Best AI Agent Observability Tools in 2026: LumiqTrace vs Langfuse vs LangSmith vs Helicone
TL;DR
LumiqTrace is the only tool in this comparison built agent-native from the ground up — spans carry agent identity, delegation chains are first-class, and setup takes under 5 minutes. Langfuse is the strongest choice for teams with open-source or self-hosting requirements, though active agent features are newer. LangSmith has the deepest LangChain/LangGraph integration but creates hard framework lock-in. Helicone is the simplest proxy-based option but lacks agent observability features and is reportedly in maintenance mode.
If you're new to agent observability concepts, start with What is AI Agent Observability.
What to Look For in an AI Agent Observability Tool
Before comparing specific products, these are the six criteria that matter most for production agent teams:
1. Agent-native architecture vs retrofitted LLM monitor Most tools in this space started as LLM call loggers and added agent features later. The difference shows up in the data model: retrofitted tools attach metadata to spans as custom properties; agent-native tools put agent identity, role, and delegation context directly in the span schema. This matters when you're debugging a failure four hops deep in a multi-agent workflow.
2. Multi-agent delegation tracking Single-agent systems are relatively easy to trace. The hard problem is multi-agent: supervisor delegates to researcher, researcher delegates to a web tool, result comes back through two intermediaries. Can your tool show you that chain end-to-end, with the payload at each handoff?
3. Built-in eval templates Writing eval logic from scratch is time-consuming. Built-in templates for faithfulness, relevance, toxicity, groundedness, and coherence let you start evaluating immediately. The difference between zero templates and twelve is the difference between "we should add evals someday" and "we're running evals this sprint."
4. Cost attribution Token spend in multi-agent systems is non-obvious. The tool that looks cheap in dev might be burning budget on a planning loop that runs unnecessarily. A cost optimizer that tells you which model swap saves 40% without quality regression is not a nice-to-have at production scale.
5. Setup friction Every hour of instrumentation is an hour not spent building. Proxy-based tools are fast but introduce a network dependency. SDK-based auto-discovery is faster than manual span instrumentation. The best outcome is meaningful traces on your first deploy.
6. Retention and data ownership 14-day retention is fine for debugging last night's incident. 90 days lets you run regression comparisons across model versions. 3 years covers compliance requirements. Know what you need before you hit the limits.
Full Comparison Table
| LumiqTrace | Langfuse | LangSmith | Helicone | |
|---|---|---|---|---|
| Architecture | Agent-native (agent identity in span schema) | Retrofitted LLM monitor + Agent Graphs (Nov 2025) | Retrofitted LLM monitor (LangChain-optimized) | Proxy-based LLM gateway |
| Open source | No | Yes (MIT) | No | Yes (Apache 2.0) |
| Free tier | 10K traces/mo, 14-day retention, no credit card | 100K units/mo (managed cloud) | 5K traces/mo, 14-day retention, 1 seat | 100K req/mo, 7-day retention |
| Paid entry price | $39/mo (Solo, 100K traces) | $29/mo (Core) | $39/seat/mo (Plus) | $25/mo (Pro) |
| Agent auto-discovery | Yes (init + 1 framework line) | No (manual instrumentation) | No (LangChain callbacks only) | No |
| Agentic traces (agent identity on spans) | Yes (every span carries agent identity) | Partial (Agent Graphs visualization, no span-level identity) | No | No |
| Multi-agent delegation tracking | Yes (delegations are first-class spans) | No | No | No |
| Built-in eval templates | 12 (faithfulness, relevance, toxicity, groundedness, instruction following, coherence, and more) | None (custom templates only) | None (custom/LLM-as-judge) | None |
| AI cost optimizer | Yes | No | No | No (multi-provider routing only) |
| AI ops assistant | Yes (LumiqPilot: analysis, action, auto-remediation) | No | No | No |
| Anomaly detection | Yes (AI-powered) | No | No | No |
| Self-hosting | Scale plan only | Free (MIT, requires ClickHouse infra) | Enterprise only | Yes (Apache 2.0) |
| Framework support | Framework-agnostic | Framework-agnostic | LangChain/LangGraph native; others manual | Framework-agnostic (proxy) |
| Setup time | Under 5 minutes | ~20 minutes (cloud); hours for self-host | ~15 minutes (LangChain); longer otherwise | Under 5 minutes (proxy) |
LumiqTrace
Website: lumiqtrace.com
LumiqTrace was designed specifically for multi-agent systems — the data model reflects this from the first line of code. Every span in a trace carries the agent's identity natively, not as a custom metadata field bolted on later. When one agent delegates to another, that delegation is a first-class span with its own parent-child relationship, payload, and timing.
