Search Results for "cpu memory usage"
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Showing 23 open source projects for "cpu memory usage"
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PicoClaw
Ultra-Efficient AI Assistant in Go
PicoClaw is an ultra-lightweight, open-source personal AI assistant written in Go, architected from the ground up to operate with extremely low memory usage (under 10 MB) and fast boot times, making it suitable for inexpensive hardware platforms and embedded devices. Inspired by earlier AI assistant projects like “nanobot,” it was refactored to emphasize resource efficiency while still supporting meaningful AI-driven interactions such as conversational workflows, planning tasks, and automation. ... -
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Hello-Agents
Building an Intelligent Agent from Scratch
Hello Agents is an open educational project designed to teach developers how to understand, design, and build AI-native agents from the ground up through structured tutorials and practical examples. The project focuses on guiding learners beyond superficial framework usage toward deeper comprehension of agent architecture, reasoning loops, and real-world implementation patterns. It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. ... -
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Hermes Web UI
The best way to use Hermes Agent from the web or from your phone
...It offers a clean, multi-panel layout that includes chat interaction, session management, and workspace file browsing. The interface allows users to manage agent sessions, configure models, and interact with persistent memory systems directly from a web environment. It is built using simple technologies like Python and vanilla JavaScript, avoiding complex frontend frameworks. The UI supports real-time interaction, context tracking, and visualization of token usage. It connects to a self-hosted agent that continuously learns and evolves over time. The project emphasizes usability, accessibility, and seamless integration with existing workflows. -
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NullClaw
Fastest, smallest, and fully autonomous AI assistant infrastructure
NullClaw is the smallest fully autonomous AI assistant infrastructure, built entirely in Zig as a single static binary with zero runtime dependencies. At just 678 KB with ~1 MB peak RAM usage, it boots in under 2 milliseconds and runs on virtually any hardware, including low-cost ARM boards. Despite its size, it delivers a complete AI stack with 22+ model providers, 18+ communication channels, integrated tools, hybrid memory, and sandboxed runtime support. Its architecture is fully modular, using vtable interfaces that allow providers, channels, tools, memory backends, and runtimes to be swapped without code changes. ... -
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QwenPaw
A personal AI assistant, easy to install
QwenPaw is an AI agent framework developed by the AgentScope ecosystem to provide a desktop-style intelligent assistant powered by Qwen language models and modular agent orchestration. The project combines conversational AI, memory systems, tool usage, workflow automation, and multimodal interaction into a unified assistant environment designed for daily productivity and experimentation. It supports structured reasoning, autonomous task execution, and integration with external tools and APIs, allowing the assistant to perform actions beyond standard chatbot conversations. ... -
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OpenAI Agents (Python)
A lightweight, powerful framework for multi-agent workflows
openai-agents-python is a library developed by OpenAI to simplify the process of creating and running agents that interact with tools and APIs using OpenAI models. It provides abstractions for tool usage, memory management, and agent workflows, enabling developers to define function-calling agents that reason through multi-step tasks. Ideal for building custom AI workflows, the library supports dynamic tool definitions and contextual memory handling. -
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OpenAI CS Agents Demo
Demo of a customer service use case implemented with the OpenAI Agents
...It also demonstrates guardrails to validate or constrain responses, memory usage to maintain context, and tracing to help debugging of workflows. -
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
AI Agent Deep Dive is a comprehensive educational repository designed to provide a deep and structured understanding of how modern AI agents work, focusing on architecture, workflows, and real-world implementation patterns. It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. The project is organized as a learning resource rather than a standalone framework, making it particularly useful for developers who want to move beyond surface-level prompt engineering into full agent system design. It explores how agents interact with environments, execute tasks, and maintain context over time, highlighting both strengths and limitations of current approaches. ... -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. ... -
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Qwen-Agent
Agent framework and applications built upon Qwen>=3.0
Qwen-Agent is a framework for building applications / agents using Qwen models (version 3.0+). It provides components for instruction following, tool usage (function calling), planning, memory, RAG (retrieval augmented generation), code interpreter, etc. It ships with example applications (Browser Assistant, Code Interpreter, Custom Assistant), supports GUI front-ends, backends, server setups. Agent workflow can maintain context / memory to perform multi-turn or more complex logic over time. ... -
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Flowise
Drag & drop UI to build your customized LLM flow
Open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Conversational agent for a chat model which utilizes chat-specific prompts and buffer memory. Open source is the core of Flowise, and it will always be free for commercial and personal usage. Flowise support different environment variables to configure your instance. You can specify the following variables in the .env file inside the packages/server folder. -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language... -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. ... -
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Agent Skills for Context Engineering
A comprehensive collection of Agent Skills for context engineering
Agent Skills for Context Engineering is a curated collection of reusable “agent skills” focused on helping AI agents perform better on long-horizon, multi-step work by managing context deliberately. Rather than being a single application, it packages practical guidance into skill modules that agents can load to improve planning, retrieval, memory usage, and overall reliability in real workflows. The repository emphasizes context engineering as a discipline, covering why agents fail when context gets too large, too noisy, or poorly structured, and how to mitigate those failure modes with repeatable patterns. It is designed to be used across modern agent environments that support skill folders and structured instructions, so teams can standardize how agents operate instead of relying on ad-hoc prompting. -
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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
...The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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Koog
Koog is the official Kotlin framework for building AI agents
...It features pure Kotlin implementation, seamless Model Control Protocol (MCP) integration for enhanced model management, vector embeddings for semantic search, and a flexible system for creating and extending tools that access external systems and APIs. Ready‑to‑use components address common AI engineering challenges, while intelligent history compression optimizes token usage and preserves context. A powerful streaming API enables real‑time response processing and parallel tool calls. Persistent memory allows agents to retain knowledge across sessions and between agents, and comprehensive tracing facilities provide detailed debugging and monitoring. -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach... -
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verl-agent
Designed for training LLM/VLM agents via RL
verl-agent is an open-source reinforcement learning framework designed to train large language model agents and vision-language model agents for complex interactive environments. Built as an extension of the veRL reinforcement learning infrastructure, the project focuses on enabling scalable training for agents that perform multi-step reasoning and decision-making tasks. The framework supports multi-turn interactions between agents and their environments, allowing the system to receive... -
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Continuous Claude v3
Context management for Claude Code. Hooks maintain state via ledgers
...It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually. -
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OpenViking
Context database designed specifically for AI Agents
OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is... -
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nanobot
🐈 nanobot: The Ultra-Lightweight Clawdbot / OpenClaw
nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to... -
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Mini Agent
A minimal yet professional single agent demo project
Mini-Agent is a minimal yet production-minded demo project that shows how to build a serious command-line AI agent around the MiniMax-M2 model. It is designed both as a reference implementation and as a usable agent, demonstrating a full execution loop that includes planning, tool calls, and iterative refinement. The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during... -
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SuperAGI
A dev-first open source autonomous AI agent framework
...Connect to multiple Vector DBs to enhance your agent’s performance. Each agent is unique, use different models of your choice. Get insights into your agent’s performance and optimize accordingly. Control token usage to manage costs effectively. Enable your agents to learn and adapt by storing their memory. Get notified when agents get stuck in the loop, and provide proactive resolution. Read and store files generated by Agents.
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