--- title: Building AI Agents with LangChain on Oracle Cloud: A... description: A practical walkthrough of building AI agents on Oracle Cloud using LangChain and the ReAct pattern, showing how LLMs reason step-by-step and call tools such... canonical: https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Building AI Agents with LangChain on Oracle Cloud: A Practical Guide to the ReAct Pattern | daily.dev og:description: A practical walkthrough of building AI agents on Oracle Cloud using LangChain and the ReAct pattern, showing how LLMs reason step-by-step and call tools such... og:url: https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm og:image: https://api.daily.dev/og/posts/1tV9xGFRM.png og:image:alt: Building AI Agents with LangChain on Oracle Cloud: A Practical Guide to the ReAct Pattern og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building AI Agents with LangChain on Oracle Cloud: A Practical Guide to the ReAct Pattern **[Medium](https://daily.dev/sources/medium_js)** · 8 min read · 0 upvotes · 0 comments ## Summary A practical walkthrough of building AI agents on Oracle Cloud using LangChain and the ReAct pattern, showing how LLMs reason step-by-step and call tools such as Oracle Database 23ai queries and OCI Generative AI vector search. Covers agent architecture, tool design with Pydantic schemas and docstrings, the full execution flow from application call to OCI Generative AI routing, and production concerns like connection pooling, security via OCI Vault, iteration limits, and observability through reasoning traces. Also demonstrates using Oracle Database 23ai's native vector search for semantic retrieval over enterprise documentation. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I build a LangChain tool that queries an Oracle database from an AI agent? Define a Python function decorated with @tool from langchain.tools, paired with a pydantic BaseModel schema describing the expected arguments (like employee_id and include_salary). Inside the function, connect using python-oracledb, run a parameterized SQL query, and return the result as a string. The docstring and type hints act as the contract the LLM uses to decide when and how to call the tool. _daily.dev surfaces practical LangChain and Oracle integration patterns for developers wiring up agent tools._ ### How does the ReAct pattern help debug failing AI agents? ReAct interleaves explicit reasoning steps (Thought) with tool calls (Action) and their results (Observation), producing a visible trace of the agent's decision-making. When an agent fails, developers can read this reasoning trace to see exactly where the logic diverged, rather than debugging an opaque black box, similar to inspecting a database EXPLAIN PLAN. _track agent reasoning and debugging techniques like this on daily.dev as agentic architectures mature._ ### How can I add semantic search over enterprise documents to a LangChain agent using Oracle Database 23ai? Use langchain-oracledb's OracleVS vector store alongside OCIGenAIEmbeddings to embed and store documents in an Oracle Database 23ai table, then wrap a similarity_search call in a @tool-decorated function. The agent invokes this tool when it lacks context, retrieves the top-k matching document chunks, and uses them to compose an accurate response. _developers combining vector search with relational data can follow this space on daily.dev._ ## Similar posts on daily.dev - [Agentic AI with LangChain — Part 4: ReAct Pattern in LangGraph](https://daily.dev/posts/agentic-ai-with-langchain-part-4-react-pattern-in-langgraph-iaseqb5th) · gitconnected · 1 upvotes · 0 comments - [An Animated Guide to ReAct Agents](https://daily.dev/posts/an-animated-guide-to-react-agents-yjlothhsy) · Machine Learning Pills · 0 upvotes · 0 comments - [How to Build an AI Agent with LangChain and LangGraph: Build an Autonomous Starbucks Agent](https://daily.dev/posts/how-to-build-an-ai-agent-with-langchain-and-langgraph-build-an-autonomous-starbucks-agent-ngn3fbgtm) · freeCodeCamp · 8 upvotes · 0 comments - [LangChain4j got a new video series](https://daily.dev/posts/langchain4j-got-a-new-video-series-5nze1qmit) · DevBlogs · 2 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#ai-agents](https://daily.dev/tags/ai-agents), [#oracle](https://daily.dev/tags/oracle), [#langchain](https://daily.dev/tags/langchain) [View this post on daily.dev](https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Building AI Agents with LangChain on Oracle Cloud: A Practical Guide to the ReAct Pattern","url":"https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm"},"datePublished":"2026-08-18T01:04:10.633Z","dateModified":"2026-09-14T06:55:31.882Z","description":"A practical walkthrough of building AI agents on Oracle Cloud using LangChain and the ReAct pattern, showing how LLMs reason step-by-step and call tools such...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4129dcde79043908308f19364786e310?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4129dcde79043908308f19364786e310?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"python,ai-agents,oracle,langchain","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Building AI Agents with LangChain on Oracle Cloud: A Practical Guide to the ReAct Pattern"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/building-ai-agents-with-langchain-on-oracle-cloud-a-practical-guide-to-the-react-pattern-1tv9xgfrm#faq","mainEntity":[{"@type":"Question","name":"How do I build a LangChain tool that queries an Oracle database from an AI agent?","acceptedAnswer":{"@type":"Answer","text":"Define a Python function decorated with @tool from langchain.tools, paired with a pydantic BaseModel schema describing the expected arguments (like employee_id and include_salary). 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