Deploy Your Own Open Agent Platform

LangChain · Intermediate ·🤖 AI Agents & Automation ·11mo ago
In this video, we show you how to deploy your own instance of Open Agent Platform (OAP) to production. This follows a previous video where we walked through how to use our managed instance of OAP. Open Agent Platform is an open-source, citizen-developer platform designed enable you to build, prototype, and deploy intelligent agents effortlessly. With its intuitive web-based interface, OAP enables users to connect agents to various tools via MCP, Retrieval-Augmented Generation (RAG) servers, and orchestrate complex multi-agent workflows. Covered in this walkthrough: 00:00 Intro 00:20 Open Agent Platform overview 01:07 Setting up the out-of-the-box Tools and Supervisor agent 05:26 Configuring collections with your own RAG server 06:29 Adding your own MCP server 07:31 Bringing it all together in the OAP UI 09:33 Adding your own custom agents 11:16 Outro Whether you're a business analyst, product manager, or developer, OAP provides a streamlined pathway to harness the power of LangChain's LangGraph agents without the need for extensive coding knowledge. Resources: 🌐 : Explore the platform: https://oap.langchain.com 📚: Documentation: - OAP: https://docs.oap.langchain.com - Custom MCP: https://docs.oap.langchain.com/setup/mcp-server - Arcade MCP: https://docs.arcade.dev/home 🛠️ : GitHub Repositories: - OAP: https://github.com/langchain-ai/open-agent-platform - Tools ReAct Agent: https://github.com/langchain-ai/oap-langgraph-tools-agent - Supervisor Agent: https://github.com/langchain-ai/oap-agent-supervisor
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2 LangChain SQL Webinar
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8 Benchmarking Question/Answering Over CSV Data
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9 LangChain "RAG Evaluation" Webinar
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10 Fine-tuning in Your Voice Webinar
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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
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16 Cognitive Architectures for Language Agents
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17 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
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20 LangChain Templates
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21 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
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25 Benchmarking Methods for Semi-Structured RAG
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26 LangSmith Highlights: Getting Started
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27 LangSmith Highlights: Debugging
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28 LangSmith Highlights: Datasets
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29 LangSmith Highlights: Evaluation
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30 LangSmith Highlights: Human Annotation
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31 LangSmith Highlights: Monitoring
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32 LangSmith Highlights: Hub
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33 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
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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
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45 Build and Deploy a RAG app with Pinecone Serverless
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
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
LangGraph: Multi-Agent Workflows
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56 Streaming Events: Introducing a new `stream_events` method
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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Chapters (8)

Intro
0:20 Open Agent Platform overview
1:07 Setting up the out-of-the-box Tools and Supervisor agent
5:26 Configuring collections with your own RAG server
6:29 Adding your own MCP server
7:31 Bringing it all together in the OAP UI
9:33 Adding your own custom agents
11:16 Outro
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