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AutoChain

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Basic Information

AutoChain is a developer-focused framework for building, customizing, and evaluating generative agents powered by large language models. It provides a lightweight pipeline that reduces abstraction compared to some alternative frameworks and is inspired by LangChain and AutoGPT. The repository is intended to help developers create conversational agents that can call custom tools, maintain simple memory of conversations and tool outputs, and use OpenAI-style function calling without extra boilerplate. AutoChain also includes a workflow evaluation framework that runs simulated multi-turn conversations between agents and LLM-generated test users so developers can automatically test agent behavior across scenarios. The project includes examples, components overview, and scripts to run interactive or batch evaluations to accelerate iteration on prompts, tools, and agent logic.

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App URL
https://github.com/Forethought-Technologies/AutoChain
Github Repository
https://github.com/Forethought-Technologies/AutoChain/blob/main/README.md

App Details

Features
AutoChain offers a compact set of features to support rapid agent development and testing. It provides an extensible generative agent pipeline and a Chain abstraction with a ConversationalAgent and memory integration. The framework supports adding custom Tool objects and transparently converts tool specs into OpenAI function calling format for compatible models. It includes a BufferMemory implementation to track conversation history and outputs. A built-in workflow evaluation system simulates test users and uses LLMs to judge whether multi-turn conversations achieve desired outcomes. Usability features include easy prompt updates and a verbose mode to visualize prompts and outputs. The repo supplies example scripts and documentation covering components and evaluation workflows.
Use Cases
AutoChain helps developers reduce the effort and friction of building and iterating on generative agents by simplifying prompt management, tool integration, and memory handling. Its minimal abstraction model lets engineers quickly modify prompts and agents while the verbose mode aids debugging and understanding model behavior. The automated workflow evaluation framework addresses the costly and manual nature of agent testing by simulating user conversations and using LLMs to assess whether intended outcomes are met, which helps detect regressions across scenarios. Support for function calling and examples for common tasks speed up integration with external tools. Overall the repository is useful for teams seeking faster iteration cycles, reproducible tests, and clearer visibility into agent behavior.

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