# Side Products

## Flowise

### Introduction

* Flowise is an open source low-code tool for developers to build customized LLM orchestration flows & AI agents.
* Flowise's Agentflows section provides a platform for building agent-based systems that can interact with external tools and data sources.

  Currently, Flowise offers two approaches for designing these systems: **Multi-Agents** and **Sequential Agents**.

### Multi-Agents

<figure><img src="https://1374779285-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MFW3x2aqEO8GF2kr3VU%2Fuploads%2FVCipJA7msFWYE035tsDL%2Fimage.png?alt=media&#x26;token=71fc3f49-e7a0-4232-ac2f-5545bf50042b" alt=""><figcaption></figcaption></figure>

* The Supervisor, as the agent governing the overall workflow and responsible for delegating tasks to the appropriate Worker,
* The Worker, as a specialized agent instructed to handle a specific task within the system
* It is designed for breaking down a complex task into a **sequence of sub-tasks**, completed one after another. Supervisor delegates to specialized Workers.

### Sequential Agents

<figure><img src="https://1374779285-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MFW3x2aqEO8GF2kr3VU%2Fuploads%2F9yOoEBaf3tC5zrfQfVnE%2Fimage.png?alt=media&#x26;token=c7b8180f-ca41-4bfb-a2c2-bafd55d8de85" alt=""><figcaption></figcaption></figure>

* Built on top of [LangGraph](https://www.langchain.com/langgraph), Flowise's Sequential Agents architecture facilitates the **development of conversational agentic systems by structuring the workflow as a directed cyclic graph (DCG)**, allowing controlled loops and iterative processes.
* Each node in the graph represents a discrete processing unit, encapsulating its own functionality like language processing, tool execution, or conditional logic.
* Edges in the graph represent the flow of data between nodes, where the output of one node becomes the input for the subsequent node, enabling a chain of processing steps.
* **It is Linear, cyclic and/or** **branching**; nodes connect in a sequence, with conditional logic for branching

## Lite LLM Proxy

* LiteLLM Proxy gives you a unified interface to access multiple LLMs
* Track LLM Usage automatically
* Load-balance across multiple deployments (e.g. Azure/OpenAI)
* Prioritizing important requests to ensure they don't fail (i.e. Queueing)
* Basic reliability logic - cooldowns, fallbacks, timeouts and retries (fixed + exponential backoff) across multiple deployments/providers.

## References

{% embed url="<https://docs.flowiseai.com/>" %}

{% embed url="<https://docs.litellm.ai/docs/>" %}
