Search Results for "action"
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Showing 13 open source projects for "action"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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LLM Action
Technical principles related to large models
LLM-Action is a knowledge/tutorial/repository that shares principles, techniques, and real-world experience related to large language models (LLMs), focusing on LLM engineering, deployment, optimization, inference, compression, and tooling. It organizes content in domains like training, inference, compression, alignment, evaluation, pipelines, and applications. -
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JamAI Base
The collaborative spreadsheet for AI
...One of the key ideas behind the platform is the concept of generative tables, which allow database columns to automatically populate with AI-generated content. The system also supports action tables and chat tables that simplify the creation of interactive AI features such as conversational interfaces and dynamic workflows. -
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LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
...The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During training, the system performs a forward execution where the agent completes a task and records the trajectory of prompts, outputs, and tool usage. A prompt-based loss function is then applied to evaluate the quality of the outcome, generating language-based gradients that guide improvements to the agent pipeline. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Integuru v0
The first AI agent that builds permissionless integrations
...Developers capture browser requests and authentication data, which the agent then uses to infer the structure of the platform’s internal API endpoints. Based on this information, the system generates executable code that can replicate the original action programmatically. This approach allows developers to automate workflows and build integrations with services that do not provide official APIs or developer tools. The project is designed as a research platform for exploring AI-driven automation and integration generation. -
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runprompt
Run LLM prompts from your shell
runprompt is an interactive command launcher and prompt utility that lets users bind shell commands, scripts, and workflows to quick keyboard shortcuts or natural-language queries, helping streamline repetitive terminal tasks and boost developer productivity. It functions as a lightweight, launcher-centric interface where you can type a phrase, partial command, or alias and have RunPrompt suggest or execute relevant actions instantly, reducing the need to memorize long commands or navigate... -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making... -
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AppAgent
Multimodal Agents as Smartphone Users, an LLM-based multimodal agent
AppAgent is an open-source multimodal agent framework designed to enable large language models to operate smartphone applications through natural interactions with graphical user interfaces. The system allows an AI agent to interpret visual information from the screen and translate natural language instructions into actions such as tapping, swiping, and navigating between application screens. Instead of requiring backend access to application APIs, the framework interacts with apps the same... -
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Code World Model (CWM)
Research code artifacts for Code World Model (CWM)
CWM (Code World Model) is a 32-billion-parameter open-weights language model. It is developed by Meta for enhancing code generation and reasoning about programs. It is explicitly trained on execution traces, action-observation trajectories, and agentic interactions in controlled environments. It has been developed to better capture how code, actions, and state interact over time. The repository provides inference code, reproducibility scripts, prompt guides, and more. It has model cards, utilities, demos, and evaluation artifacts. Inference scripts and utilities for code generation tasks. ... -
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code-act
Official Repo for ICML 2024 paper
...This approach helps unify reasoning and action planning within large language model agents by using code as the primary interface between the model and the external world. The framework also includes training data, models, and evaluation tools designed to study how language models can become more capable autonomous agents. -
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CogVLM
A state-of-the-art open visual language model
...The flagship CogVLM-17B combines ~10B visual parameters with ~7B language parameters and supports 490×490 inputs; CogAgent-18B extends this to 1120×1120 and adds plan/next-action outputs plus grounded operation coordinates for GUI tasks. The repo provides multiple ways to run models (CLI, web demo, and OpenAI-Vision–style APIs), along with quantization options that reduce VRAM needs (e.g., 4-bit). It includes checkpoints for chat, base, and grounding variants, plus recipes for model-parallel inference and LoRA fine-tuning. ... -
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ReAct Prompting
Synergizing Reasoning and Acting in Language Models
...The project implements the methodology described in the research paper “ReAct: Synergizing Reasoning and Acting in Language Models,” which combines reasoning traces with action-based interactions. Instead of generating answers in a single step, models using the ReAct approach produce intermediate reasoning steps and perform actions such as searching for information or interacting with external tools. This alternating sequence of reasoning, acting, and observing results allows the model to gather additional information and refine its decision-making process during task execution. ... -
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Repo of Tree of Thoughts (ToT)
Implementation of "Tree of Thoughts
...To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem-solving. ToT allows LMs to perform deliberate decision-making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.
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