Merge pull request #56 from pchalasani/langroid

Add Langroid Multi-Agent Framework

README.md

@@ -1003,6 +1003,37 @@ the design of agents, chains, tools, retrieval functions, and collaborative work
 
 </details>
 
+## [Langroid](https://github.com/langroid/langroid)
+
+Multi-Agent framework for building LLM Applications.
+
+<details>
+
+### Description
+
+
+`Langroid` is an intuitive, lightweight, extensible and principled
+Python framework to easily build LLM-powered applications.
+You set up Agents, equip them with optional components (LLM,
+vector-store and methods), assign them tasks, and have them
+collaboratively solve a problem by exchanging messages.
+This Multi-Agent paradigm is inspired by the
+[Actor Framework](https://en.wikipedia.org/wiki/Actor_model)
+(but you do not need to know anything about this!).
+
+`Langroid` is a fresh take on LLM app-development, where considerable thought has gone
+into simplifying the developer experience; it does not use `Langchain`.
+
+- Works with most commercial/remote and open/local LLMs.
+- Set up Multi-agent, multi-LLM system: use stronger LLMs for agents requiring strong reasoning and instruction-following, and delegate simpler tasks to weaker/local LLMs. 
+- Supports OpenAI function-calling as well as native equivalent called `ToolMessage`, which works with LLMs that 
+  do not have built-in function-calling. Simply specify structure as a (nested) Pydantic object.
+- Batteries-included: vector-databases for RAG (Retrieval-Augmented Generation), caching, logging/observability.
+- Specialized agents available: `DocChatAgent`, `SQLChatAgent`, `TableChatAgent` (for tabular data, e.g. csv/dataframes).
+- `DocChatAgent` handles text, PDF, Docx files/URLS, and has state-of-the art techniques 
+   for retrieval combining lexical and semantic search.
+- Documentation: https://langroid.github.io/langroid/
+</details>
 
 ## [Lemon Agent](https://github.com/felixbrock/lemon-agent)