Harrison Chase
Co-founder & CEO, LangChain. On 3 shows, 4 appearances. The Shows tab opens the full record on each.
founderexecutiveengineer@hwchase17 ↗LinkedIn ↗langchain.com ↗
Harrison Chase created LangChain in October 2022 and leads the company in developing developer frameworks and tools for building LLM applications. Prior to founding LangChain, he served as Head of Machine Learning at Robust Intelligence and led machine learning engineering teams at Kensho Technologies.
3shows
4appearances
51statements
5resolved
5supported
0contradicted
100%fully supported
21said about them ↓ Everything Harrison Chase said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Chase: Open-source models still lag behind Claude 3 and GPT-4
“Like there's, I think we see increasingly interest in open source, but the reasoning abilities are still just like lagging behind Cloud three or GPT four. And I think like for a lot of the applications that it kind of, it probably depends on the types of appli…”
Chase: Agent harnesses matter more for performance than underlying models
“I, the, so I don't know what happens, but I do know the harness is really, really important. Like, I think this is the thing that matters.”
Chase: Basically all AI agents will write code
“You know, if agents never write any code, then okay, maybe they're not useful, but I think it's trending where Basically all agents will write code, so that's a very interesting piece, I think.”
Chase: Nobody in the AI industry knows how to properly solve memory.
“I don't think anyone knows how to deal with memory, and so I think all these different approaches are...”
Chase: Long context windows will not replace chaining and AI agents
“There are also things where it requires iterations. You need to like decide what to do, interact with the environment, get that back. So this whole idea of chaining and agents, I don't like,
That's less around context windows and more around interacting with t…”
Chase: Needle-in-a-haystack benchmarks fail to represent real RAG reasoning
“That, that actually really doesn't reflect a lot of RAG use cases in, in my opinion, because like that's the needle in the haystack is like, okay, given this long context, can I find a single information point? But oftentimes RAG is about seeing multiple infor…”
Anthropic's Claude Code uses custom harness tools over model-level RL tools
“It doesn't actually use the tools that are RL into the model. So like anthropic models have some like file editing tools. They have a completely different set of tools in, in the actual harness.”
LangChain adds tools for AI agents to autonomously trigger context compaction
“One interesting thing there, actually, that we haven't yet released as of this recording, but will probably be released by the time it comes out, is we actually give the agent a tool to trigger its own compaction.”
Harrison Chase: AI agents will evolve into synchronous interfaces orchestrating asynchronous sub-agents
“Like, I do think we'll get to a place where we have this kind of like synchronous conversational agent kicking off kind of like longer running asynchronous agents in the background.”
Chase: AI builder differentiation lies in domain knowledge, not harness infrastructure
“I think a lot of the differentiation is in like the instructions and the tools and the skills and that basically, yeah, knowledge of how to do a process that you encode into natural language and give the agent and then the tools and the skills that you let it …”
Harrison Chase says coding AI agents are not yet a proven success
“There's a bunch of people doing coding stuff.
We've already talked about that.
I think that's a little bit, I wouldn't say that's a success yet, but there's a lot of excitement and stuff there.”
Chase: AI agent memory is extremely nascent and lacks interesting developments
“I feel it's like a field that's just, like, super, super nascent. Like, I don't, I actually am underwhelmed at the amount of, like, really interesting stuff that's going on there.”
Chase: Successful production AI agents operate as controlled state machines
“And I think the things that we see making it into production and informed a lot of the development of laying graph is, or is something in the middle where it's like this controlled state machine type thing.”
Chase: Developers only implement model fine-tuning after reaching critical scale
“We see people experimenting with it. I think the only real place where they're doing it is when they've reached like really critical scale which I still don't think is that many applications to date.”
Chase: High reliability AI agents require structured, graph-like scaffolding workflows
“So people ended up building scaffolding around the models to make them do things in a more predictable and reliable way. And that's why we at link chain, we built lane graph, which was another framework really aimed at that kind of like. Graph like workflows a…”
Chase: Major AI labs' RL focus on code makes coding agents perform best
“All the big model labs have been RL and code and bash and editing files into those models. And so that is the stuff that works the best.”
Harrison Chase: Most current agent harnesses feature built-in sub-agents and skills
“Most harnesses today have sub agents built in. They have skills built in.”
Chase: Human standard operating procedures should guide AI agent system prompts
“If you have a standard operating procedure for how a human should do things like that, that should influence a lot of what the system prompt is.”
Harrison Chase: Modern AI agent harnesses don't strictly enforce execution plans
“And so now what most things do is they just have that plan in the text file, and the main agent can, like, use that to help guide its actions, but there's nothing that says I'm explicitly doing this step, or I'm explicitly doing another step.”
Harrison Chase: Inter-agent communication is the hardest part of working with AI agents
“Communication is the hardest part of life, by the way, it's the hardest part of startups, hardest part of relationships, hardest part of working with agents is getting them to communicate.”
Chase: Virtual file systems let LLMs manage their own context windows
“And the way that I think about a file system is it basically lets the LLM manage its own context window. So it can decide what to read from files.”
Chase: AI agent context compaction must retain recent messages for flow
“You want to keep around like the last N messages, let's say the last like 10 or so messages, because if you compact everything, it actually like throws it off completely.”
Chase: AI agents will clearly be long-running and stateful
“So I think, like, pretty clearly agents will be long-running and stateful, and so I think we have a deployments product. I think a lot of the I think deployments products that let you build long-running stateful things will be kind of, like, interesting no mat…”
The other half of the tape: Harrison Chase's own voice is left out of every number here. Other people bring the name up 21 times in 13 episodes across the shows. every mention, with the transcript →
Who brings them up most Shawn Wang 7Alessio Fanelli 4Aymeric (Emmerich) 3Youssef Rizk 1Lance Martin 1Elad Gil 1Andrew White 1
Every mention by year
tap a year for its mentions
Latent Space 20
No Priors 1
2026 1 mention in 1 episode
2025 9 mentions in 6 episodes 2 per episode
- smol agents are all you need
- 2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents
- Context Engineering for Agents - Lance Martin, LangChain
- The AI Coding Factory
- DeepSeek V3, SGLang, and the state of Open Model Inference in 2025 (Quantization, MoEs, Pricing)
- Beating Google at Search with Neural PageRank and $5M of H200s — with Will Bryk of Exa.ai
- every mention in 2025, scene by scene →
2024 1 mention in 1 episode
2023 10 mentions in 5 episodes 2 per episode
- RAG is a hack - with Jerry Liu of LlamaIndex
- Generating your AI Media Empire - with Youssef Rizk of Wondercraft.ai
- Beating GPT-4 with Open Source Models - with Michael Royzen of Phind
- Powering your Copilot for Data - with Artem Keydunov from Cube.dev
- No Priors Ep. 26 | With Weights & Biases CEO Lukas Biewald
- every mention in 2023, scene by scene →
One line per show, most statements first. The link opens Harrison's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
 MADLEDGER | Co-founder & CEO, LangChain | 1 | 23 | 100% 2/2 | full record on the MAD Podcast → |
| LATENT SPACELEDGER | Co-founder & CEO, LangChain | 2 | 14 | 100% 3/3 | full record on Latent Space → |
| NO PRIORSLEDGER | Co-founder & CEO, LangChain | 1 | 14 | — | full record on No Priors → |