Setup
# Python
pip install lumiqtrace
# Node.js / TypeScript
npm install @lumiqtrace/sdk
import lumiqtrace
lumiqtrace.init(api_key="YOUR_KEY")
import { lumiqtrace } from "@lumiqtrace/sdk";
lumiqtrace.init({ apiKey: process.env.LT_KEY });
That's the full installation. LumiqTrace init auto-patches all LLM provider calls (OpenAI, Anthropic, Gemini, Bedrock, Mistral) — zero changes to your LLM code. For framework-level agent tracing, add one framework handler (e.g., LumiqtraceCallbackHandler() for LangChain, LumiqtraceCrewAIListener() for CrewAI). You get full traces on your first run.
Agent-native traces
The core differentiator is how delegation is modeled. In a system where a supervisor agent spawns a research agent which calls three tools, LumiqTrace produces a trace tree that reflects that actual structure — each agent is an identified actor, not an anonymous "step." When that system fails, you can see exactly which agent was executing and what it received.
Built-in eval templates
LumiqTrace ships 12 evaluation templates:
- Faithfulness
- Relevance
- Toxicity
- Groundedness
- Instruction following
- Coherence
- (plus 6 additional domain-specific templates)
These run against your traces directly. No eval pipeline to configure before you get your first quality signal.
LumiqPilot
LumiqPilot is a three-capability AI operations assistant included in the Pro plan ($149/mo) and above:
- Deep data analysis — surfaces patterns across your trace data that would take hours to find manually (e.g., "your planning loop accounts for 34% of total latency on 8% of requests")
- Instant action from insight — converts findings into immediate operational changes without leaving the dashboard
- Proactive auto-remediation — detects anomalies before they affect users and takes predefined corrective actions
AI cost optimizer
Analyzes token spend across model calls, identifies which specific calls could use a cheaper model without quality regression, and surfaces the projected savings. Useful at any scale; essential at production scale.
Pricing
| Plan | Price | Traces/mo | Notes |
|---|---|---|---|
| Free | $0 | 10K | 14-day retention, no credit card |
| Solo | $39/mo | 100K | — |
| Pro | $149/mo | 500K | LumiqPilot included |
| Team | $299/mo | 2M | — |
| Scale | Custom | Custom | Self-hosting option |
Best for
Teams building multi-agent systems who need accurate agent-identity tracing from day one, want built-in evals without configuration overhead, and are on any framework (not just LangChain).
Langfuse
Website: langfuse.com | License: MIT
Langfuse is the leading open-source LLM observability platform and was acquired by ClickHouse in January 2026. It's framework-agnostic, has a large community, and remains the default answer for teams with open-source requirements or data residency constraints that require self-hosting.
What Langfuse does well
Open-source with real self-hosting. The MIT license means you can run Langfuse in your own infrastructure with no per-seat restrictions and no vendor dependency. This is a genuine advantage for regulated industries, enterprises with data residency requirements, and teams that have been burned by vendor lock-in.
Agent Graphs (shipped November 2025). Langfuse added multi-step agent visualization with tool visibility. This is newer than its core tracing product and continues to mature. Note that while Agent Graphs provide visualization, spans don't carry agent identity natively at the schema level — it's a visualization layer on top of existing trace data.
Custom evaluation pipelines. Langfuse has strong support for building custom eval workflows and annotation queues. There are no built-in eval templates, but the infrastructure for running your own evals is solid.
Trade-offs
Self-hosting with real production workloads requires ClickHouse (post-acquisition, ClickHouse is the recommended storage backend). Production ClickHouse infra typically costs $200–800/month on cloud providers, which means the "free self-hosted" framing needs that asterisk. For teams with the DevOps capacity to manage this, it's still a good deal. For smaller teams, it shifts significant operational burden.
