An async message queue that lives in NodeJS's memory. Good for simulating queues in development environments. You may wish to use in combination with [iterator-worker](https://www.npmjs.com/package/iterator-worker).
Lean and customizable in-memory message queue
RabbitMQ-compatible in-memory message queue with priority support, TTL, retries, and exchange routing
**ConsisQueue** is an in-memory message queue that guarantees that messages with the same key are processed in order by a single consumer. There is no limitation of partitions like in Apache Kafka. Messages for each key can be processed in parallel.
DRY Castore MessageQueue definition using FastQ
Promise queue with concurrency control
Task and Message Queues with Multiple Providers
Tiny queue data structure
A simple queue node that store incoming messages in memory queue and uses a feedback from a following action or trigger message to release the next message stored in the queue.
Fast, in memory work queue
Easy in-memory message queue
A really fast memory store for better-queue
In memory queue system prioritizing tasks
fast, tiny `queueMicrotask` shim for modern engines
A Node.js library for interacting with the Vercel Queue Service API
Simple JS queue with auto run for node and browsers
A lightweight, in-memory message queue with topics, retries, and backoff — Kafka-like but no infra required.
Local development World implementation for Workflow SDK
Simple JS queue with auto run for node and browsers
Miscellaneous indices for graphology.
A shim for the setImmediate efficient script yielding API
Compatible version of p-queue
High-performance streaming multipart/mixed parser for Node.js
High-priority task queue for Node.js and browsers
Stomp messaging server with file/dbm/memory/activerecord based FIFO queues, queue monitoring, and basic authentication. == SYNOPSYS: Handles basic message queue processing
Ruby extension that binds System V Inter-Process Communication: message queues, semaphores, and shared memory.
Message provides flexible & reliable background/asynchronous job processing mechanism on top of simple queue interface. Any developer can create queue adapter for Message to hook up different messaging/queue system. One in-memory queue is included with Message for you to start development and test, and you can easily swap in other queues later.
# Sparrow is a really fast lightweight queue written in Ruby that speaks memcached. # That means you can use Sparrow with any memcached client library (Ruby or otherwise). # # Basic tests shows that Sparrow processes messages at a rate of 850-900 per second. # The load Sparrow can cope with increases exponentially as you add to the cluster. # Sparrow also takes advantage of eventmachine, which uses a non-blocking io, offering great performance. # # Sparrow is a in-memory queue but will persist the data to disk when receiving a term signal. # # Sparrow comes with built in support for daemonization and clustering. # Also included are example libraries and clients. For example: # # require 'memcache' # m = MemCache.new('127.0.0.1:11212') # m['queue_name'] = '1' # Publish to queue # m['queue_name'] #=> 1 Pull next msg from queue # m['queue_name'] #=> nil # m.delete('queue_name) # Delete queue # # # or using the included client: # # class MyQueue < MQ3::Queue # def on_message # logger.info "Received msg with args: #{args.inspect}" # end # end # # MyQueue.servers = [ # MQ3::Protocols::Memcache.new({:host => '127.0.0.1', :port => 11212, :weight => 1}) # ] # MyQueue.publish('test msg') # MyQueue.run # # Messages are deleted as soon as they're read and the order you add messages to the queue probably won't # be the same order when they're removed. # # Additional memcached commands that are supported are: # flush_all # Deletes all queues # version # quit # The memcached commands 'add', and 'replace' just call 'set'. # # Call sparrow with --help for usage options # # The daemonization won't work on Windows. # # Check out the code: # svn checkout http://sparrow.googlecode.com/svn/trunk/ sparrow # # Sparrow was inspired by Twitter's Starling
RedisRpc is the easiest to use RPC library in the world. (No small claim!). This version is a repackage that only has Ruby implementation. Redis is a powerful in-memory data structure server that is useful for building fast distributed systems. Redis implements message queue functionality with its use of list data structures and the `LPOP`, `BLPOP`, and `RPUSH` commands. RedisRpc implements a lightweight RPC mechanism using Redis message queues to temporarily hold RPC request and response messages. These messages are encoded as JSON strings for portability. Many other RPC mechanisms are either programming language specific (e.g. Java RMI) or require boiler-plate code for explicit typing (e.g. Thrift). RedisRpc was designed to be extremely easy to use by eliminating boiler-plate code while also being programming language neutral. High performance was not an initial goal of RedisRpc and other RPC libraries are likely to have better performance. Instead, RedisRpc has better programmer performance; it lets you get something working immediately.
RedisRPC is the easiest to use RPC library in the world. (No small claim!) It has implementations in Ruby, PHP, and Python. Redis is a powerful in-memory data structure server that is useful for building fast distributed systems. Redis implements message queue functionality with its use of list data structures and the `LPOP`, `BLPOP`, and `RPUSH` commands. RedisRPC implements a lightweight RPC mechanism using Redis message queues to temporarily hold RPC request and response messages. These messages are encoded as JSON strings for portability. Many other RPC mechanisms are either programming language specific (e.g. Java RMI) or require boiler-plate code for explicit typing (e.g. Thrift). RedisRPC was designed to be extremely easy to use by eliminating boiler-plate code while also being programming language neutral. High performance was not an initial goal of RedisRPC and other RPC libraries are likely to have better performance. Instead, RedisRPC has better programmer performance; it lets you get something working immediately.
RedisRpc is the easiest to use RPC library in the world. (No small claim!). This version is a repackage that only has Ruby implementation. Redis is a powerful in-memory data structure server that is useful for building fast distributed systems. Redis implements message queue functionality with its use of list data structures and the `LPOP`, `BLPOP`, and `RPUSH` commands. RedisRpc implements a lightweight RPC mechanism using Redis message queues to temporarily hold RPC request and response messages. These messages are encoded as JSON strings for portability. Many other RPC mechanisms are either programming language specific (e.g. Java RMI) or require boiler-plate code for explicit typing (e.g. Thrift). RedisRpc was designed to be extremely easy to use by eliminating boiler-plate code while also being programming language neutral. High performance was not an initial goal of RedisRpc and other RPC libraries are likely to have better performance. Instead, RedisRpc has better programmer performance; it lets you get something working immediately.
pikuri-memory gives a pikuri-core agent durable, long-lived memory: facts about the user and their work that persist across conversations. It wires a +recall+ tool plus an automatic per-turn prefetch onto an agent via +c.add_extension Pikuri::Memory::Extension.new(...)+ inside the +Agent.new+ block — same opt-in shape as +pikuri-tasks+ / +pikuri-vectordb+. Recall is automatic and synchronous (embed + vector search, milliseconds); capture is automatic and asynchronous (an off-the-interaction-path extraction queue), so a turn never blocks on "what should I remember?". Storage is mem0 (https://github.com/mem0ai/mem0) reached over a thin Faraday HTTP client — the append-only +add+ / read-time +search+ model. Only the *user's own words* are fed to extraction (a write-side hygiene rule that structurally drops system/assistant/tool-sourced junk), and recalled context enters the chat as a +:system+ message so it is provenance-tagged and excluded from the next extraction pass. This release ships the Ruby client + extension + tool against a *bring-your-own* mem0 endpoint; a self-managed mem0 sidecar supervisor (the +ChromaServer+-style docker pattern) is a follow-on.
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