Share Claude Code and Codex sessions as public links
Azure AI Agents client library.
A streaming way to send data to a Node.js Worker Thread
Client library for interacting with the LangGraph API
Cross-agent thread reader — resolve agents:// URIs for Amp, Codex, Claude, Gemini, Pi, OpenCode
Runs the following loaders in a worker pool
An ACP-compatible coding agent powered by Codex
TypeScript SDK for Codex APIs.
A agent component for Convex.
Properly hijack require, i.e., properly define require hooks and customizations
An Undici interceptor that routes requests over a worker thread
Use Rollup with workers and ES6 modules today.
Library with base interfaces for LangGraph checkpoint savers.
A transport for pino that sends messages to Loki
A mutex for guarding async workflows
Turn a function into an `http.Agent` instance
An HTTP(s) proxy `http.Agent` implementation for HTTPS
Maps proxy protocols to `http.Agent` implementations
No description provided.
[](https://www.npmjs.com/package/@aws-sdk/util-user-agent-node) [](https://www.npmjs.com/
Browser automation for Mastra agents using agent-browser
Thread comment integration for Univer Sheets.
Shared thread comment UI components and services for Univer.
Thread comment UI integration for Univer Docs.
Modern concurrency tools including agents, futures, promises, thread pools, actors, supervisors, and more. Inspired by Erlang, Clojure, Go, JavaScript, actors, and classic concurrency patterns.
Compute values asynchronously as seen with Clojure agents, but uses multi-processing instead of multi-threading.
Modern concurrency tools including agents, futures, promises, thread pools, actors, supervisors, and more. Inspired by Erlang, Clojure, Go, JavaScript, actors, and classic concurrency patterns.
Maze-like problem spaces for agentic reasoning — paths, dead ends, backtracking, breadcrumb trails, Ariadne's thread (guiding heuristic), and Minotaur encounters (dangerous misconceptions)
llm.rb is Ruby's most capable AI runtime. It runs on Ruby's standard library by default. loads optional pieces only when needed, and offers a single runtime for providers, agents, tools, skills, MCP, A2A (Agent2Agent), RAG (vector stores & embeddings), streaming, files, and persisted state. As a bonus, llm.rb is also available for mruby. It supports OpenAI, OpenAI-compatible endpoints, Anthropic, Google Gemini, DeepSeek, xAI, Z.ai, AWS Bedrock, Ollama, and llama.cpp. It also includes built-in ActiveRecord and Sequel support, plus concurrent tool execution through threads, tasks (via async gem), fibers, ractors, and fork (via xchan.rb gem).
Provides RobotLab::DocumentStore — a thread-safe, in-memory semantic search store backed by fastembed (BAAI/bge-small-en-v1.5). Store text documents by key and retrieve the closest matches to a natural-language query using cosine similarity. Works standalone or as a drop-in extension for robot_lab agents and networks.
A comprehensive Ruby implementation of a Knowledge-Based System featuring: • RETE Algorithm: Optimized forward-chaining inference engine with unlinking optimization for high-performance pattern matching • Declarative DSL: Readable, expressive syntax for rule definition with built-in condition helpers • Blackboard Architecture: Multi-agent coordination with message passing and knowledge source registration • Flexible Persistence: SQLite (durable), Redis (fast), and hybrid storage backends with audit trails • Concurrent Execution: Thread-safe auto-inference mode for real-time processing • AI Integration: Native support for LLM integration (Ollama, OpenAI) for hybrid symbolic/neural reasoning • Production Features: Session management, fact history, query API, statistics tracking Perfect for expert systems, trading algorithms, IoT monitoring, portfolio management, and AI-enhanced decision systems.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.