI traces and debugging - **Datadog / Grafana / existing APM** → infrastructure monitoring - **Braintrust / Phoenix / custom evals** → regression testing [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/?utm_source=chatgpt.com) If I had to pick a shortlist: - **Solo developer / startup:** Langfuse - **LangGraph agent:** LangSmith - **Enterprise AI platform:** Arize - **Existing observability team:** Datadog - **Evaluation-first AI team:** Braintrust or Phoenix The most important criterion is not the dashboard - it is whether the tool lets you reconstruct *why an agent made a decision*. That is the difference between ordinary LLM logging and real agent observability.

Here’s a breakdown of the **best observability tools for AI agents in 2025**, based on recent industry comparisons: ## 🔎 Why AI Agent Observability Matters Traditional monitoring tools (Datadog, New Relic) track latency and errors but **don’t capture reasoning, tool usage, or emergent failures** in AI agents. Specialized observability platforms provide: * **Execution tracing** (chain-of-thought, tool calls, retries) * **Token usage & cost tracking** * **Failure diagnosis** (loops, hallucinations, wrong tool choices) * **Artifact monitoring** (files, code, or data generated)[Best Observability Tools for AI Agents (2025 Guide) | Fastio](https://fast.io/resources/best-observability-tools-ai-agents/) ## 🏆 Leading AI Agent Observability Tools (2025) Tool| Best For| Key Strengths| Open Source? ---|---|---|--- **Maxim AI**| End-to-end lifecycle (simulation, evaluation, observability)| Deep distributed tracing, enterprise compliance, human-in-the-loop evals| No[Top 5 AI Observability Tools Compared (2025)](https://www.getmaxim.ai/articles/top-5-ai-observability-tools-compared-2025/) **LangSmith**| LangChain ecosystem| Native integration, chain-level tracing, dataset curation| No[Best Observability Tools for AI Agents (2025 Guide) | Fastio](https://fast.io/resources/best-observability-tools-ai-agents/) **Arize Phoenix / Arize AI**| ML \+ LLM drift detection| OpenTelemetry-native, strong evaluation framework, enterprise-grade| Yes (Phoenix)[Best Observability Tools for AI Agents (2025 Guide) | Fastio](https://fast.io/resources/best-observability-tools-ai-agents/)[The Digital Insider | 10 Best AI Observability Tools (May 2025)](https://thedigitalinsider.com/10-best-ai-observability-tools-may-2025/) **Langfuse**| Self-hosting & open-source| Flexible tracing, multi-modal monitoring, cost attribution| Yes[Best Observability Tools for AI Agents (2025 Guide) | Fastio](https://fast.io/resources/best-observability-tools-ai-agents/)[Top 5 Agent Observability Tools in December 2025](https://www.getmaxim.ai/articles/top-5-agent-observability-tools-in-december-2025/) **Helicone**| Lightweight API monitoring| Request-response logging, caching, latency tracking| Yes[Top 5 AI Observability Tools Compared (2025)](https://www.getmaxim.ai/articles/top-5-ai-observability-tools-compared-2025/) **Weights & Biases (W&B)**| Enterprise MLOps| Experiment tracking extended to LLM workflows| No[Best Observability Tools for AI Agents (2025 Guide) | Fastio](https://fast.io/resources/best-observability-tools-ai-agents/)[Top AI Observability Tools in 2025: The Ultimate Guide](https://www.getmaxim.ai/articles/top-ai-observability-tools-in-2025-the-ultimate-guide/) **Galileo**| AI reliability & guardrails| Proprietary evaluation metrics, safety checks| No[Top 5 Agent Observability Tools in December 2025](https://www.getmaxim.ai/articles/top-5-agent-observability-tools-in-december-2025/) **Fiddler AI**| Explainability & compliance| Bias detection, SOC2/HIPAA compliance| No[The Digital Insider | 10 Best AI Observability Tools (May 2025)](https://thedigitalinsider.com/10-best-ai-observability-tools-may-2025/) ## ⚖️ Choosing the Right Tool * **For LangChain-heavy projects** → LangSmith * **For enterprise-scale monitoring** → Maxim AI or Arize AI * **For open-source flexibility** → Langfuse or Arize Phoenix * **For lightweight API-level monitoring** → Helicone * **For reliability & safety guardrails** → Galileo or Fiddler AI * **For experiment-heavy workflows** → Weights & Biases 👉 If you’re building **production-grade AI agents**, I’d recommend **Maxim AI** for full lifecycle coverage, or **Langfuse** if you want open-source control. Would you like me to create a **decision matrix** that matches tools to specific use cases (e.g., debugging, compliance, cost optimization)?

