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Basic Information

ThinkGPT is a Python library designed to implement Chain of Thought techniques and generative agent capabilities on top of large language models. It provides programmatic building blocks to let LLMs think, reason and act with persistent memory and higher-order reasoning primitives. The project targets developers who want to augment LLM behavior with long-term and compressed memory, iterative self-refinement, rule induction from observations, context-aware inference, and natural language driven conditions and selections. The README includes concrete API examples showing how to create a ThinkGPT instance, memorize and recall knowledge, predict using remembered context, summarize large content in chunks, abstract observations into rules and run self-refinement workflows. Example scripts demonstrate agent memory replay, memory expansion, and critic-driven refinement to support generative agent experiments and reproducible demos.

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Features
The repository documents a set of reusable thinking building blocks: persistent Memory for storing and retrieving experiences, Self-refinement to iteratively improve model outputs using critics, Knowledge compression via summarization and chunked summarization, Inference utilities to make educated guesses, and Natural Language Conditions and Selectors to express program logic in plain text. The API is Pythonic and integrates with DocArray for multi-modal document handling. Available methods include memorize, remember, predict, refine, summarize, chunked_summarize, abstract, condition and select. The project also provides example scripts for teaching a new language, using memory in code generation, replaying agent memory, inducing new observations, and critic-based refinement to update stored knowledge.
Use Cases
ThinkGPT helps developers enhance LLM applications by addressing limited context windows through long-term memory and compressed representations. It enables improved one-shot reasoning by exposing higher-order primitives such as abstraction, rule induction and iterative refinement, which can be used to make models perform intelligent decisions inside software. Practical benefits shown in the README include self-healing code generation via error-driven refinement, chunked summarization to fit large content into model context, replaying and expanding agent memory to infer new observations, and using natural language selection or conditions to drive branching logic. The examples and simple API make it suitable for prototyping generative agents and research workflows that require measurable context management and memory-driven behavior.

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