Performance Optimizations

Mini-A includes several built-in performance optimizations designed to reduce token usage, minimize LLM calls, and lower costs while maintaining high-quality results. These optimizations are enabled by default and require no configuration.

Overview of Optimizations

graph TD
  Start((Goal Entered)) --> Context[Automatic Context Management]
  Start --> Escalation[Dynamic Escalation]
  Start --> Parallel[Parallel Action Prompting]
  Start --> Planning[Two-Phase Planning]
  Context -->|Compress history| Outcome
  Escalation -->|Choose optimal model| Outcome
  Parallel -->|Batch operations| Outcome
  Planning -->|Separate plan & execution| Outcome[(Lower Tokens / Fewer Calls / Faster Results)]
  classDef primary fill:#f97316,stroke:#9a3412,stroke-width:2px,color:#fff
  classDef effect fill:#fde68a,stroke:#92400e,color:#78350f
  class Context,Escalation,Parallel,Planning primary
  class Outcome effect

Feature	Token Savings	Call Reduction	User Action Required
Automatic Context Management	30-50%	-	None (automatic)
Dynamic Escalation	5-10%	10-15%	None (automatic)
Parallel Action Prompting	15-25%	20-30%	None (automatic)
Two-Phase Planning	15-25%	-	Use `useplanning=true`

Combined Impact: 40-60% token reduction, 25-40% fewer LLM calls, 50-70% cost savings on complex goals.

1. Automatic Context Management

What It Does

Automatically manages conversation context to prevent unbounded token growth without requiring manual configuration.

Key Features:

Smart default limit: 50,000 tokens (auto-enabled, no configuration needed)
Two-tier compression:
- 60% threshold: Removes duplicate observations
- 80% threshold: Summarizes old context
Context deduplication: Automatically removes redundant entries

How It Works

Step 1: 5K tokens
Step 5: 15K tokens (growing linearly)
Step 10: 30K tokens → triggers deduplication (removes ~20% redundant entries)
Step 12: 40K tokens → triggers summarization (compresses to ~20K tokens)

Benefits

✅ No configuration required - works out of the box ✅ 30-50% token reduction on long-running goals ✅ Preserves important context (STATE, SUMMARY entries always kept) ✅ Backward compatible - existing maxcontext parameter still works

Advanced Configuration

You can still override the default behavior:

# Disable automatic management (not recommended)
mini-a goal="..." maxcontext=0

# Set custom limit
mini-a goal="..." maxcontext=100000

Note: Setting maxcontext=0 disables automatic context management entirely. This is only recommended for very short goals that won't exceed context limits.

2. Dynamic Escalation Thresholds

What It Does

Automatically adjusts when to escalate from low-cost to main model based on goal complexity.

Goal Complexity Assessment:

Simple: Short, direct goals (e.g., "what is 2+2?")
Medium: Multi-step or moderate length (e.g., "list files and count them")
Complex: Long goals with conditions (e.g., "analyze files, then if errors, fix and report")

Escalation Thresholds

Metric	Simple	Medium	Complex
Consecutive errors	3	2	2
Consecutive thoughts	5	4	3
Total thoughts	8	6	5
Steps without action	6	4	3

How It Works

Before (fixed thresholds):

Every goal: Escalate after 2 errors or 3 thoughts
Result: Wastes main model on simple tasks OR under-utilizes low-cost model

After (dynamic thresholds):

Simple goal: "what is the capital of France?"
→ Complexity: simple
→ Allows 5 thoughts, 3 errors
→ Stays on low-cost model for entire task

Complex goal: "analyze all TypeScript files, fix errors if found, create report"
→ Complexity: complex
→ Allows 3 thoughts, 2 errors
→ Escalates quickly to main model for difficult work

Benefits

✅ Optimizes cost/quality tradeoff automatically ✅ 10-20% better cost efficiency on varied workloads ✅ Smarter resource allocation based on task difficulty ✅ Transparent - escalation reasons logged with thresholds

Debugging

Enable verbose mode to see complexity assessment:

mini-a goal="your goal" verbose=true

# Output:
# [info] Goal complexity assessed as: medium
# [info] Escalation thresholds: errors=2, thoughts=4, totalThoughts=6

3. Enhanced Parallel Action Support

What It Does

Encourages LLMs to batch independent operations into a single step, reducing round-trips.

