AgentKit

The intelligent orchestration layer that cuts Claude Code costs by 70%

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$ npx agentkit-ai@latest init

AgentKit Installer v0.5.4
─────────────────────────

Detecting platforms...
  ✓ Claude Code  (full)
  ✓ Cursor       (partial)

Installing Backend Pro bundle (22 skills)...
  ✓ Skills converted for Claude Code (SKILL.md native)
  ✓ Skills converted for Cursor (.mdc format)
  ✓ Model routing enabled  →  Haiku / Sonnet / Opus
  ✓ Memory graph initialised
  ✓ Quality gates wired into hooks

✓ AgentKit installed!

  Estimated savings:
    Tokens:  ~40,000 → ~5,000/session  (89% reduction)
    Cost:    ~$200/mo → ~$60/mo        (70% reduction)

Demo GIF coming soon — record yours and open a PR!

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Before vs After

Real numbers from AgentKit smoke tests, measured across a 50-turn coding session.

Metric	Without AgentKit	With AgentKit	Improvement
Tokens per session	45,000	~5,000	89% less
Cost per session (Sonnet)	~$1.35	~$0.40	70% cheaper
Skill activation rate	20% (ad-hoc)	84% (hook-enforced)	4× more reliable
Model used for simple tasks	Sonnet ($0.003/K)	Haiku ($0.00025/K)	12× cheaper
Model used for subagents	Sonnet	Haiku (always)	12× cheaper
Context at session start	Full 10K token dump	2K relevant nodes	80% less noise
Memory across sessions	None	SQLite graph + handoff	Persistent
Coding without a plan	Allowed	Blocked by hook	Zero skipped steps

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One Command Install

npx agentkit-ai@latest init

That's it. AgentKit detects your platforms, installs the right skill format for each, wires all hooks, and configures model routing automatically.

Or install globally (then use agentkit as a command anywhere):

npm install -g agentkit-ai
agentkit init

Note: The npm package name is agentkit-ai. After a global install, the CLI command is agentkit.

Requirements: Node.js ≥ 18 · Python ≥ 3.9 · Claude Code (for full feature set)

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What It Does

AgentKit is a 6-layer runtime that sits between your prompts and the model:

• Layer 0 — Spawn Engine: 3-tier analyzer detects complex tasks and automatically decomposes them into N specialized agents running in parallel waves — no configuration required
• Layer 1 — Skill Router: Classifies every prompt in < 10ms → loads only relevant skills → 45,000 tokens/session down to 5,000 (89% reduction)
• Layer 2 — Memory Graph: SQLite knowledge graph captures files, functions, decisions across sessions → Haiku-compressed handoffs so context survives restarts
• Layer 3 — Token Budget: Auto-routes Haiku / Sonnet / Opus by task complexity + proactive context compaction at 60% fill + real-time cost dashboard in your status bar
• Layer 4 — Workflow Engine: Enforces Research → Plan → Execute → Review → Ship via hooks — can't skip planning, quality gates (syntax/lint/types/tests) run after every edit
• Layer 5 — Platform Layer: One SKILL.md file auto-converted to 10 platform formats — Cursor .mdc, Codex AGENTS.md, Gemini CLI config, and more

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Works With

Platform	Support	Install format
	Full — skills + hooks + memory + routing	Native SKILL.md
	Skills + model routing rules	.cursor/rules/*.mdc
	Skills via system prompt	.gemini/GEMINI.md
	Skills via Cascade rules	.windsurf/rules.md
	Skills via config	.opencode/config.json
	Skills as plugins	.kilo/plugins/*.yaml
	Skills injected	AGENTS.md
	Skills as conventions	.aider.conf.yml
	Skills as context	.augment/context.md
	Full plugin system	.antigravity/plugins/

Ruflo: AgentKit makes your Ruflo swarms 3× cheaper by routing worker agents to Haiku and injecting only relevant skills per agent. See issue #1 →

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Dynamic Agent Spawning

AgentKit v0.5.4 adds a zero-config spawn engine that automatically detects when a task needs multiple agents and orchestrates them for you.

$ claude "Build a REST API with auth, tests, and a security audit"

[AgentKit] Multi-agent task detected (confidence: 0.90)
[AgentKit] Spawning 5 agents in 4 waves...

