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DALanggraph is how you control AI flow when projects get complex!
#langgraph
#genai
#agenticai
#ai
#datascience
@datastoryteller.ai
#Langgraph Multi Agent Workflow
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115
IDLangChain is what allows AI to interact with the real world.
It connects language models with tools, data sources, and actions — turning AI into a working system.
This is a core building block of AI agents.
Day 16 / 90 of Agentic AI.
Comment “TOOLS” if you’re starting to see the bigger picture 🔗🚀
@idigitialrita
1.3K
GEWant to build your own AI Assistant?
Then you need to understand one powerful tool: LangChain.
LangChain isn’t about training models.
It’s about connecting them.
It helps your AI:
• Access external data
• Use tools & APIs
• Store memory
• Maintain context
• Perform multi-step reasoning
LLMs alone just generate text.
But when you connect them to tools and memory?
That’s when they become real AI systems.
Ever wondered how advanced chatbots give structured, context-aware answers?
That’s orchestration.
That’s chaining.
That’s LangChain.
And no — it’s not as complicated as it sounds.
In the coming days, let’s learn it step by step with GetSkilled 🚀
Stay tuned.
#ArtificialIntelligence #LangChain #AIAssistant #LLM #AIAgents
@getskilledofficial
111
GEHow AI Agents “Talk” to Each Other: Understanding State in LangGraph 🧠🤝
Caption:
Ever wondered how to make multiple AI agents collaborate on a single task? It all comes down to State.
Think of State as a shared “clipboard” or project file that gets passed around a team of workers. 📋
Here is the workflow from the diagram:
1️⃣ The Writer (Node 1): Writes a draft and saves it to the State.
2️⃣ The Critique (Node 2): Reads the draft from the State, adds feedback, and updates the clipboard.
3️⃣ The Editor (Node 3): Reads both the original draft and the feedback to create the final polished version.
Without State, Node 3 would have “amnesia”—it wouldn’t know what Node 1 wrote or what Node 2 changed. The State is the persistent memory that makes agentic workflows possible. 🔗
SAVE this for your next Agent project! 👇
#LangGraph #GenerativeAI #AIagents #genaixpiyush genaixpiyush
@genaixpiyush
128
LOLangchain Introduces Modular Skill Packages for Smarter DeepAgents
LangChain DeepAgents introduce Skills - modular capability packages that define how an agent should handle specific tasks or domains. Instead of overloading the agent with all instructions upfront, these Skill modules activate only when relevant.
This keeps AI agents efficient, precise, and context-aware - while making workflows reusable and structured.
For enterprises, Skills mean scalable automation without architectural complexity.
Modular Skills. Smarter Agents. Better Automation.
#AI #LangChain #DeepAgents #AIAgents #SkillModules #LangChainSkills
@lowtouch.ai
341
CHDay 8/15 of langchain series 🚀
Chains execute.
Agents decide.
Agents in langchain allow llms to:
• choose tools
• make decisions
• perform multi-step reasoning
• interact with external systems
This is what makes ai systems autonomous.
Agents power:
rag pipelines
ai assistants
autonomous workflows
If chains are predefined logic, agents are dynamic intelligence.
Save this if you’re building ai agents.
#langchain
#aiagents
#generativeai
#aiengineering
#rag
@chaostoai
458
AIAI development tooling is splitting into layers.
Some tools are built around reliable API execution for agents.
Some are built around shipping code with repo-aware agents inside the editor.
Some are frameworks that help you compose tool-calling workflows and orchestration patterns.
And some are the data layer that keeps outputs structured and usable by teams.
Postman AI Agent Builder fits when you want API-first agents with collections, testing, and deployable agent or MCP server setups.
Cursor fits when you want agentic coding and repo-wide edits directly in an editor workflow.
LangChain fits when you want reusable components for AI apps and agents, plus orchestration patterns via LangGraph when workflows get more stateful.
Trae.ai fits when you want AI-first planning and execution with built-in agents and support for custom agents.
NocoDB fits when you need a spreadsheet-style database UI with views and API access that agents and automations can write into.
There isn’t a universal “best.”
There is the right layer for your workflow.
#ArtificialIntelligence #AITools #AIAgents
#AgentDevelopment #APIAutomation #AIDevelopment
#Developers #StartupBuilders #ProductTeams
@ainnovatespace
4.4K
LEBuild AI Agents in LangGraph 🦜🔗
Meet LangChain — the framework that’s changing how developers build AI applications.
Here’s what makes it powerful:
→ Connect to OpenAI, Anthropic, Google & more
→ Build smart agents that use tools & APIs
→ Add memory so your AI remembers context
→ Create multi-agent workflows with LangGraph
→ Debug everything with LangSmith
Whether you’re building chatbots, automating tasks, or creating RAG systems — LangChain makes it simple.
The future of AI development is modular, flexible, and open-source. 🚀
⚠️ Note: SmolAgents (not “Small Agents”) is also mentioned — it’s an experimental Framework great for quick prototyping
Save this for later ✅ Tag someone learning AI 👇
📝 Note: Animations provided are for easy understanding and educational purposes only.
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𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: LangChain, AI Agents, LLM Framework, LangGraph, LangSmith, AI Development, Machine Learning, OpenAI, Anthropic, RAG, Retrieval Augmented Generation, Python AI, AI Automation, Multi Agent Systems, AI Tools
#LangChain #AIAgents #MachineLearning #ArtificialIntelligence #LearnAI
@learning_intelligence_tamil
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