FLaNK-python-processors VS autogen

Compare FLaNK-python-processors vs autogen and see what are their differences.

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FLaNK-python-processors autogen
11 32
4 25,506
- 7.7%
7.1 9.9
24 days ago 3 days ago
Python Jupyter Notebook
Apache License 2.0 Creative Commons Attribution 4.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

FLaNK-python-processors

Posts with mentions or reviews of FLaNK-python-processors. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-06.

autogen

Posts with mentions or reviews of autogen. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-02.

What are some alternatives?

When comparing FLaNK-python-processors and autogen you can also consider the following projects:

grok-1 - Grok open release

Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]

lapdev - Self-Hosted Remote Dev Environment

semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps

CML_AMP-to-Airgapped - Download the AMP catalog for an offline (airgapped) deployment of the AMP catalog.

SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.

spring-ai - An Application Framework for AI Engineering

haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

DataDreamer - DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models.   🤖💤

AgentVerse - 🤖 AgentVerse 🪐 is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation

doc-genius-ai - DocGenius AI - Generative AI Chatbot for your Documents - Powered by Cloudera Machine Learning (CML)

langchain - 🦜🔗 Build context-aware reasoning applications