Wave 1 →  architect  (opus-4.6)      Design schema + endpoints
Wave 2 →  writer     (haiku-4.5)     Implement API + auth          [waits: architect]
Wave 3 →  tester     (haiku-4.5)     Write pytest suite            [waits: writer]
         security   (sonnet-4.6)    OWASP audit                   [waits: writer]
Wave 4 →  reviewer   (sonnet-4.6)    Final code review             [waits: writer + tester]

• 3-tier detection: keyword signals (<5ms) → heuristic scoring (<10ms) → Haiku LLM fallback (~$0.0003) for ambiguous cases
• Smart model routing per role: Architect gets Opus, implementation gets Haiku, security/review get Sonnet
• DAG execution: parallel where possible, sequential where dependencies require it
• Recursion-safe: spawned agents never re-spawn (infinite loop prevention built-in)

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How AgentKit Compares

Feature	AgentKit	Superpowers	claude-mem	ClaudeFast
Dynamic agent spawning	✅ Auto-detects, N agents, DAG waves	❌	❌	❌
Smart skill loading	✅ Auto-routed, 89% token reduction	✅ Manual SKILL.md	❌	❌
Skill library	✅ 50 skills, 7 role bundles	❌ BYO only	❌	❌
Persistent memory	✅ SQLite graph + session handoffs	❌	✅ Basic	❌
Auto model routing	✅ Haiku/Sonnet/Opus by complexity	❌	❌	⚠️ Basic
Workflow enforcement	✅ Research→Plan→Execute→Review→Ship	⚠️ Suggested only	❌	❌
Quality gates	✅ syntax+lint+types+tests on every edit	❌	❌	❌
Multi-platform	✅ 10 platforms, 1 config	❌ Claude Code only	❌	❌
Subagent cost routing	✅ Per-role model (12× cheaper)	❌	❌	❌
Cost dashboard	✅ Real-time status bar	❌	❌	✅
npx install	✅ One command	❌ Manual	❌ Manual	❌

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CLI Reference

Without global install (use npx agentkit-ai <command>):

npx agentkit-ai@latest init    # First install
npx agentkit-ai sync           # Re-sync after adding skills
npx agentkit-ai status         # Health check + cost summary
npx agentkit-ai costs --days 7 # Weekly cost analytics

With global install (npm install -g agentkit-ai, then use agentkit):

agentkit init              # Detect platforms → install
agentkit sync              # Re-sync after adding skills
agentkit status            # Health check + cost summary
agentkit costs --days 7    # Weekly cost analytics
agentkit skills list       # Browse all 50 skills
agentkit workflow status   # Current Research/Plan/Execute state
agentkit workflow approve  # Approve plan → unlock coding
agentkit detect            # Show detected AI coding tools
agentkit uninstall         # Remove all AgentKit files
agentkit uninstall --purge # Also delete runtime data (costs/memory/state)

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Skill Bundles

Pick a bundle at install time or pass --bundle <name>:

Bundle	Skills	Best for
backend-pro	python-debugger, go-debugger, pytest, rest-api, grpc, sql, mongodb, redis, auth, owasp, docker, nginx + 10 more	Python/Go backend engineers
frontend-wizard	js-debugger, jest, cypress, playwright, react, vue, nextjs, css, state-mgmt, a11y, graphql + 2 more	Frontend / React developers
full-stack-hero	All 50 skills	Full-stack teams
ai-engineer	llm-prompting, rag-pipeline, function-calling, agent-design, eval-testing + 5 more	LLM / AI application developers
devops-master	docker, kubernetes, github-actions, terraform, monitoring, nginx + 3 more	DevOps / Platform engineers
data-scientist	pandas, data-viz, ml-pipeline, sql, pytest + 2 more	Data scientists / ML engineers
mobile-dev	react-native, flutter, rest-api, auth-jwt + 3 more	Mobile developers

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All 50 Skills

Click to expand full skill list

Category	Skills
Debugging	python-debugger, js-debugger, go-debugger, network-debugger
Testing	tdd-workflow, jest-testing, pytest-workflow, cypress-e2e, playwright-testing, contract-testing
API	rest-api, graphql, grpc, openapi-design, webhook-design
Database	sql-query, prisma-orm, mongodb, redis-caching, database-migrations
Frontend	react-patterns, nextjs-patterns, css-layout, vue-patterns, state-management, accessibility
DevOps	docker, kubernetes, github-actions, terraform, monitoring-observability, nginx-config
Security	auth-jwt, owasp-top10, secrets-management, api-security
Refactoring	clean-code, performance-optimization, code-review, legacy-modernization
AI Engineering	llm-prompting, rag-pipeline, function-calling, agent-design, eval-testing
Data Science	pandas-workflow, data-visualization, ml-pipeline
Mobile	react-native, flutter

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Built on the shoulders of giants: Superpowers (108K ⭐) · claude-mem (39.9K ⭐) · awesome-claude-code (30.9K ⭐)

npm · GitHub · Issues · MIT License