LangGraph
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Durable Functions library for Node.js Azure Functions
AWS Durable Execution Language SDK for TypeScript
The leading workflow orchestration platform. Run stateful step functions and AI workflows on serverless, servers, or the edge.
Build real-time applications powered by [Durable Objects](https://developers.cloudflare.com/durable-objects/), inspired by [PartyKit](https://www.partykit.io/).
AWS Durable Execution Testing SDK for TypeScript
The Output.ai Framework
Genkit AI framework plugin for Firebase including Firestore trace/state store and deployment helpers for Cloud Functions for Firebase.
A Typescript framework built on the database
The core module of the output framework
Convex component for durably executing workflows.
Experimental sandboxed JS execution and filesystem runtime for Cloudflare Workers agents
Next.js plugin for Genkit
Local-first project memory for AI coding agents.
[EXPERIMENTAL] An opinionated AI agent implementation for Temporal
- Flexible: Leverage the strenghts of Cloudflare abstractions and let app developers choose right setup for their use case (configurable transports/storage) - Efficient: Let's Durable Objects hibernate when not possible to avoid CPU billing while still st
MCP server providing unified access to Claude Code, Codex, Gemini, Grok, and Mistral Vibe CLIs with session management, retry logic, async job orchestration, durable job results, and cross-LLM validation.
Enforce real-time token budgets and spending limits for OpenAI, Anthropic Claude, and Google Gemini API calls in Node.js
Utilities for Cloudflare Durable Objects and Workers, including SQL migrations, sharding and retry utilities, and more.
A Durable Task Javascript SDK compatible with Dapr Workflow and its underlying Durable Task engine
TypeScript client for the Durable Streams protocol
Framework-agnostic Remnic memory engine — orchestrator, storage, extraction, search, trust zones
Durable execution for Zapier SDK.
Durable-LLM is a unified interface for interacting with multiple Large Language Model APIs, simplifying integration of AI capabilities into Ruby applications.
HTM (Hierarchical Temporal Memory) provides intelligent memory/context management for LLM-based applications. It implements a two-tier memory system with durable long-term storage (PostgreSQL) and token-limited working memory, enabling applications to recall context from past conversations using RAG (Retrieval-Augmented Generation) techniques.
RobotLab is a Ruby framework for building and orchestrating multi-robot LLM workflows. Built on ruby_llm, it provides robots with template-based prompts, tools, and shared memory; networks for coordinating multiple robots with intelligent routing; MCP (Model Context Protocol) integration for external tool servers; and a memory system with Redis backend and semantic caching. Optional gems add Rails integration (robot_lab-rails), durable learning (robot_lab-durable), Ractor concurrency (robot_lab-ractor), and document storage (robot_lab-document_store).
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.
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