Search Results for "distributed shared memory"
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Showing 465 open source projects for "distributed shared memory"
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codebase-memory-mcp
High-performance code intelligence MCP server
...It is built for speed, with average repositories indexed in milliseconds and structural queries answered in under one millisecond. It ships as a single static binary for macOS, Linux, and Windows with no shared library dependencies. It also includes semantic search, BM25 search, graph visualization, auto-indexing, and cross-repository intelligence. -
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Olric
Distributed, in-memory key/value store and cache
A lightweight, distributed in-memory data store designed for key-value caching and ephemeral storage. -
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I hate money
A simple shared budget manager web application
I hate money is a web application made to ease shared budget management. It keeps track of who bought what, when, and for whom; and helps to settle the bills. I hate money is written in python, using the flask framework. It’s developed with ease of use in mind and is trying to keep things simple. Hope you (will) like it! The code is distributed under a BSD beerware derivative: if you meet the people in person and you want to pay them a craft beer, you are highly encouraged to do so. -
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Jaeger
Monitor and troubleshoot transactions in complex distributed systems
...It is used for monitoring and troubleshooting microservices-based distributed systems. OpenTracing compatible data model and instrumentation libraries include Go, Java, Node, Python, C++ and C#. Jaeger uses consistent upfront sampling with individual per service/endpoint probabilities and it has multiple storage backends: Cassandra, Elasticsearch, memory. -
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PolarDB-X
PolarDB-X is a cloud native distributed SQL Database
PolarDB-X is a cloud-native distributed SQL database designed to handle high concurrency, massive storage, and complex querying scenarios. It features a shared-nothing architecture that decouples computing from storage, providing scalability and flexibility for various applications. -
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MemClaw
Persistent memory for AI agent fleets (OSS)
MemClaw is an open-source governed shared memory platform for AI agent fleets. It is designed to help agents remember information across sessions, teams, tools, and models instead of keeping knowledge trapped inside isolated conversations. The project emphasizes enterprise-style governance, including permissions, tenant isolation, audit trails, visibility scopes, and agent trust tiers. -
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PolarDB for PostgreSQL
A cloud-native database based on PostgreSQL developed by Alibaba Cloud
PolarDB for PostgreSQL is Alibaba Cloud's cloud-native, distributed version of PostgreSQL designed for high availability, scalability, and performance. It enhances standard PostgreSQL with features like shared storage, compute-storage separation, and parallel processing. PolarDB supports cloud-native workloads, offering enterprise-grade capabilities while maintaining PostgreSQL compatibility. -
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Hivemind
Hivemind turns your traces into reusable skills across agents
Hivemind is a shared memory and skill system for AI coding agents. It captures agent sessions as structured traces, including prompts, tool calls, responses, and subagent activity. The system mines those traces for repeated patterns and turns them into reusable skills that can be shared across a team. It supports agents such as Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi. -
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OpenSquilla
Token-Efficient AI Agent with same budget, higher intelligence density
OpenSquilla is a token-efficient microkernel AI agent runtime designed for CLI, web UI, and chat-based workflows. It routes each turn through a shared loop that can select lower-cost models when appropriate while preserving tool dispatch, retries, memory, and decision logging. The project supports multiple LLM providers through a pluggable provider layer, making it adaptable to different model ecosystems. It includes persistent memory, built-in web search, on-device embeddings, and sandboxing for safer execution. ... -
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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Wandesk
Give intelligence shape. Let AI sculpt your desktop
...Built-in apps include Chat, Notebook, Ledger, Memory, Files, Tasks, Settings, Claude Code, Codex, and Open Source Radar. It is useful for users who want an AI workspace where apps, memory, and coding agents share context instead of living in separate chat threads. -
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Infinispan
Infinispan is an open source data grid platform
Infinispan is a distributed in-memory data grid and caching system designed for high-performance computing. It allows applications to scale dynamically by distributing data across multiple nodes, reducing latency and improving resilience. -
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etcd
Distributed reliable key-value store for the most critical data
...Mainly used as a separate coordination service, in distributed systems. And designed to hold small amounts of data that can fit entirely in memory. Read and write values using standard HTTP tools, such as curl. Store data in hierarchically organized directories, as in a standard filesystem. Watch specific keys or directories for changes and react to changes in values. -
