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Stop LLM hallucinations
before they reach
your users.

Fact-check any content in real-time with one API call.

Get Free API Key Free: 500 claims/month
View Documentation
from factivelabs import FactiveLabs

client = FactiveLabs(api_key="YOUR_API_KEY")

result = client.verify(
    content="The Earth is 4.5 billion years
      old. The Great Wall is visible
      from space.",
    mode="text"
)

for claim in result.claims:
    print(f"{claim.verdict}: {claim.text}")
▶ Response
Confirmed "The Earth is 4.5 billion years old"
Disputed "The Great Wall is visible from space"
2 claims · 1.2s · 6 sources

The most rigorously benchmarked fact-checking API available.

Academic Benchmark
Our score on FEVER
95.0%
Accuracy
95.7%
Macro F1
95.9%
Precision
95.4%
Recall
The best academic systems score in the low-to-mid 80s. We show that live-web retrieval outperforms static-corpus approaches.
Methodology & full results
Accuracy95.0%
Macro Precision95.9%
Macro Recall95.4%
Macro F195.7%
“Supported” F195.2%
“Disputed” F196.2%
Factive’s API was benchmarked against FEVER, the standard academic dataset for automated fact-checking, on a balanced sample of 200 claims (100 supported, 100 refuted). After independent adjudication of ground truth labels — correcting for outdated or erroneous annotations in the original dataset — the pipeline achieved 95.0% accuracy with a macro F1 of 95.7% and balanced precision and recall above 95% across both verdict classes. Notably, several of the pipeline’s apparent “errors” reflected cases where our live-web retrieval returned more current and accurate information than the dataset’s 2017-era Wikipedia ground truth, demonstrating the advantage of real-time evidence gathering over static reference sets.
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Academic Benchmark
Our score on AVeriTeC
88.0%
Accuracy
87.3%
Macro F1
88.2%
Precision
86.4%
Recall
Real claims from professional fact-checkers — not synthetic data. Tests the messy, ambiguous claims that matter in production.
Methodology & full results
Accuracy88.0%
Macro Precision88.2%
Macro Recall86.4%
Macro F187.3%
“Supported” F182.1%
“Disputed” F192.6%
Factive’s API was benchmarked against AVeriTeC, a curated set of real-world claims from professional fact-checking organizations spanning August–October 2020. After deduplication of repeated claims, independent review and correction of ground truth labels, and exclusion of unevaluable entries, 440 claims were scored. All claims were temporally grounded to end-of-2020 knowledge to match the original fact-checkers’ context window. The pipeline achieved 88.0% accuracy with a macro F1 of 87.3%, strong refutation detection (F1 = 92.6%), and precision above 82% in both verdict classes. Unlike static-corpus benchmarks, AVeriTeC tests real-time web retrieval against real-world misinformation — the pipeline’s residual errors primarily reflect limited web coverage for regional data sources rather than reasoning failures.
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Internal Benchmark
Our score on WONDERS
95.6%
Accuracy
90.4%
Macro F1
87.1%
Precision
94.2%
Recall
Assembles real-world claims in realistic real-world paragraph context. Consistent performance across 10 independent runs (σ = 1.1%).
Methodology & full results
Accuracy95.6%
Macro Precision87.1%
Macro Recall94.2%
Macro F190.4%
“Supported” F195.5%
“Disputed” F185.3%
Factive’s API was benchmarked against the Wonders internal benchmark, a curated set of 129 real-world claims spanning history, science, geography, and public figures — each embedded in realistic paragraph context designed to mirror how misinformation appears in the wild. Across 10 independent runs, the pipeline achieved a mean accuracy of 95.6% (range 93.8–97.7%, σ = 1.1%) with a macro F1 of 90.4%, confirming true claims at 95.5% F1 and correctly flagging false claims at 85.3% F1. Over 96% of claims returned a consistent verdict across 8/10 or more independent runs. With live web retrieval against naturally deceptive framing, these results demonstrate robust, repeatable performance on the kind of nuanced, context-dependent claims that trip up simpler fact-checking approaches.
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Not just an LLM wrapper — a multi-stage pipeline with dispute resolution

Every claim passes through seven independent stages before a verdict is returned.

Identify domain & framing flags
Classify
Domain & framing detection
Direct to the right model & sources
Route
Model & source selection
Verify against live web sources
Research
Live source retrieval
Cross-reference evidence & assign verdict
Verify
Evidence cross-check
Independent recheck for split verdicts
Dispute
Multi-round resolution
Catch edge cases & contradictions
QA Gate
Error pattern detection
Final structured result delivered
Verdict
Structured result returned
How it works
1
Classification

Each claim is analyzed by parallel classifiers that identify its knowledge domain (science, politics, history, etc.) and detect framing flags like negation, misconception, or attribution — so downstream stages know exactly what they’re dealing with.

2
Routing

Based on classification, each claim is directed to the appropriate verification path: general-knowledge claims go to one model, time-sensitive claims get live search, weather claims hit a weather API, and idioms or opinions are skipped entirely.

3
Research

A structured query is built from the claim and its context, then run against live web sources. Results are retrieved from authoritative sources — not a static corpus — ensuring evidence is current and relevant.

4
Verification

The claim is cross-referenced against retrieved evidence. A verdict (confirmed, disputed, or inconclusive) is assigned along with a human-readable explanation citing the specific sources used.

5
Dispute Resolution

When initial evidence is ambiguous or verdicts conflict, independent recheck rounds run with fresh queries and different models. Split decisions are resolved through majority consensus across rounds.

