Evidence-gated runner for Codex, Claude Code, OpenCode, and local coding agents. Routes tasks into scoped DAG lanes with replayable artifacts.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
git clone https://github.com/dmae97/open-multi-agent-kit{
"mcpServers": {
"open-multi-agent-kit": {
"command": "node",
"args": ["/path/to/open-multi-agent-kit/dist/index.js"]
}
}
}24 items in this repository
Persistent memory, recall, session replay, and memory-governance workflow adapted from rohitg00/agentmemory for OMK. Use when setting up agent memory, deciding what to remember, importing/replaying sessions, reducing repeated context, or auditing memory safety.
Minimal, goal-driven, surgical coding workflow adapted from forrestchang/andrej-karpathy-skills for OMK. Use for coding, refactoring, debugging, and review tasks where assumptions, overengineering, or broad edits could cause regressions.
Legal workflow drafting, triage, review, research planning, legal operations, law-student or clinic support, and legal AI governance adapted from Anthropic claude-for-legal. Use for commercial, privacy, product, corporate, employment, regulatory, AI governance, IP, litigation, legal-clinic, and law-student tasks. Draft-only; attorney review and current source verification required.
Real-engineering alignment, shared-language, TDD, diagnosis, and architecture-review workflow adapted from mattpocock/skills for OMK. Use before non-trivial implementation, ambiguous product work, debugging loops, test-first changes, or codebase architecture cleanup.
Managed-agent teamwork, issue assignment, progress tracking, reusable-skill compounding, and handoff workflow adapted from multica-ai/multica for OMK. Use when coordinating multiple agents, converting work into agent-ready tasks, tracking blockers, or turning repeated solutions into skills.
Review AdaptOrch, OMK, and similar DAG multi-agent orchestration frameworks. Use when assessing DAG node responsibility, dependency edges, worker write authority, fallback/retry/timeout/evidence gates, review/merge boundaries, or reproducible decision traces.
Optional read-only OMK web/social/video research workflow inspired by Panniantong/Agent-Reach. Use for web search, current social evidence, YouTube/Bilibili/Reddit/Twitter/X/RSS/GitHub public research, and Agent Reach availability checks without auto-installing or collecting credentials.
Backend API review for NestJS, Express, FastAPI, database access, validation, auth, error handling, and API contracts.
Adversarial code review for diffs, logic correctness, type safety, test coverage, and security risk.
Context and memory policy for long-running Kimi coding sessions, DAG workers, and repeated project work.
Diagnose agent, runtime, tool, hook, context, provider-routing, DAG, retry, fallback, and evidence-gate failures using an industrial automation feedback-control loop. Use when workflows oscillate, stall, lose context, misroute tools, or need setpoint/sensor/controller/actuator/disturbance/correction analysis.
Build and maintain a terminal-native, orchestration-first design system for agent team HUDs, status surfaces, and control planes.
Documentation, changelog, release note, README, migration guide, and developer handoff workflow.
Generate task-specific evidence contracts before a done/completed claim. Use for feature, bugfix, refactor, research, release, security, docs, or orchestration tasks that need changed files, non-empty diffs, test/build/typecheck results, citations, uncertainty, conflicting evidence, and final risk notes.
Implement frontend UI components from DESIGN.md, mockups, or screenshots with strict adherence to existing component system and accessibility.
Frontend UI review for React, Next.js, Tailwind, accessibility, responsive layout, state handling, and component architecture.
Git commit and pull request summary workflow using Conventional Commits and evidence from the current diff.
Industrial automation and feedback-control inspired review for AI workflows, agents, orchestration loops, state machines, and reliability.
Read-only planning workflow before implementation. Use for architecture, refactor, feature development, risky edits, and ambiguous tasks.
Project-level operating rules extracted from AGENTS.md, DESIGN.md, and .omk/memory/. Apply silently before implementation.
Python typed development standards using type hints, pytest, ruff, pyright, uv, and maintainable package structure.
Run lint, typecheck, test, and build gates before completing any implementation task.
Efficient repository exploration without dumping the whole codebase. Use before feature work, debugging, refactoring, and review.
Web research and source verification workflow for current technical facts, libraries, APIs, pricing, policies, and documentation.
MCP Servers overview
What people ask about open-multi-agent-kit
What is dmae97/open-multi-agent-kit?
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dmae97/open-multi-agent-kit is mcp servers for the Claude AI ecosystem. Evidence-gated runner for Codex, Claude Code, OpenCode, and local coding agents. Routes tasks into scoped DAG lanes with replayable artifacts. It has 84 GitHub stars and was last updated today.
How do I install open-multi-agent-kit?
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You can install open-multi-agent-kit by cloning the repository (https://github.com/dmae97/open-multi-agent-kit) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is dmae97/open-multi-agent-kit safe to use?
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Our security agent has analyzed dmae97/open-multi-agent-kit and assigned a Trust Score of 87/100 (tier: Trusted). See the full breakdown of passed checks and flags on this page.
Who maintains dmae97/open-multi-agent-kit?
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dmae97/open-multi-agent-kit is maintained by dmae97. The last recorded GitHub activity is from today, with 14 open issues.
Are there alternatives to open-multi-agent-kit?
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Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
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