# Technicals

As mentioned previously, Luigi is a reasoning agent, below we'll describe some of the technical aspects that brings Luigi to life.

<figure><img src="/files/pcqU9KvM4cKoAW6hvW47" alt=""><figcaption></figcaption></figure>

## 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.

<figure><img src="/files/lQihFifbTjtwYy6Bp19G" alt=""><figcaption></figcaption></figure>

Overall Market Analysis is then performed

<figure><img src="/files/lEgGoI0DptW3WqkYBG8T" alt=""><figcaption></figcaption></figure>

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.

<figure><img src="/files/z2Fo5qa4gAxiRN9VXAU1" alt=""><figcaption></figcaption></figure>

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.

<figure><img src="/files/8PEeXxvc05oN7HzoJ10I" alt=""><figcaption></figcaption></figure>


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