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<CodeGroup>
```python Google
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="google_genai:gemini-3.6-flash")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python OpenAI
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="openai:gpt-5.5")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python Anthropic
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="anthropic:claude-sonnet-4-6")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python OpenRouter
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="openrouter:z-ai/glm-5.2")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python Fireworks
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="fireworks:accounts/fireworks/models/glm-5p2")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python Baseten
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="baseten:zai-org/GLM-5.2")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
```python Ollama
from dataclasses import dataclass
from langchain.agents import create_agent
from langchain.tools import tool, ToolRuntime
from langchain_core.utils.uuid import uuid7
from langchain_openai import ChatOpenAI
USER_DATABASE = {
"user123": {
"name": "Alice Johnson",
"account_type": "Premium",
"balance": 5000,
"email": "alice@example.com",
},
"user456": {
"name": "Bob Smith",
"account_type": "Standard",
"balance": 1200,
"email": "bob@example.com",
},
}
@dataclass
class UserContext:
user_id: str
@tool
def get_account_info(runtime: ToolRuntime[UserContext]) -> str:
"""Get the current user's account information."""
user_id = runtime.context.user_id
if user_id in USER_DATABASE:
user = USER_DATABASE[user_id]
return (
f"Account holder: {user['name']}\n"
f"Type: {user['account_type']}\n"
f"Balance: ${user['balance']}"
)
return "User not found"
model = ChatOpenAI(model="ollama:north-mini-code-1.0")
agent = create_agent(
model,
tools=[get_account_info],
context_schema=UserContext,
system_prompt="You are a financial assistant.",
)
result = agent.invoke(
{"messages": [{"role": "user", "content": "What's my current balance?"}]},
config={"configurable": {"thread_id": str(uuid7())}},
context=UserContext(user_id="user123"),
)
```
</CodeGroup>
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