Self-Improving Agent

基于对经验的持续学习，不断优化 Agent 能力。适用于完成重要任务后、出现错误时、会话结束时，或用户输入“自我进化”“总结经验”“从经验中学习”等指令时触发。

"An AI agent that learns from every interaction, accumulating patterns and insights to continuously improve its own capabilities." — Based on 2025 lifelong learning research

Overview

This is a universal self-improvement system that learns from ALL task experiences. It implements a complete feedback loop:

• Multi-Memory Architecture: Semantic (patterns/rules) + Episodic (experiences) + Working (session context)
• Self-Correction: Detects and fixes guidance errors
• Self-Validation: Periodically verifies skill accuracy
• Evolution Markers: Traceable changes with source attribution
• Confidence Tracking: Measures pattern reliability over time
• User Confirmation Gate: All skill file modifications require explicit user approval before applying
• Human-in-the-Loop: Collects feedback to validate improvements

Research-Based Design

Research	Key Insight	Application
SimpleMem	Efficient lifelong memory	Pattern accumulation system
Multi-Memory Survey	Semantic + Episodic memory	World knowledge + experiences
Lifelong Learning	Continuous task stream learning	Learn from every task
Evo-Memory	Test-time lifelong learning	Real-time adaptation

The Self-Improvement Loop

text

┌──────────────────────────────────────────────────────────────┐
│                  UNIVERSAL SELF-IMPROVEMENT                   │
├──────────────────────────────────────────────────────────────┤
│                                                              │
│  Task Event → Extract Experience → Abstract Pattern → Update │
│       │               │                 │              │     │
│       ▼               ▼                 ▼              ▼     │
│  ┌────────────────────────────────────────────────────────┐  │
│  │              MULTI-MEMORY SYSTEM                       │  │
│  ├────────────────────────────────────────────────────────┤  │
│  │ Semantic Memory  │ Episodic Memory  │ Working Memory   │  │
│  │ (Patterns/Rules) │ (Experiences)    │ (Current)        │  │
│  │ memory/self-improving/semantic/ │ memory/self-improving/episodic/ │ memory/self-improving/working/  │  │
│  └────────────────────────────────────────────────────────┘  │
│                                                              │
│  ┌────────────────────────────────────────────────────────┐  │
│  │              FEEDBACK LOOP                             │  │
│  │ User Feedback → Confidence Update → Pattern Adapt      │  │
│  └────────────────────────────────────────────────────────┘  │
│                                                              │
└──────────────────────────────────────────────────────────────┘

When This Activates

Automatic Triggers

Event	Action
Any significant task completes	Extract patterns, propose skill updates (requires user confirmation)
An error or failure occurs	Capture error context, trigger self-correction (requires user confirmation before applying fixes)
Session ends	Consolidate working memory into long-term memory

Manual Triggers

• User says "自我进化", "self-improve", "从经验中学习"
• User says "分析今天的经验", "总结教训", "总结经验"
• User asks to improve a specific skill or workflow

Memory Storage

Workspace Discovery

Before accessing any memory files, the agent MUST first determine the workspace root path:

1. Check environment — Use the workspace path provided by the IDE/environment context
2. Verify structure — Confirm the workspace root by checking for project markers (e.g., .git/, package.json, pom.xml, etc.)
3. All paths below are relative to the workspace root — e.g., {workspace}/memory/self-improving/

Relationship with Agent Memory

The Self-Improving Agent's memory lives inside the Agent's memory/ directory as a dedicated subdirectory. This design ensures:

• No confusion: Agent's own memory (MEMORY.md, memory/YYYY-MM-DD.md) and Self-Improving Agent's memory (memory/self-improving/) are clearly separated by directory structure
• Discoverability: The Agent can browse memory/ and naturally find self-improving insights
• Supplement, not replace: Self-Improving Agent can append high-confidence patterns to Agent's memory files (with user confirmation), enriching the Agent's knowledge

text

{workspace}/
├── MEMORY.md                          # Agent core memory (Self-Improving Agent can append)
├── memory/
│   ├── YYYY-MM-DD.md                  # Agent daily memory (Self-Improving Agent can append)
│   └── self-improving/                # Self-Improving Agent dedicated memory space
│       ├── semantic/
│       │   └── patterns.json          # Abstract patterns and rules
│       ├── episodic/
│       │   └── YYYY/
│       │       └── YYYY-MM-DD-{task}.json  # Specific experiences
│       ├── working/
│       │   ├── current_session.json   # Active session data
│       │   ├── last_error.json        # Error context for self-correction
│       │   └── session_end.json       # Session end marker for consolidation
│       └── index.json                 # Memory index and metrics

