AI Development Environment
Memory. Agents. Workflows. Intelligence that grows.
The infrastructure that makes AI development reliable
The Challenge
Every conversation starts from scratch. Past solutions and learnings are forgotten.
Errors compound over time. Earlier mistakes pollute later decisions until everything breaks.
Reading entire files when you need one function. Context windows fill with noise.
Same generalist model for everything. No specialization, no efficiency.
AI needs infrastructure to be effective. An intelligent assistant without memory, specialization, and efficiency tools is like a brilliant engineer with no tools, no notes, and no team.
The Solution
Give Claude memory, specialists, workflows, and efficiency. Click any pillar to learn more.
Click any pillar above to see how it transforms AI development
PostgreSQL + pgvector stores learnings from every session. Semantic search recalls relevant solutions, failures, and patterns automatically.
78 TypeScript hooks intercept every action. They don't just monitor - they enforce quality, route to optimal tools, and block mistakes before they happen.
18+ agents with specific expertise. Kraken for complex implementation, Spark for quick fixes, Scout for research. Right specialist for each task.
150+ pre-built workflows activated by natural language. Multi-agent pipelines orchestrated automatically with human checkpoints.
TLDR provides AST-based code analysis - structure, relationships, and complexity without reading entire files. 95% token reduction.
Pillar 1
The system learns from every session. Knowledge accumulates automatically.
Learnings captured without manual effort. What worked, what failed, decisions made.
1024-dimension BGE embeddings enable "what you mean" search, not just keyword matching.
Solutions, failures, decisions, patterns, errors, preferences, open threads - all categorized.
Memory Deep Dive
Cross-session recall means never explaining the same thing twice.
"The auth token refresh fails because..."
"I don't know about any auth issues..."
"We literally fixed this yesterday!"
"The auth is failing again"
"Found 2 relevant learnings about auth..."
"Last time this was the token refresh. Checking that first..."
Combines text search (fast keyword matching) with vector search (semantic understanding) using Reciprocal Rank Fusion. Gets the best of both approaches.
Pillar 2
78 hooks intercept every action. They don't just monitor - they enforce.
Session start, tool use, prompt submission, session end - every moment intercepted.
Hooks don't just warn - they can deny actions and redirect to better approaches.
Add relevant information automatically - memory matches, warnings, guidance.
Type check, lint, test validation
Route to optimal tools/agents
Surface relevant past learnings
Block destructive operations
Hooks Deep Dive
File read intercepted → AST analysis instead of raw file. 23K tokens → 1.2K tokens.
Grep for code patterns → Redirect to AST-grep for structural accuracy.
Git force push to main blocked. rm -rf requires explicit confirmation.
Task start → Memory query injected with relevant past learnings.
File read returns structure + relationships + complexity analysis, not raw text.
Search automatically routed to best tool: AST-grep for code, vector for semantic.
Edit hooks run type check + lint before commit. Errors caught immediately.
Relevant learnings surface automatically. "I recall from a previous session..."
Pillar 3
18+ agents with specific expertise. Right specialist for each task.
Right model for right task.
Opus for complex reasoning, Sonnet for fast execution. Each agent knows when to escalate.
Agents Deep Dive
Different tasks need different specialists. The system routes intelligently.
Pillar 4
150+ pre-built workflows. Natural language activation, multi-agent pipelines.
Scout → Architect → Kraken → Arbiter → Commit
Debug-agent → Spark → Arbiter → Commit
Scout + Oracle → Summary → Recommendations
Critic → Judge → Principal Reviewer
Arbiter → Atlas → Coverage Report
Full autonomous development cycle
PRD → Task Breakdown → Delegation Loop → Verification → Learning Extraction → Merge
Workflows Deep Dive
Relevant learnings injected automatically
Understands codebase structure & patterns
Creates implementation plan
TDD workflow, tests first
Type check, lint, security scan
Full test suite verification
New learnings extracted & saved
Verified, tested, documented
Pillar 5
95% token reduction through AST-based code analysis. Structure over raw text.
Parse code into structure, extract what matters, skip the noise.
Understand relationships between functions without reading every line.
Extract only the code paths that affect your target.
Efficiency Deep Dive
import React, { useState, useEffect, useCallback } from 'react';
import { useRouter } from 'next/router';
import { toast } from 'sonner';
// ... 800 more lines of imports, comments, JSX ...
// ... boilerplate, whitespace, type definitions ...
// ... every single line of the file ...
export const Dashboard: React.FC<DashboardProps> = ({
user,
settings,
onUpdate,
onLogout,
// ... many more props
}) => {
const [state, setState] = useState(initialState);
// ... 600 more lines of component logic ...
};
⚠ 23,000 tokens consumed. Context window filling up.
# Dashboard Component Structure ## Exports: Dashboard (FC) ## Props: user, settings, onUpdate, onLogout ## Hooks: useState(3), useEffect(2), useCallback(1) ## Functions: - handleSubmit(data) → calls onUpdate - handleLogout() → calls onLogout, redirects - validateForm(values) → returns errors ## Dependencies: - External: react, next/router, sonner - Internal: useAuth, useSettings ## Complexity: Medium (cyclomatic: 8)
✅ 1,200 tokens. 20x more fits in context.
Integration
Example: "Build authentication for the dashboard"
Recalls: "Last auth used JWT + refresh tokens. Token storage in httpOnly cookies worked well."
Routes to TLDR for efficient context. Injects security patterns. Blocks insecure defaults.
Explores existing auth patterns. Finds related components. Maps dependencies.
Designs: Auth context, protected routes, token refresh hook. User approves plan.
Implements with TDD. Tests first, then code. Hooks validate in real-time.
New learnings extracted: "Dashboard auth uses AuthContext at /contexts/auth"
Components amplify each other.
Memory informs agents. Hooks optimize everything. Efficiency enables scale.
The Difference
Re-explain everything
Same model for all
Full files every time
No verification
Remembers everything
Right agent per task
95% token savings
78 quality gates
Growth
Every session makes the system smarter. Intelligence accumulates.
Basic memory established. First patterns captured. System learning your codebase.
50+ learnings accumulated. Common solutions recalled instantly. Failures avoided.
200+ learnings. Deep codebase knowledge. Architectural decisions informed by history.
Summary
Zero knowledge loss between sessions. Solutions recalled instantly. Failures never repeated.
20x more code fits in context. Lower costs. Better understanding through structure.
Right agent for each task. Opus for complex reasoning, Sonnet for fast execution.
Best practices built-in. Multi-agent pipelines orchestrated automatically.
Every action intercepted. Mistakes blocked before they happen. Quality enforced, not just monitored.
Accessibility
Yes - that's the whole point. You describe outcomes, agents execute.
"I need a dashboard that shows sales by region with filters"
Scout explores, Architect designs, Kraken implements, Arbiter tests
Approve plans before execution. See what changed. Request adjustments.
You're the director. Agents are your technical team.
Get Started
Guided orchestration
/maestro
Autonomous development
/ralph
Transform AI development from prompt-and-pray into systematic engineering.
docs/ARCHITECTURE.mddocs/memory-architecture.mddocs/hooks/README.mddocs/agents/README.md/help - Interactive discovery/build - Feature workflow/fix - Bug fix workflow/explore - Codebase research