{
  "$type": "site.standard.document",
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  "path": "/t/new-and-a-thought-could-there-be-an-a-team-fantasy-draft-pick-scenario/173724#post_4",
  "publishedAt": "2026-02-24T23:54:52.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "OpenAI Developers",
    "GitHub",
    "Anthropic",
    "LangCain ReferenceDocument",
    "CrewAI Documentation",
    "LM Studio",
    "Ollama",
    "Hugging Face",
    "Model Context Protocol",
    "LlamaIndex",
    "Haystack Documentation",
    "LangChain Docs",
    "OWASP",
    "NCSC",
    "DeepLearning.AI",
    "LangChain Academy"
  ],
  "textContent": "For now, I’ve gathered the most relevant current frameworks:\n\n* * *\n\n## What already exists that matches your “QB + worker bees” idea\n\nYour concept maps to a known family of designs called **multi-agent orchestration** (a coordinator routes tasks to specialist agents, then merges results). Below is a curated set of **papers + projects + docs + courses + real-world issues** that directly apply to your “phone/tablet/laptop” setup.\n\n* * *\n\n## 1) Core multi-agent patterns (the “QB delegates, workers execute” part)\n\n### Practical pattern write-ups + reference implementations\n\n  * **OpenAI Cookbook: “Routines and Handoffs”**\nClear description of a coordinator handing off work to specialized agents, with implementation patterns. (OpenAI Developers)\n  * **OpenAI Swarm (GitHub)**\nLightweight, educational multi-agent orchestration framework built around those ideas. Good to read for architecture and minimal code patterns. (GitHub)\n  * **Anthropic engineering: “How we built our multi-agent research system”**\nReal production-style multi-agent research design: parallel subagents, coordinator synthesis, and lessons learned. (Anthropic)\n\n\n\n### Frameworks you can build with (pick one to start)\n\n  * **LangGraph “Supervisor” / multi-agent docs + tutorial**\nGraph/state-machine approach that helps prevent uncontrolled loops by making the flow explicit. (LangCain ReferenceDocument)\n  * **Microsoft AutoGen (GitHub + docs)**\nMulti-agent “agent chat” style framework; good for prototyping coordinated agents. (GitHub)\n  * **Microsoft Agent Framework (GitHub)**\nMicrosoft’s newer “build/orchestrate/deploy agents” framework; useful if you want something oriented toward production workflows. (GitHub)\n  * **CrewAI (docs)**\nRole-based “crews” and “flows”; beginner-friendly mental model for specialist agents + coordinator. (CrewAI Documentation)\n\n\n\n* * *\n\n## 2) Offline / local running (how your laptop becomes the “hub”)\n\nYour “devices connected to a QB” becomes much easier if the laptop runs a **local model server** and your phone/tablet act as clients.\n\n### Local model serving (laptop)\n\n  * **LM Studio as a local LLM API server** (localhost or LAN)\nLets you serve a model from the laptop and call it via REST (including compatibility endpoints). (LM Studio)\n  * **Ollama: OpenAI-compatible endpoints + tool support**\nUseful because many orchestration examples assume OpenAI-shaped APIs; Ollama bridges local models into that tooling ecosystem and supports tool calls. (Ollama)\n\n\n\n### Local inference fundamentals (what “space” and “performance” actually mean)\n\n  * **llama.cpp (GitHub)**\nCanonical local inference project; strong documentation trail around model formats (GGUF) and deployment constraints. (GitHub)\n  * **llama.cpp quantization docs (GGUF quantize tool README)**\nQuantization is the real lever for fitting models into limited memory (phones/tablets). (GitHub)\n  * **KV cache explainer (why long context uses extra memory while running)**\nHelps understand why “it fits on disk” ≠ “it runs comfortably.” (Hugging Face)\n\n\n\n* * *\n\n## 3) “Tools” and device-to-device capabilities (the “dev-to-dev orders” part)\n\nIf you want your QB to “call” worker capabilities cleanly (search, files, calendar, scraping, etc.), there’s a growing standard approach:\n\n  * **Model Context Protocol (MCP) specification**\nAn open protocol for connecting LLM apps to external tools/data sources in a standardized way. (Model Context Protocol)\n  * **MCP official GitHub repo** (spec + schemas + docs) (GitHub)\n\n\n\nThis is relevant to your setup because it’s essentially “plug in a worker/tool server and let the QB call it.”