Helicone Alternatives in 2026: Best Options for AI Agent Observability

Helicone built its reputation on one specific thing: the simplest possible way to log LLM calls. Change your API base URL to route through Helicone's proxy, and you get a dashboard of requests, costs, and latency with no SDK installation required.

That's still a genuine value for very simple use cases. But a meaningful number of teams have been moving away from Helicone over the past year, driven by three specific problems: the project appears to be in maintenance mode, it has no eval capabilities, and its proxy architecture creates an availability dependency teams aren't comfortable with in production.

This post covers what's actually driving the migration, what to evaluate when picking an alternative, and an honest breakdown of the five strongest options available in 2026.

Why Teams Are Looking for Helicone Alternatives

Maintenance mode concerns

Helicone is Apache 2.0 open source. That transparency cuts both ways — it's easy to see that commit activity and issue response times have slowed significantly. Teams evaluating Helicone for new production deployments are finding that the project isn't keeping pace with the rate at which AI tooling is evolving.

No eval capabilities

Helicone logs what went to the LLM and what came back. There's no mechanism to evaluate whether responses were faithful, relevant, grounded, or toxic. For teams that care about output quality at scale — not just cost and latency — this is a hard gap. There are no built-in eval templates, no LLM-as-judge scoring, no automated quality metrics.

Proxy architecture creates an availability dependency

Helicone's proxy model means it sits in the critical path between your application and the LLM API. If Helicone has downtime or elevated latency, your application inherits it. For internal tooling or low-stakes applications this may be acceptable. For production customer-facing systems, teams increasingly want observability infrastructure that never touches the critical path.

Zero support for multi-agent systems

Helicone intercepts HTTP calls. That's all it can see. It has no concept of agents, tools, delegation, planning loops, or handoffs between agents. If you're running multi-agent workflows — even basic ones — Helicone records disjointed LLM API calls with no connective tissue between them.

If you're new to what agent observability covers, start with What is AI Agent Observability.

Short retention on the free tier

Helicone's free tier offers 7-day retention. Debugging production incidents that happened eight days ago requires a paid plan. Langfuse's free tier, by comparison, offers longer retention, and LumiqTrace offers 14-day retention on the free plan.

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What to Look for in a Helicone Alternative

Before evaluating options, establish your actual requirements:

1. Agent support — Do you need visibility into agent decisions, tool calls, and delegation? Or just raw LLM call logging?
2. Eval capabilities — Do you need LLM-as-judge scoring running automatically on traces, or are you comfortable writing custom scoring?
3. No proxy dependency — Is it acceptable for observability infrastructure to sit in your application's critical path?
4. Retention — How far back do you need to query traces for debugging and analysis?
5. Cost attribution — Do you need basic per-call cost tracking, or automated analysis that surfaces cost reduction opportunities?
6. Framework support — Are you on a single framework, or running agents across LangChain, CrewAI, AutoGen, and custom implementations?

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Quick Comparison

	LumiqTrace	Langfuse	LangSmith	Arize Phoenix	Braintrust
Agent identity on spans	✓	✗	✗	✗	✗
Delegation tracing	✓	✗	✗	✗	✗
Provider auto-patch + framework handler	✓	✗	✗	✗	✗
Built-in eval templates	12	0	Custom	Custom	Custom
AI cost optimizer	✓	✗	✗	✗	✗
Anomaly detection	✓	✗	✗	✗	✗
Proxy-free architecture	✓	✓	✓	✓	✓
Open source	✗	✓ (MIT)	✗	✓	✗
Self-hosted	Enterprise	✓	✗	✓	✗
Free tier traces/mo	10K	100K events	5K	Unlimited (local)	Limited
Free tier retention	14 days	Configurable	7 days	Local only	Limited
Starting paid price	$39/mo	$29/mo	$39/seat	Custom	Custom

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Detailed Breakdown

1. LumiqTrace — Best for Agent-Heavy Production Teams

LumiqTrace is purpose-built for AI agent observability. The distinction from every other tool on this list — including Helicone — is that agent identity, delegation, and tool execution are first-class primitives in the data model, not features bolted onto an LLM call logger.

What it does differently:

Agentic traces. Every span carries agent identity. When Agent A delegates to Agent B, that handoff is recorded as its own span: which agent initiated, which agent received, what context was passed, what came back, and the full cost and latency of the sub-execution. You can see the live agent topology of your system built automatically from real execution data.

