agent building & harness

10 notes, read out of this brain and free to use. Each one was extracted from a source and is re-checked against its exam.

Adding long-term memory to agents

To add long-term memory to an agent, create a store and pass it to the create_agent function. This function accepts a store parameter that enables persistent memory functionality.

create_agent function basic usage

In Python, create an agent with: agent = create_agent('claude-sonnet-4-6', tools=[get_weather]). In TypeScript, use: const agent = createAgent({ model: 'claude-sonnet-4-6', tools: [getWeather] }).

createAgent function for TypeScript agents

createAgent is the factory function for creating agents in TypeScript/JavaScript. The minimal configuration includes: model (required, can be a string identifier like 'gpt-5.5' or an initialized model object from initChatModel), tools (required, an array of tool objects created with the tool() function), and systemPrompt (optional, a string defining the agent's role). Additional optional parameters include checkpointer for memory management. Tools are created using the tool() function with a handler function, name, description, and schema (Zod or JSON schema).

Deep agents vs LangChain agents comparison

Deep agents (from deepagents package) and LangChain agents both provide fine-grained control over tools, memory, and agent behavior. The main difference is that deep agents come with built-in capabilities including: planning (write_todos tool), file system access (grep, read_file tools), subagent delegation, and context management. Deep agents are created with create_deep_agent() function with same parameters as create_agent. Use deep agents for maximum capability with minimal setup; choose LangChain agents for fine-grained control and customization.

Basic agent example with OpenAI in Python

Example of creating a basic LangChain agent in Python with OpenAI: from langchain.agents import create_agent def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" agent = create_agent( model="openai:gpt-5.5", tools=[get_weather], system_prompt="You are a helpful assistant", ) result = agent.invoke( {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]} ) print(result["messages"][-1].content_blocks)

Basic agent example with OpenAI in TypeScript

Example of creating a basic LangChain agent in TypeScript with OpenAI: import { createAgent, tool } from "langchain"; import * as z from "zod"; const getWeather = tool( (input) => `It's always sunny in ${input.city}!`, { name: "get_weather", description: "Get the weather for a given city", schema: z.object({ city: z.string().describe("The city to get the weather for"), }), } ); const agent = createAgent({ model: "gpt-5.5", tools: [getWeather], }); console.log( await agent.invoke({ messages: [{ role: "user", content: "What's the weather in San Francisco?" }], }) );

Node.js version requirement for LangChain JavaScript

LangChain for JavaScript requires Node.js 22+ or Bun v1.0.0+. This applies when using npm, pnpm, yarn, or bun package managers to install langchain and @langchain/core.

Full agent implementation example Python

A complete Python example showing all components together: import urllib.error import urllib.request from langchain.agents import create_agent from deepagents import create_deep_agent from langchain.chat_models import init_chat_model from langchain.tools import tool from langgraph.checkpoint.memory import InMemorySaver SYSTEM_PROMPT = """You are a literary data assistant. ## Capabilities - `fetch_text_from_url`: loads document text from a URL into the conversation. Do not guess line counts or positions—ground them in tool results from the saved file.""" @tool def fetch_text_from_url(url: str) -> str: """Fetch the document from a URL.""" req = urllib.request.Request( url, headers={"User-Agent": "Mozilla/5.0 (compatible; quickstart-research/1.0)"}, ) try: with urllib.request.urlopen(req, timeout=120) as resp: raw = resp.read() except urllib.error.URLError as e: return f"Fetch failed: {e}" text = raw.decode("utf-8", errors="replace") return text model = init_chat_model( "gemini-3.1-pro-preview", model_provider="google-genai", temperature=0.5, timeout=600, max_tokens=25000, streaming=True, ) checkpointer = InMemorySaver() agent = create_agent( model=model, tools=[fetch_text_from_url], system_prompt=SYSTEM_PROMPT, checkpointer=checkpointer, ) deep_agent = create_deep_agent( model=model, tools=[fetch_text_from_url], system_prompt=SYSTEM_PROMPT, checkpointer=checkpointer, ) content = """Project Gutenberg hosts a full plain-text copy of F. Scott Fitzgerald's The Great Gatsby. URL: https://www.gutenberg.org/files/64317/64317-0.txt Answer as much as you can: 1) How many lines in the complete Gutenberg file contain the substring `Gatsby`? 2) The 1-based line number of the first line in the file that contains `Daisy`. 3) A two-sentence neutral synopsis.""" agent_result = agent.invoke( {"messages": [{"role": "user", "content": content}]}, config={"configurable": {"thread_id": "great-gatsby-lc"}}, ) deep_agent_result = deep_agent.invoke( {"messages": [{"role": "user", "content": content}]}, config={"configurable": {"thread_id": "great-gatsby-da"}}, ) print(agent_result["messages"][-1].content_blocks) print("\n") print(deep_agent_result["messages"][-1].content_blocks)

