Laminar vs Probe: Which is Better in 2026?
A comprehensive comparison of Laminar and Probe covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Laminar if:
- →You want more affordable paid plans (from $5/mo)
- →You need signals — describe a failure in plain english and get slack alerts when it happens or readable agent-run transcripts with inputs, reasoning, tool calls and sub-agents
Choose Probe if:
- →You need 44 checks in ~30 seconds with a 0-100 score, letter grade, and readme badge or x402 payment discovery and validation, plus erc-8004 onchain identity checks
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Laminar vs Probe: At a Glance
Pricing Comparison: Laminar vs Probe
Understanding the pricing differences between Laminar and Probe is crucial for making the right choice. Here's how their plans compare side by side.
Laminar Pricing
Probe Pricing
💡 Pricing takeaway: Both Laminar and Probe offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Laminar and Probe stacks up.
What Makes Each Tool Unique
🔵 Unique to Laminar
Features available in Laminar but not in Probe:
- ✓Signals — describe a failure in plain English and get Slack alerts when it happens
- ✓Readable agent-run transcripts with inputs, reasoning, tool calls and sub-agents
- ✓Ask questions about a run and get answers that reference specific steps
- ✓OTLP trace ingestion, full-text search, custom dashboards and a SQL editor
- ✓Evaluations, datasets, labelling queues and browser session recording
- ✓MCP access plus SOC 2 Type II, HIPAA and server-side PII removal on paid tiers
🟣 Unique to Probe
Features available in Probe but not in Laminar:
- ✓44 checks in ~30 seconds with a 0-100 score, letter grade, and README badge
- ✓x402 payment discovery and validation, plus ERC-8004 onchain identity checks
- ✓MCP endpoint discovery, tool declarations, and transport security
- ✓A2A agent.json discovery and skill declaration validation
- ✓EU AI Act Article 14 human oversight, GDPR, and FATF Travel Rule checks
- ✓Up to 24x daily continuous monitoring with automatic email alerts
Use Case Recommendations
Best for: Laminar
Laminar is an open-source observability platform built specifically for agents rather than for LLM calls, and the distinction shows in its core primitive. Instead of asking you to write assertions, it lets you describe a failure in plain English — "agent is stuck in a loop" — as a Signal; Laminar then reads every agent run, evaluates it against that description and pings Slack when the pattern occurs. That inverts the usual observability workflow, where you only catch the failures you thought to instrument. Once alerted, the debugging path is designed to be short: the run is rendered as a readable transcript and timeline surfacing inputs, LLM reasoning, tool calls and sub-agents, and you can ask questions of the run directly and get answers that cite specific steps. Around that sit the rest of an evaluation stack — OTLP trace ingestion, full-text trace search, custom dashboards, a SQL editor, datasets, labelling queues, browser session recording and MCP access. Billing is metered on two axes that reflect how the product works: gigabytes of trace data, and dollars of Signals evaluation spend, with published input and output token rates for the overage. The free tier is a real one at 1 GB and $5 in Signals with no overage, and paid tiers offer SOC 2 Type II, HIPAA and server-side PII removal.
Ideal use cases:
- •Teams or individuals who need signals — describe a failure in plain english and get slack alerts when it happens
- •Teams or individuals who need readable agent-run transcripts with inputs, reasoning, tool calls and sub-agents
- •Teams or individuals who need ask questions about a run and get answers that reference specific steps
- •Teams or individuals who need otlp trace ingestion, full-text search, custom dashboards and a sql editor
- •Anyone focused on agent-observability workflows
- •Anyone focused on tracing workflows
Best for: Probe
Probe is a compliance scanner for AI agent APIs and MCP servers. You paste a URL and it runs forty-four checks in about thirty seconds, returning a 0-100 score, a letter grade, and a README badge, with no account required. The check set is unusually specific to the agent ecosystem rather than being a generic web scanner with AI branding. It covers the x402 agent payment standard by auto-discovering /.well-known/x402.json and validating payment metadata; MCP endpoint discovery, tool declarations, and transport security; Google's A2A protocol via agent.json discovery and skill declarations; ERC-8004 onchain agent identity registration on Base and Ethereum; and llms.txt and AI plugin discovery. Alongside those it runs conventional infrastructure checks — HSTS, CSP, X-Content-Type-Options, X-Frame-Options, Referrer-Policy, CORS, rate limiting, DNSSEC, CAA, DMARC and SPF, TLS, OpenAPI spec validation, API versioning, and Content-Type consistency — plus a regulatory cluster covering EU AI Act human oversight under Article 14, AI disclosure and risk classification, GDPR privacy and data-processing disclosure, FATF Travel Rule VASP disclosure, and ten voice-AI-specific checks for synthetic voice labeling, call recording consent, and operator identity. The grading is framed around listing readiness: A means listing-ready with minor fixes, C means the endpoint will fail x402 validation, D and F mean critical gaps. Subscribing adds up to 24 scans a day with email alerts. The vendor states the product is entirely free with no paid tier, funded by the compliance dataset it builds.
Ideal use cases:
- •Teams or individuals who need 44 checks in ~30 seconds with a 0-100 score, letter grade, and readme badge
- •Teams or individuals who need x402 payment discovery and validation, plus erc-8004 onchain identity checks
- •Teams or individuals who need mcp endpoint discovery, tool declarations, and transport security
- •Teams or individuals who need a2a agent.json discovery and skill declaration validation
- •Anyone focused on mcp workflows
- •Anyone focused on x402 workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Laminar and Probe aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Laminar better than Probe?
It depends on your needs. Laminar offers 6 key features including Signals — describe a failure in plain English and get Slack alerts when it happens and Readable agent-run transcripts with inputs, reasoning, tool calls and sub-agents, while Probe provides 6 features including 44 checks in ~30 seconds with a 0-100 score, letter grade, and README badge and x402 payment discovery and validation, plus ERC-8004 onchain identity checks. Laminar uses a freemium model with a free tier, while Probe is free with free access available. Choose based on which features and pricing model align with your requirements.
Is Laminar cheaper than Probe?
Probe doesn't have standard paid plans, while Laminar starts at $5/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Laminar and Probe together?
Yes, many users combine Laminar and Probe in their workflow. Laminar excels at signals — describe a failure in plain english and get slack alerts when it happens, while Probe shines with 44 checks in ~30 seconds with a 0-100 score, letter grade, and readme badge. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Laminar and Probe?
While both are ai agent infrastructure tools, Laminar emphasizes signals — describe a failure in plain english and get slack alerts when it happens, whereas Probe is known for 44 checks in ~30 seconds with a 0-100 score, letter grade, and readme badge. The best choice depends on your specific workflow and feature priorities.
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