langchain-fundamentals

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• SKILL.md

SKILL.md (first lines)

<oneliner>
Build production agents using `create_agent()`, middleware patterns, and the `@tool` decorator / `tool()` function. When creating LangChain agents, you MUST use create_agent(), with middleware for custom flows. All other alternatives are outdated.
</oneliner>

<create_agent>
## Creating Agents with create_agent

`create_agent()` is the recommended way to build agents. It handles the agent loop, tool execution, and state management.

### Agent Configuration Options

| Parameter | Purpose | Example |
|-----------|---------|---------|
| `model` | LLM to use | `"anthropic:claude-sonnet-4-5"` or model instance |
| `tools` | List of tools | `[search, calculator]` |
| `system_prompt` / `systemPrompt` | Agent instructions | `"You are a helpful assistant"` |
| `checkpointer` | State persistence | `MemorySaver()` |
| `middleware` | Processing hooks | `[HumanInTheLoopMiddleware]` (Python) / `[humanInTheLoopMiddleware({...})]` (TypeScript) |
</create_agent>

<ex-basic-agent>
<python>
```python
from langchain.agents import create_agent
from langchain_core.tools import tool

@tool
def get_weather(location: str) -> str:
    """Get current weather for a location.

    Args:
        location: City name
    """
    return f"Weather in {location}: Sunny, 72F"

agent = create_agent(
    model="anthropic:claude-sonnet-4-5",
    tools=[get_weather],
    system_prompt="You are a helpful assistant."
)

result = agent.invoke({
    "messages": [{"role": "user", "content": "What's the weather in Paris?"}]
})
print(result["messages"][-1].content)
```
</python>
<typescript>
```typescript
import { createAgent } from "langchain";
import { tool } from "@langchain/core/tools";
import { z } from "zod";

const getWeather = tool(
  async ({ location }) => `Weather in ${location}: Sunny, 72F`,
  {
    name: "get_weather",
    description: "Get current weather for a location.",
    schema: z.object({ location: z.string().describe("City name") }),
  }
);

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-5",
  tools: [getWeather],
  systemPrompt: "You are a helpful assistant.",
});

const result = await agent.invoke({
  messages: [{ role: "user", content: "What's the weather in Paris?" }],
});
console.log(result.messages[result.messages.length - 1].content);
```
</typescript>
</ex-basic-agent>

<ex-agent-with-persistence>
<python>
Add MemorySaver checkpointer to maintain conversation state across invocations.
```python
from langchain.agents import create_agent
from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()

agent = create_agent(
    model="anthropic:claude-sonnet-4-5",
    tools=[search],
    checkpointer=checkpointer,
)

config = {"configurable": {"thread_id": "user-123"}}
agent.invoke({"messages": [{"role": "user", "content": "My name is Alice"}]}, config=config)
result = agent.invoke({"messages": [{"role": "user", "content": "What's my name?"}]}, config=config)
# Agent remembers: "Your name is Alice"
```
</python>
<typescript>
Add MemorySaver checkpointer to maintain conversation state across invocations.
```typescript
import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";

const checkpointer = new MemorySaver();

const agent = createAgent({
  model: "anthropic:claude-sonnet-4-5",
  tools: [search],
  checkpointer,
});

const config = { configurable: { thread_id: "user-123" } };
await agent.invoke({ messages: [{ role: "user", content: "My name is Alice" }] }, config);
const result = await agent.invoke({ messages: [{ role: "user", content: "What's my name?" }] }, config);
// Agent remembers: "Your name is Alice"
```
</typescript>
</ex-agent-with-persistence>

From Harmeet10000/skills, MIT.

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