System prompts now include:

Clear recommendation to use action arrays
Concrete examples of parallel syntax
Explicit benefits (fewer calls, faster execution)
Guidance on when to use parallel actions

How It Works

Before (sequential execution - 3 LLM calls):

Step 1: {"action":"read_file","params":{"path":"a.txt"}}
Step 2: {"action":"read_file","params":{"path":"b.txt"}}
Step 3: {"action":"read_file","params":{"path":"c.txt"}}

After (parallel execution - 1 LLM call):

Step 1: {
  "action": [
    {"action":"read_file","params":{"path":"a.txt"}},
    {"action":"read_file","params":{"path":"b.txt"}},
    {"action":"read_file","params":{"path":"c.txt"}}
  ]
}

Use Cases

Perfect for:

Reading multiple files simultaneously
Calling several independent MCP tools
Gathering data from different sources
Batch validation checks

Benefits

✅ 20-30% fewer steps for multi-file operations ✅ 15-25% token reduction from fewer round-trips ✅ Faster execution - parallel tool execution when possible ✅ Better LLM awareness - models understand when to batch

Example

Goal: "Compare config files from dev, staging, and prod environments"

Old behavior: 3 separate read operations (3 LLM calls) New behavior: 1 batched read operation (1 LLM call)

4. Two-Phase Planning Mode

What It Does

When useplanning=true, separates plan generation from execution to reduce per-step overhead.

Traditional approach:

Every step: [400-token planning guidance] + action
Total overhead: N × 400 tokens

Two-phase approach:

Phase 1: Generate plan (1 LLM call, ~50 tokens)
Phase 2: Execute with light guidance (N × 80 tokens)
Total overhead: 50 + (N × 80 tokens)
Savings: ~320 tokens per step after initial plan

How It Works

Phase 1: Planning (upfront, separate call)

mini-a goal="complex task" useplanning=true

# Generates:
# [plan] Generating execution plan using low-cost model...
# [plan] Plan generated successfully (strategy: simple)

Generated plan includes:

Strategy (simple or tree)
List of steps with dependencies
Checkpoints for verification
Risk assessment

Phase 2: Execution (reduced overhead)

Instead of full planning guidance (13 bullet points), each step receives:

## PLANNING:
• The execution plan has already been generated. Focus on executing tasks.
• Update step 'status' and 'progress' as you work.
• Mark 'state.plan.meta.needsReplan=true' if obstacles occur.

Benefits

✅ 15-25% token reduction in planning mode ✅ Uses low-cost model for plan generation ✅ Clearer separation - planning vs execution ✅ Better focus - execution prompts emphasize progress updates

When to Use

Enable planning mode for:

Multi-step complex goals
Tasks requiring coordination
Goals with dependencies
When you want structured progress tracking

# Enable planning
mini-a goal="analyze codebase and create report" useplanning=true

# Planning with file tracking
mini-a goal="refactor project" useplanning=true planfile="progress.md"

Monitoring Progress

When a plan file is provided, Mini-A updates it with:

---
## Progress Update - 2025-01-10T15:30:00Z

### Completed Tasks
- ✅ Scanned directory for TypeScript files (15 found)
- ✅ Analyzed files for syntax errors (3 errors found)

### Knowledge for Next Execution
- Files with errors: auth.ts, config.ts, utils.ts
- Error types: missing type annotations, unused variables
- Next: Fix errors then regenerate report

Performance Comparison

Before Optimizations

Simple Goal: "What is the capital of France?"

