MEAH (Multi External Agent Harness)
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Alternatives to MEAH (Multi External Agent Harness)
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- AlicenseNot gradedqualityAmaintenanceEnables MCP clients such as Claude Code to delegate bounded tasks like code review, boilerplate generation, commit messages, format conversion, and embeddings to local or cloud OpenAI-compatible LLMs, reducing token costs while keeping the main model in control.MIT
- AlicenseNot gradedqualityCmaintenanceEnables Claude and Codex agents to delegate implementation, testing, research, or review tasks to each other through local MCP tools, while supporting persistent sessions, isolated editing workspaces, and reviewed integration. It also allows progress tracking, cancellation, model selection, and management handoff between providers.2MIT
- FlicenseNot gradedqualityAmaintenanceEnables MCP clients like Claude Code to delegate bounded work—code review, boilerplate, commit messages, format conversion, embeddings—to local or cloud OpenAI-compatible models, keeping large files out of the main model's context to cut token costs and provide a second opinion. It routes and profiles the target model automatically, while the orchestrating client retains planning and quality checks.-
- AlicenseNot gradedqualityBmaintenanceProvides 300+ MCP tools for orchestrating AI agents — including swarm coordination, self-learning memory, and task management — enabling MCP clients like Claude Code and Cursor to spawn, coordinate, and learn from specialist agents across sessions.21,333 npm1MIT
- FlicenseNot gradedqualityAmaintenanceEnables Claude Code and other MCP clients to hand bounded work such as code review, boilerplate generation, commit messages, format conversion and embeddings to a local GPU model, LiteLLM router, OpenRouter, or cheaper cloud API like OpenAI, DeepSeek, Groq or Cerebras. Keeps the orchestrating model as architect and reviewer, cutting token spend and providing a second opinion on code.-
- AlicenseAqualityDmaintenanceEnables Claude to orchestrate tasks across 27 AI providers, run multi-agent plans, and conduct multi-model councils for decision-making.15231 npmMIT
TDQS
Scored across 8 tools
Tools are mostly distinct: list_models vs list_agents, get_agent vs cancel_agent, and parallel_agents are clearly separated. However, meah_run_agent and meah_delegate_task both start 'one isolated external worker' and differ mainly in async vs sync behavior, which could initially confuse selection. Descriptions do clarify the distinction, so overlap is minor.
Almost all names follow a consistent meah_verb_noun pattern: list_models, get_agent, run_agent, delegate_task, list_agents, cancel_agent, cleanup_workspace. The outlier is meah_parallel_agents, which uses an adjective_noun form instead of a verb. This is a minor deviation in an otherwise predictable scheme.
Eight tools are well-scoped for an external agent harness, covering discovery, execution (sync, async, parallel), monitoring, cancellation, and workspace cleanup. No tool feels redundant or missing from a count perspective. The set is appropriately sized.
The surface covers the full worker lifecycle: listing models, running agents (single/parallel), polling state, listing agents, cancelling, and cleaning workspaces. A minor gap is the lack of tools to modify provider/model configuration or role routing defaults, which are only readable. This is a small omission agents can likely work around via external configuration.