Provider auto-patch + one framework handler. LumiqTrace init silently patches all LLM providers (OpenAI, Anthropic, Gemini, Bedrock). For framework-level agent tracing, add one handler:

import lumiqtrace
lumiqtrace.init(api_key="YOUR_KEY")

import { lumiqtrace } from "@lumiqtrace/sdk";
lumiqtrace.init({ apiKey: process.env.LT_KEY });

That's it for provider-level tracing — OpenAI, Anthropic, Gemini, and Bedrock calls are captured automatically, including the OpenAI Agents SDK. For framework-level agent tracing, add one handler matching your stack:

# LangChain
from lumiqtrace.integrations import LumiqtraceCallbackHandler
handler = LumiqtraceCallbackHandler()

# CrewAI
from lumiqtrace.integrations import LumiqtraceCrewAIListener
crew = Crew(..., listeners=[LumiqtraceCrewAIListener()])

# Google ADK
from lumiqtrace.integrations import LumiqtraceADKHandler
runner = Runner(..., handlers=[LumiqtraceADKHandler()])

Works across LangChain, CrewAI, AutoGen, OpenAI Agents SDK, Google ADK, and custom agent implementations.

12 built-in eval templates. LLM-as-judge scoring runs automatically on every trace from day one. Faithfulness, relevance, toxicity, groundedness, coherence, and more — no scoring functions to write. On the Solo plan and above, these run continuously without manual configuration.

AI cost optimizer. LumiqTrace doesn't just track token costs per call. It analyzes your full trace history to surface where money is actually going: which agent patterns are expensive, which prompts are running unnecessarily long, which model could be swapped with no quality impact. Cost reduction opportunities come with dollar estimates.

LumiqPilot. An AI ops assistant with three distinct capabilities. On Pro: deep analysis (ask "what caused the cost spike at 2am?" and Pilot reads your live traces) and instant action from insight (create alerts, switch models, or roll back prompts from the same conversation). On Scale: proactive auto-remediation — Pilot surfaces anomalies before you notice them and acts on rules you define.

AI anomaly detection. Statistical baselines built from your trace history. Alerts fire when latency, error rates, or cost behavior deviates, without you writing monitoring rules.

Honest trade-offs:

• Not open source — no self-hosted option below the Enterprise tier
• Smaller community than Langfuse or LangSmith
• Newer product; some edge-case framework quirks are still being ironed out

Pricing:

• Free: $0, 10K traces/month, 14-day retention, 2 projects, 1 seat
• Solo: $39/month, 100K traces, 30-day retention, auto LLM-as-judge evals
• Pro: $149/month, 500K traces, 90-day retention, LumiqPilot (200 queries), A/B testing, custom guardrails
• Team: $299/month, 2M traces, 180-day retention, SSO, PagerDuty, 15 seats
• Scale: Custom, 10M+ traces, 365-day retention, self-hosted option, LumiqPilot auto-remediation

Best for: Production teams running multi-agent systems who need automated evals, cost optimization, and real agent visibility without building observability infrastructure from scratch.

For a head-to-head breakdown of LangSmith, Langfuse, and LumiqTrace, see our LangSmith vs Langfuse vs LumiqTrace comparison.

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2. Langfuse — Best for Open Source / Self-Hosted Requirements

Langfuse is MIT-licensed, self-hostable, and actively maintained. It's the most credible open-source option in this space and has a genuine community moat built over years of development.

What it does well:

• Full self-hosting: you control your data entirely (Postgres + ClickHouse + Redis stack)
• Multi-framework support with no LangChain lock-in
• Free cloud tier with 100K events/month
• Agent Graphs shipped in November 2025, adding visualization for multi-agent patterns
• Scoring API that supports custom LLM-as-judge functions when you build them

Honest trade-offs:

• No built-in eval templates — you write all scoring logic from scratch
• No agent identity on spans — spans don't carry which agent owns them
• Delegation tracking is not a first-class primitive
• No automated cost optimization; token costs are tracked but not analyzed
• Self-hosting adds real operational overhead (database administration, version upgrades, Redis ops)
• No auto-discovery; instrumentation is manual per framework

Pricing (cloud):

• Free: 100K events/month
• Core: $29/month
• Pro: $199/month

Best for: Teams with data residency or open-source requirements who are willing to invest engineering time in custom eval scoring and self-hosted infrastructure.

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3. LangSmith — Best for Teams Fully on LangChain/LangGraph

LangSmith is LangChain's native observability layer. If your entire stack is LangChain or LangGraph, the integration depth is unmatched — tracing is largely automatic and the LangGraph-specific visualization is genuinely useful.