Full agent implementation example TypeScript

A complete TypeScript example showing all components together: import { MemorySaver } from "@langchain/langgraph"; import { createDeepAgent } from "deepagents"; import { tool } from "@langchain/core/tools"; import { createAgent, initChatModel } from "langchain"; import { z } from "zod"; const SYSTEM_PROMPT = `You are a literary data assistant. ## Capabilities - \`fetch_text_from_url\`: loads document text from a URL into the conversation. Do not guess line counts or positions—ground them in tool results from the saved file.`; const fetchTextFromUrl = tool( async ({ url }: { url: string }): Promise<string> => { const controller = new AbortController(); const timeoutId = setTimeout(() => controller.abort(), 120_000); try { const resp = await fetch(url, { headers: { "User-Agent": "Mozilla/5.0 (compatible; quickstart-research/1.0)", }, signal: controller.signal, }); if (!resp.ok) { return `Fetch failed: HTTP ${resp.status} ${resp.statusText}`; } return await resp.text(); } catch (e) { const msg = e instanceof Error ? e.message : String(e); return `Fetch failed: ${msg}`; } finally { clearTimeout(timeoutId); } }, { name: "fetch_text_from_url", description: "Fetch the document from a URL.", schema: z.object({ url: z.string().url() }), }, ); const model = await initChatModel("gemini-3.1-pro-preview", { modelProvider: "google-genai", temperature: 0.5, timeout: 600_000, maxTokens: 25000, streaming: true, }); const checkpointer = new MemorySaver(); async function main() { const agent = createAgent({ model, tools: [fetchTextFromUrl], systemPrompt: SYSTEM_PROMPT, checkpointer, }); const deepAgent = createDeepAgent({ model, tools: [fetchTextFromUrl], systemPrompt: SYSTEM_PROMPT, checkpointer, }); const content = `Project Gutenberg hosts a full plain-text copy of F. Scott Fitzgerald's The Great Gatsby. URL: https://www.gutenberg.org/files/64317/64317-0.txt Answer as much as you can: 1) How many lines in the complete Gutenberg file contain the substring \`Gatsby\`? 2) The 1-based line number of the first line in the file that contains \`Daisy\`. 3) A two-sentence neutral synopsis.`; const agentResult = await agent.invoke( { messages: [{ role: "user", content }] }, { configurable: { thread_id: "great-gatsby-lc" } }, ); const deepAgentResult = await deepAgent.invoke( { messages: [{ role: "user", content }] }, { configurable: { thread_id: "great-gatsby-da" } }, ); const agentMessages = agentResult.messages; const deepMessages = deepAgentResult.messages; console.log(agentMessages[agentMessages.length - 1]!.contentBlocks); console.log("\n"); console.log(deepMessages[deepMessages.length - 1]!.contentBlocks); } main().catch((err) => { console.error(err); process.exitCode = 1; });

create_agent function for basic LangChain agent

create_agent is the main factory function for creating a basic LangChain agent. The minimal configuration includes: model (required, can be a string identifier like 'openai:gpt-5.5' or an initialized model object), tools (required, a list of tool functions), and system_prompt (required, a string defining the agent's role and behavior). The function signature is: create_agent(model, tools, system_prompt, checkpointer=None). The agent is invoked with invoke() method taking a dictionary with 'messages' key containing a list of message objects, each with 'role' and 'content' keys.

Give your agent this brain