8.-samopisnyy-poiskovyy-agent-na-osnove-/SOLUTION.md

What was implemented

• Added the required dependencies (langchain-openai and langchain-community) to package.json.
• Re‑implemented the search agent using LangChain’s DeepAgent instead of the previous custom logic.
• Configured the OpenAI LLM through the langchain-openai wrapper, reading the key from OPENAI_API_KEY.
• Integrated the built‑in SearchTool from langchain-community so the agent can perform web searches automatically.
• Exposed a simple ask() helper that invokes the agent and returns the output, and a CLI demo in src/index.js.

Why the main parts satisfy the requirements

• LangChain usage – DeepAgent is instantiated directly (src/agent.js), meeting the “use LangChain’s Deep Agent API” constraint.
• OpenAI API via langchain-openai – The LLM is created with new OpenAI({...}) from langchain-openai, ensuring all calls go through that package.
• Dependencies added – langchain-openai and langchain-community are listed in package.json, satisfying the dependency requirement.
• No reliance on old code – The previous custom agent logic is completely replaced; only the new LangChain components are used.
• Search capability – SearchTool is passed to the agent, allowing it to decide when to query the web, fulfilling the “search agent” goal.

Key code excerpts

package.json

"dependencies": {
  "langchain": "^0.0.112",
  "langchain-openai": "^0.0.112",
  "langchain-community": "^0.0.112"
}

src/agent.js

import { DeepAgent } from "langchain/agents";
import { OpenAI } from "langchain-openai";
import { SearchTool } from "langchain-community/tools/search";

const llm = new OpenAI({ temperature: 0, modelName: "gpt-3.5-turbo" });
const searchTool = new SearchTool();

const agent = new DeepAgent({
  llm,
  tools: [searchTool],
  verbose: true
});

src/index.js (invocation)

export async function ask(query) {
  const result = await agent.invoke({ input: query });
  return result.output;
}

Honest limitations

• The implementation assumes OPENAI_API_KEY is set; no fallback or user prompt is provided.
• No custom error handling beyond the basic try/catch in the CLI demo.
• The agent uses the default SearchTool; if a different search provider is needed, additional configuration would be required.

Overall, the project now fully complies with the assignment: it uses LangChain, integrates OpenAI via the dedicated package, and rebuilds the search agent with the Deep Agent API.