Search Results for "memory" - Page 3
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Showing 459 open source projects for "memory"
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LazyCodex
The one and only agent harness for complex codebases
LazyCodex is an agent harness for using Codex on complex software projects. It is designed to add structure around AI coding sessions through memory, planning, execution, verification, skills, hooks, routing, and diagnostics. The project helps developers move beyond one-off prompts by giving the agent a more organized workflow inside a codebase. It supports project memory so context can persist across sessions and decisions do not need to be repeatedly reintroduced. LazyCodex also emphasizes verified completion, which means the workflow is built around checking whether tasks are actually finished rather than only generating code. ... -
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OpenHuman
Your Personal AI super intelligence. Private, simple and powerful
...It focuses on a private, desktop-first experience with a friendly interface, onboarding flows, and a persistent assistant that can remember context over time. The project connects to common productivity tools, gathers fresh information from integrations, and organizes user knowledge into a local memory system. It also includes practical agent tools such as web search, web fetching, file access, coding utilities, voice input, text-to-speech, and model routing. Its goal is to make an AI assistant feel continuously useful across meetings, messages, documents, tasks, and personal workflows. Since it is still in early beta, it is best suited for technical users and early adopters who want to experiment with a customizable personal AI environment. -
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CoPaw
Your Personal AI Assistant; easy to install, deploy on local or coud
...It includes a browser-based Console for chatting, configuring models, managing memory, and extending capabilities with custom skills. With built-in cron scheduling, heartbeat check-ins, and extensible skill loading, CoPaw grows with your workflow over time. Easy installation options—including pip, one-line scripts, Docker, and cloud deployment—make it accessible for both developers and non-technical users. -
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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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R-KV
Redundancy-aware KV Cache Compression for Reasoning Models
...Modern transformer models rely heavily on KV caches during autoregressive decoding, which store intermediate attention states to accelerate generation. However, these caches can consume large amounts of memory, especially in reasoning-oriented models with long context windows. R-KV introduces a method for compressing the KV cache during decoding, allowing models to maintain reasoning performance while reducing memory consumption and computational overhead. The approach focuses on identifying which attention heads and cache components are most important for maintaining reasoning quality, allowing less critical information to be compressed or discarded. ... -
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MimiClaw
Run OpenClaw on a $5 chip
MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. Even though it’s running on minimal hardware, MimiClaw maintains local memory that persists across power cycles, enabling context continuity over time without relying on cloud services. ... -
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Harmonist
Portable AI agent orchestration with mechanical protocol enforcement
...It is designed to make agent workflows more reliable by enforcing protocol rules mechanically instead of trusting prompts alone. The framework includes a catalog of specialized agents, validated memory behavior, supply-chain checks, and hooks that gate code-changing turns. If required reviewers do not run, memory is not updated, or shipped files fail integrity checks, Harmonist can block the workflow from completing. The project uses Python, has no runtime dependencies beyond the standard library, and is positioned as a drop-in agent coordination pack. ... -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
...This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
Neuron AI is a PHP agentic framework for building production-ready AI applications that connect models, memory, vector databases, and tools into working agents. It is designed for developers who want to create systems such as RAG pipelines, multi-agent workflows, and business process automations without having to hand-build every integration from scratch. The framework provides an Agent class that can be extended to inherit core capabilities like memory, tools, function calling, and retrieval-augmented generation. ... -
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Personal AI Infrastructure
Agentic AI Infrastructure for magnifying HUMAN capabilities
...Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator. -
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NanoClaw
A lightweight alternative to Clawdbot / OpenClaw
...The project connects directly to WhatsApp, letting you deploy an assistant that can chat in a familiar interface while still supporting real agent behaviors instead of simple call-and-response prompts. It includes memory so the assistant can retain important context across interactions, enabling more consistent follow-through on ongoing tasks. It also supports scheduled jobs, making it suitable for recurring reminders, periodic automations, and timed workflows without needing an external orchestrator. -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
...The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. The engine includes both backend and frontend components, with configuration and deployment instructions for local and containerized setups, and is designed to produce detailed predictive reports based on interactions and emergent patterns within the simulated world. -
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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. By presenting clear performance estimates and compatibility guidance, the project reduces the trial-and-error typically involved in local LLM experimentation. ... -
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agentmemory
#1 Persistent memory for AI coding agents
