Search Results for "code framework" - Page 2
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140 projects for "code framework" with 2 filters applied:
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OpenAGI
When LLM Meets Domain Experts
OpenAGI is a package for AI agent creation designed to connect large language models with domain-specific tools and workflows in the AIOS (AI Operating System) ecosystem. It provides a structured Python framework, pyopenagi, for defining agents as modular units that encapsulate execution logic, configuration, and dependency metadata. Agents are organized in a well-defined folder structure that includes code (agent.py), configuration (config.json), and extra requirements (meta_requirements.txt), which makes them easy to package, share, and reuse. ... -
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Google Research
This repository contains code released by Google Research
Google Research is a massive monorepo that hosts a wide range of research code released by Google Research teams across machine learning, artificial intelligence, robotics, natural language processing, and other advanced domains. Rather than being a single framework, the repository serves as a centralized collection of experimental projects, reference implementations, and reproducible research artifacts. It is intended primarily for researchers and advanced practitioners who want to explore cutting-edge techniques directly from the teams that developed them. ... -
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II Agent
A new open-source framework to build and deploy intelligent agents
II-Agent is an open-source intelligent assistant framework designed to automate complex workflows across multiple domains using large language models and external tools. The platform allows users to interact with multiple AI models within a single environment while connecting those models to external services and knowledge sources. Through a unified interface, users can switch between models, access specialized tools, and execute tasks that require information retrieval, code execution, or file analysis. ... -
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typeui.sh
Design system skills for agentic tools
typeui.sh is a type-safe UI framework designed to streamline the development of user interfaces by leveraging strong typing and structured definitions. It focuses on improving developer productivity by reducing runtime errors and ensuring consistency across UI components. The framework integrates closely with TypeScript, allowing developers to define interfaces and components with strict type validation. -
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Pro Workflow
Claude Code learns from your corrections: self-correcting memory
Pro Workflow is a productivity framework for Claude Code that introduces self-improving workflows through memory, context engineering, and structured agent orchestration. The system learns from user corrections over time, storing feedback and refining its behavior across sessions to improve accuracy and efficiency. It supports advanced development setups such as parallel worktrees, enabling multiple tasks to be handled simultaneously without interference. -
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Sail
A drop-in Apache Spark replacement written in Rust
Sail is an open-source distributed computation framework designed to unify batch processing, stream processing, and AI workloads into a single, high-performance engine. It is built entirely in Rust, eliminating JVM overhead and enabling predictable performance, fast startup times, and improved memory safety compared to traditional big data frameworks. Sail is compatible with the Spark Connect protocol, which means existing Spark SQL and DataFrame workloads can run without code changes, making adoption seamless for teams already using Spark-based pipelines. ... -
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TypedAI
TypeScript AI platform with AI chat, Autonomous agents
TypedAI is an open-source TypeScript platform designed for building and running AI agents, chatbots, and large language model workflows. The framework provides developers with a full-featured environment for designing autonomous agents capable of performing complex tasks such as code analysis, workflow automation, or conversational assistance. Written in TypeScript, the platform emphasizes strong typing and structured development patterns to improve reliability when building AI-driven systems. ... -
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Lagent
A lightweight framework for building LLM-based agents
Lagent is a lightweight open-source framework designed to help developers build autonomous agents powered by large language models. The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. ... -
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Lingvo
Framework for building neural networks
Lingvo is a TensorFlow based framework focused on building and training sequence models, especially for language and speech tasks. It was originally developed for internal research and later open sourced to support reproducible experiments and shared model implementations. The framework provides a structured way to define models, input pipelines, and training configurations using a common interface for layers, which encourages reuse across different tasks. It has been used to implement state... -
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HybridClaw
The enterprise operating layer for open agents
HybridClaw is an emerging open-source framework focused on enabling hybrid AI agent systems that combine local execution, tool integration, and multi-agent orchestration into a cohesive development environment. It is designed to work alongside modern agent ecosystems such as OpenClaw, Claude Code, and similar agentic coding tools, providing a flexible infrastructure for managing agent behaviors, workflows, and capabilities. -
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Modelence
Modelence is an all-in-one TypeScript platform
...Modelence also focuses on “standard features” that most apps require, so developers can spend more time on product logic rather than setup and glue code. -
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OpenSpec
Spec-driven development (SDD) for AI coding assistants
