# melih-unsal/DemoGPT

🤖 Create LLM agents in a second with your prompts. Everything you need to create an LLM Agent - tools, prompts, frameworks, and models - all in one place.

Repository: https://github.com/melih-unsal/DemoGPT
Canonical: https://ross.abutalabs.com/products/demogpt
Language: Python
License: MIT
License Family: permissive
Topics: chatgpt, demo, langchain, llms, streamlit, streamlit-application, chatgpt-api, langchain-python, langchain-app, agent, autogpt, openai, ai, python, artificial-intelligence, autonomous-agents, gpt-4, agents, deepseek, o1
Last push: 2026-04-01T12:30:12+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 75, release rhythm 8, longevity 84
- inputs: {"age_days": 1183, "days_push": 154, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1905, forks 223 (observed 2026-08-28T04:05:51.914690+00:00)

## What it is
DemoGPT is a Python framework and autonomous agent that generates LangChain-based LLM agent applications from natural-language prompts, bundling tools, prompts, frameworks, and model knowledge into one toolkit. It supports RAG, knowledge graphs, and vector databases to streamline building and demoing Gen-AI apps.

## Use cases
- create llm agents from prompts
- generate langchain pipelines automatically
- build gen-ai demo apps without boilerplate
- add rag and vector database knowledge to agents
- prototype autonomous agent workflows quickly
- compare and pick llm models for agents

## When to choose
- you want to spin up LLM agents or demos from a single prompt
- you are building on LangChain and want auto-generated pipelines
- you need integrated tools, RAG, and vector DB support in one place

## When to avoid
- you need production-grade, fully controlled agent code rather than generated demos
- your stack is not Python/LangChain based
- you require fine-grained custom agent architectures beyond prompt-driven generation

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, llm-inference, rag, prompt-engineering, chatbot
- domain: artificial-intelligence, large-language-models, developer-tools
- platform: python, cross-platform, cli
- tags: langchain, streamlit, openai, gpt-4, autonomous-agents, gen-ai-app-generation, vector-databases, knowledge-graph, deepseek, no-code-agents, ai-agents, retrieval-augmented-generation, web-server

## Member repositories
- melih-unsal/DemoGPT (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.914690+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:11:43.500527+00:00, confidence not recorded.
  - readme: https://github.com/melih-unsal/DemoGPT (fetched 2026-08-28T04:05:51.914690+00:00, sha e3018e9d810f)
  - registry_pypi: https://pypi.org/pypi/demogpt/json (fetched 2026-08-29T10:50:43.218947+00:00, sha 938e9e79ba65)
- Data as of 2026-08-30T08:39:29.467469+00:00.