Temporal
Trigger.dev
n8n.io
Pipedream
Amazon AWS
Apache Airflow
Aditya Protocol
Molted
OrientDB
ArangoDB
Redis
neo4j
MongoDB
Apache Cassandra
Azure Cosmos DB
CouchBase
Temporal OrientDBNo features have been listed yet.
Based on our record, Temporal seems to be a lot more popular than OrientDB. While we know about 16 links to Temporal, we've tracked only 1 mention of OrientDB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Durable execution โ the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows โ treats waiting as a continuation rather than a loop:. - Source: dev.to / 4 days ago
Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal โ Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / about 1 month ago
We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of โ a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines โ an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate โ plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 2 months ago
The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago
A typical production stack for teams using Claude or Gemini as the reasoning layer includes an LLM provider API, an orchestration layer (n8n, Temporal, or a custom Python service), application infrastructure (a server running the orchestration code), and a data layer (a database for storing results). Each boundary introduces a failure point. When the LLM provider changes its rate limits, as OpenAI did repeatedly... - Source: dev.to / 4 months ago
First, you need to choose a specific graph database platform to work with, such as Neo4j, OrientDB, JanusGraph, Arangodb or Amazon Neptune. Once you have selected a platform, you can then start working with graph data using the platform's query language. - Source: dev.to / over 3 years ago
Trigger.dev - Trigger workflows from APIs, on a schedule, or on demand. API calls are easy with authentication handled for you. Add durable delays that survive server restarts.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Pipedream - Integration platform for developers
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
