9 Repos
Modules that integrate code interpretation to solve problems through reasoning and execution.
Distinguishing note: Focuses on code-based reasoning, distinct from standard text-based agents.
Explore 9 awesome GitHub repositories matching artificial intelligence & ml · Code Execution Agents. Refine with filters or upvote what's useful.
This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin
Demonstrates how to build agents that write and execute programming code to solve technical problems and derive insights.
DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-
Utilizes code interpretation to solve problems through iterative reasoning and execution loops.
Roo-Code is an integrated development environment extension that functions as an autonomous software engineering agent. It connects large language models directly to your local file system and terminal, enabling the agent to interpret natural language requirements and execute complex development workflows. The project distinguishes itself through a model-agnostic orchestration layer that allows developers to connect various large language model backends to their local workspace. By utilizing an iterative tool-use loop, the agent decomposes high-level tasks into sequential steps, interacting w
Integrates AI agents with direct terminal and file system access to perform complex project maintenance and code generation.
Forem is an open-source platform designed for building and managing technical communities. It functions as a social publishing engine that enables members to share long-form content, participate in threaded discussions, and engage through social interactions. The platform provides tools for organizations to maintain branded profiles, host community hackathons, and facilitate collaborative learning through structured educational tracks. Beyond its social features, Forem integrates advanced capabilities for AI agent workflow orchestration and codebase knowledge graphing. It allows developers to
Executes interactive coding tasks by reasoning across project files and performing multi-file edits.
The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web
Integrates code interpretation to solve problems through reasoning and execution.
This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
Enables agents to run code snippets to perform computations and solve technical problems.
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
Generates and executes native code directly to solve complex problems through system interactions.
TaskWeaver is an LLM agent framework that interprets natural language requests and executes them as Python code, SQL queries, or shell commands. It functions as a conversational code interpreter that maintains stateful data structures across turns, generating executable code from user prompts within a session-based environment. The system is designed as a self-hosted AI agent platform that can be deployed in Docker, managing sessions and providing a web UI for data analytics and automation tasks. The framework distinguishes itself through a role-based multi-agent architecture that divides the
Ships an agent framework that interprets natural language and executes Python, SQL, or shell commands.
Runs arbitrary Python scripts within the agent's workflow for computations and data processing.