Autogen: Enable next-gen large language model applications
41–50 of 59 posts
Re: Autogen: Enable next-gen large language model applications
#42Use cases for multi agents?
Re: Autogen: Enable next-gen large language model applications
#43Are these 'safer' than using langchain-based agents that directly execute (arbitrary!) Python code? That was always my main issue with langchain
i noticed autogen creates a new docker container each time code is executed by agents (default behaviour, can be turned off), so it's safe as you think docker is safe
Re: Autogen: Enable next-gen large language model applications
#44Have been working with this and very impressed so far - it’s a step ahead of LangChain agents and seems to be receiving more attention/development than LangChain was interested in committing to agents. FWIW the “group research” and “chess” examples from the notebooks folder in their repo have been the best for explaining the utility of this tech to others - the meme generator does a good job showing functions strippe…
And the chess one: https://github.com/microsoft/autogen/blob/main/notebook/agen...
Re: Autogen: Enable next-gen large language model applications
#45This just reminds me: I have been wondering, if you get multiple instances of GPT-4 talking to each other, each seeded with a different personality prompt, do they have interesting conversations? I suspect it would devolve in to nonsense quickly, but I’ve never seen any chat log of two GPT instances talking. Does anyone have a reference for this? Thanks.
easy enough to test, copy and paste the responses after initial prompt
Re: Autogen: Enable next-gen large language model applications
#46Having conversations amongst agents is it like treating each agent as your traditional nodes? Maybe in the future there would be millions of nodes(agents) conversing and maybe this is how next gen AGI will form
Re: Autogen: Enable next-gen large language model applications
#47Are these 'safer' than using langchain-based agents that directly execute (arbitrary!) Python code? That was always my main issue with langchain
Re: Autogen: Enable next-gen large language model applications
#48A question for people researching LLMs and their capabilities: Is there any reason to believe that the interaction of multiple agents (using the same model) will yield some emergent property that is beyond the capabilities of the agent model? I'm not working with LLMs, but my intuition is that whatever these multi agent setups come up with could also be achieved by a single agent just talking to itself, as they all a…
* sometimes we want an LLM with longer context, faster speed, higher quality, etc: so even in a model family, in the same job, diff model configs
* we do a lot of prompt tuning for agent calls, like what a good Splunk query is, what SQL tables are currently available, what a good chart is, how to using a graph library, ...
* we also do accompanying code-level work, like running a generated python data analysis in a sandbox and feeding back exceptions to the LLM, or checking for parse errors when running a DB query, which feed back to the LLM
* When working directly on data, we might run it through the LLM, which might get into parallel chunked calls, a summary tree, etc, where a single LLM call would be insufficient, costly, slow, etc
Re: Autogen: Enable next-gen large language model applications
#49This just reminds me: I have been wondering, if you get multiple instances of GPT-4 talking to each other, each seeded with a different personality prompt, do they have interesting conversations? I suspect it would devolve in to nonsense quickly, but I’ve never seen any chat log of two GPT instances talking. Does anyone have a reference for this? Thanks.
Re: Autogen: Enable next-gen large language model applications
#50Earlier quoted context omitted.
easy enough to test, copy and paste the responses after initial prompt
Right but someone with API access could do this much more easily. I don’t really want to sit there copying and pasting back and forth. I’d rather write two or three starting prompts and have a few agents do all the work of talking.