\n\n* * *\n\n## 4) Memory, storage, and “keep only what’s useful”\n\nYour QB needs a plan for:\n\n  * what to store (summaries, citations, extracted facts)\n  * how to retrieve later (search/relevance)\n  * how to stop memory from becoming a junk pile\n\n\n\nGood starting docs:\n\n  * **LlamaIndex Agents (and agentic workflows)**\nTreats RAG/search pipelines as tools the agent can call; useful for “QB stores + retrieves.” (LlamaIndex)\n  * **Haystack Agents**\nExplicit “loop until exit_conditions” design is very relevant to preventing runaway agent behavior. (Haystack Documentation)\n\n\n\n* * *\n\n## 5) Evals + observability (how you keep a multi-agent system from becoming chaos)\n\nMulti-agent systems fail in ways that are hard to debug unless you log and evaluate systematically.\n\n  * **OpenAI Cookbook Evals hub**\nMany examples for testing prompts/tools/web-search/structured outputs and building an evaluation loop. (OpenAI Developers)\n  * **OpenAI “evaluation flywheel” guide**\nA concrete “analyze → measure → improve” method for making agent behaviors reliable. (OpenAI Developers)\n  * **LangSmith observability quickstart**\nTracing and debugging agent/tool calls end-to-end. (LangChain Docs)\n\n\n\n* * *\n\n## 6) Security (important once agents can use tools)\n\nOnce agents can browse, call tools, and act on outputs, prompt injection becomes a system problem.\n\n  * **OWASP Top 10 for LLM Applications**\nA baseline checklist; useful for “what can go wrong” in agentic apps. (OWASP)\n  * **UK NCSC: “Prompt injection is not SQL injection (it may be worse)”**\nHigh-quality explanation of why mitigations are tricky and what mindset to adopt. (NCSC)\n\n\n\n* * *\n\n## 7) Courses (beginner-friendly on-ramp)\n\n  * **DeepLearning.AI: AI Agents in LangGraph** (DeepLearning.AI)\n  * **DeepLearning.AI: Multi AI Agent Systems with CrewAI** (DeepLearning.AI)\n  * **LangChain Academy: Intro to LangGraph (Python)** (LangChain Academy)\n  * **Hugging Face Agents Course: LangGraph intro** (Hugging Face)\n\n\n\n* * *\n\n## 8) Real issues people hit (read these to avoid common traps)\n\nThese are useful because they show what breaks in practice.\n\n### Runaway loops / recursion limits\n\n  * LangChain issue: **infinite tool-call loop** (GitHub)\n  * LangGraph issue: **infinite looping until recursion limit** (GitHub)\n  * LangGraph docs: **GRAPH_RECURSION_LIMIT** (what it means and typical causes) (LangChain Docs)\n  * LangChain built-in middleware: **model call limit** to cap runaway behavior (LangChain Docs)\n\n\n\n### “Where do I store agent state/history per user?”\n\n  * AutoGen issue: storing **multiple agents + histories** in multi-user production scenarios (GitHub)\n\n\n\n### Governance of handoffs (who is allowed to do what)\n\n  * Swarm issues list includes proposals like **governance guardrails for handoffs** (GitHub)\n\n\n\n* * *\n\n## A tight “learning path” tailored to your phone/tablet/laptop idea\n\n  1. **Understand the orchestration pattern** : OpenAI “handoffs” + Swarm (OpenAI Developers)\n  2. **Pick one orchestrator framework** : LangGraph _or_ CrewAI _or_ AutoGen (LangCain ReferenceDocument)\n  3. **Make laptop a model hub** : LM Studio server or Ollama compatibility (LM Studio)\n  4. **Add memory + retrieval** : LlamaIndex or Haystack agents (LlamaIndex)\n  5. **Add evals + tracing** : OpenAI evals hub + LangSmith tracing (OpenAI Developers)\n  6. **Add guardrails/security** : OWASP + NCSC guidance (OWASP)\n\n",
  "title": "New and a thought could there be an A-team fantasy draft pick scenario"
}