...By separating intent from execution, it helps teams reduce ambiguity and improve reproducibility in AI-driven development. Overall, OpenSpec serves as an enabling framework for spec-driven software engineering in the age of AI coding tools. -
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GitHub Agentic Workflows
GitHub Agentic Workflows
GitHub Agentic Workflows is an experimental CLI extension and framework for the gh GitHub CLI that lets developers author automation driven by natural language specifications instead of hand-written code, compiling those descriptions into GitHub Actions workflows that run AI agents (like Copilot, Claude Code, or Codex) on schedule or in response to repository events. By writing intent in markdown files, a developer can quickly generate .yml Actions workflows that perform tasks such as summarizing issues, automating triage, generating reports, or maintaining documentation, all without manually crafting YAML logic from scratch. ... -
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FlowGram
Extensible workflow development framework
FlowGram is an open-source, node-based workflow development framework and toolkit aimed at helping developers build custom AI-workflow platforms or automation systems through a visual, drag-and-drop interface. Instead of shipping as a ready-made product, it provides the building blocks — a canvas for wiring together nodes, a form engine for configuring node parameters, a variable-scope and type-inference engine, and a set of “materials” (pre-built node types such as code execution, conditional logic, LLM calls, etc.) that can be composed into larger workflows. ... -
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Claude Subconscious
Give Claude Code a subconscious
Claude Subconscious is an experimental plugin that enhances AI coding workflows by introducing a persistent “memory layer” for Claude Code through integration with Letta’s agent framework. It operates as a background agent that continuously observes user interactions, reads project files, and processes session transcripts to build long-term contextual memory. Unlike standard AI interactions that reset between sessions, this system accumulates knowledge over time, allowing it to recall user preferences, project structures, and recurring patterns across multiple sessions. ... -
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Agentless
An agentless approach to automatically solve software development
Agentless is an open-source framework that applies large language models to automatically resolve software development issues without relying on complex autonomous agent systems. The project proposes an alternative approach to AI-driven code repair that avoids the overhead of multi-agent orchestration by using a structured pipeline for identifying and fixing bugs. -
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Agently 4
Build GenAI application quick and easy
Agently is a Python framework for building generative-AI (“GenAI”) applications; it focuses on enabling developers to orchestrate AI agents, workflows, and event-driven logic in a robust, reusable way. With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions,... -
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
HumanLayer is an open source development environment designed to help developers orchestrate and manage AI coding agents working within complex software projects. It provides a framework and tooling that allow AI agents to research, plan, and implement changes in large codebases while maintaining structured workflows. It focuses on enabling AI-assisted development through coordinated agent workflows rather than isolated code generation tasks. HumanLayer integrates with modern AI models and coding assistants to automate tasks such as code research, planning, and implementation while maintaining a structured development process. ... -
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Plano
Delivery infrastructure for agentic apps
Plano is an AI-native proxy and data plane designed to simplify the infrastructure required to deploy and operate agentic applications in production environments. It removes repetitive plumbing work from application code by centralizing capabilities such as agent routing, orchestration, guardrails, observability, and model selection. Built on modern proxy technology and compatible with any language or AI framework, Plano enables developers to focus on core agent logic instead of infrastructure complexity. The system provides intelligent LLM routing APIs that support model agility, along with filter chains for safety, moderation, and memory hooks. ... -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ... -
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DocETL
A system for agentic LLM-powered data processing and ETL
...The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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ADK Go
Code-first Go toolkit for building, evaluating, and deploying AI agent
ADK-Go is an open source toolkit designed to help developers build, evaluate, and deploy sophisticated AI agents using the Go programming language. It is part of the Agent Development Kit ecosystem and follows a code-first approach that allows developers to define agent behavior, tools, and orchestration logic directly in Go code. ADK-Go applies traditional software engineering principles to agent development, making it easier to structure, test, and maintain complex agent-based systems. It... -
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AutoTrain Advanced
Faster and easier training and deployments
AutoTrain Advanced is an open-source machine learning training framework developed by Hugging Face that simplifies the process of training and fine-tuning state-of-the-art AI models. The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. ... -
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GitClaw
A universal git-native AI agent framework
GitClaw is an open-source framework for building AI agents whose entire identity, configuration, memory, and capabilities live inside a Git repository. Instead of storing agent state in databases or application code, the framework treats a repository itself as the agent’s environment, allowing developers to version, inspect, and collaborate on agents using standard Git workflows.
