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- Add status fields to Receipt model (status, claimed_at, claimed_by) - Add RECEIPT_CLAIM handling to state_transition.py with validation and reward minting - Add type field to Transaction model for reliable transaction type storage - Update router to use TransactionRequest model to preserve type field - Update poa.py to extract type from mempool transaction content and store only original payload - Add RECEIPT_CLAIM to GasType enum with gas schedule
161 lines
4.0 KiB
Markdown
161 lines
4.0 KiB
Markdown
---
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description: Atomic OpenClaw agent performance tuning and optimization with deterministic outputs
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title: openclaw-performance-optimizer
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version: 1.1
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---
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# OpenClaw Performance Optimizer
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## Purpose
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Optimize agent performance, tune execution parameters, and improve efficiency for OpenClaw agents through systematic analysis and adjustment.
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## Activation
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Trigger when user requests performance optimization: agent tuning, parameter adjustment, efficiency improvements, or performance benchmarking.
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## Input
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```json
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{
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"operation": "tune|benchmark|optimize|profile",
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"agent": "agent_name",
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"target": "speed|memory|throughput|latency|all",
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"parameters": {
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"max_tokens": "number (optional)",
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"temperature": "number (optional)",
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"timeout": "number (optional)"
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}
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}
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```
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## Output
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```json
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{
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"summary": "Agent performance optimization completed successfully",
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"operation": "tune|benchmark|optimize|profile",
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"agent": "agent_name",
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"target": "speed|memory|throughput|latency|all",
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"before_metrics": {
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"execution_time": "number",
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"memory_usage": "number",
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"throughput": "number",
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"latency": "number"
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},
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"after_metrics": {
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"execution_time": "number",
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"memory_usage": "number",
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"throughput": "number",
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"latency": "number"
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},
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"improvement": {
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"speed": "percentage",
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"memory": "percentage",
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"throughput": "percentage",
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"latency": "percentage"
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},
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"issues": [],
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"recommendations": [],
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"confidence": 1.0,
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"execution_time": "number",
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"validation_status": "success|partial|failed"
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}
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```
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## Process
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### 1. Analyze
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- Profile current agent performance
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- Identify bottlenecks
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- Assess optimization opportunities
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- Validate agent state
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### 2. Plan
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- Select optimization strategy
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- Define parameter adjustments
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- Set performance targets
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- Plan validation approach
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### 3. Execute
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- Apply parameter adjustments
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- Run performance benchmarks
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- Measure improvements
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- Validate stability
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### 4. Validate
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- Verify performance gains
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- Check for regressions
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- Validate parameter stability
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- Confirm agent functionality
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## Constraints
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- **MUST NOT** modify agent core functionality
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- **MUST NOT** exceed 90 seconds for optimization
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- **MUST** validate parameter ranges
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- **MUST** preserve agent behavior
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- **MUST** rollback on critical failures
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## Environment Assumptions
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- Agent operational and accessible
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- Performance monitoring available
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- Parameter configuration accessible
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- Benchmarking tools available
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- Agent state persistence functional
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## Error Handling
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- Parameter validation failure → Revert to previous parameters
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- Performance regression → Rollback optimization
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- Agent instability → Restore baseline configuration
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- Timeout during optimization → Return partial results
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## Example Usage Prompt
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```
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Optimize main agent for speed and memory efficiency
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```
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## Expected Output Example
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```json
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{
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"summary": "Main agent optimized for speed and memory efficiency",
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"operation": "optimize",
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"agent": "main",
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"target": "all",
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"before_metrics": {
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"execution_time": 15.2,
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"memory_usage": 250,
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"throughput": 8.5,
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"latency": 2.1
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},
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"after_metrics": {
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"execution_time": 11.8,
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"memory_usage": 180,
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"throughput": 12.3,
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"latency": 1.5
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},
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"improvement": {
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"speed": "22%",
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"memory": "28%",
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"throughput": "45%",
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"latency": "29%"
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},
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"issues": [],
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"recommendations": ["Consider further optimization for memory-intensive tasks"],
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"confidence": 1.0,
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"execution_time": 35.7,
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"validation_status": "success"
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}
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```
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## Model Routing Suggestion
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**Reasoning Model** (Claude Sonnet, GPT-4)
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- Complex parameter optimization
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- Performance analysis and tuning
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- Benchmark interpretation
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- Regression detection
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**Performance Notes**
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- **Execution Time**: 20-60 seconds for optimization, 5-15 seconds for benchmarking
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- **Memory Usage**: <200MB for optimization operations
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- **Network Requirements**: Agent communication for profiling
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- **Concurrency**: Safe for sequential optimization of different agents
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