AgentsProof vs LangSmith: Which is Better in 2026?
A comprehensive comparison of AgentsProof and LangSmith covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose AgentsProof if:
- →You want more affordable paid plans (from $29/mo)
- →You need a broader feature set (6 features vs 5)
- →You need trace llm and tool calls with one wrapper and get a scored report url or scores across goal completion, tool accuracy, step efficiency, output quality and safety
Choose LangSmith if:
- →You need full trace visualization for chains and agents or prompt hub and versioning
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AgentsProof vs LangSmith: At a Glance
Pricing Comparison: AgentsProof vs LangSmith
Understanding the pricing differences between AgentsProof and LangSmith is crucial for making the right choice. Here's how their plans compare side by side.
AgentsProof Pricing
💡 Pricing takeaway: Both AgentsProof and LangSmith offer free tiers, making it easy to try before you buy. Visit each tool's website for the latest pricing details.
Feature-by-Feature Comparison
Here's how every feature from AgentsProof and LangSmith stacks up.
What Makes Each Tool Unique
🔵 Unique to AgentsProof
Features available in AgentsProof but not in LangSmith:
- ✓Trace LLM and tool calls with one wrapper and get a scored report URL
- ✓Scores across goal completion, tool accuracy, step efficiency, output quality and safety
- ✓Custom graders written as plain-English rules checked on every run
- ✓Golden test cases grouped into proof suites for regression checking
- ✓Publicly shareable proof reports as external evidence
- ✓Drop-in support for OpenAI, Anthropic, LangChain, CrewAI, Vercel AI SDK and LlamaIndex
🟣 Unique to LangSmith
Features available in LangSmith but not in AgentsProof:
- ✓Full trace visualization for chains and agents
- ✓Prompt hub and versioning
- ✓Evaluation datasets and automated tests
- ✓Production monitoring
- ✓Dataset curation
Use Case Recommendations
Best for: AgentsProof
AgentsProof exists to replace vibe-checking an AI agent with a shareable artifact. The integration is intentionally tiny: install one npm package, call `ap.startRun()` with the input and a goal string that anchors grading, wrap each LLM and tool call in `run.trace()`, and call `run.complete()` — which returns a public URL to a proof report. The report scores the run out of 100 with a breakdown across goal completion, tool accuracy, step efficiency, output quality, safety and anomaly detection, alongside the traced step sequence. Beyond the default LLM grader you write custom graders as plain-English rules that are checked against every run automatically, and you define golden test cases — approved input-output pairs the agent must keep satisfying — grouped into proof suites. The problem it targets is the specific failure mode of shipping agents: someone changes a prompt, something breaks silently, and a user notices before the team does. Because a proof report has a public URL, it also works as external evidence — a way to show a customer or a reviewer that the agent behaves, rather than asserting it. Published drop-in support covers OpenAI, Anthropic, LangChain, CrewAI, the Vercel AI SDK and LlamaIndex, with TypeScript and Python SDKs. The product is in beta, and free-tier eval runs return a 402 from the SDK when the monthly limit is reached rather than silently degrading.
Ideal use cases:
- •Teams or individuals who need trace llm and tool calls with one wrapper and get a scored report url
- •Teams or individuals who need scores across goal completion, tool accuracy, step efficiency, output quality and safety
- •Teams or individuals who need custom graders written as plain-english rules checked on every run
- •Teams or individuals who need golden test cases grouped into proof suites for regression checking
- •Anyone focused on agent-evals workflows
- •Anyone focused on observability workflows
Best for: LangSmith
LLMOps platform by LangChain for debugging, testing, evaluating, and monitoring LLM applications. LangSmith provides full trace visibility into complex chains, agents, and RAG pipelines built with LangChain or any framework.
Ideal use cases:
- •Teams or individuals who need full trace visualization for chains and agents
- •Teams or individuals who need prompt hub and versioning
- •Teams or individuals who need evaluation datasets and automated tests
- •Teams or individuals who need production monitoring
- •Anyone focused on LLMOps workflows
- •Anyone focused on LangChain workflows
🤖 Other AI Agent Infrastructure Tools to Consider
AgentsProof and LangSmith 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 AgentsProof better than LangSmith?
It depends on your needs. AgentsProof offers 6 key features including Trace LLM and tool calls with one wrapper and get a scored report URL and Scores across goal completion, tool accuracy, step efficiency, output quality and safety, while LangSmith provides 5 features including Full trace visualization for chains and agents and Prompt hub and versioning. AgentsProof uses a freemium model with a free tier, while LangSmith is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is AgentsProof cheaper than LangSmith?
LangSmith doesn't have standard paid plans, while AgentsProof starts at $29/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 AgentsProof and LangSmith together?
Yes, many users combine AgentsProof and LangSmith in their workflow. AgentsProof excels at trace llm and tool calls with one wrapper and get a scored report url, while LangSmith shines with full trace visualization for chains and agents. 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 AgentsProof and LangSmith?
While both are ai agent infrastructure tools, AgentsProof emphasizes trace llm and tool calls with one wrapper and get a scored report url, whereas LangSmith is known for full trace visualization for chains and agents. The best choice depends on your specific workflow and feature priorities.
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