Prefactor

Prefactor

Prefactor is an agent observability and reliability platform that scores every AI agent run in real time (quality, drift and risk) and wires those evaluations into enforcement actions so failing or risky runs can be paused, blocked or routed for human approval before they act.

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Free API 70/100
One of 617 tools in AI Agents
Added 2 months ago
Data reviewed Jul 28, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: AI Agents

What it does

AI Tools software for decision-makers comparing workflow fit and alternatives.

Best fit

AI Agents

Pricing snapshot

Free from Free for first 25,000 spans per month

Next step

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Prefactor is a production-focused platform for agent observability, evaluation and enforcement. It captures every agent run as structured trace data (each LLM call, tool invocation and decision), scores runs with custom evals (including LLM-as-judge, technical and qualitative metrics), and wires those evaluations into actions so risky or failing runs can be paused, blocked or routed for human approval at runtime. The product is aimed at engineering and AI platform teams running agents in production and integrates via a CLI, TypeScript and Python SDKs, and native integrations for frameworks like LangChain and Claude.

The platform emphasizes a closed-loop reliability model: observe every run, evaluate with the evals you define, and act automatically or with human-in-the-loop enforcement. Prefactor also provides runtime visibility (traces & spans), custom spans to attach external context, versioning and environment promotion (dev → staging → prod) gated by evals, and enterprise security features such as least-privilege access, auditing, and sensitive-data detection.

Evaluate your AI Agents in real-time Discussion | Link

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Key Features

Real-time evaluation

Scores every agent run in production the moment it happens for quality, drift and risk using configurable evals including LLM-as-judge, technical checks and qualitative metrics.

Closed-loop enforcement

Wires evaluations directly into action: hold, approve or block runs automatically or route high-risk actions to humans via the SDK or API so failures are caught live, not just charted.

SDKs & CLI

TypeScript and Python SDKs plus a CLI (prefactor init) that discovers agents across runtimes and instruments calls into streaming spans and live runs.

Native integrations

Native support for agent frameworks and platforms (LangChain, Claude, Vercel AI, OpenClaw, LiveKit) and connectors for coding and workflow tools.

Custom spans & grounding

Attach any datasource (GitHub, Linear, Jira, DBs, internal APIs) as custom spans to a run so evals can be grounded in external context and factual signals.

Versioning & lifecycle promotion

Immutable agent versioning (semver/commit/tag), schema management, dev/staging/prod environments and eval-gated promotion with instant rollback.

Sensitive-data detection & audit

Sensitive-data detection across categories, auditable records of decisions and scoped least-privilege access for enforcement actions.

Pricing

Free Tier Available

Free for your first 25,000 spans a month.

Free

Free for first 25,000 spans per month
  • First 25,000 spans free each month
  • Drop-in SDK instrumentation and live scoring

Use Cases

Preventing harmful or risky agent actions

Pause or block high-risk actions (for example, PII leaks or destructive operations) at runtime and route them to human reviewers for approval before execution.

Continuous agent quality monitoring

Score agent outputs on real production traffic to detect drift and regressions using LLM-as-judge, technical checks and qualitative metrics, with metrics available per run and per agent version.

Agent development lifecycle and promotion

Version agents, validate them against production traffic, and promote only when evals pass to ensure newer agent versions improve performance and safety.

Grounded evaluations with external context

Attach data from GitHub, Jira, databases or internal APIs to runs so evaluations are grounded in the factual context of each decision.

Enterprise compliance and auditing

Provide auditable records, scoped access and sensitive-data detection to meet enterprise governance and security requirements.

Integrations

LangChain

Native SDK integration for instrumenting LangChain agents and capturing spans for every run.

Claude

Native integration to evaluate and enforce runs using Claude-based agents.

Vercel AI

Native integration for agents running on Vercel AI.

OpenClaw

Native integration for OpenClaw agent frameworks.

LiveKit

Native integration for LiveKit-powered agents.

Coding & workflow tools

Works with VS Code, GitHub Copilot, Cursor, n8n and other tools via native SDKs or OpenTelemetry spans.

Benefits

Catch and stop failing or risky agent runs in real time instead of discovering problems after the fact.
Automate enforcement and human-in-the-loop flows to scale oversight across many agents.
Maintain traceable, versioned agent deployments with eval-gated promotion and instant rollback.
Ground evaluations in external context for more accurate quality and risk assessments.
Enterprise-ready security posture with least-privilege access, auditing and sensitive-data detection.

Limitations

SOC 2 Type II is listed as in progress (not yet completed).
RBAC (role-based access control) is on the roadmap but not yet available.

Frequently Asked Questions

What is agent observability?
Capturing every agent run as structured trace data (each LLM call, tool invocation and decision) so you can see exactly what an agent did, how well it performed, and what it cost.
What is agent evaluation?
Continuously scoring agent outputs with the evals you define (LLM-as-judge, technical checks and qualitative metrics) on real production traffic to catch drift and regressions.
How do I instrument my agent with Prefactor?
Install the CLI (prefactor init) and add the TypeScript or Python SDK, native for LangChain, Claude, Vercel AI, OpenClaw and LiveKit. For coding and workflow tools, send OpenTelemetry spans or instrument anything else with the core SDK.
What are custom spans, and how do they measure quality?
A custom span marks a step that matters (a retrieval, a tool call, a sub-agent). Attach external context to the span and run your own evals on it (LLM-as-judge, technical and qualitative metrics) so every evaluation is grounded in what actually happened.
How does human-in-the-loop work?
High-risk actions can be paused and routed to a person to approve, modify or reject before they execute, via the SDK or the API, which enforces the decision at runtime. Every decision is logged.
How does Prefactor enforce at runtime?
Through the SDK or API, Prefactor can pause a run and hold a high-risk action for human approval before it executes; sensitive-data detection and risk classification drive what gets held.
Why do AI agents need identity and scoped access?
So each action ties to a specific agent, task and user context, enabling least privilege, traceability and revocation.

Getting Started

  1. 1 Step 1: Install the Prefactor CLI (prefactor init) to connect your workspace and discover agents.
  2. 2 Step 2: Add the TypeScript or Python SDK (native for LangChain, Claude, Vercel AI, OpenClaw & LiveKit) to instrument agent runs as spans.
  3. 3 Step 3: Define evals (LLM-as-judge, technical & qualitative metrics) and attach any custom spans or external context.
  4. 4 Step 4: Watch runs stream in live with traces, scores, latency and cost, then configure enforcement (block, hold, throttle, or route to humans).
  5. 5 Step 5: Use versioning and eval-gated promotion to move agents through dev → staging → prod and enable instant rollback when needed.

Support

docs

Documentation and SDK reference linked from the product site (Read the docs).

demo

Book a 30-minute walkthrough with an engineer (Book a demo).

community

Slack Community referenced on the site for community support and discussion.

self-serve

Get started — free (signup to the app) and CLI-based installation for self-serve onboarding.

API

Available: Yes
Documentation:

https://prefactor.tech/

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