by Pravko-Solutions · Agent Tool · ★ 608
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Flash Learn - Agents made simple FlashLearn provides a simple interface and orchestration (up to 1000 calls/min) for incorporating Agent LLMs into your typical workflows and ETL pipelines. Conduct data transformations, classifications, summarizations, rewriting, and custom multi-step tasks, just like you’d do with any standard ML library, harnessing the power of LLMs under the hood. Each step and task has a compact JSON definition which makes pipelines simple to understand and maintain. It supports LiteLLM, Ollama, OpenAI, DeepSeek, and all other OpenAI-compatible clients. 🚀 Examples 📖 Full Documentation Installation Add the API keys for the provider you want to use to your .env file. High-Level Concept Flow mermaid flowchart TB classDef smallBox font-size:12px, padding:0px; H[Your Data] -- I[Load Skill / Learn Skill] I -- J[Create Tasks] J -- K[Run Tasks] K -- L[Structured Results] L -
| Stars | 608 |
| Forks | 40 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.8510662339074/100 |
| Last Updated | 2025-03-10 |
| Created | 2025-01-16 |
| Platforms | python |
| Est. Tokens | ~96k |
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FlashLearn is Integrate LLM in any pipeline - fit/predict pattern, JSON driven flows, and built in concurency support.. It is categorized as a Agent Tool with 608 GitHub stars.
FlashLearn is primarily written in Python. It covers topics such as agentic-ai-development, ai, ai-agents.
You can find installation instructions and usage details in the FlashLearn GitHub repository at github.com/Pravko-Solutions/FlashLearn. The project has 608 stars and 40 forks, indicating an active community.
FlashLearn is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to FlashLearn on Agent Skills Hub include ralph-orchestrator, arxiv-mcp-server, code-graph-rag. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.