Ralph Mode for Deep Agents: Running an Agent Forever

LangChain ยท Intermediate ยท๐Ÿค– AI Agents & Automation ยท4mo ago
๐Ÿซก Ralph Mode for Deep Agents ๐Ÿซก What if you could give an AI agent a task and just let it run forever? We built Ralph Mode to test this, built on Deep Agents. Ralph Mode loops an agent with fresh context each iteration, using the filesystem as memory. Just start it, walk away, and Ctrl+C when you're satisfied (or set some limits beforehand). In this video, we go over how to run Ralph Mode with Deep Agents to build an entire Python course autonomously. --- ๐Ÿ”— Repo: https://github.com/langchain-ai/deepagents/tree/master/examples/ralph_mode
Watch on YouTube โ†— (saves to browser)
Sign in to unlock AI tutor explanation ยท โšก30

Playlist

Uploads from LangChain ยท LangChain ยท 0 of 60

โ† Previous Next โ†’
1 Chat With Your Documents Using LangChain + JavaScript
Chat With Your Documents Using LangChain + JavaScript
LangChain
 2 LangChain SQL Webinar
LangChain SQL Webinar
LangChain
 3 LangChain "OpenAI functions" Webinar
LangChain "OpenAI functions" Webinar
LangChain
 4 LangSmith Launch
LangSmith Launch
LangChain
 5 LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain x Pinecone: Supercharging Llama-2 with RAG
LangChain
 6 LangChain Expression Language
LangChain Expression Language
LangChain
 7 Building LLM applications with LangChain with Lance
Building LLM applications with LangChain with Lance
LangChain
 8 Benchmarking Question/Answering Over CSV Data
Benchmarking Question/Answering Over CSV Data
LangChain
 9 LangChain "RAG Evaluation" Webinar
LangChain "RAG Evaluation" Webinar
LangChain
 10 Fine-tuning in Your Voice Webinar
Fine-tuning in Your Voice Webinar
LangChain
 11 Tabular Data Retrieval
Tabular Data Retrieval
LangChain
 12 Building an LLM Application with Audio by AssemblyAI
Building an LLM Application with Audio by AssemblyAI
LangChain
 13 Superagent Deepdive Webinar
Superagent Deepdive Webinar
LangChain
 14 Lessons from Deploying LLMs with LangSmith
Lessons from Deploying LLMs with LangSmith
LangChain
 15 Shortwave Assistant Deepdive Webinar
Shortwave Assistant Deepdive Webinar
LangChain
 16 Cognitive Architectures for Language Agents
Cognitive Architectures for Language Agents
LangChain
 17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
LangChain
 18 Data Privacy for LLMs
Data Privacy for LLMs
LangChain
 19 "Theory of Mind" Webinar with Plastic Labs
"Theory of Mind" Webinar with Plastic Labs
LangChain
 20 LangChain Templates
LangChain Templates
LangChain
 21 Using Natural Language to Query Postgres with Jacob
Using Natural Language to Query Postgres with Jacob
LangChain
 22 Building a Research Assistant from Scratch
Building a Research Assistant from Scratch
LangChain
 23 Benchmarking RAG over LangChain Docs
Benchmarking RAG over LangChain Docs
LangChain
 24 Skeleton-of-Thought: Building a New Template from Scratch
Skeleton-of-Thought: Building a New Template from Scratch
LangChain
 25 Benchmarking Methods for Semi-Structured RAG
Benchmarking Methods for Semi-Structured RAG
LangChain
 26 LangSmith Highlights: Getting Started
LangSmith Highlights: Getting Started
LangChain
 27 LangSmith Highlights: Debugging
LangSmith Highlights: Debugging
LangChain
 28 LangSmith Highlights: Datasets
LangSmith Highlights: Datasets
LangChain
 29 LangSmith Highlights: Evaluation
LangSmith Highlights: Evaluation
LangChain
 30 LangSmith Highlights: Human Annotation
LangSmith Highlights: Human Annotation
LangChain
 31 LangSmith Highlights: Monitoring
LangSmith Highlights: Monitoring
LangChain
