---
license: mit
language:
  - en
task_categories:
  - text-classification
  - text-generation
tags:
  - agents
  - rag
  - llmops
  - mcp
  - multi-agent
  - github
  - tags
pretty_name: Agentic GitHub Meta
size_categories:
  - n<1K
dataset_info:
  features:
    - name: input
      dtype: string
    - name: target
      dtype: string
  splits:
    - name: train
      num_examples: 687
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet
---

# Agentic GitHub Meta

Text2text dataset of **agentic AI / RAG / LLMOps / multi-agent** GitHub-style descriptions
paired with comma-separated tags. Schema mirrors [`zamal/github-meta-data`](https://huggingface.co/datasets/zamal/github-meta-data)
(`input`, `target`) but the content focuses on projects from
[`hharsha98`](https://github.com/hharsha98) / [`hharsha`](https://huggingface.co/hharsha)
plus synthetic paraphrases and search-style queries.

| Column | Meaning |
| --- | --- |
| `input` | Repo description, search query, or paraphrase |
| `target` | Comma-separated tags (deduplicated, lowercased) |

**Train rows:** 687

## Sources

- Rows expanded from [`hharsha/agentic-systems-showcase`](https://huggingface.co/datasets/hharsha/agentic-systems-showcase)
- Public GitHub repos under `hharsha98` (descriptions + README paraphrases)
- Synthetic queries such as “looking for multi-agent orchestration with MCP”, “RAG hybrid search recall@k”
- Tags are honest topic labels (agents, multi-agent, rag, llmops, mcp, langgraph, fastapi, nextjs, observability, evals, docker, kubernetes, …) — not keyword spam

## Intended use

Training small text2text models (e.g. T5 + LoRA) for **GitHub-style tag generation** over agentic/RAG projects.

Companion model: [`hharsha/agentic-github-tagger`](https://huggingface.co/hharsha/agentic-github-tagger).

Studio: [https://agentic-systems-studio.com](https://agentic-systems-studio.com) · GitHub: [https://github.com/hharsha98](https://github.com/hharsha98)

## License

MIT