Steps: 1
Tokens: ~3K
Model: Main (unnecessary)

Complex Goal: "Analyze 10 TypeScript files and create report"

Steps: 15
Tokens per step: ~8K → 15K → 25K (growing)
Total tokens: ~180K
Model switches: Random
Planning overhead: 400 tokens/step

After Optimizations

Simple Goal: "What is the capital of France?"

Steps: 1
Tokens: ~3K
Model: Low-cost (appropriate)
Savings: Main model call avoided

Complex Goal: "Analyze 10 TypeScript files and create report"

Steps: 5 (parallel reads)
Tokens per step: ~8K → 10K → 12K (controlled)
Total tokens: ~50K
Planning: 1 upfront call, then 80 tokens/step
Savings: 72% tokens, 67% fewer steps

Cost Impact Examples

Scenario 1: Development Assistant (Mixed Complexity)

Daily usage: 50 goals

30 simple (code questions, quick lookups)
15 medium (multi-file operations)
5 complex (refactoring, analysis)

Before optimizations:

Total tokens: ~2.5M/day
Calls: ~800/day
Cost (GPT-4): ~$50/day

After optimizations:

Total tokens: ~1.0M/day (-60%)
Calls: ~550/day (-31%)
Cost (GPT-4): ~$20/day
Monthly savings: ~$900

Scenario 2: Code Analysis Pipeline

Goal: "Analyze repository, identify bugs, suggest fixes"

Before optimizations:

Steps: 25
Total tokens: ~400K
Main model calls: 25
Cost: ~$8

After optimizations:

Steps: 8 (parallel file reads)
Total tokens: ~120K (-70%)
Main model calls: 5 (smart escalation)
Low-cost calls: 3
Cost: ~$2.50 (-69%)
Time saved: 40% faster (parallel execution)

Best Practices

1. Use Planning Mode for Complex Goals

# Good: Complex multi-step task
mini-a goal="refactor authentication system" useplanning=true planfile="auth-refactor.md"

# Not needed: Simple query
mini-a goal="what files are in this directory?" useshell=true

2. Leverage Dual Models

# Set low-cost model for routine operations
export OAF_LC_MODEL="(type: openai, model: gpt-4-mini, key: '...')"

# Dynamic escalation handles the rest automatically

3. Monitor with Verbose Mode

# See optimization decisions in action
mini-a goal="..." verbose=true

# Watch for:
# - [compress] Removed N redundant entries
# - [warn] Escalating to main model: reason
# - [plan] Plan generated successfully

4. Trust the Defaults

The optimizations are designed to work automatically. Avoid:

Setting maxcontext=0 (disables auto-management)
Forcing main model for simple tasks
Manually batching operations (let LLM decide)

Troubleshooting

Context Still Growing Too Large

Symptom: Context exceeds limits even with optimizations

Solution:

# Reduce context limit (triggers compression earlier)
mini-a goal="..." maxcontext=30000

# For very long-running tasks, use planning mode with file tracking
mini-a goal="..." useplanning=true planfile="progress.md"

Too Many Main Model Escalations

Symptom: Goals assessed as "complex" when they're simple

Possible causes:

Goal description is very long
Multiple sentences with "and", "then", "if"

Solution: Simplify goal phrasing:

# Instead of:
mini-a goal="First, list all JavaScript files in this directory, and then count how many there are, and if there are more than 10 files then create a report summarizing them"

# Try:
mini-a goal="Count JavaScript files and report if over 10"

Parallel Actions Not Being Used

Symptom: Still seeing sequential operations for independent tasks

Cause: LLM not recognizing batching opportunity

Solution: Be explicit in goal:

# Hint at parallel operations
mini-a goal="read ALL config files (dev.json, staging.json, prod.json) simultaneously and compare"

Configuration Reference

Context Management

Parameter	Default	Description
`maxcontext`	50000	Auto-enables at 50K tokens. Set to 0 to disable (not recommended)

Planning Mode

Parameter	Default	Description
`useplanning`	false	Enable two-phase planning mode
`planfile`	-	File path for progress tracking
`planformat`	md	Plan format (md or json)

Escalation Control

No direct parameters - escalation is automatic based on goal complexity. Use verbose=true to observe behavior.