Manual instrumentation is the other friction point. There's no auto-discovery — you instrument your code explicitly. For established codebases with stable architecture, this is manageable. For teams iterating quickly on agent design, it means re-instrumenting when you restructure.
Pricing (managed cloud)
| Plan | Price | Notes |
|---|---|---|
| Hobby | $0 | 100K units/mo |
| Core | $29/mo | — |
| Pro | $199/mo | SOC2, HIPAA, 3-year retention |
| Enterprise | $2,499/mo | Advanced compliance, SLAs |
Self-hosted: MIT license, free. Infrastructure costs separate.
For a detailed breakdown of Langfuse's limitations and alternatives, see our Langfuse alternatives guide.
Best for
Teams with open-source requirements, data residency or compliance constraints requiring self-hosting, or organizations with existing ClickHouse infrastructure. Also a strong fit for teams that want a large open-source community and long-term vendor independence.
LangSmith
Website: smith.langchain.com | License: Closed source
LangSmith is LangChain's observability product, and if your stack is deeply committed to the LangChain ecosystem, the integration is unmatched. LangGraph state diffs — the ability to see how agent state changed between nodes in a LangGraph execution — is a feature that doesn't exist elsewhere.
What LangSmith does well
LangChain/LangGraph native integration. If you're using LangChain abstractions, tracing is near-automatic. Spans attach to chain runs, agent executions, and LangGraph nodes without manual instrumentation at every callsite.
LangGraph state diffs. For multi-agent systems built on LangGraph, LangSmith shows exactly how the agent's state object changed at each node — not just what was called, but what changed. This is genuinely useful for debugging LangGraph workflows and doesn't exist in any other tool.
Dataset management and annotation. LangSmith's annotation workflow for collecting and labeling examples is well-developed. The pipeline from "interesting trace" to "labeled training example" is smoother than alternatives.
Trade-offs
Framework lock-in. The near-automatic tracing only works with LangChain abstractions. On OpenAI Agents SDK, AutoGen, CrewAI, or a custom orchestrator, you're back to manual span instrumentation — which eliminates most of the setup advantage.
No agent identity on spans. Like Langfuse, LangSmith records execution trees but spans don't carry agent identity natively. Multi-agent delegation is visible as a nested trace but isn't a first-class modeling concept.
No built-in evals, no cost optimizer, no AI ops assistant. LangSmith's evals are custom-built via LLM-as-judge. There's no automated cost analysis or ops assistant.
Pricing at scale. Plus is $39/seat/month with 10K base traces and $5 per additional 1K traces at extended retention. At 500K traces/month with a 5-person team, the numbers add up quickly.
Pricing
| Plan | Price | Notes |
|---|---|---|
| Free | $0 | 5K traces/mo, 14-day retention, 1 seat |
| Plus | $39/seat/mo | 10K base traces, $5/1K for extended retention |
| Enterprise | Custom | SSO, RBAC, self-hosting |
For a full breakdown of why teams leave LangSmith, see our LangSmith alternatives guide.
Best for
Teams fully committed to LangChain/LangGraph who need the tightest possible integration with the LangChain ecosystem and don't require multi-framework support.
Helicone
Website: helicone.ai | License: Apache 2.0
Helicone is a proxy-based LLM observability tool. It sits between your application and your LLM provider, logging every request and response. Setup genuinely is one line — point your base_url at Helicone's proxy endpoint and you're logging immediately.
What Helicone does well
Proxy simplicity. If you need LLM request logging in 60 seconds, Helicone delivers. No SDK installation, no instrumentation — just a URL change.
Multi-provider routing. Helicone supports routing across multiple LLM providers from a single endpoint, which is useful if you're experimenting with models or want failover.
Open source. Apache 2.0 means you can self-host, fork, and inspect the full codebase.
Trade-offs
No agent observability. Helicone logs LLM calls, not agents. There's no agent identity on spans, no delegation tracking, no multi-agent workflow visualization. For a chatbot or single-model application, this is sufficient. For multi-agent systems, it only sees the LLM calls — the agent orchestration layer is invisible.