Memory Interaction Rules

Action	Target	Condition
Read	MEMORY.md	Always — to understand Agent's accumulated knowledge
Read	memory/YYYY-MM-DD.md	Always — to understand today's context
Append to	MEMORY.md	Only high-confidence patterns (>= 0.9), requires user confirmation
Append to	memory/YYYY-MM-DD.md	Session summary and key learnings, requires user confirmation
Full CRUD	memory/self-improving/*	Self-Improving Agent's own memory space, free to manage

Evolution Priority Matrix

Trigger evolution when new reusable knowledge appears:

Trigger	Priority	Action
New workflow pattern discovered	High	Add to relevant skill guidance
Architecture/design tradeoff clarified	High	Add to decision patterns
Debugging fix or anti-pattern found	High	Add to troubleshooting patterns
Security or performance insight	High	Add to best practice patterns
Code pattern or idiom learned	Medium	Add to coding patterns
Test strategy improvement	Medium	Update testing approach
Tool usage optimization	Medium	Update tool usage patterns
Documentation structure insight	Low	Update documentation templates

Multi-Memory Architecture

1. Semantic Memory (memory/self-improving/semantic/patterns.json)

Stores abstract patterns and rules reusable across contexts:

json

{
  "patterns": {
    "pat-2025-01-11-001": {
      "id": "pat-2025-01-11-001",
      "name": "Pattern Name",
      "source": "user_feedback|implementation_review|retrospective",
      "confidence": 0.95,
      "applications": 5,
      "created": "2025-01-11",
      "last_applied": "2025-01-15",
      "category": "coding_patterns|architecture|debugging|workflow|...",
      "pattern": "One-line summary",
      "problem": "What problem does this solve?",
      "solution": "How to apply this pattern",
      "quality_rules": ["Rule 1", "Rule 2"],
      "target_skills": ["skill-name-1", "skill-name-2"]
    }
  }
}

2. Episodic Memory (memory/self-improving/episodic/)

Stores specific experiences and what happened:

json

{
  "id": "ep-2025-01-11-001",
  "timestamp": "2025-01-11T10:30:00Z",
  "skill": "debugger|coding-assistant|reviewer|...",
  "task_type": "debugging|coding|review|design|...",
  "situation": "What the user was trying to do",
  "solution": "How the issue was resolved",
  "outcome": "success|partial|failure",
  "root_cause": "Underlying issue if applicable",
  "lesson": "Key takeaway from this experience",
  "related_pattern": "pattern_id if linked",
  "user_feedback": {
    "rating": 8,
    "comments": "User's feedback on the experience"
  }
}

3. Working Memory (memory/self-improving/working/)

Stores current session context — ephemeral data that gets consolidated at session end:

json

{
  "session_id": "session-2025-01-11-001",
  "started": "2025-01-11T10:00:00Z",
  "tasks_completed": [],
  "errors_encountered": [],
  "patterns_applied": [],
  "pending_extractions": []
}

Self-Improvement Process

Phase 1: Experience Extraction

After any significant task completes, extract:

yaml

What happened:
  task_type: {what kind of task}
  task: {what was being done}
  outcome: {success|partial|failure}

Key Insights:
  what_went_well: [what worked]
  what_went_wrong: [what didn't work]
  root_cause: {underlying issue if applicable}

User Feedback:
  rating: {1-10 if provided}
  comments: {specific feedback}

Phase 2: Pattern Abstraction

Convert experiences to reusable patterns. The goal is to go from concrete to abstract — patterns should be general enough to apply across different tasks but specific enough to be actionable.