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OpenWork
An open-source alternative to Claude Cowork, powered by opencode
OpenWork is a framework for building decentralized collaborative work environments powered by AI and human contributions. At its core, the project enables contributors to define tasks, workflows, and goals that can be split, shared, and recombined across distributed nodes while agents and humans cooperate to advance progress. It offers structured templates for work items, decision logic for task allocation, and consensus mechanisms that let groups verify and validate results toward shared objectives. This project also includes moderation and reputation layers so that contributor trust and quality can be assessed and integrated into future task assignments. ... -
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Macro
Macro is a unified interface for email, messages, tasks, calls, agents
Macro is an open-source all-in-one workspace that combines email, messages, docs, tasks, calls, agents, pull requests, files, and CRM into one connected interface. It is designed around shared team memory, so information across the workspace can be linked, queried, and used by people or AI agents without losing context. The product emphasizes keyboard-first speed, unified communication, and a single inbox for emails, mentions, tasks, channel updates, and agent responses. Its docs system is collaborative, markdown-native, version-controlled, and built for real-time work. ... -
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Memcached
memcached development tree
Memcached is a high-performance, open-source distributed memory caching system, typically used to accelerate dynamic, database-driven websites by storing frequently accessed data in RAM. To build memcached on your machine from the local repo, you will have to install autotools, automake, and libevent. If you want TLS support, install OpenSSL's development packages and change the configure line. libseccomp (optional, experimental, Linux), enables process restrictions for better security. ... -
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vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
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ORG-2
Open-source Cursor-style agent IDE
...It presents AI agents as persistent, observable collaborators inside a structured software organization rather than as isolated coding assistants. The project focuses on reviewability, control, replayable execution, cross-session memory, and shared workspace state. It supports local-first execution and is designed to stay lightweight on disk. ORG2 includes GUI, CLI, terminal, Git, browser, LSP, timeline, and database tooling for coordinating development activity. Overall, it is an experimental environment for teams and builders who want AI coding agents to be easier to audit, schedule, supervise, and align with human goals. -
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node-rate-limiter-flexible
Count and limit requests by key with atomic increments
rate-limiter-flexible counts and limits number of actions by key and protects from DDoS and brute force attacks at any scale. It works with Redis, process Memory, Cluster or PM2, Memcached, MongoDB, MySQL, PostgreSQL and allows to control of requests rate in single process or distributed environment. All operations in memory or distributed environments use atomic increments against race conditions. Combine limiters, block key for some duration, delay actions, manage failover with insurance options, configure smart key blocking in memory and many others. ... -
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Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. ... -
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PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
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Gocache
Go cache library that brings you multiple ways to manage caches
Guess what is Gocache? a Go cache library. This is an extendable cache library that brings you a lot of features for caching data. Multiple cache stores: actually in memory, redis, or your own custom store. A chain cache: use multiple cache with a priority order (memory then fallback to a redis shared cache for instance) A loadable cache: allow you to call a callback function to put your data back in cache. A metric cache to let you store metrics about your caches usage (hits, miss, set success, set error, ...) ... -
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Citus
Distributed PostgreSQL as an extension
Citus is a PostgreSQL extension that transforms Postgres into a distributed database, so you can achieve high performance at any scale. With Citus, you extend your PostgreSQL database with new superpowers. Distributed tables are sharded across a cluster of PostgreSQL nodes to combine their CPU, memory, storage and I/O capacity. References tables are replicated to all nodes for joins and foreign keys from distributed tables and maximum read performance. ... -
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JetCache
JetCache is a Java cache framework
...It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
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Perfetto
Production-grade client-side tracing, profiling, and analysis
Perfetto is a production-grade tracing platform for Android, Linux, and Chrome that captures extremely detailed information about what a system is doing over time. It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ... -
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Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning.