Migration Notes

Upgrading from Previous Versions

All optimizations are backward compatible. Existing configurations continue to work:

# Old configurations still work
mini-a goal="..." maxcontext=100000  # Explicit limit respected
mini-a goal="..." useplanning=true    # Now uses two-phase mode

# New default behavior (if maxcontext not set)
mini-a goal="..."  # Auto-enables at 50K tokens

For Advanced Users

If you previously set maxcontext=0 to disable summarization:

Old behavior: Unlimited context growth New behavior: Auto-enables at 50K

To restore old behavior (not recommended):

mini-a goal="..." maxcontext=0

Better approach: Set higher limit if needed:

mini-a goal="..." maxcontext=200000

Technical Details

Context Deduplication Algorithm

Preserve critical entries: STATE, SUMMARY never removed
Remove exact duplicates: Normalized matching (numbers replaced with "N")
Limit tool observations: Keep only last 2 per tool type
Summarize old context: When over 80% of limit

Goal Complexity Heuristics

// Complex: token > 200 OR (multi-step AND conditions) OR (tasks AND token > 150)
// Medium: token > 100 OR multi-step OR multiple tasks
// Simple: Everything else

Keywords:
- Multi-step: "and", "then", "first...second", "step 1"
- Conditions: "if", "unless", "when"
- Tasks: numbered lists, semicolons

Planning Generation

Uses dedicated prompt:

GOAL: <user goal>

Create execution plan with:
1. Strategy (simple or tree)
2. Steps with dependencies
3. Checkpoints
4. Risk assessment

Respond with JSON...

Metrics and Observability

All optimizations expose counters through MiniA.getMetrics() and are registered under the mini-a namespace in the OpenAF metrics registry, making them available to Prometheus/Grafana scrapers.

var agent = new MiniA()
agent.start({ goal: "...", useshell: true })
log(agent.getMetrics())

// Poll OpenAF registry from another job:
// ow.metrics.get("mini-a")

The table below lists the metric groups most relevant to performance and optimization. For the complete metric reference (all groups and all counters) see USAGE.md § Metric breakdown.

Group	Key counters	What it tells you
`llm_calls`	`normal`, `low_cost`, `total`, `fallback_to_main`	How often the main vs low-cost model was used, and how many escalations occurred
`performance`	`steps_taken`, `total_session_time_ms`, `avg_step_time_ms`, `max_context_tokens`, `llm_estimated_tokens`, `llm_actual_tokens`, `step_prompt_build_ms`, `step_llm_wait_ms`, `step_tool_exec_ms`, `step_context_maintenance_ms`	End-to-end timing broken down by phase so you can identify where time is spent
`summarization`	`summaries_made`, `summaries_skipped`, `context_summarizations`, `summaries_tokens_reduced`, `summaries_original_tokens`, `summaries_final_tokens`	Auto-summarization activity and tokens reclaimed
`context_compression`	`prompt_context_compressed`, `prompt_context_tokens_saved`, `goal_block_compressed`, `goal_block_tokens_saved`, `hook_context_compressed`, `hook_context_tokens_saved`	Per-block compression savings (prompt, goal, hook context)
`system_prompt`	`system_prompt_builds`, `system_prompt_tokens_last`, `system_prompt_budget_applied`, `system_prompt_budget_tokens_saved`, `system_prompt_examples_dropped`, `system_prompt_skill_descriptions_dropped`, `system_prompt_tool_details_dropped`, `system_prompt_planning_details_dropped`, `system_prompt_skills_trimmed`	System prompt construction cost and budget trimming decisions
`tool_cache`	`hits`, `misses`, `total_requests`, `hit_rate`	Cache effectiveness for deterministic/read-only MCP tools
`tool_selection`	`dynamic_used`, `keyword`, `llm_lc`, `llm_main`, `connection_chooser_lc`, `connection_chooser_main`, `fallback_all`	Which dynamic tool-selection strategy fired (`mcpdynamic=true`)
`mcp_resilience`	`circuit_breaker_trips`, `circuit_breaker_resets`, `lazy_init_success`, `lazy_init_failed`	MCP connection health: circuit breaker activity and lazy-init outcomes
`behavior_patterns`	`escalations`, `escalation_consecutive_errors`, `escalation_consecutive_thoughts`, `escalation_thought_loop`, `escalation_steps_without_action`, `escalation_similar_thoughts`, `escalation_context_window`	Escalation reasons—helps diagnose why the agent switched to the main model
`planning`	`plans_generated`, `plans_validated`, `plans_validation_failed`, `plans_replanned`, `planning_disabled_simple_goal`	Planning engine activity and validation pass/fail rates
`deep_research`	`sessions`, `cycles`, `validations_passed`, `validations_failed`, `early_success`, `max_cycles_reached`	Research-and-validate loop outcomes when `deepresearch=true`
`delegation`	`total`, `completed`, `failed`, `timedout`, `retried`, `worker_hint_matched`, `workers_healthy`	Sub-agent delegation lifecycle and worker pool health
`history`	`sessions_started`, `sessions_resumed`, `files_kept`, `files_deleted_by_period`, `files_deleted_by_count`	Console history session tracking and pruning activity