Choosing the best observability tool for AI agents depends heavily on your stack, whether you prioritize open-source self-hosting, and whether you need integrated evaluation or runtime guardrails. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Portkey%20is%20the%20most%20relevant%20option%20here,Phoenix%20for%20self%2Dhosted%2C%20open%20source%20agent%20observability.) The leading observability tools for AI agents are categorized below by their primary strengths: ### 1\. Best Overall & Agent Tracing * **Arize AX (with Phoenix OSS)** [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/) * **Best For:** End-to-end tracing, evaluation, and production monitoring using open standards. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Instrumentation%20and%20deployment.%20Published%20starting%20point*%20Main,and%20LangChain%20development%20teams.%20SDKs%20and%20OpenTelemetry%3B) * **Why it shines:** Built heavily on OpenTelemetry and **OpenInference** , Arize provides some of the deepest multi-step agent trajectory visualizations. You can seamlessly use **Phoenix** (its open-source counterpart) for local development or self-hosting, and scale up to **Arize AX** for managed enterprise governance. [10 best AI observability tools for monitoring and evaluating agents in](https://www.mintlify.com/library/best-ai-observability-tools#:~:text=*%202.%20Arize%20AX%20and%20Phoenix.%20Arize,evaluations%3B%20and%20send%20examples%20for%20human%20review.) ### 2\. Best for LangChain & LangGraph Ecosystems * **LangSmith** [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/) * **Best For:** Teams building complex, multi-agent workflows using LangChain or LangGraph. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Quick%20answer%3A%20Arize%20AX%20and%20Phoenix%20offer,with%20an%20existing%20application%20and%20infrastructure%20stack.) * **Why it shines:** Because it’s built by the creators of LangChain, its native integration offers unmatched insight into agent loops, tool call retries, state management, and debugging. It includes great features for annotation queues and dataset creation. ### 3\. Best for Evaluation-First Engineering * **Braintrust** [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=*%20Braintrust%3A%20Best%20for%20evaluation%2Dfirst%20engineering.) * **Best For:** Tying production observability directly into regression testing, CI pipelines, and multi-user prompt collaboration. * **Why it shines:** Braintrust treats evaluation as a first-class citizen. It not only tracks your agent's tokens, costs, and tool selections in production, but lets you immediately turn failing production traces into test datasets and run continuous evaluations. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Quick%20answer%3A%20Arize%20AX%20and%20Phoenix%20offer,with%20an%20existing%20application%20and%20infrastructure%20stack.)[10 best AI observability tools for monitoring and evaluating agents in](https://www.mintlify.com/library/best-ai-observability-tools#:~:text=The%2010%20best%20AI%20observability%20tools%20in,latency%2C%20token%20usage%2C%20cost%2C%20metadata%2C%20and%20errors.) ### 4\. Best Open-Source & Self-Hosted Alternatives * **Langfuse** [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=*%20Langfuse%3A%20Best%20open%2Dsource%20platform%20with%20a%20managed%20cloud.) * **Best For:** Open-source transparency (MIT licensed) with robust prompt management and low overhead. [10 LLM Observability Tools to Evaluate & Monitor AI in 2026](https://www.confident-ai.com/knowledge-base/compare/10-llm-observability-tools-to-evaluate-and-monitor-ai-2026#:~:text=Table_title%3A%20The%20Best%20LLM%20Observability%20Tools%20at,Multi%2Dagent%20observability%20%26%20testing%20%7C%20Free%20tier%3B) * **Why it shines:** It is developer-friendly, natively supports OpenTelemetry, provides detailed tracing for multi-step agent decisions, and can be completely self-hosted with minimal hassle. [15 Best AI Observability Tools for Production Teams in 2026](https://www.honeycomb.io/blog/best-ai-observability-tools#:~:text=Table_title%3A%20Quick%20comparison%20of%20the%20best%20AI,Token%20%2B%20usage%20in%2Dplatform%20%7C%20SaaS%20usage%2Dbased%3B) * **Comet Opik** [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Get%20started%20View%20AX%20docs.