> For the complete documentation index, see [llms.txt](https://usestrawberry.gitbook.io/strawberryai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://usestrawberry.gitbook.io/strawberryai/product-suite/0xluigi/technicals.md).
# Technicals
As mentioned previously, Luigi is a reasoning agent, below we'll describe some of the technical aspects that brings Luigi to life.
## Architecture
* Built on [LangChain's](https://www.langchain.com/langchain) StateGraph (learn more [here](https://medium.com/@gitmaxd/understanding-state-in-langgraph-a-comprehensive-guide-191462220997)).
* Token-Aware System utilizing 32,000 tokens for dynamic chunking and recursive summary.
* DAG workflow for modular execution and intra-agent cooperation.
* Custom APIs to feed the model pipelines with social media/market data.
* Semantic clustering allows groups' related information to preserve overall coherence across data processing stages.
* Iterative refinement employs asychronous node execution and state persistence for enhanced decision-making.
Below, we can see the flow of information `in` to a resulting publication `out`. Summarized below as,
* Raw data ingestion
* Chunking
* Market data processing
* Semantic/sentiment analysis and clustering
* Ranking
* Iterative looping and refinement
* Synthesis and publication production
First, market summary node fetches data, implements dynamic chunking, token-aware, maintaining context windows of 32,000 tokens.
Overall Market Analysis is then performed
From here, ranking system takes over via ticker and narrative rankings.
* Ticker: frequency analysis, sentiment scoring, volume metrics, historical correlates are all applied.
* Narrative: topic modeling and trend detection, as well as semantic clustering.
Next, the iterative refinement looping (three loops maximum to prevent overfitting), where Luigi refines its analysis in multiple stages: identification, verification, and storage via DAG.
Within Luigi's DAG,
* Nodes represent tastks like data digestion, rankings, and synthesis
* Edges define dependencies *between* tasks
* Token management ensures efficient processing of the dataset, which is important for semantic and narrative preservation.
