How do I install LLM Agents Ecosystem Handbook?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does LLM Agents Ecosystem Handbook need an API key?

No required environment variable is listed on this record.

What transport does LLM Agents Ecosystem Handbook use?

LLM Agents Ecosystem Handbook runs locally over stdio.

When was LLM Agents Ecosystem Handbook last checked?

Last checked 2026-10-26. One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.

LLM Agents Ecosystem Handbook MCP server

LLM Agents Ecosystem Handbook

A practical operating manual for building, evaluating, securing, and shipping modern LLM agent systems.

Modern agents are not "a prompt + a tool." They are systems — with identity, memory, skills, tools, MCP integrations, guardrails, observability, evals, and a provider strategy. This handbook teaches the whole stack and ships templates, blueprints, runnable adapters, and curated examples you can adopt today.

What's in this repo

A curated, opinionated, production-oriented handbook in seven parts:

Concepts — Agent OS, identity, memory, skills, MCP, safety, observability — every layer of the modern agent stack
Provider ecosystem — adapters + docs for 24+ LLM providers (frontier APIs, fast inference, marketplaces, enterprise clouds, specialty, local runtimes), with a router for fallback chains
Skills ecosystem — design guide, taxonomy, maturity model, security checklist, and a curated skill catalog
Prompt engineering — agent prompt patterns, instruction hierarchy, context engineering, prompt-injection defense
Coding-agent workflows — for Claude Code, Cursor, Codex, Aider, Cline, and custom runtimes — repo instructions, prompts, review checklist, safe refactoring
Design docs — agent / technical design docs, ADR guide, design reviews, rollout plans, the DESIGN.md machine-readable spec
Curated catalog — 100+ existing agent skeletons, framework comparisons, evaluation tools, tutorials — preserved and improved

Who this is for

You are…	Start at
New to agents	docs/beginners_guide.md → agent_os/README.md
Building a production agent	blueprints/ → checklists/production_readiness_checklist.md
Picking / wiring providers	providers/README.md → providers/provider_matrix.md
Comparing frameworks	docs/framework_comparison.md
Adding memory / RAG	memory/ → tutorials/rag_tutorials
Adding MCP	mcp/ → mcp/mcp_security.md
Designing Skills	skills/ → skills/skill_design_guide.md
Working with coding agents	coding_agents/ → coding_agents/prompts/

Modern Agent Stack

Layer	Purpose	Where in this repo
Model / Provider	LLM choice + abstraction + routing	providers/
Orchestration	Agent loops, planning, handoffs	docs/framework_comparison.md, blueprints/
Tool	Function calling and external actions	agent_os/mcp_layer.md
MCP	Standardized external context and tools	mcp/
Memory	Durable user/project/semantic memory	memory/
Skills	Reusable, progressive-loading workflows	skills/
Identity	Personality, mission, refusal style	agent_os/agent_identity.md, templates/
Prompt	System prompt design, instruction hierarchy, defenses	prompt_engineering/
Safety	Guardrails, approvals, policy	safety/

📖 Deep dive: agent_os/README.md

Provider ecosystem

The handbook ships an LLMProvider abstraction with 24+ providers across six families. Most providers go through a single OpenAI-compatible code path; specialty / local providers are first-class.

Provider type	Examples	Best for
Frontier APIs	OpenAI, Anthropic, Google Gemini	Reasoning, tool use, production agents
Fast inference	Groq, Cerebras, SambaNova	Low-latency workloads
Marketplaces	OpenRouter, Together, Fireworks, DeepInfra	Model choice and routing
Enterprise clouds	Azure OpenAI, AWS Bedrock, Vertex AI	Compliance, governance
Specialty	xAI, Perplexity, Mistral, Cohere, DeepSeek, Hugging Face, Replicate, NVIDIA NIM, MiniMax	Domain-specific
Local runtimes	Ollama, LM Studio, vLLM, llama.cpp	Privacy, cost control, offline dev

If you want a governed OpenAI-compatible control plane in front of those providers, Tuning Engines is a useful runtime option for policy enforcement, approval gates, MCP and agent tracing, and usage or cost visibility without changing the surrounding agent framework.