%20Prefer%20open,tracing%20plus%20agent%20testing%20and%20evaluation%20%7C) * **Best For:** Open-source agent testing, prompt experimentation, and lightweight tracing. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Get%20started%20View%20AX%20docs.%20Prefer%20open,tracing%20plus%20agent%20testing%20and%20evaluation%20%7C) ### 5\. Best for Real-Time Safety & Guardrails * **Galileo** [6 Best AI Agent Observability Platforms (2026) - Galileo AI](https://galileo.ai/blog/best-ai-agent-observability-platforms#:~:text=Galileo%20is%20an%20agent%20observability%20and%20guardrails,intercepts%20unsafe%20outputs%20before%20they%20reach%20users.) * **Best For:** Production environments requiring low-latency hallucination checks and runtime guardrails. [6 Best AI Agent Observability Platforms (2026) - Galileo AI](https://galileo.ai/blog/best-ai-agent-observability-platforms#:~:text=AI%20engineering%20teams%20that%20need%20comprehensive%20observability%2C,deployment%2C%20and%20scalable%20infrastructure%20across%20regulated%20industries.) * **Why it shines:** Galileo uses lightweight evaluation models (like Luna-2) to score agent reasoning coherence, tool selection quality, and action completion on the fly, blocking unsafe behaviors _before_ they output to the user. [5 best AI agent observability tools for agent reliability in 2026 - Articles](https://www.braintrust.dev/articles/best-ai-agent-observability-tools-2026#:~:text=Galileo%20evaluates%20agent%20outputs%20using%20lightweight%20models,real%2Dtime%2C%20low%2Dcost%20safety%20evaluations%20on%20every%20request.) ### 6\. Best for Infrastructure & Enterprise APM Integration * **Datadog LLM Observability** [10 LLM Observability Tools to Evaluate & Monitor AI in 2026](https://www.confident-ai.com/knowledge-base/compare/10-llm-observability-tools-to-evaluate-and-monitor-ai-2026#:~:text=from%20%2429%2Fmo%20%7C%20Yes%20\(MIT\)%20%7C%20Self%2Dhosted,observability%20%26%20AI%20gateway%20%7C%20Free%20tier%3B) * **Best For:** Engineering teams already using Datadog for traditional application performance monitoring (APM). [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Get%20started%20View%20AX%20docs.%20Prefer%20open,tracing%20plus%20agent%20testing%20and%20evaluation%20%7C) * **Why it shines:** It allows you to seamlessly correlate your AI agents' token spend, latency, and errors directly alongside your backend microservices, databases, and network logs. ### How to Choose: * Choose **Arize AX / Phoenix** if you want industry-standard OpenTelemetry tracking for complex agent graphs. * Choose **LangSmith** if your agent framework of choice is LangGraph. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Quick%20answer%3A%20Arize%20AX%20and%20Phoenix%20offer,with%20an%20existing%20application%20and%20infrastructure%20stack.) * Choose **Braintrust** or **Galileo** if your primary bottleneck is evaluating agent quality and preventing regressions. * Choose **Langfuse** if you need a fully open-source, self-hosted stack. [14 Best AI Observability Tools for Agents in 2026 | Arize](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/#:~:text=Langfuse%20and%20Comet%20Opik%20are%20strong%20open%2Dsource,runs%20today.%20Get%20started%20View%20AX%20docs.) Are you currently using a specific framework (like LangGraph, CrewAI, or custom Python code) to build your AI agents?

