Auto-Claude Optimization

Performance tuning, cost reduction, and efficiency improvements.

Performance Overview

Key Metrics

Metric	Impact	Optimization
API latency	Build speed	Model selection, caching
Token usage	Cost	Prompt efficiency, context limits
Memory queries	Speed	Embedding model, index tuning
Build iterations	Time	Spec quality, QA settings

Model Optimization

Model Selection

Model	Speed	Cost	Quality	Use Case
claude-opus-4-5-20251101	Slow	High	Best	Complex features
claude-sonnet-4-5-20250929	Fast	Medium	Good	Standard features

# Override model in .env
AUTO_BUILD_MODEL=claude-sonnet-4-5-20250929

Extended Thinking Tokens

Configure thinking budget per agent:

Agent	Default	Recommended
Spec creation	16000	Keep default for quality
Planning	5000	Reduce to 3000 for speed
Coding	0	Keep disabled
QA Review	10000	Reduce to 5000 for speed

# In agent configuration
max_thinking_tokens=5000  # or None to disable

Token Optimization

Reduce Context Size

1. Smaller spec files

   # Keep specs concise
   # Bad: 5000 word spec
   # Good: 500 word spec with clear criteria
   

2. Limit codebase scanning

   # In context/builder.py
   MAX_CONTEXT_FILES = 50  # Reduce from 100
   

3. Use targeted searches

   # Instead of full codebase scan
   # Focus on relevant directories
   

Efficient Prompts

Optimize system prompts in

apps/backend/prompts/

:

<!-- Bad: Verbose -->
You are an expert software developer who specializes in building
high-quality, production-ready applications. You have extensive
experience with many programming languages and frameworks...

<!-- Good: Concise -->
Expert full-stack developer. Build production-quality code.
Follow existing patterns. Test thoroughly.

Memory Optimization

# Use efficient embedding model
OPENAI_EMBEDDING_MODEL=text-embedding-3-small

# Or offline with smaller model
OLLAMA_EMBEDDING_MODEL=all-minilm
OLLAMA_EMBEDDING_DIM=384

Speed Optimization

Parallel Execution

# Enable more parallel agents (default: 4)
MAX_PARALLEL_AGENTS=8

Reduce QA Iterations

# Limit QA loop iterations
MAX_QA_ITERATIONS=10  # Default: 50

# Skip QA for quick iterations
python run.py --spec 001 --skip-qa

Faster Spec Creation

# Force simple complexity for quick tasks
python spec_runner.py --task "Fix typo" --complexity simple

# Skip research phase
SKIP_RESEARCH_PHASE=true python spec_runner.py --task "..."

API Timeout Tuning

# Reduce timeout for faster failure detection
API_TIMEOUT_MS=120000  # 2 minutes (default: 10 minutes)

Cost Management

Monitor Token Usage

# Enable cost tracking
ENABLE_COST_TRACKING=true

# View usage report
python usage_report.py --spec 001

Cost Reduction Strategies

1. Use cheaper models for simple tasks

   # For simple specs
   AUTO_BUILD_MODEL=claude-sonnet-4-5-20250929 python spec_runner.py --task "..."
   

2. Limit context window

   MAX_CONTEXT_TOKENS=50000  # Reduce from 100000
   

3. Batch similar tasks

   # Create specs together, run together
   python spec_runner.py --task "Add feature A"
   python spec_runner.py --task "Add feature B"
   python run.py --spec 001
   python run.py --spec 002
   

4. Use local models for memory

   # Ollama for memory (free)
   GRAPHITI_LLM_PROVIDER=ollama
   GRAPHITI_EMBEDDER_PROVIDER=ollama
   

Cost Estimation

Operation	Estimated Tokens	Cost (Opus)	Cost (Sonnet)
Simple spec	10k	~$0.30	~$0.06
Standard spec	50k	~$1.50	~$0.30
Complex spec	200k	~$6.00	~$1.20
Build (simple)	50k	~$1.50	~$0.30
Build (standard)	200k	~$6.00	~$1.20
Build (complex)	500k	~$15.00	~$3.00

