orka-reasoning

Orchestrator Kit for Agentic Reasoning - OrKa is a modular AI orchestration system that transforms Large Language Models (LLMs) into composable agents capable of reasoning, fact-checking, and constructing answers with transparent traceability.

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2Distinct people

2Mentions

0Explicit recommenders

96GitHub stars

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What it is

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marcosomma/orka- · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour2 dated mentions

Observed mentions: 2025-06-24 14:22:25 UTC to 2025-06-24 17:35:08 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
Jun 24, 14:00	2025-06-24 14:00:00 UTC	2025-06-24 15:00:00 UTC	1	1	0
Jun 24, 15:00	2025-06-24 15:00:00 UTC	2025-06-24 16:00:00 UTC	0	0	0
Jun 24, 16:00	2025-06-24 16:00:00 UTC	2025-06-24 17:00:00 UTC	0	0	0
Jun 24, 17:00	2025-06-24 17:00:00 UTC	2025-06-24 18:00:00 UTC	1	0	1

What Hacker News says

2 sources

pablo1979·Jun 24, 2025

Hi HN, I'm Marco — ex-ethologist turned AI systems engineer — and I built something I needed but couldn't find: an open-source framework to wire cognition like circuits — not spaghetti prompt chains. It's called OrKa: the Orchestrator Kit for Agents. WHY I BUILT IT • Tired of black-box LLM chains (LangChain, AutoGPT, etc.) • Needed a way to fork/join reasoning paths • Wanted true traceability, memory, and reproducibility • Inspired by how cognition decays, splits, reconverges • Built from scratch with Redis Streams, YAML logic, and local model support I wanted reasoning I could see, debug, version, and replay — like functional circuits. WHAT ORKA IS > YAML-defined cognition graphs (versioned, inspectable) > Fork/Join execution with trace replay > Router agents w/ confidence-weighted branching > Redis or Kafka backends > Scoped memory (epi

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sodic, procedural, etc.) > Visual UI (React-based): https://orka-ui.web.app > ServiceNodes: RAG, Memory fetch, Writer, Embedder > Full local+remote LLM support via LiteLLM / OpenAI / Ollama > 76%+ test coverage, deterministic behavior BENCHMARK (REAL) • 1000 orchestration runs (2-agent pipeline) • DeepSeek-R1 (1.5B) via Ollama on Pop!_OS • Avg latency: ~7.6s per agent • Zero agent drift across runs • Total cost (simulated): ~$0.49 • CPU temp: stable 88–89°C • RAM: < 5.3 GB • RESULTS: https://github.com/marcosomma/orka-reasoning/blob/master/doc... LINKS • PyPI → https://pypi.org/project/orka-reasoning/ • GitHub → https://github.com/marcosomma/orka-reasoning • Examples → https://github.com/marcosomma/orka- reasoning/tree/master/examples • UI (Docker)→ https://hub.docker.com/r/marcosomma/orka-ui FEEDBACK WELCOME • Is this the right abstraction layer for agentic reasoning? • Should this remain infra-first or move toward hosted cognition-as-a-service? • Anyone else frustrated with current LLM toolchains? Thanks for reading. AMA

In [dead]

marcosomma-orka·Jun 24, 2025Submission

I built orKa-reasoning: a modular orchestration layer for explainable AI agents

I built OrKa: a modular orchestration layer for explainable AI agents Hi HN, I'm Marco — ex-ethologist turned AI systems engineer — and I built something I needed but couldn't find: an open-source framework to wire *cognition like circuits* — not spaghetti prompt chains. It's called *OrKa*: the *Orchestrator Kit for Agents*. WHY I BUILT IT • Tired of black-box LLM chains (LangChain, AutoGPT, etc.) • Needed a way to fork/join reasoning paths • Wanted true traceability, memory, and reproducibility • Inspired by how cognition decays, splits, reconverges • Built from scratch with Redis Streams, YAML logic, and local model support I wanted reasoning I could *see*, *debug*, *version*, and *replay* — like functional circuits. WHAT ORKA IS > YAML-defined cognition graphs (versioned, inspectable) > Fork/Join execution with trace replay > Router ag

Read the rest

ents w/ confidence-weighted branching > Redis or Kafka backends > Scoped memory (episodic, procedural, etc.) > Visual UI (React-based): https://orka-ui.web.app > ServiceNodes: RAG, Memory fetch, Writer, Embedder > Full local+remote LLM support via LiteLLM / OpenAI / Ollama > 76%+ test coverage, deterministic behavior BENCHMARK (REAL) • 1000 orchestration runs (2-agent pipeline) • DeepSeek-R1 (1.5B) via Ollama on Pop!_OS • Avg latency: ~7.6s per agent • Zero agent drift across runs • Total cost (simulated): ~$0.49 • CPU temp: stable 88–89°C • RAM: < 5.3 GB • RESULTS: https://github.com/marcosomma/orka-reasoning/blob/master/docs/orka_linux_stressTes… LINKS • PyPI → https://pypi.org/project/orka-reasoning/ • GitHub → https://github.com/marcosomma/orka-reasoning • Examples → https://github.com/marcosomma/orka- reasoning/tree/master/examples • UI (Docker)→ https://hub.docker.com/r/marcosomma/orka-ui FEEDBACK WELCOME • Is this the right abstraction layer for agentic reasoning? • Should this remain infra-first or move toward hosted cognition-as-a-service? • Anyone else frustrated with current LLM toolchains? Thanks for reading. AMA. _

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