# Shared 0 files Observability for AI agents goes beyond traditional LLM logging. Because agents execute multi-step reasoning, dynamic tool calls, and autonomous loops, you need tools capable of **session replay, chain-of-thought tracking, and cost/latency attribution**. [LangSmith vs Langfuse vs AgentOps: Best AI Monitoring](https://www.youtube.com/watch?v=DtDz1iJTVSI)[Ultimate Guide to AI Agent Observability and Production](https://www.youtube.com/watch?v=DevAyoh_4bU&t=161) The best observability tools for AI agents fit into distinct categories depending on whether you need open-source privacy, multi-agent tracking, or enterprise-grade APM integration. * * * 1\. Specialized Agent & LLM Observability Platforms * **AgentOps** * **Best for:** Autonomous and multi-agent systems (CrewAI, AutoGen, LangChain). * **Key Features:** Laser-focused on agent workflows, offering **time-travel debugging** (session replay with point-in-time accuracy), visual tracking of tool execution, environment tracking, and cost monitoring. * **Trade-off:** Carries slightly higher runtime overhead compared to basic APM tracers, but unmatched for complex agent loops. [15 AI Agent Observability Tools: AgentOps & Langfuse](https://aimultiple.com/agentic-monitoring) * **Langfuse** * **Best for:** Open-source, self-hosted, and privacy-first engineering. * **Key Features:** MIT-licensed, framework-agnostic tracing, prompt management, cost tracking, and evaluation metrics. You can self-host it to keep sensitive agent payloads completely in-house. * **Trade-off:** Requires more manual setup if you are piecing together custom evaluation pipelines. [Langfuse vs. Arize AX / Arize Phoenix](https://langfuse.com/resources/engineering/best-phoenix-arize-alternatives)[The Open Source LLM Observability Platform Explained](https://www.youtube.com/watch?v=kIf1Ng76cmc)[Top 5 AI Agent Evaluation Tools (2025): Maxim AI, Langfuse,](https://www.youtube.com/watch?v=_-2i3pSRbCY&t=106) * **LangSmith** * **Best for:** Deep integration with LangChain/LangGraph ecosystems and production-ready monitoring. * **Key Features:** Highly mature, exceptional performance efficiency with minimal latency overhead, robust dataset regression testing, and collaborative debugging. * **Trade-off:** Most powerful when deeply tied into the LangChain ecosystem, though usable elsewhere. [AIMultiple +1] * **Arize Phoenix** / **Arize AX** * **Best for:** Evaluation, local-first debugging, and notebook environments. * **Key Features:** Excellent for tracing embeddings, RAG evaluation, and troubleshooting localized agent failures in Jupyter notebooks before pushing to production. _(Note: Arize was acquired by Dynatrace, but the Phoenix/AX tooling remains core to their AI-native observability stack)._ [Langfuse +1] * * * 2\. Enterprise & Full-Stack APM Giants If your AI agents talk heavily to microservices, traditional enterprise observability platforms have evolved dedicated AI/agent tracing capabilities: * **Datadog** * **Best for:** Correlating agent execution metrics with traditional cloud infrastructure, databases, and microservices. * **Key Features:** Uses its ML engine (Watchdog) to surface latency outliers and anomalies across downstream services called by an agent without manual threshold definitions. * **Dynatrace** * **Best for:** Automated, zero-config enterprise-scale root-cause analysis. * **Key Features:** Automatically maps AI agent actions into broader application topologies and business workflows using DAVIS AI. * **IBM Instana** * **Best for:** Automated, real-time tracking of microservices and agent framework interactions (tracking tool usage and costs out-of-the-box). * * * Summary Checklist: Which one should you pick? * Choose **AgentOps** if you are building untethered, multi-agent loops that frequently break during complex tool execution. * Choose **Langfuse** if you want an open-source, cost-effective, and self-hostable stack for data privacy. * Choose **LangSmith** if your architecture is built on LangGraph/LangChain and you need zero-friction performance. * Choose **Datadog or Dynatrace** if you are an enterprise needing to unify agent telemetry with existing infrastructure APM. [YouTube·AI Quality Nerd +6] If you can share **which agent framework you are using** (e.g., AutoGen, CrewAI, LangGraph, or custom code) and whether you prefer an **open-source or managed SaaS** tool, I can give you a tailored implementation recommendation.

