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AI Agent Chat with LangChain & Gemini

AI Agent Chat with LangChain & Gemini is a 4-node automation blueprint connecting LangChain, Google Gemini. Inspect its trigger, execution graph, branches, and implementation requirements.

LangChainGoogle Gemini

Interactive system map

Execution flows, separated by connected path.

Each horizontal lane is one connected flow. Solid lines show runtime execution; dashed lines show AI, model, memory, or tool dependencies.

4

Nodes

4

Links

1

Flows

Connected tools

2 integrations

  • LangChain
  • Google AI
AI Agent Chat with LangChain & Gemini execution lanes1 connected flows containing 4 workflow nodes and 4 visible links. Solid lines are execution links and dashed lines are support dependencies. Focus any node to inspect its immediate path.Google Gemini Chat Model. Google AI node, lmChatGoogleGemini. 0 incoming and 1 outgoing connections.Google Gemini Chat ModelGOOGLE AILMCHATGOOGLEGEMINI0 IN / 1 OUTStore conversation history. ai node, memoryBufferWindow. 0 incoming and 2 outgoing connections.Store conversation historyAIMEMORYBUFFERWINDOW0 IN / 2 OUTWhen chat message received. trigger node, chatTrigger. 1 incoming and 1 outgoing connections.When chat message receivedTRIGGERCHATTRIGGER1 IN / 1 OUTConstruct & Execute LLM Prompt. ai node, code. 3 incoming and 0 outgoing connections.Construct & Execute LLM P…AICODE3 IN / 0 OUT

Signal inspector

Focus, hover, or select a node to illuminate its real path.

Execution linkSupport dependencytriggeraidecisiontransformintegration
Scroll horizontally to follow every connected flow.4 connected nodesEnter or click to lock · Escape to clear

Execution breakdown

Follow each connected flow from start to outcome.

The readable view below uses the same graph relationships as the interactive map. Each row is a connected flow; columns advance only when an actual link connects the nodes.

01

Connected Flow

When chat message received

1 Execution Link3 Support Dependencies

  1. Start

    Google Gemini Chat Model

    AI reasoning

    Store conversation history

    AI reasoning

  2. Step 2

    When chat message received

    Trigger

  3. Outcome

    Construct & Execute LLM Prompt

    AI reasoning

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