Memory System Optimization

Embedding Performance

# Faster embeddings
OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # 1536 dim, fast

# Higher quality (slower)
OPENAI_EMBEDDING_MODEL=text-embedding-3-large  # 3072 dim

# Offline (fastest, free)
OLLAMA_EMBEDDING_MODEL=all-minilm
OLLAMA_EMBEDDING_DIM=384

Query Optimization

# Limit search results
memory.search("query", limit=10)  # Instead of 100

# Use semantic caching
ENABLE_MEMORY_CACHE=true

Database Maintenance

# Compact database periodically
python -c "from integrations.graphiti.memory import compact_database; compact_database()"

# Clear old episodes
python query_memory.py --cleanup --older-than 30d

Build Efficiency

Spec Quality = Build Speed

High-quality specs reduce iterations:

# Good spec (fewer iterations)
## Acceptance Criteria
- [ ] User can log in with email/password
- [ ] Invalid credentials show error message
- [ ] Successful login redirects to /dashboard
- [ ] Session persists for 24 hours

# Bad spec (more iterations)
## Acceptance Criteria
- [ ] Login works

Subtask Granularity

Optimal subtask size:

• Too large: Agent gets stuck, needs recovery
• Too small: Overhead per subtask
• Optimal: 30-60 minutes of work each

Parallel Work

Let agents spawn subagents for parallel execution:

Main Coder
├── Subagent 1: Frontend (parallel)
├── Subagent 2: Backend (parallel)
└── Subagent 3: Tests (parallel)

Environment Tuning

Optimal .env Configuration

# Performance-focused configuration
AUTO_BUILD_MODEL=claude-sonnet-4-5-20250929
API_TIMEOUT_MS=180000
MAX_PARALLEL_AGENTS=6

# Memory optimization
GRAPHITI_LLM_PROVIDER=ollama
GRAPHITI_EMBEDDER_PROVIDER=ollama
OLLAMA_LLM_MODEL=llama3.2:3b
OLLAMA_EMBEDDING_MODEL=all-minilm
OLLAMA_EMBEDDING_DIM=384

# Reduce verbosity
DEBUG=false
ENABLE_FANCY_UI=false

Resource Limits

# Limit Python memory
export PYTHONMALLOC=malloc

# Set max file descriptors
ulimit -n 4096

Benchmarking

Measure Build Time

# Time a build
time python run.py --spec 001

# Compare models
time AUTO_BUILD_MODEL=claude-opus-4-5-20251101 python run.py --spec 001
time AUTO_BUILD_MODEL=claude-sonnet-4-5-20250929 python run.py --spec 001

Profile Memory Usage

# Monitor memory
watch -n 1 'ps aux | grep python | head -5'

# Profile script
python -m cProfile -o profile.stats run.py --spec 001
python -c "import pstats; p = pstats.Stats('profile.stats'); p.sort_stats('cumulative').print_stats(20)"

Quick Wins

Immediate Optimizations

1. Switch to Sonnet for most tasks

   AUTO_BUILD_MODEL=claude-sonnet-4-5-20250929
   

2. Use Ollama for memory

   GRAPHITI_LLM_PROVIDER=ollama
   GRAPHITI_EMBEDDER_PROVIDER=ollama
   

3. Skip QA for prototypes

   python run.py --spec 001 --skip-qa
   

4. Force simple complexity for small tasks

   python spec_runner.py --task "..." --complexity simple
   

Medium-Term Improvements

1. Optimize prompts in

   apps/backend/prompts/

2. Configure project-specific security allowlist
3. Set up memory caching
4. Tune parallel agent count

Long-Term Strategies

1. Self-hosted LLM for memory (Ollama)
2. Caching layer for common operations
3. Incremental context building
4. Project-specific prompt optimization

Related Skills

• auto-claude-memory: Memory configuration
• auto-claude-build: Build process
• auto-claude-troubleshooting: Debugging