start adding dummy nodes

agent/agent.ts

@@ -0,0 +1,30 @@
+import { END, START, StateGraph } from "@langchain/langgraph";
+import { MessagesState } from "./state";
+import { toolNode } from "./nodes/tool";
+import { createToolConditional } from "./conditionals/tool_end";
+import { normalizationSetup } from "./nodes/normalizationSetup";
+import { dummyNormalisationModel } from "./nodes/dummyNormalisationModel";
+import { dummyTriggerEventModel } from "./nodes/dummyTriggerEventModel";
+
+const triggerEventToolConditional = createToolConditional(toolNode.name, END)
+const agent = new StateGraph(MessagesState)
+  
+  //NODES
+  .addNode("toolNode", toolNode)
+  .addNode(normalizationSetup.name, normalizationSetup)
+  .addNode(dummyNormalisationModel.name, dummyNormalisationModel)
+  .addNode(dummyTriggerEventModel.name, dummyTriggerEventModel)
+  
+  .addEdge(START, normalizationSetup.name)
+  .addEdge(normalizationSetup.name, dummyNormalisationModel.name)
+  .addEdge(dummyNormalisationModel.name, dummyTriggerEventModel.name)
+  
+  // @ts-expect-error
+  .addConditionalEdges(dummyTriggerEventModel.name, triggerEventToolConditional, [toolNode.name, END])
+  
+  .addEdge(toolNode.name, dummyTriggerEventModel.name)
+  
+  .addEdge(dummyTriggerEventModel.name, END)
+  .compile();
+
+  export {agent}

agent/conditionals/tool_end.ts

@@ -0,0 +1,31 @@
+import { ConditionalEdgeRouter, END } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage } from "@langchain/core/messages";
+
+export function createToolConditional(a: String, b: String): ConditionalEdgeRouter<typeof MessagesState, String> {
+  // @ts-expect-error
+  var genericToolConditional: ConditionalEdgeRouter<typeof MessagesState, String> = (state) => {
+    const lastMessage = state.messages.at(-1);
+    
+    //STARTTEMP
+    if (lastMessage?.content?.toString().indexOf("qwe") != -1) {
+      return a
+    }
+    //ENDTEMP
+
+    // Check if it's an AIMessage before accessing tool_calls
+    if (!lastMessage || !AIMessage.isInstance(lastMessage)) {
+      return b;
+    }
+
+    // If the LLM makes a tool call, then perform an action
+    if (lastMessage.tool_calls?.length) {
+      return a;
+    }
+
+    // Otherwise, we stop (reply to the user)
+    return b;
+  };
+  return genericToolConditional
+}
+

agent/index.ts

@@ -1,33 +0,0 @@
-import { addMessages, entrypoint } from "@langchain/langgraph";
-import { type BaseMessage } from "@langchain/core/messages";
-import { HumanMessage } from "@langchain/core/messages";
-import { modelNode } from "./nodes/model";
-import { toolNode } from "./nodes/tool";
-import 'dotenv/config';
-
-const agent = entrypoint({ name: "agent" }, async (messages: BaseMessage[]) => {
-  let modelResponse = await modelNode(messages);
-
-  while (true) {
-    if (!modelResponse.tool_calls?.length) {
-      break;
-    }
-
-    // Execute tools
-    const toolResults = await Promise.all(
-      modelResponse.tool_calls.map((toolCall) => toolNode(toolCall))
-    );
-    messages = addMessages(messages, [modelResponse, ...toolResults]);
-    modelResponse = await modelNode(messages);
-  }
-
-  return messages;
-});
-
-export {agent}
-
-const result = await agent.invoke([new HumanMessage("Add 3 and 4.")]);
-
-for (const message of result) {
-  console.log(`[${message.getType()}]: ${message.text}`);
-}

agent/langgraph.json

@@ -1,7 +1,7 @@
 {
   "dependencies": ["."],
   "graphs": {
-    "agent": "./index.ts:agent"
+    "agent": "./agent.ts:agent"
   },
   "env": ".env"
 }

agent/nodes/dummyNormalisationModel.ts

@@ -0,0 +1,11 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage, HumanMessage } from "@langchain/core/messages";
+
+export const dummyNormalisationModel: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: call AI model with collected data
+
+  return {
+    messages: [ new AIMessage(state.messages.at(-1)?.content + " Processed")]
+  };
+};

agent/nodes/dummyRagasMetrics.ts

@@ -0,0 +1,11 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage, HumanMessage } from "@langchain/core/messages";
+
+export const dummyRagasMetrics: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: get ragas metrics
+  
+  return {
+    messages: [ new AIMessage("RAGASSED : " + state.messages.at(-1)?.content)]
+  };
+};

agent/nodes/dummyTriggerEventModel.ts

@@ -0,0 +1,11 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage, HumanMessage } from "@langchain/core/messages";
+
+export const dummyTriggerEventModel: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: call AI model with collected data
+  
+  return {
+    messages: [ new AIMessage("Trigger events of: " + state.messages.at(-1)?.content)]
+  };
+};