Observability for AI agents focuses on tracing, evaluation, cost, and governance across agent lifecycles. Here are strong options to consider in 2026, with strengths aligned to common needs. - Arize Phoenix (and AX): Best for end-to-end agent tracing and evaluations; supports production monitoring, online/offline evaluations, and experiments; open standards help you instrument across models and frameworks. - Langfuse: Strong open-source option for end-to-end tracing with per-turn visibility and cost tracking; good when you want self-hosted, OpenTelemetry–driven instrumentation. - Braintrust: Comprehensive platform with automated evaluation, real-time monitoring, and production feedback loops; strong for regulated environments and governance. - Galileo AI: Agent observability with runtime guardrails and protection; useful if you want active safety controls alongside monitoring. - AgentOps: Open-source, agent-first observability with dashboards and playback; appealing if you prefer a fully auditable, self-hosted stack. - Fiddler: Enterprise-grade observability with governance features; strong in compliance-heavy industries. - Datadog / Helicone / OpenTelemetry ecosystems: Helpful if you already live in these ecosystems or need broad telemetry, dashboards, and alerting across models and tools. - Open-source tracing + eval combo (Langfuse + Phoenix pairing, or Langtrace with eval layers): A cost-conscious, flexible approach that emphasizes transparency and reproducibility. Key criteria to evaluate for your use case - Tracing completeness: Do you capture per-turn steps, tool invocations, and retrievals, not just high-level model calls? - Evaluation capability: Can you automatically run evaluations on prompts, outputs, and tool results in production? - Cost visibility: Are per-request or per-turn costs visible and attributable to specific components? - Guardrails and safety: Do you get runtime protections or anomaly detection to prevent unsafe outputs? - Deployability: Is the solution self-hosted, cloud, or hybrid, and does it integrate with your existing stack (models, tools, orchestrators)? - Data governance: How well does the platform handle data lineage, access controls, and compliance requirements? Illustrative example - If your AI agents perform multi-step plans with tools and web retrieval, you might pair an open-source tracer for per-turn visibility with an evaluation engine to rate each step’s quality, plus an enterprise-grade guardrails layer for safety checks. Would you like a concise comparison table tailored to your stack (e.g., your LLM providers, orchestration framework, and whether you prefer self-hosted vs. SaaS)? I can also help outline a shortlist and a 2-week pilot plan. Citations: For a general landscape of AI agent observability tools and their capabilities, see industry roundups and platform comparisons from 2026 coverage by multiple sources.[8 Best AI Agent Observability Tools in 2026](https://www.ayautomate.com/blog/best-ai-agent-observability-tools)[The 17 Best AI Observability Tools In Aug 2026 - Monte Carlo](https://montecarlo.ai/blog-best-ai-observability-tools)[14 best AI agent observability tools in 2026: A practical comparison](https://arize.com/blog/best-ai-observability-tools-for-autonomous-agents-in-2026/)[AI observability tools: A buyer's guide to monitoring AI agents ...](https://www.braintrust.dev/articles/best-ai-observability-tools-2026)[6 Best AI Agent Observability Platforms (2026)](https://galileo.ai/blog/best-ai-agent-observability-platforms)