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  4. ExtractBench
PythonNiche visibilityFresh activityNo linked package signalPartial snapshot

run-llama/ExtractBench

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Sep 16, 2026

ExtractBench - A Benchmark for Schema-Guided Enterprise Document Extraction

Compare closest alternativesOpen GitHub

First read

Promising movement, but the picture is still partial

run-llama/ExtractBench is active enough to inspect further, but the surrounding proof is thinner. Use the source repository and nearby comparisons to decide whether this is rising substance or just short-term visibility.

Visible

95 public stars in the current GitStar snapshot.

Needs a narrower context read

Active

Last commit Sep 16, 2026.

Fresh activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 95 stars
  • 13 forks
  • Last commit Sep 16, 2026
  • Package usage not mapped yet

Compare lens

ray-project/ray and The-Vibe-Company/quivr are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Sep 16, 2026

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for run-llama/ExtractBench, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

benchmarkcoding-agentdocument-aievaluationextractextract-datallamaindexllmvision-language-model
🛡️ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines fresh update.

Fresh update
📈 Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 95
30d ago7d ago1d agoNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars95
🧭 Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Python repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

ray-project/ray

Shares the data category footprint with run-llama/ExtractBench, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

The-Vibe-Company/quivr

Shares the data category footprint with run-llama/ExtractBench, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

vanna-ai/vanna

Shares the data category footprint with run-llama/ExtractBench, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

sinaptik-ai/pandas-ai

Shares the data category footprint with run-llama/ExtractBench, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked ray + quivr as the closest next comparison from the related repository set.

Open compare presetCompare with rayCompare with quivr
Repo utility

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Wider nearby ecosystem

Compare with rayCompare with quivr
Python
43.8K stars
ray-project/ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Python
39.5K stars
The-Vibe-Company/quivr

Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Python
23.8K stars
vanna-ai/vanna

🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.

Python
23.8K stars
sinaptik-ai/pandas-ai

Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.

Python
20K stars
eosphoros-ai/DB-GPT

open-source agentic AI data assistant for the next generation of AI + Data products.

Python
14.6K stars
microsoft/RD-Agent

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesView the organizationRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of run-llama/ExtractBench based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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