Open Deep Research

LangChain · Advanced ·🤖 AI Agents & Automation ·10mo ago
Open Deep Research is an open source agent that is built on LangGraph and can be hooked up to your own data sources, LLMs, and MCP servers. In this video, we walk through the agent’s architecture and explain our design philosophy. We also show you how to run the agent locally with LangGraph Studio, and how you can test it out quickly with Open Agent Platform. 📘 Learn more in our blog: https://blog.langchain.com/open-deep-research/ 👋 Try it out on Open Agent Platform: https://oap.langchain.com 🧑‍💻 Check out the code: https://github.com/langchain-ai/open_deep_research/tree/main
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1 Chat With Your Documents Using LangChain + JavaScript
Chat With Your Documents Using LangChain + JavaScript
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 11 Tabular Data Retrieval
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 12 Building an LLM Application with Audio by AssemblyAI
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 13 Superagent Deepdive Webinar
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 14 Lessons from Deploying LLMs with LangSmith
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 15 Shortwave Assistant Deepdive Webinar
Shortwave Assistant Deepdive Webinar
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 16 Cognitive Architectures for Language Agents
Cognitive Architectures for Language Agents
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 17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
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 18 Data Privacy for LLMs
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 19 "Theory of Mind" Webinar with Plastic Labs
"Theory of Mind" Webinar with Plastic Labs
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 20 LangChain Templates
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 21 Using Natural Language to Query Postgres with Jacob
Using Natural Language to Query Postgres with Jacob
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 22 Building a Research Assistant from Scratch
Building a Research Assistant from Scratch
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 23 Benchmarking RAG over LangChain Docs
Benchmarking RAG over LangChain Docs
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 24 Skeleton-of-Thought: Building a New Template from Scratch
Skeleton-of-Thought: Building a New Template from Scratch
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 25 Benchmarking Methods for Semi-Structured RAG
Benchmarking Methods for Semi-Structured RAG
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 26 LangSmith Highlights: Getting Started
LangSmith Highlights: Getting Started
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 27 LangSmith Highlights: Debugging
LangSmith Highlights: Debugging
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 28 LangSmith Highlights: Datasets
LangSmith Highlights: Datasets
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 29 LangSmith Highlights: Evaluation
LangSmith Highlights: Evaluation
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 30 LangSmith Highlights: Human Annotation
LangSmith Highlights: Human Annotation
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 31 LangSmith Highlights: Monitoring
LangSmith Highlights: Monitoring
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 32 LangSmith Highlights: Hub
LangSmith Highlights: Hub
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 33 SQL Research Assistant
SQL Research Assistant
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 34 Getting Started with Multi-Modal LLMs
Getting Started with Multi-Modal LLMs
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 35 Build a Full Stack RAG App With TypeScript
Build a Full Stack RAG App With TypeScript
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 36 Auto-Prompt Builder (with Hosted LangServe)
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 37 LangChain v0.1.0 Launch: Introduction
LangChain v0.1.0 Launch: Introduction
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 38 LangChain v0.1.0 Launch: Observability
LangChain v0.1.0 Launch: Observability
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 39 LangChain v0.1.0 Launch: Integrations
LangChain v0.1.0 Launch: Integrations
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 40 LangChain v0.1.0 Launch: Composability
LangChain v0.1.0 Launch: Composability
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 41 LangChain v0.1.0 Launch: Streaming
LangChain v0.1.0 Launch: Streaming
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 42 LangChain v0.1.0 Launch: Output Parsing
LangChain v0.1.0 Launch: Output Parsing
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 43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
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 44 LangChain v0.1.0 Launch: Agents
LangChain v0.1.0 Launch: Agents
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 45 Build and Deploy a RAG app with Pinecone Serverless
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 46 Hosted LangServe + LangChain Templates
Hosted LangServe + LangChain Templates
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 47 LangGraph: Intro
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 48 LangGraph: Agent Executor
LangGraph: Agent Executor
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 49 LangGraph: Chat Agent Executor
LangGraph: Chat Agent Executor
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 50 LangGraph: Human-in-the-Loop
LangGraph: Human-in-the-Loop
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 51 LangGraph: Dynamically Returning a Tool Output Directly
LangGraph: Dynamically Returning a Tool Output Directly
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 52 LangGraph: Respond in a Specific Format
LangGraph: Respond in a Specific Format
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 53 LangGraph: Managing Agent Steps
LangGraph: Managing Agent Steps
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 54 LangGraph: Force-Calling a Tool
LangGraph: Force-Calling a Tool
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 55 LangGraph: Multi-Agent Workflows
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 56 Streaming Events: Introducing a new `stream_events` method
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 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
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 58 OpenGPTs
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 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)
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 60 LangGraph: Persistence
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