XAgent supports autonomous complex task solving with a large language model agent.
Open source
24 alternativesThinkGPT
ThinkGPT provides agent techniques intended to extend the capabilities of large language models.
About ThinkGPT
ThinkGPT is an open-source Python library for chain-of-thought techniques with large language models. It provides methods to memorize information, retrieve it as context for predictions, refine output against critics, compress knowledge by summarization or rule abstraction, and express conditions in natural language.
It installs with pip from GitHub and the documented example uses model gpt-3.5-turbo. One-shot summarization requires content within the model's context length, and the remember method does not count separator tokens in its token total.
Key features
- Memorize text and retrieve relevant memories with document limit
- Retrieve memories constrained by maximum token count
- Predict with retrieved long-term memory as context
- Refine generated content using critics and instruction hints
- Summarize content in one-shot or chunked mode
- Abstract general rules from specific observations
Best for: Python developers building language model workflows that need memory, refinement, and context management.
Good for
- Open-source Python library installed with pip from GitHub
- Memory retrieval supports document count and token count controls
- Provides one-shot and chunked summarization methods
Watch out for
- One-shot summarization requires content within the model's context length
- Remember method does not account for separator tokens
ThinkGPT at a glance
- Category
- AI & Machine Learning › AI Agents & Automation
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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ThinkGPT FAQ
How is ThinkGPT installed?
With pip using pip install git+https://github.com/alaeddine-13/thinkgpt.git.
How can retrieved memory be limited?
Use the limit parameter for maximum documents and the max_tokens parameter for maximum tokens.
When should chunked_summarize be used instead of summarize?
Use summarize for content within the model's context length in a single call; chunked_summarize splits very large content into chunks, summarizes each, then combines them.
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