# LangChain (/docs/providers/langchain)

The LangChain provider transforms Devcaster tools into LangChain's [StructuredTool](https://python.langchain.com/docs/how_to/custom_tools/) format with built-in execution.

**Install**

**Python:**

```bash
pip install devcaster devcaster_langchain langchain langchain_openai
```

**TypeScript:**

```bash
npm install @devcaster/core @devcaster/langchain @langchain/openai @langchain/langgraph @langchain/core
```

**Configure API Keys**

> Set `DEVCASTER_API_KEY` with your API key from [Settings](https://platform.devcaster.dev/?next_page=/settings) and `OPENAI_API_KEY` with your [OpenAI API key](https://platform.openai.com/api-keys).

```txt title=".env"
DEVCASTER_API_KEY=xxxxxxxxx
OPENAI_API_KEY=xxxxxxxxx
```
**Create session and run**

**Python:**

```python
from devcaster import Devcaster
from devcaster_langchain import LangchainProvider
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI

devcaster = Devcaster(provider=LangchainProvider())
llm = ChatOpenAI(model="gpt-5.2")

# Create a session for your user
session = devcaster.create(user_id="user_123")
tools = session.tools()

agent = create_agent(tools=tools, model=llm)
result = agent.invoke({"messages": [("user", "Send an email to john@example.com with the subject 'Hello' and body 'Hello from Devcaster!'")]})

print(result["messages"][-1].content)
```
**TypeScript:**

```typescript
import { ChatOpenAI } from '@langchain/openai';
import { HumanMessage, AIMessage } from '@langchain/core/messages';
import { ToolNode } from '@langchain/langgraph/prebuilt';
import { StateGraph, MessagesAnnotation } from '@langchain/langgraph';
import { Devcaster } from '@devcaster/core';
import { LangchainProvider } from '@devcaster/langchain';

const devcaster = new Devcaster({
  provider: new LangchainProvider(),
});

// Create a session for your user
const session = await devcaster.create("user_123");
const tools = await session.tools();

const toolNode = new ToolNode(tools);

const model = new ChatOpenAI({
  model: 'gpt-5.2',
  temperature: 0,
}).bindTools(tools);

function shouldContinue({ messages }: typeof MessagesAnnotation.State) {
  const lastMessage = messages[messages.length - 1] as AIMessage;
  if (lastMessage.tool_calls?.length) {
    return 'tools';

  return '__end__';

async function callModel(state: typeof MessagesAnnotation.State) {
  const response = await model.invoke(state.messages);
  return { messages: [response] };

const workflow = new StateGraph(MessagesAnnotation)
  .addNode('agent', callModel)
  .addEdge('__start__', 'agent')
  .addNode('tools', toolNode)
  .addEdge('tools', 'agent')
  .addConditionalEdges('agent', shouldContinue);

const app = workflow.compile();

const finalState = await app.invoke({
  messages: [new HumanMessage("Send an email to john@example.com with the subject 'Hello' and body 'Hello from Devcaster!'")],
});
console.log(finalState.messages[finalState.messages.length - 1].content);
```

---

📚 **More documentation:** [View all docs](https://docs.devcaster.dev/llms.txt) | [Cookbooks](https://docs.devcaster.dev/llms.mdx/cookbooks) | [API Reference](https://docs.devcaster.dev/llms.mdx/reference)

---

# Devcaster SDK — Instructions for AI Code Generators

**Purpose:** Enforce only the **current** and **correct** patterns for integrating [Devcaster](https://devcaster.dev/) tools into AI agents.
**Scope:** All AI-generated code using Devcaster must follow these guardrails.

---

## 1. Correct Devcaster Integration

Devcaster supports two integration modes: **Native Tools** (with a provider package) and **MCP** (no provider package needed).

### Native Tools

```python
# ✅ CORRECT — Python (defaults to OpenAI)
from devcaster import Devcaster

devcaster = Devcaster()
session = devcaster.create(user_id="user_123")
tools = session.tools()
# Pass tools to your agent/LLM framework
```

```typescript
// ✅ CORRECT — TypeScript (defaults to OpenAI)
import { Devcaster } from "@devcaster/core";

const devcaster = new Devcaster();
const session = await devcaster.create("user_123");
const tools = await session.tools();
// Pass tools to your agent/LLM framework
```

For other providers, pass the provider explicitly. Provider packages follow the naming convention: `devcaster_<provider>` for Python, `@devcaster/<provider>` for TypeScript.

### MCP

Use `session.mcp.url` and `session.mcp.headers` with any MCP-compatible client (Claude Desktop, Cursor, OpenAI Agents, etc.). No provider package needed.

---

## 2. CRITICAL INSTRUCTIONS FOR AI MODELS

### 2.1 — ALWAYS DO THE FOLLOWING

1. **Use `devcaster.create(user_id)`** to create a session — this is the entry point for all Devcaster integrations.
2. **Use `session.tools()`** for native tool integration or **`session.mcp.url`** for MCP integration.
3. **Import** from the correct provider package (`devcaster_<provider>` for Python, `@devcaster/<provider>` for TypeScript).
4. **Pass `user_id` to `devcaster.create()`**, not to individual tool calls.

### 2.2 — NEVER DO THE FOLLOWING

1. **Do not** instruct users to manually create auth configs or connected accounts before running.
2. **Do not** import from `devcaster_openai` / `@devcaster/openai` when using OpenAI Agents SDK — use `devcaster_openai_agents` / `@devcaster/openai-agents` instead.

### 2.3 — DISCOURAGED (do not use unless user explicitly asks)

Do **not** generate `devcaster.tools.get()`, `devcaster.tools.execute()`, or `devcaster.provider.handle_tool_calls()` unless the user explicitly requests direct tool execution. These are a supported but **not recommended** low-level interface.

