Projects sharing features with AlphaCodium

Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove

Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and

This project is an agentic development framework and autonomous software engineering system. It utilizes a coordinated network of specialized LLM agents to automate the full software development lifecycle, from codebase exploration and architectural planning to implementation and automated refactoring. The system is distinguished by an agentic memory system and a test-driven development orchestrator. It maintains project continuity across sessions by capturing architectural learnings and state in a persistent semantic database and enforces code quality through an automated cycle of generating

Paper2Code is an AI research automation suite and large language model code generation pipeline designed to transform machine learning research papers into executable code repositories. It functions as a tool for automating the translation of scientific literature and theoretical descriptions into functional machine learning implementations. The system employs a multi-stage generation pipeline that utilizes document-to-plan decomposition and automated repository scaffolding to produce complete project structures. It incorporates an automated code evaluation framework that uses an iterative cr

Open Canvas is a system for managing stateful AI agent workflows through a collaborative editor and orchestration framework. It provides a shared workspace where humans and large language models co-author documents and write code in real time, supported by a structured text editor with live rendering. The project distinguishes itself by integrating a state manager that tracks session context, user memories, and historical snapshots across conversational threads. It employs a durable execution model that allows for human-in-the-loop interventions and maintains a version tracking system for doc

Wolverine is an AI code repair tool and self-healing Python runtime designed to monitor scripts for runtime crashes and automatically recover the source code. It functions as an automated script recovery tool that identifies failures and utilizes large language models to propose and apply corrections. The system operates through an iterative debugging cycle that captures traceback data and feeds it back into a language model to refine fixes through trial and error. To ensure safety, it includes a human-in-the-loop verification mechanism that requires manual approval before generated code chan

This project is an AI software engineering tool and framework for building autonomous coding agents. It provides a system for automating program synthesis and bug fixing by integrating large language models with codebase analysis and iterative refinement loops. The framework features an agentic development server that exposes task execution interfaces to remote agents through a structured protocol. This allows for the remote execution of development tasks and the embedding of autonomous program synthesis capabilities into external software projects. The toolset covers AI-driven project scaff

SWE-bench is a software engineering benchmark and evaluation framework designed to measure the ability of large language models to resolve real-world GitHub issues. It provides datasets and evaluation suites to verify whether model-generated code patches correctly fix software bugs. The project includes a multimodal benchmark for testing visual language models on issues involving graphical interfaces. It utilizes a collection of pre-processed repository issues and gold-standard patches to train and test AI coding agents. The framework provides infrastructure for containerized patch verificat

Open Interpreter is a local language model agent framework that enables the deployment of autonomous agents capable of controlling a local operating system and its applications. It provides an execution environment where language models can run code and scripts directly on a computer to automate system tasks. The framework includes a computer control interface that allows language models to interact with web browsers and native user interfaces through programmatic commands. To ensure system stability, it utilizes a secure sandbox environment for the execution of model-generated code. The sys

Vision-agent is an AI system and visual data extraction framework that translates natural language prompts into runnable Python scripts for analyzing images and video. It functions as a multi-model vision orchestrator, using large language models to plan and generate executable code for tasks such as object detection, counting, and video tracking. The system employs a plan-and-execute cycle that iteratively generates and tests code, using an error-correction loop to refine the implementation until a solution is validated. It is configuration-driven, allowing the underlying language model back

Poml is a prompt management framework and templating engine designed for authoring, versioning, and rendering structured prompts for large language models. It uses a semantic markup language to organize prompts into reusable templates, combining them with dynamic context and data to generate formatted inputs. The system distinguishes itself by decoupling core prompt logic from final presentation through a stylesheet-based approach. It provides a dedicated JSON schema output generator to enforce strict, machine-parsable model responses and a configuration interface for managing function tool s

This project is a comprehensive generative AI prompt library and image generation toolkit designed to streamline the creation of professional visual assets. It provides a curated collection of structured text instructions and templates that guide generative models to produce specific creative outputs, ranging from marketing materials to complex infographics. The toolkit distinguishes itself through specialized capabilities for maintaining visual continuity and applying consistent aesthetic transformations. It features reference-based identity preservation to anchor facial features across mult

SillyTavern is a comprehensive interface and orchestration platform designed for immersive AI roleplay and interactive chat experiences. It functions as a unified gateway that connects users to a wide array of local and cloud-based large language models, providing a centralized environment to manage complex character personas, narrative context, and model-driven interactions. The platform distinguishes itself through its advanced prompt engineering and automation capabilities. It utilizes a sophisticated macro-based templating engine and vector-database retrieval to dynamically inject lore, c

RedInk is an AI content automation tool designed to generate coordinated social media posts, including titles, body text, and matching visual assets, from a single user-provided topic. It functions as a stateless content pipeline that uses large language models to transform topics into structured marketing copy and image prompts. The system utilizes prompt-template orchestration to combine static instructions with dynamic inputs, guiding artificial intelligence toward specific output formats. Users can manage these AI behaviors and API preferences through a web-based settings interface that t

Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with large language models. By acting as a reverse-proxy, it provides a centralized layer for routing requests across multiple AI providers, allowing developers to maintain consistent application logic while gaining deep visibility into model performance, usage, and costs. The platform distinguishes itself through a robust suite of traffic management and prompt engineering tools. It enables policy-driven control, including automatic failover between providers, rate limiting, and edge-b

Guidance is a control framework and generation orchestrator for large language models. It provides a programming layer to steer model outputs through structured templates, schema enforcement, and logical flow management. The framework distinguishes itself by interleaving model generation with local code execution, enabling the use of loops and conditional branching within a single session. It employs grammar-based token constraints and regular expressions to force models to sample only from tokens that satisfy a specific structural format, ensuring strict adherence to predefined data models.

