CoexistAI is a modular, developer-friendly research assistant framework built on FastAPI and LangChain. It enables you to build, search, summarize, and automate research workflows using LLMs, web search, Reddit, YouTube, and mapping tools—all with simple API calls or Python functions.
- Web Search API: Query the web, summarize results, and extract context using LLMs.
- Reddit Summarizer: Fetch and summarize top posts and comments from any subreddit.
- YouTube Transcript Summarizer: Summarize YouTube videos by extracting transcripts.
- Map & Route Generation: Generate maps and routes with points of interest.
- Pluggable LLMs: Use Google Gemini, OpenAI, Ollama, and more.
- Async & Parallel: Fast, scalable, and robust asynchronous execution.
- Notebook & API Ready: Use as a Python library or via a FAST API.
# Clone the repo
git clone https://github.com/SPThole/CoexistAI.git
cd CoexistAI
# Install dependencies
pip install -r requirements.txtCoexistAI can use a local or remote SearxNG instance for privacy-friendly, customizable web search aggregation.
git clone https://github.com/searxng/searxng.git
cd searxng
docker compose up -dOR
docker pull docker.io/searxng/searxng:latest- By default, SearxNG will be available at
http://localhost:8080. - You can edit
settings.ymlfor advanced configuration (seesearxng/settings.ymlin this repo for a template). - if not just copy the folder searxng from this repo, at the same relative location in your machine
-- Run the following command:
docker run --rm \
-d -p 30:8080 \
-v "${PWD}/searxng:/etc/searxng" \
-e "BASE_URL=http://localhost:$PORT/" \
-e "INSTANCE_NAME=my-instance" \
searxng/searxng- The web search utilities will automatically use your configured SearxNG instance for search aggregation.
- For best results, ensure SearxNG is running before starting the FastAPI server or using the Python API.
uvicorn app:app --reloadIf using local infinity embedding server (https://github.com/michaelfeil/infinity):
infinity_emb v2 --model-id hf_model_nameObtain your API key (Currently Gemini, OpenAI and ollama is supported) from your preferred LLM provider. Once you have the key, update the app.py file or your environment variables as follows:
import os
os.environ['GOOGLE_API_KEY'] = "YOUR_API_KEY"Alternatively, you can set the API key in your shell before starting the server:
export YOUR_LLM_API_KEY=your-api-key-hereNote: For optimal quality and speed, use Google models with
embedding-001embeddings and Gemini Flash models. They provide free API keys.
Update the place (default: India) in utils/config.py for personalized results
For local files search or summary
POST /websearch
Content-Type: application/json
{
"query": "Summarise these files",
"date": "2025-05-28",
"day": "Wednesday",
"rerank": false,
"num_results": 3,
"local_mode": true,
"split": true,
"document_paths": [["file1.pdf", "file2.pdf"]] # list of lists
}OR
POST /websearch
Content-Type: application/json
{
"query": "Top AI research conferences in december",
"date": "2025-05-28",
"day": "Wednesday",
"rerank": false,
"num_results": 3,
"local_mode": false,
"split": true,
"document_paths": []
}POST /reddit-search
Content-Type: application/json
{
"subreddit": "OpenAI",
"url_type": "hot",
"n": 5,
"k": 3,
"search_query": null,
"sort_type": "relevance"
}POST /youtube-search
Content-Type: application/json
{
"url": "https://www.youtube.com/watch?v=...",
"prompt": "Summarize the main points."
}from utils.websearch_utils import query_web_response
from utils.reddit_utils import reddit_reader_response
# Web search
result = await query_web_response(
query="latest AI research",
date="2025-05-28",
day="Wednesday",
websearcher=None,
hf_embeddings=None,
rerank=False,
cross_encoder=None,
model=None, #replace with llm
text_model=None,
num_results=3,
document_paths=[],
local_mode=False, # True if you have local files in document_paths
split=True
)
# Reddit summarization
summary = reddit_reader_response(
subreddit="OpenAI",
url_type="hot",
n=5,
k=3,
custom_url=None,# Replace with llm
time_filter="all",
search_query=None,
sort_type="relevance",
model=None
)from utils.map import generate_map
# Generate a map with route and POIs
html_path = generate_map("MG Road, Bangalore", "Indiranagar, Bangalore", 500, "hotel", 3)- The Coexist tutorial can be found in
coexist_tutorial.ipynb.
- Plug in your own LLMs: Swap out Google Gemini for OpenAI, Ollama, or any LangChain-supported model.
- Custom Tools: Add your own tools to the agent system for new capabilities (see
utils/for examples). - Async/Parallel: All web and document search utilities are asynchronous for high performance.
Pull requests, issues, and feature suggestions are welcome! Please open an issue or PR on GitHub.
Non-Commercial Use Only: CoexistAI is intended strictly for research, prototyping, and educational purposes. Commercial or production use of this project or its outputs is not permitted.
Web and Reddit Data: CoexistAI uses public web scraping and Reddit JSON endpoints. It does not use the official Reddit API. Always respect robots.txt, site terms, and copyright law when using this tool.
YouTube & Other Sources: Use responsibly and in accordance with the terms of service of each data provider.
Compliance: You are responsible for ensuring your use of this tool complies with all relevant terms, conditions, and laws.
This project is licensed under a custom Non-Commercial Research and Educational Use License. Use of this software is permitted only for non-commercial research, prototyping, and educational purposes. Commercial or production use is strictly prohibited. See the LICENSE file for full terms and conditions.
If you find this project useful, please star the repo and share it with your network!
For questions, reach out via GitHub Issues or open a discussion.