Regolo AI published a technical tutorial on September 2, 2026, showing how autonomous coding agents can gain persistent institutional memory by combining Cognee knowledge graphs with Regolo-hosted inference. The supplied excerpt describes the problem as “context amnesia”: lessons from pull requests, CI failures, and re The proposed architecture uses Cognee for episodic storage, multi-hop graph traversal, and retrieval of prior engineering decisions, then places the agent workflow in a ReAct-style loop with live, test-driven self-healing. Regolo says the stack runs on European sovereign infrastructure and is aimed at environments usin

Regolo AI published a technical blog post on August 3, 2026 describing a fork of PokéRogue, an open-source browser-based Pokémon roguelike, where its hosted gpt-oss-120b model drives enemy AI decisions in real time. The implementation is deliberately minimal: a single import line added to src/main.ts plus a new 1,300-l The post frames PokéRogue as a more rigorous test than standard Game Boy Pokémon benchmarks because the game launched in 2024, has no dedicated competitive dataset, and uses procedurally generated runs with roguelite mechanics that change optimal strategy. Regolo positions this as a reasoning evaluation of an unfamilia

Regolo.ai published a case study on 2026-07-23 covering a University of Udine bachelor's thesis by Giovanni Battista Perini that selected Regolo as the language-model infrastructure for "Oracolo," a RAG-based conversational chatbot for citizens built on the Cheshire Cat framework and developed at the Treviso-based web The case study documents a deliberate architecture choice to address hallucination and stale-knowledge limitations of generative models, using Regolo as the model-access layer alongside retrieval-augmented generation. It serves as direct, provider-relevant evidence of third-party integration with api.regolo.ai, though

With the growing need for robust AI models, many developers face challenges in scaling their applications effectively. Traditional, closed ecosystems often lock developers into proprietary solutions, which can be costly and inefficient. Regolo.ai is designed to break these barriers by offering an open-source platform t

AllAIModel's catalog page lists 13 models served via Regolo AI with per-million-token USD pricing, context window sizes, capability flags, and cutoff dates. Notable entries include Mistral Small 4 119B at $0.75 input / $3.00 output with 256K context, MiniMax 2.5 at $0.80/$3.50 with 190K context, GPT-OSS-20B at $0.40/$1 GPT-OSS-120B is listed at $1.00 input and $4.20 output per million tokens with a 128K context window, and Llama 3.3 70B Instruct and Llama 3.1 8B Instruct appear as lower-priced open-weight options. Pricing for embeddings and reranking models is unusually low at $0.10–$0.12 per million tokens, suggesting Regolo is posi