agent/nodes/dummyVerificationModel.ts

@@ -0,0 +1,11 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { AIMessage, HumanMessage } from "@langchain/core/messages";
+
+export const dummyVerificationModel: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: call AI model with collected data
+  
+  return {
+    messages: [ new AIMessage("Verified : " + state.messages.at(-1)?.content)]
+  };
+};

agent/nodes/model.ts

@@ -1,21 +1,24 @@
-import { task, entrypoint } from "@langchain/langgraph";
-import { BaseMessage, SystemMessage } from "@langchain/core/messages";
-import { ChatOpenAI } from "@langchain/openai"
-import { arithmeticTools } from "../tools/arithmetic";
+// import { SystemMessage } from "@langchain/core/messages";
+// import { GraphNode } from "@langchain/langgraph";
+// import { MessagesState } from "../state";
+// import { arithmeticTools } from "../tools/arithmetic";
+// import { ChatOpenAI } from "@langchain/openai"
 
-const model = new ChatOpenAI({
-  model: "gpt-5-mini"
-});
+// const model = new ChatOpenAI({
+//   model: "gpt-5-mini"
+// });
 
-const modelWithTools = model.bindTools(arithmeticTools);
+// const modelWithTools = model.bindTools(arithmeticTools);
 
-export const modelNode = task({ name: "callLlm" }, async (messages: BaseMessage[]) => {
-    
-
-    return modelWithTools.invoke([
-    new SystemMessage(
-      "You are a helpful assistant tasked with performing arithmetic on a set of inputs."
-    ),
-    ...messages,
-  ]);
-});
+// export const llmCall: GraphNode<typeof MessagesState> = async (state) => {
+//   const response = await modelWithTools.invoke([
+//     new SystemMessage(
+//       "You are a helpful assistant tasked with performing arithmetic on a set of inputs. Any calculation, no matter how trivial, should be done with tools.  Output the final answer with %%% on each side"
+//     ),
+//     ...state.messages,
+//   ]);
+//   return {
+//     messages: [response],
+//     llmCalls: 1,
+//   };
+// };

agent/nodes/normalizationSetup.ts

@@ -0,0 +1,9 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { HumanMessage } from "@langchain/core/messages";
+
+export const normalizationSetup: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: Implement claim normalisation, using few shot prompting and CLAN Dataset
+  
+  return { messages: [ new HumanMessage(state.disinformationTitle)] };
+};

agent/nodes/tool.ts

@@ -1,8 +1,25 @@
-import type { ToolCall } from "@langchain/core/messages/tool";
-import { task } from "@langchain/langgraph";
+import { AIMessage, ToolMessage } from "@langchain/core/messages";
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
 import { arithmeticToolsByName } from "../tools/arithmetic";
 
-export const toolNode = task({ name: "callTool" }, async (toolCall: ToolCall) => {
+export const toolNode: GraphNode<typeof MessagesState> = async (state) => {
+  const lastMessage = state.messages.at(-1);
+
+  //STARTTEMP
+  return {messages: [new AIMessage("yeman")]}
+  //ENDTEMP
+  
+  if (lastMessage == null || !AIMessage.isInstance(lastMessage)) {
+    return { messages: [] };
+  }
+
+  const result: ToolMessage[] = [];
+  for (const toolCall of lastMessage.tool_calls ?? []) {
     const tool = arithmeticToolsByName[toolCall.name];
-  return tool.invoke(toolCall);
-});
+    const observation = await tool.invoke(toolCall);
+    result.push(observation);
+  }
+
+  return { messages: result };
+};

agent/nodes/verificationSetup.ts

@@ -0,0 +1,9 @@
+import { GraphNode } from "@langchain/langgraph";
+import { MessagesState } from "../state";
+import { HumanMessage } from "@langchain/core/messages";
+
+export const verificationSetup: GraphNode<typeof MessagesState> = async (state) => {
+  //TODO: this might not be needed, looks nice on the graph tho
+  
+  return { messages: [ new HumanMessage(state.messages.at(-1)?.content ?? "undefined")] };
+};

agent/run.ts

@@ -8,9 +8,9 @@ const streamResponse = client.runs.stream(
   thread["thread_id"],
   "agent", 
   {
-    input: [
-      { role: "user", content: "3+5" }
-    ],
+    input: {
+      "messages": ["3+5" ]
+    },
     streamMode: "messages-tuple",
   }
 );

agent/state.ts

@@ -0,0 +1,17 @@
+import {
+  StateGraph,
+  StateSchema,
+  MessagesValue,
+  ReducedValue,
+  GraphNode,
+  ConditionalEdgeRouter,
+  START,
+  END,
+} from "@langchain/langgraph";
+import { z } from "zod/v4";
+
+export const MessagesState = new StateSchema({
+  messages: MessagesValue,
+  // normalizationContext: z.map(z.string(), z.string()),
+  disinformationTitle: z.string()
+});