Tip: behavior_patterns.escalation_* counters identify why the agent escalated. If escalation_context_window is high, lower maxcontext or enable useplanning. If escalation_consecutive_thoughts is high, the agent is looping—review the goal phrasing or add constraints.

FAQ

Q: Will this break my existing workflows? A: No, all optimizations are backward compatible. Existing configurations work unchanged.

Q: Can I disable these optimizations? A: Yes, but not recommended. Set maxcontext=0 to disable auto-management. Other optimizations (escalation, parallel actions) are prompt-based and can't be disabled.

Q: Do I need to update my code? A: No, benefits are automatic. Update prompt phrasing to leverage parallel actions.

Q: Will my goals behave differently? A: Goals will complete faster with fewer tokens, but results quality is unchanged or improved.

Q: What if I'm already using maxcontext? A: Your setting takes precedence. New behavior only applies when maxcontext is unset.

Additional Resources

Usage Guide - Complete configuration reference
CHEATSHEET - Quick parameter reference
GitHub Issues - Report problems or request features

Summary

✅ Automatic - No configuration required ✅ Backward Compatible - Existing setups unchanged ✅ Significant Savings - 40-60% token reduction, 50-70% cost savings ✅ Better Performance - Faster execution, smarter model usage ✅ Transparent - Verbose mode shows all optimization decisions

The optimizations work together to provide the best balance of cost, speed, and quality for all goal types.

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Performance Optimizations

Overview of Optimizations

1. Automatic Context Management

What It Does

How It Works

Benefits

Advanced Configuration

2. Dynamic Escalation Thresholds

What It Does

Escalation Thresholds

How It Works

Benefits

Debugging

3. Enhanced Parallel Action Support

What It Does

How It Works

Use Cases

Benefits

Example

4. Two-Phase Planning Mode

What It Does

How It Works

Benefits

When to Use

Monitoring Progress

Performance Comparison

Before Optimizations

After Optimizations

Cost Impact Examples

Scenario 1: Development Assistant (Mixed Complexity)

Scenario 2: Code Analysis Pipeline

Best Practices

1. Use Planning Mode for Complex Goals

2. Leverage Dual Models

3. Monitor with Verbose Mode

4. Trust the Defaults

Troubleshooting

Context Still Growing Too Large

Too Many Main Model Escalations

Parallel Actions Not Being Used

Configuration Reference

Context Management

Planning Mode

Escalation Control

Migration Notes

Upgrading from Previous Versions

For Advanced Users

Technical Details

Context Deduplication Algorithm

Goal Complexity Heuristics

Planning Generation

Metrics and Observability

FAQ

Additional Resources

Summary