Danielxu0208/rsi_eval
A lightweight Python SDK and CLI for evaluating recursive self-improvement (RSI) methods across agent benchmarks.
Star History
Add Live Star History & Verified Badges to README.md
Keep your repository README looking professional and dynamic. As our continuous crawler records new stars, these official SVG badges update in real time with zero maintenance.
Renders your high-resolution star trajectory chart right inside your GitHub README or project docs.
[](https://githubrepo.cloud/repo/Danielxu0208/rsi_eval?utm_source=readme_chart)Compact Shields-style badges for your README header. Shows real-time stars and global ranking.
[](https://githubrepo.cloud/repo/Danielxu0208/rsi_eval?utm_source=readme_badge) [](https://githubrepo.cloud/repo/Danielxu0208/rsi_eval?utm_source=readme_badge) [](https://githubrepo.cloud/repo/Danielxu0208/rsi_eval?utm_source=readme_badge)Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#2,411
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 |
rsi_eval gained 10 stars in the last 30 days, about 1 a day, and now has 103. It is about 1 year old and has averaged roughly 103 stars a year. It ranks #2,411 among Python repositories and #17,818 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#16578
BEST RANK
Oct 18, 2026
FIRST APPEARANCE
Active
STATUS TODAY
rsi_eval has maintained a continuous presence across global trending indexes, peaking at #16578. Below is the 30-day activity profile:
💡 Overview
rsi_eval is an open-source project written in Python: A lightweight Python SDK and CLI for evaluating recursive self-improvement (RSI) methods across agent benchmarks.
Engineered for speed, consistency, and developer ease, it solves common hurdles in ai-agents, benchmarking, llm-evaluation. 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 rsi_eval⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
rsi_eval 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 rsi_eval.
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 rsi_eval using your package manager: `pip install rsi_eval`
Initialize your project workspace or configuration file for rsi_eval.
Import rsi_eval into your codebase or invoke it directly from your terminal.
Execute your test suite or run `rsi_eval --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
Danielxu0208/rsi_eval is currently ranked #17,818 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #17,762 | ArtificialAnalysis/aa-agentperf-local | Python | ★ 104 | Compare ↗ |
| #17,762 | Robot2050/TreeSpark | Python | ★ 104 | Compare ↗ |
| #17,762 | sybil-solutions/dsv41-flash-offload | Python | ★ 104 | Compare ↗ |
| #17,762 | personalagentproxy/proxy | TypeScript | ★ 104 | Compare ↗ |
| #17,762 | MirroS-Lab/AgentGarten | Python | ★ 104 | Compare ↗ |
| #17,818 | Danielxu0208/rsi_eval This Project | Python | ★ 103 | |
| #17,818 | caged/lighthouse-api | Ruby | ★ 103 | Compare ↗ |
| #17,818 | purcell/darcs-to-git | Ruby | ★ 103 | Compare ↗ |
| #17,818 | yfactorial/utility_scopes | Ruby | ★ 103 | Compare ↗ |
| #17,818 | aquasync/ruby-msg | Ruby | ★ 103 | Compare ↗ |
| #17,818 | ragnard/redis-clojure | Clojure | ★ 103 | Compare ↗ |
Frequently Asked Questions
What does rsi_eval do? +
A lightweight Python SDK and CLI for evaluating recursive self-improvement (RSI) methods across agent benchmarks.
What language is rsi_eval written in? +
The primary language is Python. Topics include: ai-agents, benchmarking, llm-evaluation, python, recursive-self-improvement.
Is rsi_eval actively maintained? +
Yes, the last recorded push was on Oct 12, 2026 with 0 open issues being tracked.
How many stars does rsi_eval have? +
rsi_eval has 103 stars and 4 forks on GitHub.
How does rsi_eval rank among GitHub repositories? +
With 103 stars, Danielxu0208/rsi_eval is ranked #17,818 globally across all repositories tracked on GitHubRepo and #2,411 among Python projects.
What license is rsi_eval distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
