AgentsProof vs Prefactor: Which is Better in 2026?
A comprehensive comparison of AgentsProof and Prefactor 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 Prefactor if:
- →You need deterministic scoring and risk checks on 100% of agent activity, with no per-check cost or real-time enforcement — hold, approve or block an agent action mid-run
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AgentsProof vs Prefactor: At a Glance
Pricing Comparison: AgentsProof vs Prefactor
Understanding the pricing differences between AgentsProof and Prefactor is crucial for making the right choice. Here's how their plans compare side by side.
AgentsProof Pricing
Prefactor Pricing
💡 Pricing takeaway: Both AgentsProof and Prefactor 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 AgentsProof and Prefactor stacks up.
What Makes Each Tool Unique
🔵 Unique to AgentsProof
Features available in AgentsProof but not in Prefactor:
- ✓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 Prefactor
Features available in Prefactor but not in AgentsProof:
- ✓Deterministic scoring and risk checks on 100% of agent activity, with no per-check cost
- ✓Real-time enforcement — hold, approve or block an agent action mid-run
- ✓Eval-gated promotion across dev, staging and production, with instant version rollback
- ✓PII checks built into the reliability loop rather than sold separately
- ✓Unlimited seats on every plan; bring your own model keys with no token markup
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: Prefactor
Prefactor is an evaluation runtime for AI agents in production, and the distinction it draws against agent observability tools is that it does not stop at recording — it scores every step live and can act on the score. The unit of measurement is a span, defined as one step an agent takes: an LLM call, a tool invocation, a message turn, or a custom business step you mark. The SDK records them automatically, and then deterministic scoring, risk checks, pattern and sequence checks and PII checks run on 100% of activity rather than a sample, with no per-check cost and no tokens consumed by the deterministic path. Scores and checks never create spans, so the meter measures your agent's work rather than the observability of it. Enforcement is the part most tools in this space lack: Prefactor can hold, approve or block an action in real time, gate promotion between development, staging and production on evaluation results, and roll an agent version back instantly. The problems it names are the ones teams actually hit — hidden failures, manual evaluation, being stuck at proof-of-concept, data leakage, runaway costs and having no kill switch — and it maps its controls to the EU AI Act, GDPR, ISO 42001, NIST AI RMF, SOC 2, ISO 27001, HIPAA and the OWASP LLM Top 10. Your models and keys stay yours with no token markup, seats are unlimited on every plan, and self-hosting is available. The meter climbs fast at scale: 1M spans a month runs $2,500 monthly or $24,000 committed annually.
Ideal use cases:
- •Teams or individuals who need deterministic scoring and risk checks on 100% of agent activity, with no per-check cost
- •Teams or individuals who need real-time enforcement — hold, approve or block an agent action mid-run
- •Teams or individuals who need eval-gated promotion across dev, staging and production, with instant version rollback
- •Teams or individuals who need pii checks built into the reliability loop rather than sold separately
- •Anyone focused on agent-evaluation workflows
- •Anyone focused on observability workflows
🤖 Other AI Agent Infrastructure Tools to Consider
AgentsProof and Prefactor 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 Prefactor?
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 Prefactor provides 5 features including Deterministic scoring and risk checks on 100% of agent activity, with no per-check cost and Real-time enforcement — hold, approve or block an agent action mid-run. AgentsProof uses a freemium model with a free tier, while Prefactor is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is AgentsProof cheaper than Prefactor?
AgentsProof is cheaper, starting at $29/month compared to Prefactor's $199/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 Prefactor together?
Yes, many users combine AgentsProof and Prefactor in their workflow. AgentsProof excels at trace llm and tool calls with one wrapper and get a scored report url, while Prefactor shines with deterministic scoring and risk checks on 100% of agent activity, with no per-check cost. 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 Prefactor?
While both are ai agent infrastructure tools, AgentsProof emphasizes trace llm and tool calls with one wrapper and get a scored report url, whereas Prefactor is known for deterministic scoring and risk checks on 100% of agent activity, with no per-check cost. The best choice depends on your specific workflow and feature priorities.
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