https://github.com/reory/smart_file_agent
Smart File Agent is a tiny, self‑contained demonstration of an agentic AI pipeline — built entirely in Python
https://github.com/reory/smart_file_agent
agentic-workflow agents ai-pipeline cli-tool demo-project educational-project file-ingestion logging-library markdown-output open-source pure-python python rule-based-ai text-processing
Last synced: 3 days ago
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Smart File Agent is a tiny, self‑contained demonstration of an agentic AI pipeline — built entirely in Python
- Host: GitHub
- URL: https://github.com/reory/smart_file_agent
- Owner: reory
- License: mit
- Created: 2026-04-03T11:40:27.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-07-04T20:11:05.000Z (14 days ago)
- Last Synced: 2026-07-04T22:06:57.628Z (14 days ago)
- Topics: agentic-workflow, agents, ai-pipeline, cli-tool, demo-project, educational-project, file-ingestion, logging-library, markdown-output, open-source, pure-python, python, rule-based-ai, text-processing
- Language: Python
- Homepage: https://www.linkedin.com/in/roy-p-74980b382/
- Size: 562 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
Awesome Lists containing this project
README
# 🗄️ Smart File Agent
- A tiny, educational demonstration of an agentic AI pipeline — no LLMs, no APIs, just pure Python.
---
# 🧠 Overview
Smart File Agent is a tiny, self‑contained demonstration of an agentic
AI pipeline — built entirely in Python
It mirrors the architecture shown in the “How Agentic AI Works” diagram:
- Input Sources → file ingestion
- AI Processing → request analysis + planning
- Action Layer → tool execution (summarise, bulletify)
- Output → clean Markdown summary
This project is intentionally small and easy to understand.
---
# 📸 Screenshots
- Smart File Agent is a miniature implementation of the agentic‑AI pipeline shown below — it ingests a file, analyses the user’s request, plans the steps, executes tools, and produces a structured output exactly like the Input → Processing → Action → Output flow in the diagram.
---
---
# 🏗️ Project Structure
```
smart_file_agent/
│
├── smart_file_agent/
│ ├── __init__.py
│ ├── main.py
│ ├── agent/
│ │ ├── __init__.py
│ │ ├── agent_core.py
| | ├── logging_config.py
│ │ ├── ingestion.py
│ │ ├── processing.py
│ │ ├── tools.py
│ │ └── output.py
├──tests/
│ ├── test_agent_core.py
│ ├── test_ingestion.py
│ ├── test_logging_config.py
│ ├── test_main.py
│ ├── test_processing.py
│ ├── test_tools_hypothesis.py
│ ├── test_tools.py
│ ├── test_output.py
├── venv/
├── README.md
└── project_notes.txt
└── requirements.txt
```
---
# 🧩 How It Works (Mapped to Agentic AI Architecture)
## Input Sources → ingestion.py
- Loads the file text
- Extracts metadata (name, size, extension)
## AI Processing → processing.py
- Analyses the user’s request
- Chooses a mode (summary or bullet summary)
- Plans the steps the agent should take
## Action Layer → tools.py
- Implements two tiny rule‑based tools:
- summarise_text() → extracts first sentence, longest sentence, keywords
- bulletify() → converts sentences into bullet points
## Agent Orchestration → agent_core.py
- Runs the pipeline and executes each planned step in order
- Produces a structured result dictionary
## Output Layer → output.py
- Formats the final Markdown summary
---
# ▶️ Running the Agent
From the project root:
```Bash
python -m smart_file_agent.main
```
You’ll be prompted for:
- File path
- What you want the agent to do
## Example:
Enter file path: (**File path of any text document you have**)
What would you like to do? **bulletify**
## 💬 Supported Prompts
You can speak to the agent naturally.
Here are some example prompts:
## Summaries
- summarise this
- give me a summary
- summarise the file
- what is this file about
## Bullet summaries
- bulletify
- give me bullet points
- turn this into bullets
- bullet summary please
The agent detects the intent and chooses the correct mode.
## 🧪 Example Output
- AGENT OUTPUT
## Summary of project_notes.txt
**Mode:** bullet_summary
**Steps:** extract_text, summarise, bulletify
- Summary:
- Opening idea: ...
- Key detail: ...
- Keywords: ...
---
# 🎯 Why This Project Exists
This project demonstrates:
- how agentic systems work internally
- how to build a reasoning → planning → action pipeline
- how to structure a Python agent project
- how to simulate LLM behaviour without external dependencies
- It’s intentionally tiny, readable, and educational.
---
# 🛣️ Roadmap Features
- [ ] Add a reasoning trace (“Thought → Action → Result”)
- [ ] Add a memory system
- [ ] A reasoning trace (LLM‑style)
- [ ] Add a tool registry
- [ ] Add a FastAPI interface
---
# 📝 Notes
- It was enjoyable to build this. If you have any ideas on how to collaborate and
improve this project please let me know.
---
* **Built by Roy Peters** 😁[](https://linkedin.com/in/roy-p-74980b382/)