AIエージェント
system-prompts-and-models-of-ai-tools
FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Q…
★ 144.0k スターGPL-3.0
ガイドを見る →dspyは、stanfordnlpが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: stanfordnlp/dspy）。GitHubスターは38,554、フォークは3,406で、MITライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
Program language models with code and compilers — not fragile prompts.
Choose one concrete task — narrow beats ambitious for a first run.
Fetch the source and skim the docs folder.
Set the environment variables or keys the project needs.
Execute the example workflow and watch what happens.
LM behavior as code with signatures.
Compile and tune programs with data.
Not tied to a single provider.
Build complex pipelines from simple parts.
Measure what your tuning actually did.
From Stanford NLP, with papers behind it.
Download the source and move into the project folder.
git clone https://github.com/stanfordnlp/dspy.git
cd dspyIsolate dependencies, then install requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtLaunch using the entry point shown in the README.
python -m dspy # check README for the exact commandIt is an open-source project at stanfordnlp/dspy with 3406 forks. Check the contributors tab and recent commits to judge how active maintenance is.
Yes — it's MIT licensed: free to use, modify, and ship commercially. Just keep the license notice in copies.
Some — DSPy is a programming framework with optimizers, aimed at engineers comfortable with data-driven iteration.
Many major providers; programs are model-agnostic by design. Check the docs for the current list.
Clone the repo and follow the README quickstart — the docs at https://dspy.ai walk through setup.
FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Q…
Physical Intelligence's open vision-language-action models (pi0, pi0-FAST, pi0.5) for robot policies.
Use LLMs as plain Python functions: prompts become typed, testable code.
Turn messy documents into clean, structured data for language models.
AI-powered reverse engineering assistant that bridges IDA Pro with language models through MCP.
Hugging Face's LeRobot: datasets, models and tools to make real-world robotics AI accessible
LibreAIカタログに掲載されたAIエージェントプロジェクトの中で最もスターの多いもの — dspyが合わなかった場合に検討したい、現実的な候補リストです。
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
カタログの中でdspyとスター数が近いAIエージェントプロジェクト — 前後の棚もそのまま見て回れます。
ByteDance's open-source multimodal AI agent stack: GUI agents that see and operate real desktops
Microsoft's Playwright-powered MCP server for browser automation.
A self-evolving context database unifying agent memory, RAG, and skills.
Your self-hostable AI second brain for answers, agents, and automations.
Simple, fast retrieval-augmented generation with graph smarts.
Secure, fast microVMs that make serverless possible.