Alternatives
Products that do what Agoragentic ECF Core does
Open-source infrastructure for resilient AI agent workflows.
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Hi HN, I’m Vincent from Aden. We spent 4 years building ERP automation for construction (PO/invoice reconciliation). We had real enterprise customers but hit a technical wall: Chatbots aren't for real work. Accountants don't want to chat; they want the ledger reconciled while they sleep. They want services, not tools. Existing agent frameworks (LangChain, AutoGPT) failed in production - brittle, looping, and unable to handle messy data. General Computer Use (GCU) frameworks were even worse. My reflections: 1. The "Toy App" Ceiling & GCU Trap Most frameworks assume synchronous sessions.…
Feb 2026 · github.com
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I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…
2024 · github.com
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2024 · github.com
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I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks. The core idea is local-first compliance: – risk classification (Articles 5–15, incl. prohibited use cases) – bias evaluation using CrowS-Pairs – automatic Annex IV–oriented PDF reports – no cloud services or external APIs (browser-based + Ollama) I’m especially interested in feedback on whether this kind of technical framing of AI regulation makes sense in real-world projects.
Jan 2026 · github.com
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Nov 2025 · github.com
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Hi, I'm a Dapr CNCF project maintainer. We've recently released Dapr Agents which provides agentic AI features together with built-in durable execution to guarantee statefulness and reliable agentic workflows that run to completion and retry upon failure. It runs natively on Kubernetes, has built-in OTEL integration and uses a lightweight architecture where agents scale to zero, allowing you to run thousands of agents on commodity hardware. It'd be great if you can test it out and give us feedback.
2025 · github.com
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Hi HN, I’m building Waycore, an open-source project exploring what a flexible, offline-first field computer should look like for outdoor, survival, and off-grid scenarios. The core goals are adaptability and resilience: modular hardware (external sensor/tool modules) extensible OS with support for external apps (guidelines in progress) no required internet connection — maps, models, and knowledge work offline optional LTE/Wi-Fi when available and explicitly enabled A major focus is on-device agentic AI, not just chat or image recognition. The AI is intended to: read live sensor…
Dec 2025
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The first open model as performant as Opus 4.6, 96% cheaper
Apr 2026 · arcee.ai
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May 2026 · github.com
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2025 · github.com
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2024 · github.com
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It all started with a conversation among friends about limitations in current multi-agent orchestration frameworks. We faced issues like limited control over agent memory and state, complicated persistence, scaling problems, and lack of type safety in Python-based tools. These challenges inspired us to try something different. The result was GraphFlow, a Rust-based lean framework for orchestrating multi-agent workflows that's simple, scalable, and robust. Its key features include: Graph-based orchestration: Easily define workflows using nodes and edges. Lean Execution Engine: A minimal and…
2025 · github.com
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Hi all, I'm the creator and maintainer of Dapr. Today we announced an agentic AI framework that allows developers to run thousands of agents on a single core that can scale to/from zero with minimal latency, with a durable execution engine that supports automatic retries. Us maintainers would very much appreciate your feedback
2025 · github.com
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