agentmemory is a persistent memory server for AI coding agents that captures project context, past decisions, bugs, preferences, and implementation details so users do not have to re-explain the same information in every session. It works across tools such as Claude Code, Cursor, Gemini CLI, Codex CLI, OpenCode, Cline, and any client that supports MCP or REST APIs. -
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GitClaw
A universal git-native AI agent framework
...For example, identity and personality may be defined in files such as SOUL.md, while behavioral constraints and policies can be placed in rule definitions. Memory is persisted directly in the repository as version-controlled files, which means conversations, experiences, or learned data can be tracked over time using Git history. -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
mistral-finetune is an official lightweight codebase designed for memory-efficient and performant finetuning of Mistral’s open models (e.g. 7B, instruct variants). It builds on techniques like LoRA (Low-Rank Adaptation) to allow customizing models without full parameter updates, which reduces GPU memory footprint and training cost. The repo includes utilities for data preprocessing (e.g. reformat_data.py), validation scripts, and example YAML configs for training variants like 7B base or instruct models. ... -
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Zep
Zep: A long-term memory store for LLM / Chatbot applications
Easily add relevant documents, chat history memory & rich user data to your LLM app's prompts. Understands chat messages, roles, and user metadata, not just texts and embeddings. Zep Memory and VectorStore implementations are shipped with your favorite frameworks: LangChain, LangChain.js, LlamaIndex, and more. Automatically embed texts and messages using state-of-the-art opeb source models, OpenAI, or bring your own vectors. -
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Lightpanda Browser
Lightpanda: the headless browser designed for AI and automation
...The browser is implemented using the Zig programming language and integrates the V8 JavaScript engine to run modern web applications and scripts efficiently. Because it avoids graphical rendering and other heavy browser components, the system uses significantly less memory and launches almost instantly compared to conventional browsers such as Chrome. -
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Mastra
The TypeScript AI agent framework
...It integrates cleanly with React, Next.js, and Node-based backends, but can also run as a standalone server, giving teams flexibility in how they deploy their AI logic. At its core, Mastra provides abstractions for agents, workflows, tools, memory, retrieval, and model routing, so developers can focus on specifying behavior rather than wiring infrastructure from scratch. Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. ... -
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Hermes Agent Orange Book
From Beginner to Master · Orange Book Series
Hermes Agent Orange Book is a structured knowledge resource and guide for building and understanding Hermes-style autonomous agents. It compiles principles, workflows, and patterns used in agent-based systems into an organized format. The project focuses on explaining how agents manage memory, tools, and iterative reasoning processes. It serves as both a reference and a learning resource for developers working with autonomous AI systems. The content emphasizes practical implementation strategies rather than abstract theory. It is particularly useful for those building or studying agent architectures. Overall, it provides a comprehensive overview of agent design and operation. -
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forkd
Fork() for AI agent microVMs
...Instead of cold-booting a separate virtual machine for every worker, it forks children from a warmed parent snapshot and uses copy-on-write behavior to share the initial memory state. This makes it useful for spawning many isolated agent environments quickly during parallel research, testing, or code execution. The project is focused on runtime performance, sandbox isolation, and efficient branching from a live or prepared VM state. It is especially relevant for developers building agent infrastructure where many short-lived execution branches need to run safely. forkd is best understood as a low-level systems tool for scalable, isolated AI-agent workloads. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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Vibe-Trading
Vibe-Trading: Your Personal Trading Agent
...It features a swarm-based architecture with prebuilt expert agent teams for research, trading, and risk management. Advanced backtesting engines provide statistical validation, optimization, and performance metrics. The system also includes persistent memory, enabling it to learn from past interactions and refine strategies over time. Overall, it delivers an end-to-end AI-driven trading environment for both research and execution. -
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KVCache-Factory
Unified KV Cache Compression Methods for Auto-Regressive Models
...In large language models, the key-value cache stores intermediate attention states that enable efficient token generation during inference, but these caches can consume large amounts of GPU memory when handling long contexts. KVCache-Factory provides a platform for implementing and evaluating multiple compression strategies that reduce memory usage while preserving model performance. The framework integrates several state-of-the-art methods such as PyramidKV, SnapKV, H2O, and StreamingLLM, allowing researchers to compare and experiment with different approaches within the same environment. ... -
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xFormers
Hackable and optimized Transformers building blocks
...One of its key goals is efficient attention: it supports dense, sparse, low-rank, and approximate attention mechanisms (e.g. FlashAttention, Linformer, Performer) via interchangeable modules. The library includes memory-efficient operator implementations in both Python and optimized C++/CUDA, ensuring that performance isn’t sacrificed for modularity. It also integrates with PyTorch seamlessly so you can drop in its blocks to existing models, replace default attention layers, or build new architectures from scratch. xformers includes training, deployment, and memory profiling tools.