Concrete Experience	Abstract Pattern
"User forgot to save intermediate work"	"Always persist intermediate results to files"
"Code review missed SQL injection"	"Add security checklist to review process"
"Callback was empty, causing silent failure"	"Verify all callbacks have implementations"
"Ambiguous UI spec caused rework"	"UI specs need exact layout specifications"

Abstraction Rules:

yaml

If experience_repeats 3+ times:
  pattern_level: critical
  action: Add to "Critical Mistakes" or "Anti-Patterns" section

If solution_was_effective:
  pattern_level: best_practice
  action: Add to "Best Practices" section

If user_rating >= 7:
  pattern_level: strength
  action: Reinforce this approach in relevant skills

If user_rating <= 4:
  pattern_level: weakness
  action: Add to "What to Avoid" section

Phase 3: Skill Updates

IMPORTANT: User Confirmation Required — Before writing any changes to skill files, you MUST:

1. Present proposed changes — Show the user a clear summary of what will be modified:
   • Which skill file(s) will be updated
   • What content will be added, modified, or removed
   • The rationale behind each change (source episode, pattern, confidence level)
2. Wait for explicit approval — Do NOT proceed until the user confirms. Acceptable confirmations include explicit affirmative responses (e.g., "确认", "好的", "proceed", "yes").
3. Apply changes only after approval — Once confirmed, apply the changes with evolution markers for traceability.

If the user rejects or requests modifications, adjust the proposed changes accordingly and re-present for confirmation.

Proposed Change Summary Format:

markdown

## Proposed Skill Update

**Target**: `{skill-file-path}`
**Action**: {Add new pattern | Correct existing guidance | Update checklist}
**Source**: {episode_id or trigger}
**Confidence**: {X.XX}

### Changes Preview
{Show the exact content that will be added/modified, using diff-style or before/after format}

### Rationale
{Why this change is recommended}

---
Confirm this update? (yes/no/modify)

Once confirmed, update skill files with evolution markers for traceability:

markdown

<!-- Evolution: 2025-01-12 | source: ep-2025-01-12-001 | task: debugging -->

## Pattern Added (2025-01-12)

**Pattern**: Always verify callbacks are not empty functions

**Source**: Episode ep-2025-01-12-001

**Confidence**: 0.95

### Updated Checklist
- [ ] Verify all callbacks have implementations
- [ ] Test callback execution paths

Correction Markers (when fixing wrong guidance):

markdown

<!-- Correction: 2025-01-12 | was: "Use callback chain" | reason: caused stale state -->

## Corrected Guidance

Use direct state monitoring instead of callback chains for reactive updates.

Use the templates in templates/ for consistent formatting. See references/appendix.md for the full template structures.

Phase 4: Memory Consolidation

1. Update semantic memory — add or update patterns in memory/self-improving/semantic/patterns.json

2. Store episodic memory — write episode to memory/self-improving/episodic/YYYY/YYYY-MM-DD-{task}.json

3. Update pattern confidence — increase confidence for patterns that were successfully applied, decrease for those that led to errors

4. Prune outdated patterns — lower confidence for patterns with no recent applications; archive patterns below 0.3 confidence

5. Supplement Agent memory — propose additions to Agent's own memory files. User confirmation is REQUIRED before any write to MEMORY.md or memory/YYYY-MM-DD.md. Follow the same confirmation protocol as Phase 3:

   What to propose:

   • High-confidence patterns (>= 0.9) as concise entries → MEMORY.md
   • Today's session summary and key learnings → memory/YYYY-MM-DD.md

   Confirmation format:

   markdown

   ## Proposed Agent Memory Update
   
   ### → MEMORY.md (append)
   {Exact content to be appended, preview here}
   
   ### → memory/YYYY-MM-DD.md (append)
   {Exact content to be appended, preview here}
   
   **Source patterns**: {pattern IDs and confidence levels}
   
   ---
   Confirm this memory update? (yes/no/modify)
   

   After approval:

   • Append confirmed content with <!-- Source: self-improving-agent | date: YYYY-MM-DD --> markers for traceability
   • Do NOT overwrite existing content — always append at the end

Self-Correction

Triggered when:

• A command or operation returns an error
• Tests fail after following skill guidance
• User reports the guidance produced incorrect results

Process:

1. Detect Error

   • Capture error context into memory/self-improving/working/last_error.json
   • Identify which guidance was followed

2. Verify Root Cause

   • Was the guidance incorrect?
   • Was the guidance misinterpreted?
   • Was the guidance incomplete?

