Exploring AI Landscape and Agent Frameworks

Last week LangChain announced a $20M Series A led by Sequoia and released the paid version of LangSmith, which has already been used by 1K+ teams and driven 80K signups. On this week’s episode of Unsupervised Learning, we sat down with LangChain Co-Founder and CEO Harrison Chase to talk about the current state of LLM evaluation, observability, and the agent landscape.

(0:00) intro 

(1:07) applications of AI in the sports world 

(3:26) what does LangChain do? 

(7:51) building with LangSmith 

(10:00) best AI eval practices 

(16:51) to what extent is eval generalizable? 

(21:11) the current agent landscape 

(29:35) balancing present and future at LangChain 

(36:27) using LangServe to deploy LangChain applications 

(41:37) more complex chatbots are coming 

(45:51) current AI practices that will become obsolete 

(48:55) over-hyped/under-hyped 

(49:25) bigger surprise in building LangChain 

(51:50) how ubiquitous will open-source models be in the future? 

(52:43) most exciting AI startups 

(56:07) being an AI “celebrity” 

(58:09) Jacob and Jordan debrief 

With your co-hosts:  

@jacobeffron  

- Partner at Redpoint, Former PM Flatiron Health  

@patrickachase  

- Partner at Redpoint, Former ML Engineer LinkedIn  

@ericabrescia  

- Former COO Github, Founder Bitnami (acq’d by VMWare)  

@jordan_segall  

- Partner at Redpoint