Quick start:

from utilities import get_provider
from utilities.provider_router import ProviderRouter

# Use any single provider
out = get_provider("groq").chat(
    [{"role": "user", "content": "Summarize MCP."}],
    model="llama-3.1-8b-instant",
)

# Or route by task class with fallback
router = ProviderRouter()
out = router.chat(messages, task_class="cheap")  # Groq → DeepSeek → Together → OpenRouter

📖 providers/README.md • providers/provider_matrix.md • providers/router_patterns.md • providers/local_models.md

Repository map

.
├── README.md • llms.txt • llms-full.txt
├── agent_os/                ← the Agent OS concept, layers, workspace examples
├── providers/               ← 24+ provider docs + adapters + router patterns
├── templates/               ← AGENTS.md / SOUL.md / MEMORY.md / SKILL.md / DESIGN_DOC / ADR / …
├── skills/                  ← design guide + taxonomy + maturity model + curated catalog + 4 examples
├── memory/                  ← memory taxonomy, distillation, security, examples
├── mcp/                     ← MCP basics, architecture, security, server catalog, examples
├── prompt_engineering/      ← agent prompt patterns, instruction hierarchy, defenses
├── coding_agents/           ← Claude Code, Cursor, Codex, workflows, prompts, review
├── design_docs/             ← agent + technical design docs, ADR guide, design.md spec
├── safety/                  ← guardrails, approvals, prompt injection, secure checklist
├── observability/           ← tracing, spans, cost/latency, dashboards
├── evals/                   ← eval design, regression / tool / memory / MCP / safety / prompt
├── blueprints/              ← production architectures by use case
├── examples/                ← end-to-end runnable agent workspaces
├── checklists/              ← agent design, prod readiness, MCP security, …
├── llm_wiki/                ← LLM-friendly index, glossary, matrices, wiki pattern
├── docs/                    ← framework comparison, best practices, beginners' guide
├── tutorials/               ← RAG, memory, fine-tuning, chat-with-X
├── utilities/               ← LLMProvider + router + provider_config
├── agents/                  ← 100+ curated agent skeletons (preserved)
├── complete_apps/, web_apps/, notebooks/, datasets/, design/, resources/, scripts/, tests/, ecosystem/
└── .github/                 ← issue / PR templates

Skills ecosystem

A curated, in-repo catalog plus a clear taxonomy and maturity model:

skills/skill_design_guide.md — write triggers the model picks
skills/skill_vs_tool_vs_mcp.md — when to use which
skills/skill_taxonomy.md — domains, tags, risk
skills/skill_maturity_model.md — experimental → production
skills/skill_packaging.md — ship a portable skill
skills/skill_validation.md — lint / smoke / eval
skills/awesome_skills_catalog.md — broader ecosystem map
skills/catalog/ — index + per-domain skills
skills/examples/ — four full reference skills

Curated skills shipped: research-summarizer, repo-auditor, mcp-security-reviewer, agent-memory-curator, api-design-reviewer, pr-summarizer, adr-writer, incident-postmortem, sprint-planner, dataset-profiler.

Prompt engineering

A dedicated section, agent-focused:

Templates: SYSTEM_PROMPT, AGENT_PROMPT. Checklist: agent_prompt_checklist.

Use this repo with coding agents

The handbook is itself a great surface for coding agents. Drop your favorite tool (Claude Code, Cursor, Codex, Aider, Cline) into the repo:

llms.txt gives the agent an index in 30 seconds
coding_agents/ has tool-specific notes + prompts
coding_agents/prompts/ — repo audit, modernization, feature, bugfix, provider expansion, docs update, release review
templates/CODING_AGENT_TASK.md.template — task contract template
templates/REPO_MODERNIZATION_PROMPT.md.template — multi-phase modernization

The guidance is tool-neutral: same AGENTS.md, same workflows, regardless of harness.

Design docs

Agent + technical design docs, ADRs, reviews, rollouts, and the DESIGN.md machine-readable spec for design tokens:

design_docs/agent_design_doc.md
design_docs/technical_design_doc.md
design_docs/adr_guide.md
design_docs/design_review.md
design_docs/rollout_plan.md
design_docs/design_md_spec.md
design_docs/examples/ — research / MCP / memory / provider-router worked examples

Templates: DESIGN_DOC, ADR.

Frameworks at a glance

Framework	Best for	Lang	MCP	Tracing
OpenAI Agents SDK	Production agents	Py / JS	✅	✅ built-in
LangGraph	Stateful, branching graphs	Py / JS	✅	✅ LangSmith
CrewAI	Role-based teams	Py	✅	⚠️ via partners
AutoGen (AG2)	Event-driven multi-agent + HITL	Py	⚠️ partial	✅
LlamaIndex Workflows	Data-heavy / RAG-first	Py / TS	✅	✅
Pydantic AI	Type-safe, FastAPI-native	Py	✅	✅ Logfire
Smolagents	Code-execution mini-agents	Py	⚠️	basic
Semantic Kernel	.NET / enterprise / Azure	C# / Py / Java	✅	✅
DSPy	Programmatic prompt optimization	Py	—	✅
Strands Agents	Provider-agnostic, OpenTelemetry	Py	✅	✅ OTEL
Vercel AI SDK	App-layer agents in Next.js	TS / JS	✅	✅
Google ADK	Gemini / Vertex hierarchical tools	Py	✅	✅

📖 Full comparison + decision tree: docs/framework_comparison.md. Capability tags hedged: verify against current upstream docs.

