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https://github.com/Forethought-Technologies/AutoChain

AutoChain: Build lightweight, extensible, and testable LLM Agents
https://github.com/Forethought-Technologies/AutoChain

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AutoChain: Build lightweight, extensible, and testable LLM Agents

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README 

        
# AutoChain



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](#workflow-evaluation) 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 



## Features



- 🚀 lightweight and extensible generative agent pipeline.

- 🔗 agent that can use different custom tools and

  support OpenAI [function calling](https://platform.openai.com/docs/guides/gpt/function-calling)

- 💾 simple memory tracking for conversation history and tools' outputs

- 🤖 automated agent multi-turn conversation evaluation with simulated conversations



## Setup



Quick install



```shell

pip install autochain

```



Or install from source after cloning this repository



```shell

cd autochain

pyenv virtualenv 3.10.11 venv

pyenv local venv



pip install .

```



Set `PYTHONPATH` and `OPENAI_API_KEY`



```shell

export OPENAI_API_KEY=

export PYTHONPATH=`pwd`

```



Run your first conversation with agent interactively



```shell

python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py -i

```



## How does AutoChain simplify building agents?



AutoChain aims to provide a lightweight framework and simplifies the agent building process in a

few

ways, as compared to existing frameworks



1. 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 `-v` flag to output verbose prompt and outputs in console.

2. Up to 2 layers of abstraction  

   As part of enabling rapid iteration, AutoChain chooses to remove most of the

   abstraction layers from alternative frameworks

3. 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.



## Example usage



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`:



```python

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



```python

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](https://platform.openai.com/docs/guides/gpt/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.



```python

llm = ChatOpenAI(temperature=0)

agent = OpenAIFunctionsAgent.from_llm_and_tools(llm=llm, tools=tools)

```



See [more examples](./docs/examples.md) under `autochain/examples` and [workflow

evaluation](./docs/workflow-evaluation.md) test cases which can also be run interactively.



Read more about detailed [components overview](./docs/components_overview.md)



## Workflow Evaluation



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](./docs/workflow-evaluation.md).



### How to run workflow evaluations



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:



```shell

python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py

```



To run your tests interactively `-i`:



```shell

python autochain/workflows_evaluation/conversational_agent_eval/generate_ads_test.py -i

```



Looking for more details on how AutoChain works? See

our [components overview](./docs/components_overview.md)