6
QA Gate

A specialized review layer scans every result for eight known failure patterns: verdict/explanation mismatches, nitpicking, absence-as-disproof, circular confirmation, and more. Flagged results are routed to corrective actions.

7
Verdict

Clean results are delivered as structured JSON: verdict, confidence, explanation, sources, and metadata. Flagged results are re-verified, relabeled, or suppressed before delivery — so only quality-checked results reach your users.

Submit any content.

Text
URLs
PDF
Word
Audio
Images
YouTube YouTube
TikTok TikTok
Instagram Instagram
X X
Reddit Reddit
ChatGPT
Perplexity Perplexity
Gemini Gemini
Claude Claude

Get back structured data.

Granular Claims

Content is decomposed into precise, atomic claims before verification. Our extraction engine balances focus and coverage so every meaningful claim is captured at the right weight, fast.

Verdicts

Every claim returns confirmed, disputed, or inconclusive. Machine-readable with no parsing and no ambiguity. Use directly in conditional logic.

Source Citations

Each verdict includes the URLs and titles of the sources used to make the determination. Built-in transparency your users can trust.

Source Snippets

Get the exact passage from each source that supports or contradicts the claim. Get the evidence, not just a link.

Short & Detailed Explanations

A one-liner for compact UIs, plus a full reasoning breakdown for detail views. Choose which to display — both are included in every response.

Claim Positioning & Text

Every highlight region includes character offsets and the matched text itself — so you can pinpoint claims by position or simple string search. Build inline highlighting, annotations, or underlines with zero guesswork.

Corrective Text

When a claim is disputed, get back corrected text ready to swap in. Go beyond flagging errors — deliver the fix, not just the flag.

Document Sections

Long documents come back with a full section map — name, word count, character offsets, and a skip recommendation for non-verifiable sections like references or appendices. Use it to build a section picker or automatically exclude boilerplate before verification.

Choose from three input modes.

Send it all at once, stream it in as it's generated, or submit in bulk.

Complete Default

Send the full content in a single request. Accepts any format — text, URLs, YouTube, TikTok, PDFs, audio, images. Best for articles, documents, and finished content.

POST /api/v1/verify

Streaming Real-time

Push content chunk by chunk as it's generated. We detect paragraphs and start fact-checking before the content is finished. Built for LLM output streams, live transcription, and real-time captions.

POST /api/v1/verify/stream

Batch Bulk

Submit up to 100 items in a single request. Each is processed independently and concurrently. Best for content pipelines, CMS ingestion, and bulk verification jobs.

POST /api/v1/verify/batch

Choose from three response modes.

All at once, streamed as they come, or picked up later in batch.

Synchronous Default

Send a request, get the full result when processing completes. Best for short content and simple integrations.

"stream": false, "async": false

Streaming SSE

Receive claim-by-claim results via Server-Sent Events as they're extracted and verified. Ideal for real-time UIs.

"stream": true

Batch / Async Polling

Submit and receive a job ID immediately. Poll the status endpoint to check progress. Best for large documents and batch processing.

"async": true

Get started in 2 minutes.

Three steps from zero to your first verified claim.

1

Get your API key

Create a free account and generate your key. 500 free claims per month included.

2

Install the SDK (optional)

Use our Python SDK for the fastest integration, or call the REST API directly.

3

Verify your first claim

Send any text to the verify endpoint and get back claims with verdicts, sources, and explanations.

Built for production environments.

Integrate fact-checking into any workflow. Fact-check a single claim at a time or submit content with hundreds or even thousands of claims.

💪

AI Safety & Hallucination Detection

Add a verification layer to any LLM pipeline. Submit model output and get back structured evidence for every claim — before it reaches your users. Try it out live here.

🤖

AI Agents & Copilots

Give your AI agent the ability to verify its own output before presenting it to users. Catch hallucinations in RAG pipelines, chatbots, and copilots.

📜

Content Moderation

Flag disputed claims in user-generated content before it goes live. Run at ingestion time or as a batch job across your entire content library.

📚

Research & Journalism

Verify sources and claims at scale. Process entire articles or documents and get claim-by-claim breakdowns with source citations.

📰

Publishing & Editorial

Fact-check articles, press releases, and reports before publication. What takes hours manually takes seconds through the API.

⚖️

Compliance & Legal

Verify claims in filings, marketing materials, and regulated documents. Reduce liability by catching factual errors before they ship.

See what you can build with our API.

Toadstool is a live app built entirely on our API. Fact-check all forms of media: text docs, audio, YouTube, Instagram, links, AI shares, and more.

Factive Review Mode

Frequently asked questions:

What counts as a "claim"?

A claim is a single verifiable statement extracted from your content. For example, "The Earth is 4.5 billion years old" is one claim. A paragraph might contain 3-5 claims. The API automatically extracts and counts individual claims.

Can I try the API before signing up?

Yes. The Playground lets you test the API without creating an account. For programmatic access, create a free account to get your API key — no credit card required.

What happens if I exceed my monthly credits?

On paid plans, you’ll be billed $0.01 per additional claim automatically. On the Free plan, requests will return a 402 error — upgrade to continue.

What happens if I hit the rate limit?

You’ll receive a 429 response with a Retry-After header. Upgrade to a higher plan for increased limits.

How is the Toadstool app different from the API?

The Toadstool app is our consumer product — a fact-checking app built on the same pipeline. The API gives developers and AI agents programmatic access. Same engine, different interfaces.