What it does well:

• Near-automatic tracing for LangChain abstractions — minimal instrumentation work
• LangGraph multi-agent visualization is the best in class for that specific framework
• Human annotation and feedback collection UI
• Dataset management for regression testing

Honest trade-offs:

• Deep LangChain dependency — multi-framework teams face painful manual instrumentation
• No agent identity on spans, no delegation tracing beyond what LangGraph natively exposes
• No automated cost optimization
• 5K traces/month on the free tier is tight; extended retention costs $5 per 1,000 additional traces
• Framework lock-in is a real strategic risk as the AI framework landscape keeps shifting

Pricing:

• Free: 5K traces/month, 7-day retention
• Plus: $39/seat/month

Best for: Teams fully committed to LangChain/LangGraph who won't be adding other frameworks and want the tightest possible native integration.

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4. Arize Phoenix — Best for Eval-Depth Prioritizers

Arize Phoenix is open source and built around evaluation as the primary use case. Where other tools add evals as a feature, Phoenix's architecture treats eval pipelines as the core product.

What it does well:

• Strong eval framework with support for LLM-as-judge, retrieval metrics, and custom scorers
• Open source with a local-first deployment option — no cloud dependency required
• Good for teams doing offline evaluation on datasets before production deployment
• Tracing support across major frameworks

Honest trade-offs:

• Production monitoring and real-time alerting are secondary concerns in the product roadmap
• No agent identity on spans, no delegation tracking
• No auto-discovery, no cost optimizer
• The local-first model means you're managing your own persistence and scaling

Best for: Teams where rigorous eval pipelines are the primary engineering investment and production monitoring is a lower priority.

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5. Braintrust — Best for Eval-First Workflow Teams

Braintrust leads with offline evals and dataset management, with tracing added as a supporting capability.

What it does well:

• Flexible eval framework: combine AI scoring, human feedback, and deterministic checks
• Prompt playground with version tracking and regression detection
• Dataset management for systematic prompt iteration

Honest trade-offs:

• Production observability and real-time monitoring are not the focus
• No agent-specific tracing primitives — spans don't carry agent identity or delegation data
• No automated cost optimization or anomaly detection
• Pricing is custom and can become significant for high-volume production use

Best for: Teams where the primary workflow is offline prompt evaluation and regression testing before deployment, not continuous production monitoring.

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For a comprehensive comparison including pricing tables for all major tools, see the AI agent observability tools overview.

Recommendation by Use Case

You need to move fast and your agents are getting complex: LumiqTrace. Two lines of setup, automatic agent discovery, evals running the same day. You don't have to choose between fast setup and real agent visibility.

Open source or self-hosting is a hard requirement: Langfuse. It's the only option with a credible, actively maintained self-hosted path. Accept that you'll be writing custom eval logic and managing database infrastructure. For a full breakdown of Langfuse's strengths and limitations, see our Langfuse alternatives guide.

Your stack is entirely LangChain/LangGraph: LangSmith for the native integration depth. Switch to LumiqTrace if you add other frameworks or need cost optimization.

Evaluation pipeline depth matters more than production monitoring: Arize Phoenix if you want open source, Braintrust if you prefer a managed platform.

You want the absolute simplest LLM call logging and have no agent complexity: Helicone still works for this narrow use case. Understand the proxy risk and the maintenance trajectory before building on it.

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FAQ

Is Helicone still actively maintained?

Development pace has slowed and users have reported the project entering maintenance mode. Teams evaluating Helicone for new production deployments are finding it isn't keeping pace with the rate at which AI tooling is evolving.

What is the main risk of using a proxy-based tool like Helicone?

The proxy sits in the critical path between your application and the LLM API. If the proxy has downtime or latency spikes, your application inherits them directly. SDK-based tools like LumiqTrace instrument your application without touching the critical path — observability infrastructure failing never causes your application to fail.

Does Helicone support multi-agent systems?

No. Helicone intercepts HTTP calls to LLM APIs. It has no visibility into agent logic, tool calls, planning loops, or delegation between agents. It records what went to the API and what came back. Multi-agent execution produces a list of disconnected API calls with no relationship data between them.

Which alternative is best if I need open source?

Langfuse is the strongest open-source option: MIT-licensed, actively maintained, and self-hostable. Arize Phoenix is a solid alternative if eval depth is the priority.

Can I migrate from Helicone without touching my application code?

Migrating away from a proxy always requires some code change since you're removing the proxy URL override. For SDK-based tools like LumiqTrace, you add 2 lines of initialization code and remove the base URL override — most teams complete the migration in under an hour and immediately gain visibility they didn't have before.

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LumiqTrace is free to start — 10,000 traces per month, 14-day retention, no credit card required. Setup is init + one framework handler — under 5 minutes for any stack. If you're running agents in production and want evals, cost optimization, and real agent visibility without building infrastructure, it's worth trying before your next sprint.

import lumiqtrace
lumiqtrace.init(api_key="YOUR_KEY")

import { lumiqtrace } from "@lumiqtrace/sdk";
lumiqtrace.init({ apiKey: process.env.LT_KEY });