Coze-loop is an optimization platform and orchestration management suite for large language model agents. It functions as a comprehensive environment for the development, debugging, evaluation, and monitoring of AI agent performance. The project provides a dedicated prompt engineering playground for real-time iteration and validation of model responses. It includes an evaluation framework that runs automated assessments against datasets to generate performance metrics and verify output accuracy. The system covers observability through real-time execution tracing and historical analysis of ag

This is a curated gallery of real-world workflows demonstrating how to use Claude Code for AI-driven coding, debugging, and development automation. The showcase includes executable scripts that reproduce each AI interaction locally, allowing you to see exactly how the assistant generates, explains, and modifies code within a development environment. The project shows how to build custom AI agents with targeted prompts and multi-step slash commands, define project-wide memory that persists across sessions, and inject domain-specific knowledge through markdown files. It also demonstrates integr

Context-Engineering is a prompt engineering framework and cognitive architecture for large language models. It provides a set of patterns and methodologies for designing structured prompts and modular reasoning flows that decompose complex tasks into specialized, step-by-step problem solving templates. The project distinguishes itself through stateful prompt management and context window optimization. It maintains persistent memory across multiple interaction turns by compressing conversation history into compact internal state cells and employs techniques to maximize information density per

This project is a curated library of community-driven prompt templates and personas designed to improve interactions with large language models. It functions as a prompt engineering guide, providing interactive tutorials and examples to teach advanced design and reasoning techniques. The library can operate as a Model Context Protocol server, providing a standardized interface for AI tools and agents to access prompt data as a service. For organizations, it offers a self-hosted repository option that allows for private deployment on internal infrastructure with custom authentication and data

ChatGPT-Next-Web is a cross-platform web interface and frontend for interacting with large language models. It functions as a self-hosted client that allows users to connect to various AI model providers through a unified chat interface compatible with web browsers and desktop operating systems. The project includes a prompt template manager for creating and organizing reusable masks to standardize interactions. It supports self-hosting on private clouds to maintain data security and provides a centralized administrative panel for managing API resources and member access permissions. The app

Starcoder is a large language model and associated framework designed to generate, complete, and evaluate source code across multiple programming languages. It functions as a source code model that can produce complete function implementations and predict subsequent characters in a line of code based on provided prompts. The project provides a specialized toolkit for adapting base models to specific coding tasks and instruction-following behaviors. This includes a conversational code assistant framework for training models to generate code via natural language chat, as well as a parameter-eff

Genkit is an open-source framework for building AI-powered applications. It provides a unified interface for connecting to hundreds of generative AI models from multiple providers, enabling text, image, audio, and video generation through a single API. The framework structures multi-step AI interactions—including chat, retrieval-augmented generation, tool use, and agentic workflows—as composable, traceable flows with built-in streaming and state management. The framework distinguishes itself through a comprehensive developer toolkit that includes a command-line interface and a local developer

FluentAssertions is a library of extension methods for .NET that provides a human-readable syntax for verifying expectations in unit tests. It serves as a validation layer that integrates with MSTest, NUnit, and xUnit to throw framework-specific exceptions upon assertion failure. The project includes a structural equivalency engine that performs deep member-by-member validation of .NET objects using configurable matching rules. It is designed for test-driven and behavior-driven development by generating descriptive failure messages that explain the difference between actual and expected resul

gptme is a multi-agent orchestration platform designed for autonomous software engineering, terminal-based AI integration, and RAG-enhanced code navigation. It enables the deployment of persistent agents and specialized subagents to decompose complex tasks and execute parallel technical workflows. The system distinguishes itself through a combination of vision-based GUI automation for controlling desktop applications and surgical patching mechanisms for targeted source code modifications. It utilizes git-based memory management to maintain a versioned history of agent identities, lessons, and

Prompt-poet is a Python templating engine and software library designed for constructing, tokenizing, and rendering dynamic language model prompts. It separates content from application code by storing prompt definitions as independent files on disk, supporting optional in-memory caching and structured metadata organization with named sections, assigned roles, and conditional logic. The library features modular template composition that decomposes prompts into reusable components and shared variants. It provides runtime Python evaluation to execute custom functions directly inside template st

This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and security testing. It provides a library of advanced templates and frameworks designed to optimize the quality and specificity of model responses. The project includes resources for red teaming and security research, featuring a repository of prompts designed to bypass safety filters and operational constraints. It also provides techniques for system prompt extraction to reveal the internal instructions and configurations of AI personas. The collection covers a broader surface