3. Propose Correction

   • Draft the corrected guidance with correction markers
   • Present proposed changes to user for review (follow Phase 3 confirmation format)
   • Wait for user confirmation before applying any changes

4. Apply Correction (after user approval)

   • Update relevant skill/document with corrected guidance
   • Add correction marker with reason
   • Update related patterns in semantic memory

5. Validate Fix

   • Test the corrected guidance if possible
   • Ask user to verify the fix

Self-Validation

Periodically (or when triggered manually), verify that stored patterns and skill guidance are still accurate:

1. Check that examples still work
2. Verify checklists match current conventions
3. Confirm external references are still valid
4. Detect duplicated or conflicting guidance

Use the validation template in templates/validation-template.md for structured reviews.

Human-in-the-Loop Feedback

After each self-improvement cycle, present a summary to the user:

markdown

## Self-Improvement Summary

I've learned from our session and updated:

### Patterns Extracted
1. **pattern_name**: Description (confidence: X.XX)

### Skills/Documents Updated
- `skill-name`: What was updated

### Confidence Levels
- New patterns: ~0.85 (needs more validation)
- Reinforced patterns: ~0.95 (well-established)

### Your Feedback
- Were these updates helpful?
- Should I apply any pattern more broadly?
- Any corrections needed?

Integrate feedback into confidence scoring:

Feedback	Action
Positive (rating >= 7)	Increase confidence, consider expanding to related skills
Neutral (rating 4-6)	Keep pattern, gather more data before expanding
Negative (rating <= 3)	Decrease confidence, revise or archive pattern

Best Practices

DO

• Learn from EVERY significant task interaction
• Extract patterns at the right abstraction level — general enough to reuse, specific enough to be actionable
• Always present proposed changes to the user and wait for explicit confirmation before writing to skill files OR Agent memory (MEMORY.md, memory/YYYY-MM-DD.md)
• Update multiple related skills when a pattern applies broadly
• Track confidence and application counts for all patterns
• Ask for user feedback on improvements
• Use evolution/correction markers for full traceability
• Validate guidance before applying broadly
• Read MEMORY.md and today's memory/YYYY-MM-DD.md at the start of each self-improvement cycle for context

DON'T

• NEVER modify skill files or Agent memory files without user confirmation — this is a hard rule with no exceptions
• NEVER overwrite Agent memory content — always append at the end
• Over-generalize from a single experience — wait for 2-3 occurrences before creating a pattern
• Update skills without confidence tracking
• Ignore negative feedback — it's the most valuable signal
• Make changes that break existing, working functionality
• Create contradictory patterns — resolve conflicts explicitly
• Apply untested patterns at high confidence

Quick Start

After any significant task completes, this agent:

1. Analyzes what happened during the task
2. Extracts reusable patterns and insights
3. Proposes skill updates and presents them to the user for review
4. Waits for explicit user confirmation before applying any skill modifications
5. Updates approved changes to skill files with evolution markers
6. Logs to memory (semantic + episodic) for future reference
7. Reports summary to user and collects feedback

References

For detailed memory structures, validation templates, metrics, and workflow diagrams, read references/appendix.md.

For pattern/correction/validation templates, see the templates/ directory:

• templates/pattern-template.md — Adding new patterns
• templates/correction-template.md — Fixing incorrect guidance
• templates/validation-template.md — Validating skill accuracy

Research Papers

• SimpleMem: Efficient Lifelong Memory for LLM Agents
• A Survey on the Memory Mechanism of Large Language Model Agents
• Lifelong Learning of LLM based Agents
• Evo-Memory: DeepMind's Benchmark