A TypeScript-based implementation of Deep Research for comprehensive topic exploration. CLI and full-stack example app coming soon!
Together Deep Research is a TypeScript-based implementation that delivers in-depth research on complex topics requiring multi-hop reasoning.
It enhances traditional web search by producing comprehensive, well-cited content that mimics the human research process - planning, searching, evaluating information, and iterating until completion.
Based on the python implementation open_deep_research from Together AI.
- Comprehensive Research Reports - Generates long-form, well-cited content on complex topics
- Multi-Stage Process - Uses multiple self-reflection stages for quality information gathering
- Extensible Architecture - Built with TypeScript for type safety and better developer experience
- Model Flexibility - Supports multiple LLM models for different research stages
- Configurable Parameters - Easy customization of research depth and output format
Run pnpm install to install the dependencies, add a .env with your Together AI and Exa API keys, then pnpm run dev to run the demo.ts file that is seen below.
import { DeepResearchPipeline } from "./deepresearch/research-pipeline";
(async () => {
const pipeline = new DeepResearchPipeline();
const topic =
"Tell me about the best nba players who were bald at one point in their career";
const answer = await pipeline.runResearch(topic);
console.log(`\x1b[35m📡 Research Answer:\n\n${answer}\x1b[0m`);
})();As an LLM-based system, this tool may occasionally:
- Generate hallucinations or fabricate information that appears plausible
- Contain biases present in its training data
- Misinterpret complex queries or provide incomplete analyses
- Present outdated information
Always verify important information from generated reports with primary sources.
- Python version Together Open Deep Research
- Together.ai for LLM models inference
- Exa for the web search API
MIT