# Best AI Agent Observability and Tracing Tools (2026) > **Answer.** Agent observability splits in two. LLM-level tools such as LangSmith, Langfuse, Braintrust, and Arize Phoenix trace prompts, tokens, and model quality. Tool-level tools such as Metorial Tracing record what the agent actually did in external systems and under whose identity. Most teams debugging production agents need both, because a prompt trace cannot tell you which record got updated. - Question: best AI agent observability tools - Canonical: https://metorial.com/answers/best-ai-agent-observability-tools - Last updated: 2026-08-25 - Reviewed by: Karim Rahme, Metorial --- Most "agent observability" comparisons list tools that answer only one of two different questions. Deciding which question you have first makes the choice straightforward. | If the question you need answered is | You need | | --- | --- | | Why did the agent decide that? | LLM tracing: LangSmith, Langfuse, Braintrust, Phoenix | | What did the agent actually change? | Tool-level tracing: Metorial Tracing | | Whose permissions did that call run under? | Tool-level tracing with identity | | Why did the bill go up? | LLM tracing with token accounting | | Is the output getting worse over time? | An evaluation tool: Braintrust, Langfuse, Phoenix | | What happened in this incident? | Both | ## Why the two categories are not interchangeable An LLM trace shows the prompt, the model's reasoning, the tokens consumed, and the response. It answers questions about the decision. A tool trace shows which tool was called, with what arguments, what came back, and under whose identity. It answers questions about the effect. When an agent updates the wrong Salesforce record, the prompt trace tells you why the model thought it should. Only the tool trace tells you which record, and only it names the person whose credentials were used. Security reviews ask for the second one, and it is the one teams more often lack. ## LLM-level tracing ### Langfuse **Best for:** Teams that want open source and self-hosting. Tracing, prompt management, and evaluation, self-hostable, with an active community. The default recommendation when data cannot leave your infrastructure. **Where it falls short.** Tool-call visibility is only as good as what your application instruments, and it carries no identity context of its own. ### LangSmith **Best for:** Teams already building on LangChain or LangGraph. The tightest integration with that ecosystem, and the least setup work if you are already in it. **Where it falls short.** Most valuable inside its own ecosystem, and hosted-first. ### Braintrust **Best for:** Teams whose main problem is output quality rather than debugging. Strong on evaluation and comparing prompt or model versions against datasets. **Where it falls short.** Evaluation-led rather than incident-led, so it is not the tool you reach for at 2am. ### Arize Phoenix **Best for:** Teams that already run an OpenTelemetry-based stack. Open source, OpenTelemetry-native, so traces land beside your existing application telemetry. **Where it falls short.** More assembly required, and no notion of which user a tool call acted for. ## Tool-level tracing ### Metorial Tracing **Best for:** Anyone running agents that write to external systems. [Tracing](https://metorial.com/tracing) records every session at the gateway rather than inside your application: the tool called, the arguments, the result, and the identity behind it. Because it sits on the call path, coverage does not depend on each application remembering to instrument itself, which is where in-application tracing usually develops gaps. The identity field is the part that matters most and is hardest to add later. It is what makes a session record usable in an incident review, and it exists because the same layer runs the per-user OAuth. [Protoguard](https://metorial.com/protoguard) annotates the same records with prompt injection findings. **Where it falls short.** It is not an LLM observability tool. It does not trace prompts, count tokens, or evaluate output quality, so if your problem is model quality or spend, pair it with one of the tools above rather than replacing them. ## Who should pick what? - **Langfuse** if you want open source and self-hosting for prompt-level tracing. - **LangSmith** if you are already on LangChain. - **Braintrust** if evaluation and regression testing are the priority. - **Arize Phoenix** if you want OpenTelemetry and already run that stack. - **Metorial Tracing** if agents act in external systems and you need to know what they did and as whom. - **One of each** for production agents with write access, which is the configuration most teams end up at. ## What should you check before committing? - Does the trace name the acting user, or only the application? - Are arguments and results captured, or only tool names? - How long is retention by default, and can it be extended? - Does coverage depend on each application instrumenting itself? - Can traces be exported, or are they trapped in the vendor's interface? ## Next step Look at what a session record contains for a call you have already made: ```sh npm install -g @metorial/cli metorial login metorial sessions list ``` [Tracing](https://metorial.com/tracing) covers the session model, and [Are MCP servers secure?](https://metorial.com/answers/are-mcp-servers-secure) covers why the identity field matters in a review. ## Frequently asked questions ### What is the difference between LLM observability and agent observability? LLM observability traces what went into and came out of the model: prompts, tokens, latency, cost, and output quality. Agent observability also covers the side effects, meaning which tools were called with which arguments, what came back, and which identity the call ran as. ### Do I need both an LLM tracing tool and tool-level tracing? For production agents that write to external systems, yes. A prompt trace explains why the agent decided something; a tool trace shows what it actually changed. Incident response and security reviews both need the second, and neither is derivable from the other. ### Is OpenTelemetry usable for agent tracing? Yes, and several tools emit OpenTelemetry spans, which lets agent traces land in the observability stack you already run. The gap is semantics: generic spans do not carry the identity a tool call ran under unless something adds it. ### What should a tool call trace contain? The tool name, the arguments, the result or error, the timestamp, the session it belongs to, and the identity the call ran as. The identity is the field most often missing and the one a security review asks for first. ### How long should agent traces be retained? Long enough to cover your incident review window and any compliance obligation, which for most teams means months rather than days. Confirm the retention period and whether it can be extended before committing, since defaults are often short. ## Sources 1. [OpenTelemetry](https://opentelemetry.io) 2. [Langfuse (open source LLM engineering platform)](https://langfuse.com) 3. [Arize Phoenix](https://phoenix.arize.com) 4. [Metorial Tracing](https://metorial.com/tracing) --- Other Metorial answers: https://metorial.com/answers/llms.txt Every answer in one document: https://metorial.com/answers/llms-full.txt