Large language models (LLMs) have shown huge success in different text generation tasks and enable developers to build generative agents based on objectives expressed in natural language.
However, most generative agents require heavy customization for specific purposes, and supporting different use cases can sometimes be overwhelming using existing tools and frameworks. As a result, it is still very challenging to build a custom generative agent.
In addition, evaluating such generative agents, which is usually done by manually trying different scenarios, is a very manual, repetitive, and expensive task.
AutoChain takes inspiration from LangChain and AutoGPT and aims to solve both problems by providing a lightweight and extensible framework for developers to build their own agents using LLMs with custom tools and automatically evaluating different user scenarios with simulated conversations. Experienced user of LangChain would find AutoChain is easy to navigate since they share similar but simpler concepts.
The goal is to enable rapid iteration on generative agents, both by simplifying agent customization and evaluation.
If you have any questions, please feel free to reach out to Yi Lu [email protected]
- 🚀 lightweight and extensible generative agent pipeline.
- 🔗 agent that can use different custom tools and support OpenAI function calling
- 💾 simple memory tracking for conversation history and tools' outputs
- 🤖 automated agent multi-turn conversation evaluation with simulated conversations
Quick install
pip install autochainOr install from source after cloning this repository
cd autochain
pyenv virtualenv 3.10.11 venv
pyenv local venv
pip install .Set PYTHONPATH and OPENAI_API_KEY
export OPENAI_API_KEY=
export PYTHONPATH=`pwd`Run your first conversation with agent interactively
python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py -iAutoChain aims to provide a lightweight framework and simplifies the agent building process in a few ways, as compared to existing frameworks
- Easy prompt update
Engineering and iterating over prompts is a crucial part of building generative agent. AutoChain makes it very easy to update prompts and visualize prompt outputs. Run with-vflag to output verbose prompt and outputs in console. - Up to 2 layers of abstraction
As part of enabling rapid iteration, AutoChain chooses to remove most of the abstraction layers from alternative frameworks - Automated multi-turn evaluation
Evaluation is the most painful and undefined part of building generative agents. Updating the agent to better perform in one scenario often causes regression in other use cases. AutoChain provides a testing framework to automatically evaluate agent's ability under different user scenarios.
If you have experience with LangChain, you already know 80% of the AutoChain interfaces.
AutoChain aims to make building custom generative agents as straightforward as possible, with as little abstractions as possible.
The most basic example uses the default chain and ConversationalAgent:
from autochain.chain.chain import Chain
from autochain.memory.buffer_memory import BufferMemory
from autochain.models.chat_openai import ChatOpenAI
from autochain.agent.conversational_agent.conversational_agent import ConversationalAgent
llm = ChatOpenAI(temperature=0)
memory = BufferMemory()
agent = ConversationalAgent.from_llm_and_tools(llm=llm)
chain = Chain(agent=agent, memory=memory)
print(chain.run("Write me a poem about AI")['message'])Just like in LangChain, you can add a list of tools to the agent
tools = [
Tool(
name="Get weather",
func=lambda *args, **kwargs: "Today is a sunny day",
description="""This function returns the weather information"""
)
]
memory = BufferMemory()
agent = ConversationalAgent.from_llm_and_tools(llm=llm, tools=tools)
chain = Chain(agent=agent, memory=memory)
print(chain.run("What is the weather today")['message'])AutoChain also added support
for function calling
in OpenAI models. Behind the scenes, it turns the function spec into OpenAI format without explicit
instruction, so you can keep following the same Tool interface you are familiar with.
llm = ChatOpenAI(temperature=0)
agent = OpenAIFunctionsAgent.from_llm_and_tools(llm=llm, tools=tools)See more examples under autochain/examples and workflow
evaluation test cases which can also be run interactively.
Read more about detailed components overview
It is notoriously hard to evaluate generative agents in LangChain or AutoGPT. An agent's behavior is nondeterministic and susceptible to small changes to the prompt or model. As such, it is hard to know what effects an update to the agent will have on all relevant use cases.
The current path for evaluation is running the agent through a large number of preset queries and evaluate the generated responses. However, that is limited to single turn conversation, general and not specific to tasks and expensive to verify.
To facilitate agent evaluation, AutoChain introduces the workflow evaluation framework. This framework runs conversations between a generative agent and LLM-simulated test users. The test users incorporate various user contexts and desired conversation outcomes, which enables easy addition of test cases for new user scenarios and fast evaluation. The framework leverages LLMs to evaluate whether a given multi-turn conversation has achieved the intended outcome.
Read more about our evaluation strategy.
You can either run your tests in interactive mode, or run the full suite of test cases at once.
autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py contains a few
example test cases.
To run all the cases defined in a test file:
python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.pyTo run your tests interactively -i:
python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py -iLooking for more details on how AutoChain works? See our components overview