Proxy dependency. Every LLM call routes through Helicone's servers. This adds a network hop and creates a dependency: if Helicone has an outage or latency spike, it affects your application. Self-hosting eliminates this but adds operational overhead.
Maintenance mode. Helicone is reportedly in maintenance mode as of 2026. Active feature development has slowed significantly. Teams evaluating for production multi-agent systems should factor this into their decision.
No evals, no cost optimizer, no AI ops assistant.
Pricing
| Plan | Price | Notes |
|---|---|---|
| Free | $0 | 100K req/mo, 7-day retention |
| Pro | $25/mo | — |
| Enterprise | Custom | — |
For teams actively migrating away from Helicone, see our Helicone alternatives guide.
Best for
Teams that only need lightweight LLM request logging, want multi-provider routing, need a one-line integration, and are not running multi-agent systems.
Arize Phoenix (Honorable Mention)
Arize Phoenix is an open-source (MIT) evaluation-first observability platform worth mentioning for teams where evaluation depth is the primary concern. It has strong support for running and comparing evals at scale. It's less focused on tracing and agent visualization than the tools above. If your primary use case is offline eval harnesses and you're already handling tracing separately, Phoenix is worth evaluating.
How to Choose
For a head-to-head comparison of all three leading tools, see our LangSmith vs Langfuse vs LumiqTrace comparison.
Use LumiqTrace if:
- You're running multi-agent systems and need accurate agent identity in your traces
- You want setup done in under 5 minutes on any framework
- You want 12 built-in eval templates without building eval infrastructure first
- AI cost optimization or an AI ops assistant matters to your team
- You're not committed to a specific framework
Use Langfuse if:
- You have open-source requirements or a mandate against SaaS data storage
- You need self-hosting for data residency or compliance (HIPAA, GDPR)
- You have DevOps capacity to manage ClickHouse infrastructure
- Vendor independence is a priority
Use LangSmith if:
- Your entire stack runs on LangChain or LangGraph
- LangGraph state diffs are valuable for your debugging workflow
- You're not using and don't plan to use other orchestration frameworks
- You're doing intensive prompt engineering within the LangChain ecosystem
Use Helicone if:
- You need LLM request logging in 60 seconds and nothing more
- Multi-provider routing is your primary requirement
- You're building a simple chatbot or single-model application, not a multi-agent system
FAQ
What is the best AI agent observability tool?
It depends on your stack. LumiqTrace is the strongest choice for teams running multi-agent systems that need agent auto-discovery, agentic traces, and built-in evals out of the box. Langfuse is the best option for teams with open-source or self-hosting requirements. LangSmith is the right pick if your entire stack runs on LangChain/LangGraph. Helicone suits teams that only need lightweight LLM request logging with multi-provider routing.
What's the difference between LLM monitoring and AI agent observability?
LLM monitoring records inputs, outputs, latency, and token counts for individual model calls. AI agent observability goes deeper: it tracks agent identity across every span, maps delegation chains between agents, surfaces which sub-agent failed in a multi-hop workflow, and correlates cost back to specific agent roles. The distinction matters in production — an LLM monitor tells you a call failed; an agent observability tool tells you which agent in which delegation chain caused the failure and why.
Is Langfuse free?
Langfuse's managed cloud has a free tier with up to 100K units per month. The open-source self-hosted version is free under the MIT license, but you're responsible for infrastructure — typically ClickHouse plus supporting services, which costs roughly $200–800/month depending on scale and cloud provider.
Does LangSmith work with frameworks other than LangChain?
LangSmith has an API and SDKs that work outside LangChain, but the integration is manual. The automatic, near-zero-config tracing only works when you use LangChain or LangGraph abstractions. If you're using OpenAI Agents SDK, CrewAI, AutoGen, or a custom orchestration layer, you'll need to instrument spans by hand — which erases most of the setup advantage.
What happened to Helicone?
Helicone is reportedly in maintenance mode as of 2026. The product continues to work and is open source under Apache 2.0, but active feature development has slowed significantly. Teams that depend on it for production multi-agent workloads should evaluate alternatives. For simple LLM logging or multi-provider routing needs, it remains a functional lightweight option.
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