GraphCanon updated 2d · GitHub synced 2d · 28 views this month
Decision brief
Decision-critical facts for 'rags':
Good fit when
- When leveraging natural language queries over proprietary user data using OpenAI services.
- If you require a framework that integrates seamlessly with Streamlit to build conversational agents.
Avoid when
- Not suitable if you seek solutions not dependent on OpenAI's services as the underlying framework is tightly coupled with OpenAI APIs.
- Avoid using rags if your project involves sensitive or highly confidential data since it requires integrating API keys, potentially posing security concerns.
- Requirements:
- Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (865d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- 38 low (38 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for LlamaIndex. Display-only - separate from trust and ranking.
- Company
- LlamaIndex·GitHub org profile·1mo
- Funding
- $19,000,000 (2024-02)·GraphCanon curated seed (public press)·1mo
- Commercial model
- Open core·GraphCanon curated seed·1mo
Install
pip install ragsHow it fits your stack(17)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A framework for building conversational agents that leverage user data using natural language queries and OpenAI services.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 18, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 18, 2026)
e `rags` project folder. We recommend creating a virtual env for dependencies (`python3 -m venv .venv`).Source link
Tags
README
Installation and Setup
Clone this project, go into the rags project folder. We recommend creating a virtual env for dependencies (python3 -m venv .venv).
poetry install --with dev
By default, we use OpenAI for both the builder agent as well as the generated RAG agent.
Add .streamlit/secrets.toml in the home folder.
Then put the following:
openai_key = "<openai_key>"
Then run the app from the "home page" file.
streamlit run 1_🏠_Home.py
NOTE: If you've upgraded the version of RAGs, and you're running into issues on launch, you may need to delete the cache folder in your home directory (we may have introduced breaking changes in the stored data structure between versions).
For agents
This page has a .md twin and JSON over the API.