Welcome to the codebase for our paper, "Language Models can Solve Computer Tasks". In this codebase, you will find the implementation of our RCI agent, which uses a pre-trained language model to execute computer tasks in MiniWoB++ benchmark guided by natural language. The agent employs a simple…
الميزات الرئيسية لـ posgnu/rci-agent هي: Agent Memory Systems, Task Automation Agents.
تشمل البدائل مفتوحة المصدر لـ posgnu/rci-agent: mnemox-ai/tradememory-protocol — The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial… aiwaves-cn/agents — This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It… baai-agents/cradle — The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer… canner/wrenai — WrenAI is a platform designed to enable natural language interaction with relational and analytical databases. By… cpacker/memgpt — MemGPT is a memory management framework and external memory layer for large language models. It functions as a… airi-institute/arigraph — AriGraph functions as the external memory architecture for large language models (LLMs), featuring a knowledge graph…
The Tradememory Protocol is a persistent, multi-layered memory and audit framework designed for artificial intelligence trading agents. It provides a structured architecture for agents to maintain episodic, semantic, and procedural knowledge across trading sessions, ensuring that decision-making is informed by long-term recall and historical context. The framework distinguishes itself through a combination of cryptographic integrity and cognitive modeling. It employs a tamper-evident logging system that uses hashed sequences to verify the history of trading decisions, alongside an outcome-wei
This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes. The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent. The system
The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.
AriGraph functions as the external memory architecture for large language models (LLMs), featuring a knowledge graph that is built from the ground up. This memory, configured as a semantic knowledge graph with added episodic vertices and edges, greatly improves the performance of…