Skills, MCP, and Memory in one minute

Skills are reusable, model-loaded workflows (SKILL.md + scripts + references). Use when a task is repeatable, multi-step, and benefits from progressive disclosure. → skills/
MCP (Model Context Protocol) is a standard for exposing tools/context to any agent. Use when integrations should be reusable (GitHub, filesystem, browser, internal APIs). → mcp/
Memory is durable state across runs (MEMORY.md, vector stores, decision logs). → memory/

A useful rule of thumb:

If the thing is…	Use
A repeatable workflow with steps and references	Skill
An external system with tools to call	MCP server
State that should outlive the current run	Memory
A single function the model needs once	Plain tool

📖 Decision matrix: skills/skill_vs_tool_vs_mcp.md

Guardrails & safety

Production agents need risk-tiered tool controls and human approval gates for high-impact actions.

Risk level	Examples	Approval
Low	read-only search, summarization	none
Medium	drafting files, creating tickets	sometimes
High	sending email, modifying repos, running shell	required
Critical	deleting data, spending money, changing permissions	always + audit

📖 safety/README.md • safety/prompt_injection.md • safety/secure_agent_checklist.md

Observability & evals

You cannot ship what you cannot measure. The handbook ships:

A tracing primer (observability/tracing.md) and span model (observability/spans.md)
Cost / latency / failure analysis playbooks
Eval design + datasets (evals/): regression, tool-call, memory, MCP, safety, prompt evals
A curated guide to evaluation_frameworks — Promptfoo, DeepEval, RAGAs, Langfuse, Phoenix, TruLens, LangSmith, MLflow

Templates (copy-paste ready)

File	Purpose
AGENTS.md	Repo-specific agent instructions
SOUL.md	Identity, voice, values, refusal style
MEMORY.md	Durable project + user memory index
USER.md	User profile and preferences
TOOLS.md	Allowed/restricted/approval-gated tools
SKILL.md	Skill spec with progressive loading
MCP_SERVER.md	Documenting an MCP integration
SYSTEM_PROMPT.md	Long-lived system prompt
AGENT_PROMPT.md	Per-task / per-session prompt
DESIGN_DOC.md	Agent / technical design doc
ADR.md	Architecture Decision Record
EVAL_PLAN.md	What you'll evaluate and how
GUARDRAILS.md

Merged knowledge areas (1.0.1)

This release merged seven external projects into the handbook. Each was adapted (not bulk-copied) into the structure above:

Source theme	Lives in
Skills catalog + taxonomy patterns	skills/ — taxonomy, maturity, packaging, validation, awesome catalog
Personal-wiki / self-maintaining KB	llm_wiki/wiki_pattern.md, docs/llm_readable_docs.md
Agent prompt research patterns	prompt_engineering/
Production coding-agent prompts + workflows	coding_agents/ — prompts, workflows, review
Machine-readable design specs	design_docs/design_md_spec.md, templates/DESIGN_DOC.md.template
ADRs + design reviews	design_docs/adr_guide.md, design_docs/design_review.md

📖 Full migration plan: MIGRATION_AND_PROVIDER_EXPANSION_PLAN.md

Supported LLM providers

The utilities/llm_provider.py module exposes a single LLMProvider interface (and a backwards-compatible complete() function). Switch via LLM_PROVIDER without touching agent code; route automatically with ProviderRouter.

24+ providers across frontier / fast / marketplace / enterprise / specialty / local. See:

providers/provider_matrix.md — capability comparison
providers/env_vars.md — every variable
.env.example — copy-and-fill

Contributing

Contributions are very welcome — new examples, framework updates, fixes, and translations all help. Start with:

CONTRIBUTING.md — workflow, scope, quality bar
.github/PULL_REQUEST_TEMPLATE.md
.github/ISSUE_TEMPLATE/
checklists/open_source_quality_checklist.md

Roadmap & changelog

ROADMAP.md — what's next
CHANGELOG.md — what shipped

License

MIT — see LICENSE.

Maintainer

Curated & maintained by Sayed Allam (oxbshw). If this handbook helped you ship, please ⭐ the repo and open a PR with what you learned along the way.

LLM Agents Ecosystem Handbook

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Questions

How do I install LLM Agents Ecosystem Handbook?

Does LLM Agents Ecosystem Handbook need an API key?

What transport does LLM Agents Ecosystem Handbook use?

When was LLM Agents Ecosystem Handbook last checked?

Documentation

LLM Agents Ecosystem Handbook

What's in this repo

Who this is for

Modern Agent Stack

Provider ecosystem

Repository map

Skills ecosystem

Prompt engineering

Use this repo with coding agents

Design docs

Frameworks at a glance

Skills, MCP, and Memory in one minute

Guardrails & safety

Observability & evals

Templates (copy-paste ready)

Merged knowledge areas (1.0.1)

Supported LLM providers

Contributing

Roadmap & changelog

License

Maintainer