lotchuazzz-crypto/papergraph-mcp
PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents: extract results, trace proof evidence, plan reading order, and review external dependencies without guessing.
⚡ Executive Summary & Quick Facts
Authoritative Sourcepapergraph-mcp is papergraph-mcp is an open-source project written in Python: PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents: extract results, trace proof evidence, plan reading order, and review external dependencies without guessing..
| Repository | lotchuazzz-crypto/papergraph-mcp ↗ |
|---|---|
| Primary Category | ai agents |
| Language & Stack | Python |
| Software License | MIT |
| Community Traction | ★ 285 stars · ⑂ 4 forks · #12,218 globally |
| Maintenance | ● Active (Last commit: Oct 18, 2026) · 1 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
Star History
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+10
STARS · LAST 30 DAYS
1
PER DAY
#1,583
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
papergraph-mcp gained 10 stars in the last 30 days, about 1 a day, and now has 285. It is about 1 year old and has averaged roughly 285 stars a year. It ranks #1,583 among Python repositories and #12,218 across all languages on GitHubRepo.
Trending Record
4
DAYS ON TRENDING
#11600
BEST RANK
Oct 18, 2026
FIRST APPEARANCE
Active
STATUS TODAY
papergraph-mcp has maintained a continuous presence across global trending indexes, peaking at #11600. Below is the 30-day activity profile:
💡 Overview
papergraph-mcp is an open-source project written in Python: PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents: extract results, trace proof evidence, plan reading order, and review external dependencies without guessing.
Engineered for speed, consistency, and developer ease, it solves common hurdles in ai-agents, arxiv, knowledge-graph. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install papergraph-mcp⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
papergraph-mcp coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with papergraph-mcp.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install papergraph-mcp using your package manager: `pip install papergraph-mcp`
Initialize your project workspace or configuration file for papergraph-mcp.
Import papergraph-mcp into your codebase or invoke it directly from your terminal.
Execute your test suite or run `papergraph-mcp --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
lotchuazzz-crypto/papergraph-mcp is currently ranked #12,218 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
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|---|---|---|---|---|
| #12,198 | bmf-san/ggc | Go | ★ 286 | Compare ↗ |
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| #12,218 | lotchuazzz-crypto/papergraph-mcp This Project | Python | ★ 285 | |
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Frequently Asked Questions
What does papergraph-mcp do? +
PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents: extract results, trace proof evidence, plan reading order, and review external dependencies without guessing.
What language is papergraph-mcp written in? +
The primary language is Python. Topics include: ai-agents, arxiv, knowledge-graph, latex, mathematics.
Is papergraph-mcp actively maintained? +
Yes, the last recorded push was on Oct 18, 2026 with 1 open issues being tracked.
How many stars does papergraph-mcp have? +
papergraph-mcp has 285 stars and 4 forks on GitHub.
How does papergraph-mcp rank among GitHub repositories? +
With 285 stars, lotchuazzz-crypto/papergraph-mcp is ranked #12,218 globally across all repositories tracked on GitHubRepo and #1,583 among Python projects.
What license is papergraph-mcp distributed under? +
The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.
