Quickstart

uv python pin 3.11
uv init
uv add langchain
uv sync

# Install Python 3.11+ separately if needed.
pip install -U langchain

# Install Python 3.11+ separately if needed.
python3.11 -m venv .venv
source .venv/bin/activate
# Windows: .venv\Scripts\activate
pip install -U langchain

export OPENAI_API_KEY="your-api-key"

OPENAI_API_KEY=your-api-key

export GOOGLE_API_KEY="your-api-key"

GOOGLE_API_KEY=your-api-key

export ANTHROPIC_API_KEY="your-api-key"

ANTHROPIC_API_KEY=your-api-key

export OPENROUTER_API_KEY="your-api-key"

OPENROUTER_API_KEY=your-api-key

export FIREWORKS_API_KEY="your-api-key"

FIREWORKS_API_KEY=your-api-key

export BASETEN_API_KEY="your-api-key"

BASETEN_API_KEY=your-api-key

# Local: Ollama must be running (https://ollama.com)
# Cloud: Set your Ollama API key for hosted inference
export OLLAMA_API_KEY="your-api-key"

# Local: Ollama must be running (https://ollama.com)
# Cloud: Set your Ollama API key for hosted inference
OLLAMA_API_KEY=your-api-key

export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"
export AZURE_OPENAI_DEPLOYMENT_NAME="your-deployment"

AZURE_OPENAI_API_KEY=your-api-key
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_DEPLOYMENT_NAME=your-deployment

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="us-east-1"

AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_REGION=us-east-1

export HUGGINGFACEHUB_API_TOKEN="hf_..."

HUGGINGFACEHUB_API_TOKEN=hf_...

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)

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="google_genai:gemini-2.5-flash-lite",
    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)

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="claude-sonnet-4-6",
    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)

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="openrouter:anthropic/claude-sonnet-4-6",
    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)

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="fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    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)

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="baseten:zai-org/GLM-5.2",
    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)

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="ollama:devstral-2",
    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)

import os
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

model = init_chat_model(
    "azure_openai:gpt-5.5",
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)
agent = create_agent(
    model=model,
    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)

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="bedrock_converse:us.anthropic.claude-sonnet-4-6",
    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)

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="huggingface:microsoft/Phi-3-mini-4k-instruct",
    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)

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."""

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "openai:gpt-5.5",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "gemini-3.1-pro-preview",
    model_provider="google-genai",
    temperature=0.5,
    timeout=600,
    max_tokens=25000,
    streaming=True,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "claude-sonnet-4-6",
    temperature=0.5,
    timeout=600,
    max_tokens=25000,
    streaming=True,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "openrouter:anthropic/claude-sonnet-4-6",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "fireworks:accounts/fireworks/models/qwen3p5-397b-a17b",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "baseten:zai-org/GLM-5.2",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "ollama:devstral-2",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

import os
from langchain.chat_models import init_chat_model

model = init_chat_model(
    "azure_openai:gpt-5.5",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "us.anthropic.claude-sonnet-4-6",
    model_provider="bedrock_converse",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

from langchain.chat_models import init_chat_model

model = init_chat_model(
    "microsoft/Phi-3-mini-4k-instruct",
    model_provider="huggingface",
    temperature=0.5,
    timeout=300,
    max_tokens=25000,
)

uv add deepagents

pip install -U deepagents

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 = f"""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` (count lines, not occurrences within a line, each line ends with a line break).
2) The 1-based line number of the first line in the file that contains `Daisy`.
3) A two-sentence neutral synopsis.

Do your best on (1) and (2). If at any point you realize you cannot **verify** an exact answer with
your available tools and reasoning, do not fabricate numbers: use `null` for that field and spell out
the limitation in `how_you_computed_counts`. If you encounter any errors please report what the error was and what the error message was."""

print("Running create_agent...", flush=True)
agent_result = agent.invoke(
    {"messages": [{"role": "user", "content": content}]},
    config={"configurable": {"thread_id": "great-gatsby-lc"}},
)
print("Running create_deep_agent...", flush=True)
deep_agent_result = deep_agent.invoke(
    {"messages": [{"role": "user", "content": content}]},
    config={"configurable": {"thread_id": "great-gatsby-da"}},
)
print("\ncreate_agent:")
print(agent_result["messages"][-1].content_blocks)
print("\ncreate_deep_agent:")
print(deep_agent_result["messages"][-1].content_blocks)

**1) Number of lines containing `Gatsby`:** `null`

**2) First line containing `Daisy`:** `null`

**3) Synopsis:**
The Great Gatsby follows the mysterious millionaire Jay Gatsby and his obsession with reuniting with his former lover, Daisy Buchanan, as narrated by his neighbor Nick Carraway. Set against the backdrop of the Roaring Twenties on Long Island, the novel explores themes of wealth, class, and the elusive nature of the American Dream.

**how_you_computed_counts:**
I successfully fetched the full text of the eBook using the `fetch_text_from_url` tool. However, because I do not have access to a code execution environment (like Python) or text-processing tools (like `grep`), I cannot deterministically split the text by line breaks, iterate through the thousands of lines, and verify the exact line numbers or match counts. LLMs cannot reliably perform exact line-counting or indexing over massive texts within their context window without external computational tools. As instructed, rather than fabricating or guessing a number, I have output `null` for the exact counts and positions.

Based on the text fetched directly from the Gutenberg URL and analyzed using filesystem search tools, here are the answers to your questions:

**1) Lines containing the substring `Gatsby`**
**258** lines contain the exact substring `Gatsby`.

**2) First line containing `Daisy`**
Line **181** is the first line in the file that contains the exact substring `Daisy`.
*(For context, the line reads: "Buchanans. Daisy was my second cousin once removed, and I’d known Tom")*

**3) Two-sentence neutral synopsis**
*The Great Gatsby* follows the mysterious millionaire Jay Gatsby and his obsessive pursuit to reunite with his former lover, Daisy Buchanan, in 1920s Long Island. The story is narrated by Nick Carraway, who observes the tragic consequences of Gatsby's relentless ambition and the shallow materialism of the era's wealthy elite.

***

**How counts were computed:**
When fetching the document from the URL, the file was too large for the standard output and was automatically saved to the local filesystem by the system (`/large_tool_results/x246ax2x`). I then used the `grep` tool to search the saved file for the exact literal substrings `Gatsby` and `Daisy`. The `grep` tool returned every matching line along with its 1-based line number. I manually counted the exact number of lines returned for `Gatsby` (which totaled 258) and identified the first line number returned for `Daisy` (which was 181). I also verified there were no uppercase variations (`GATSBY` or `DAISY`) that would have been missed. No errors were encountered during this process.

export LANGSMITH_TRACING="true"
export LANGSMITH_API_KEY="..."