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How to use RLMs in Deep Agents

2026-07-01 Science & Technology
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LangChain
LangChain
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Description

Sydney Runkle, an open source engineer at LangChain walks through Recursive Language Models (RLMs), a pattern where a model can call itself, and shows how deep agents uses them to tackle large-scale data tasks that break standard agents. She demos the full RLM workflow using dcode, LangChain's terminal coding agent, including a live haiku tournament run across 16 parallel subagents, then benchmarks RLM-enabled deep agents against the Oolong dataset to show how performance holds up at 128k token context lengths where plain agents give up. 00:00 Intro: RLMs + deep agents 00:07 What is a recursive language model 00:44 Why use RLMs: reliability, finite context, deterministic coverage 01:32 How RLMs work in deep agents (code interpreter + task function) 02:30 Getting started + dcode haiku-tournament demo 03:38 Benchmark: the Oolong long-context dataset 05:14 Results: plain vs. RLM-enabled deep agent 06:29 Wrap-up

Top Comments (2)

@hamidraza1584 2026-07-05

Means , rlf is s a coding agents into agentic graph in Lanchchin langraph echo system? Correct?

0
@naveenkusakula8511 2026-07-03

How oolang data is provided to benchmark in normal agent without rlm ?

0

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