Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for engineering teams, AI agent developers, automation builders, founders, creators, power users, AI-curious readers, and any developer building a multi-agent workflow with a long-running company of role-staffed agents -- and who can pair HKUDS/OpenOPC with an Anthropic / OpenAI / DeepSeek / Xiaomi Mimo / Claude / GPT API key for the model surface (LiteLLM is the universal adapter), 14 chan
Who should use it
Who should skip it
Skip HKUDS/OpenOPC unless the captured evidence suggests it solves a problem you are actively working on.
About this signal
HKUDS/OpenOPC is tracked by RepoRadar as a autonomous multi-agent company f in the Multi-Agent Orchestration Framework section. It was first seen on 2026-07-06 and last updated on 2026-07-06. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. Across RepoRadar's eight signals, HKUDS/OpenOPC is strongest on workflow potential (9.7) and open-source/build quality (8.4) and weakest on setup ease (6.4) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned HKUDS/OpenOPC a composite score of 8.2 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
The 508* / 8988 KB monorepo is at active maintenance but the framework is research-grade and relatively new (508* is modest for the breadth of features) -- treat the first evaluation cycle as a smoke test (install via `uv pip install -e .; uv run opc init; uv run opc ui` + run the Office UI + send a brief + watch the kanban + role progress update in real time + click into a role to inspect detailed execution records) before relying on the company runtime in production; the Self-Grown layer (experience profiles + shared playbooks) accumulates organizational knowledge across runs -- the consumer SHOULD review the promotion policy before enabling organizational learning in production.