Ocean Li

[Qingyun Wu (Hiring) @qingyun_wu]

🚀 AG2 v0.5.0 is here! Two super cool features:

1. ReasoningAgent: Your o1 alternative
- Tree of Thoughts with beam search
- Systematic multi-path reasoning
- Smart solution evaluation

2. Structured Output & GraphRAG
- Pydantic schemas for perfect JSON responses @OpenAI
- Graph-based knowledge retrieval @falkordb
- See demo: Trip planner with both features!

See it in action: https://t.co/KZpY6YF5Bt and https://t.co/7OI02eYC73

👯‍♀️ Join +20k agent builders on Discord: https://t.co/yiHAG0yUPf
⭐ Star AG2: https://t.co/GaEetk82tn

#AG2 #AgentOS #AI #OpenSource

[Ocean Li @quantoceanli]

Thank you @rUv , very happy to see gets applied.

@llama_index

[rUv @rUv]

I create an autonomous agent in Python using LionAGI and Llama Index.

[Ocean Li @quantoceanli]

How to compare different local models quickly with @ollama and lionagi ?

-----works with lionagi==0.0.207

[Ocean Li @quantoceanli]

How to compare different local models quickly with @ollama and lionagi ?

-----works with lionagi==0.0.207

[Ocean Li @quantoceanli]

Can a LLM solve a multi-step problem with only the most basic tools?

Try do the problem by hand first.

Full notebook:

https://t.co/a9OhRK6ndh

#lionagi #AI #AGI #LLM #OpenAI #GenAI

[Ocean Li @quantoceanli]

More Lions is All you need

speed up llama2 by 1700%

https://t.co/I5MruSs6NU

[Ocean Li @quantoceanli]

@5122Oliver @llama_index Yeah, but context length and tool calling for Ollama is still on the way.

[Jerry Liu @jerryjliu0]

Legit a 2.2 million line PR: https://t.co/wA5FarXEsx

We did this so we never have to do this ever again

[Ocean Li @quantoceanli]

@jerryjliu0 wow

[Ocean Li @quantoceanli]

@llama_index repo: https://t.co/F4CRP3KxFO

[Ocean Li @quantoceanli]

@llama_index Thanks a lot,

[LlamaIndex 🦙 @llama_index]

Define a Research Workflow for a RAG-powered agent 📖🤖

This is a cool notebook by @quantoceanli showing how you can build an agentic workflow to do scientific research from ArXiv, Wikipedia, textbooks, and more.

Have it first fetch relevant abstracts from ArXiv, propose an idea, and lookup information through sources. Afterwards trace through intermediate responses in between tool lookups.

Built with @llama_index + LionAGI - check it out!

https://t.co/gpedEXHMps

[Ocean Li @quantoceanli]

More Lions is All you need

speed up llama2 by 1700%

https://t.co/I5MruSs6NU

[Ocean Li @quantoceanli]

Can a LLM solve a multi-step problem with only the most basic tools?

Try do the problem by hand first.

Full notebook:

https://t.co/a9OhRK6ndh

#lionagi #AI #AGI #LLM #OpenAI #GenAI

[Ocean Li @quantoceanli]

@MertLovesAI @llama_index yes, that way, the whole process can iteratively updated and truly 'auto' , but code interpreter doesn't do pip install, so can't do integration test.

[Ocean Li @quantoceanli]

@cyrusofeden @llama_index you certainly can give it a go 😀

[LlamaIndex 🦙 @llama_index]

RAG Assisted Auto Developer 🔎🧑‍💻

Here’s a neat cookbook by @quantoceanli to build a devbot that can 1) understand a codebase, 2) write additional code based on the codebase.

It’s a nice mix of different tools: @llama_index to index an existing codebase, Autogen / OpenAI Code Interpreter to write/test code, and https://t.co/Y2mLO3pRVB as the orchestration layer to define the flow.

Check it out: https://t.co/7E5RIbSktl

[Jerry Liu @jerryjliu0]

Here's how you can easily build an ArXiv research assistant 🧑‍🔬🤖 with @llama_index + LionAGI by @quantoceanli 👇

1) Define a @llama_index RAG pipeline as an agent tool
2) Plug tool into LionAGI, letting you compose custom agentic pipelines

Check it out: https://t.co/GaSf1ie59N

[LlamaIndex 🦙 @llama_index]

Community Showcase 🌟: LionAGI, a simple but powerful agent framework by @quantoceanli designed for interacting with data 🛠️

✅ Easily compose your own workflow - can be sequential or a loop. Can include chain-of-thought and use of external tools.
✅ Efficient data operations for reading, chunking, binning, writing, storing and managing data.
✅ Supports concurrent calls, function calling, JSON mode with @OpenAI out of the box.

Best of all, you can easily combine with a @llama_index RAG pipeline to build automated AI assistants over your data. Check out how to build an ArXiv research assistant below! 🧑‍🔬🤖

ArXiv research assistant with LionAGI + LlamaIndex: https://t.co/fBpN5UTqJ9

LionAGI repo: https://t.co/9xE10vF0f2

LionAGI Docs: https://t.co/BAuP5wXq3l

Full credits to @quantoceanli. This is a great addition to the LLM developer community 🙌