 32 LangSmith Highlights: Hub
LangSmith Highlights: Hub
LangChain
 33 SQL Research Assistant
SQL Research Assistant
LangChain
 34 Getting Started with Multi-Modal LLMs
Getting Started with Multi-Modal LLMs
LangChain
 35 Build a Full Stack RAG App With TypeScript
Build a Full Stack RAG App With TypeScript
LangChain
 36 Auto-Prompt Builder (with Hosted LangServe)
Auto-Prompt Builder (with Hosted LangServe)
LangChain
 37 LangChain v0.1.0 Launch: Introduction
LangChain v0.1.0 Launch: Introduction
LangChain
 38 LangChain v0.1.0 Launch: Observability
LangChain v0.1.0 Launch: Observability
LangChain
 39 LangChain v0.1.0 Launch: Integrations
LangChain v0.1.0 Launch: Integrations
LangChain
 40 LangChain v0.1.0 Launch: Composability
LangChain v0.1.0 Launch: Composability
LangChain
 41 LangChain v0.1.0 Launch: Streaming
LangChain v0.1.0 Launch: Streaming
LangChain
 42 LangChain v0.1.0 Launch: Output Parsing
LangChain v0.1.0 Launch: Output Parsing
LangChain
 43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
LangChain
 44 LangChain v0.1.0 Launch: Agents
LangChain v0.1.0 Launch: Agents
LangChain
 45 Build and Deploy a RAG app with Pinecone Serverless
Build and Deploy a RAG app with Pinecone Serverless
LangChain
 46 Hosted LangServe + LangChain Templates
Hosted LangServe + LangChain Templates
LangChain
 47 LangGraph: Intro
LangGraph: Intro
LangChain
 48 LangGraph: Agent Executor
LangGraph: Agent Executor
LangChain
 49 LangGraph: Chat Agent Executor
LangGraph: Chat Agent Executor
LangChain
 50 LangGraph: Human-in-the-Loop
LangGraph: Human-in-the-Loop
LangChain
 51 LangGraph: Dynamically Returning a Tool Output Directly
LangGraph: Dynamically Returning a Tool Output Directly
LangChain
 52 LangGraph: Respond in a Specific Format
LangGraph: Respond in a Specific Format
LangChain
 53 LangGraph: Managing Agent Steps
LangGraph: Managing Agent Steps
LangChain
 54 LangGraph: Force-Calling a Tool
LangGraph: Force-Calling a Tool
LangChain
 55 LangGraph: Multi-Agent Workflows
LangGraph: Multi-Agent Workflows
LangChain
 56 Streaming Events: Introducing a new `stream_events` method
Streaming Events: Introducing a new `stream_events` method
LangChain
 57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
LangChain
 58 OpenGPTs
OpenGPTs
LangChain
 59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
LangChain
 60 LangGraph: Persistence
LangGraph: Persistence
LangChain

Related AI Lessons

โšก
Recepcionista virtual vs contestadora automรกtica en Argentina: flujo de intake para llamadas, WhatsApp y derivaciรณn
Learn how a virtual receptionist AI can improve intake flow for calls, WhatsApp, and referrals in Argentina, and why it's better than an automated answering machine
Dev.to AI
 โšก
Quรฉ es una recepcionista virtual IA en Argentina: flujo de intake para llamadas, WhatsApp y derivaciรณn
Learn how a virtual AI receptionist works in Argentina, handling calls, WhatsApp, and redirection as a designed intake flow, not just a free bot
Dev.to AI
 โšก
Mejor recepcionista virtual para PyMEs en Argentina: flujo de intake para llamadas, WhatsApp y derivaciรณn
Learn how to design a virtual receptionist AI for small and medium-sized enterprises (PyMEs) in Argentina using a flow-based intake system
Dev.to AI
 โšก
AI Answering Service Ireland: 24/7 Call Handling for SMEs: an intake workflow for missed calls
Implement a 24/7 AI answering service to streamline call handling for SMEs in Ireland, improving customer experience and reducing missed calls
Dev.to AI
Up next
New Kimi Web Bridge Browser AI Agent is INSANE!
Julian Goldie SEO
Watch โ†’