langchain-autotools 0.1.0

Automatic Langchain Tool and Toolkit creation from any Python SDK!

langchain-autotools

Generate Tools and Toolkits from any Python SDK -- no extra code required

How to use AutoTools to make a dynamic toolkit

AutoTools allows you to wrap any SDK client that exposes various CRUD operations (get, update, create, delete) and turn them into LLM-enabled toolkits without having to write a custom toolkit.

There are two core aspects to AutoTools:

1. AutoToolWrapper -- wraps the SDK you pass in
2. CrudControls -- allows you to control the access an agent has to Create, Read, Update, Delete verbs with flexible pattern matching

Pattern Matching in CrudControls

CrudControls now supports both regex patterns and glob-style wildcards for matching function names:

# Using glob patterns (simple wildcards)
crud_controls = CrudControls(
    read=True,
    read_list=["get_thing*"]  # Matches: get_thing, get_thing_by_id, get_thing_generator, etc.
)

# Using regex patterns (more precise control)
crud_controls = CrudControls(
    read=True,
    read_list=[
        r"^get_thing$",      # Exact match only
        r"^get_thing_\w+$"   # Match get_thing_something but require at least one word char after _
    ]
)

# Mix and match both styles
crud_controls = CrudControls(
    read=True,
    read_list=[
        "get_thing*",        # Glob pattern for broad matching
        r"^get_other_\w+$"   # Regex pattern for precise matching
    ]
)

Glob patterns support familiar shell-style wildcards:

• * matches everything
• ? matches any single character
• [seq] matches any character in seq
• [!seq] matches any character not in seq

Quick Start Example

import boto3
from langchain_autotools import AutoToolWrapper, CrudControls

# Create your SDK client
s3 = boto3.client("s3")
client = {"client": s3}

# Configure access controls
crud_controls = CrudControls(
    read=True,
    read_list=["list_buckets"],  # Exact match
    create=True,
    create_list=["create_bucket*"]  # Wildcard match
)

# Create the toolkit
autotool = AutoToolWrapper(client=client, crud_controls=crud_controls)

# View available tools
print([tool.name for tool in autotool.operations])

Working with Agents

Since autotool.operations is a list of tools, we can pass that into an Agent upon initialization:

from langchain import hub
from langchain.agents import AgentExecutor, create_structured_chat_agent
from langchain_anthropic import ChatAnthropic

# Get the prompt to use
prompt = hub.pull("hwchase17/structured-chat-agent")

# Create the agent
llm = ChatAnthropic(model="claude-3-sonnet-20240229", temperature=0)
agent = create_structured_chat_agent(llm, autotool.operations, prompt)
agent_executor = AgentExecutor(
    agent=agent,
    tools=autotool.operations,
    verbose=True,
    handle_parsing_errors=True,
    max_iterations=3,
)

# Use the agent
agent_executor.invoke(
    {"input": "How many S3 buckets do I have? Pass an empty string as the 'action_input'."}
)

NOTE: If your SDK has many callable functions, your tool list could exceed your model's context length. Use CrudControls pattern matching to limit the tools your Agent has access to.

AWS Authentication Example

If using AWS services, ensure you have your credentials available:

import os
import getpass

os.environ["AWS_ACCESS_KEY_ID"] = getpass.getpass(prompt="AWS Access Key ID: ")
os.environ["AWS_SECRET_ACCESS_KEY"] = getpass.getpass(prompt="AWS Secret Access Key: ")
os.environ["AWS_DEFAULT_REGION"] = "us-east-1"

Cost Note

As of writing, using Anthropic's Claude3 Sonnet model in this notebook will cost less than USD 0.50 to complete.