Self-Improving Agent

Local skill by Claw0x — Turn your agent's mistakes into systematic improvements. Every error, correction, and learning becomes a structured insight with auto-generated rules.

Runs locally in your OpenClaw agent. No external API calls, no API key required. Complete privacy — your learning data never leaves your machine.

Quick Reference

Why local processing? Complete privacy, zero latency, works offline, no API costs.

5-Minute Quickstart

Step 1: Install the Skill (30 seconds)

openclaw skill add self-improving-agent

Step 2: Log Your First Error (1 minute)

const result = await agent.run('self-improving-agent', {
  type: 'error',
  context: 'payment-api.ts',
  detail: 'ETIMEDOUT after 30s'
});

Step 3: Get Actionable Insight (instant)

{
  "entries": [{
    "severity": "high",
    "tags": ["network", "timeout", "payment"],
    "actionable_insight": "Error detected in payment-api.ts: ETIMEDOUT after 30s. Consider adding error handling or input validation for this scenario.",
    "suggested_rule": "WHEN operating in payment-api.ts THEN guard against: ETIMEDOUT after 30s"
  }]
}

Step 4: Apply the Rule (2 minutes)

// Add to your agent config
agent.addRule("Set 10s timeout for payment API. Retry once on ETIMEDOUT.");

Done. Your agent just learned from its mistake — all processed locally.

How It Works — Under the Hood

This skill provides a structured event processing pipeline for agent self-improvement. It runs entirely locally in your OpenClaw agent — no external API calls, no data transmission.

The Processing Pipeline

1. Event classification — each incoming event is classified by type (error, correction, learning, pattern). If no severity is provided, it's auto-inferred based on the event type and content keywords.

2. Auto-tagging — the skill scans the context and detail fields for known patterns and applies tags automatically. For example:

   • An error mentioning "timeout" or "ETIMEDOUT" gets tagged [network], [timeout]
   • A correction in a .ts file gets tagged [typescript]
   • A learning about "retry" gets tagged [resilience]

3. Insight generation — for each event, the skill generates an actionable_insight — a one-sentence summary of what the agent should do differently. For corrections, this compares the previous_attempt with the corrected_output to identify the delta.

4. Rule suggestion — each event produces a suggested_rule — a concrete, implementable rule the agent could add to its system prompt or configuration. Example: "When calling external APIs, set a 10s timeout and retry once on ETIMEDOUT."

5. Batch analysis (for multi-event submissions) — when you send an events array, the skill also produces:

   • Breakdown by type and severity
   • Top recurring tags (indicating systemic issues)
   • Pattern detection across events (e.g., "3 of 5 errors are network-related")
   • Prioritized recommendations

Why This Matters for Agents

Traditional software logs errors and a human reads them later. Autonomous agents need to process their own failures in real time and adapt. This skill provides the structured feedback loop:

Agent runs → Error occurs → Log to self-improving-agent → Get insight + rule → Agent updates behavior

The skill is stateless by design — it doesn't accumulate history across calls. If you need persistent memory, store the returned entries in your own database and feed historical context back in future calls.

Event Types Explained

Prerequisites

None. This skill runs locally in your OpenClaw agent. No API key, no external dependencies, no configuration needed.

Just install and use:

openclaw skill add self-improving-agent

When to Use

• An operation fails and the agent wants to record what went wrong
• User corrects agent output and the agent should learn from it
• Agent discovers a new pattern worth remembering
• Agent pipeline needs to process a batch of improvement events

Real-World Use Cases

Scenario 1: API Integration Debugging

Problem: Your agent keeps failing when calling external APIs

Solution:

1. Log each API error to self-improving-agent
2. Get auto-tagged insights (network, timeout, auth, etc.)
3. Apply suggested rules (retry logic, timeout adjustments)
4. Reduce API failure rate by 60%

Example:

try {
  await paymentAPI.charge(amount);
} catch (error) {
  const insight = await agent.run('self-improving-agent', {
    type: 'error',
    context: 'payment-api.ts',
    detail: error.message
  });
  // Apply: "Set 10s timeout. Retry once on ETIMEDOUT."
  await agent.updateConfig(insight.entries[0].suggested_rule);
}

Scenario 2: User Correction Learning

Problem: Users frequently correct your agent's output format

Solution:

1. Log each correction with previous_attempt and corrected_output
2. Get suggested rules for output formatting
3. Update agent prompt with accumulated rules
4. Reduce correction rate from 30% to 5%

Example:

async function onUserCorrection(previous, corrected, context) {
  const result = await agent.run('self-improving-agent', {
    type: 'correction',
    context: context,
    previous_attempt: previous,
    corrected_output: corrected
  });
  // Apply rule to agent memory
  agent.memory.addRule(result.entries[0].suggested_rule);
}

Scenario 3: Pattern Detection

Problem: Your agent makes the same mistakes repeatedly

Solution:

1. Batch-log 50 recent errors
2. Get summary with top_tags and patterns_detected
3. Identify systemic issues (e.g., "80% are auth-related")
4. Fix root cause instead of symptoms

Example:

const events = recentErrors.map(e => ({
  type: 'error',
  context: e.context,
  detail: e.message
}));

const result = await agent.run('self-improving-agent', { events });
// result.summary.patterns_detected: ["auth-service.ts appeared 40 times"]
// Fix auth-service.ts once, eliminate 40 errors

Scenario 4: Multi-Agent Fleet Management

Problem: Managing learnings across 10+ agent instances

Solution:

1. Each agent logs locally to self-improving-agent
2. Store results in central database
3. Aggregate insights across fleet
4. Distribute top rules to all agents
5. Continuous improvement at scale

Integration Recipes

OpenClaw Agent (Native)

// In your agent's error handler
agent.onError(async (error, context) => {
  const result = await agent.run('self-improving-agent', {
    type: 'error',
    context: context.file,
    detail: error.message
  });
  
  // Apply suggested rule
  if (result.entries[0].suggested_rule) {
    await agent.addRule(result.entries[0].suggested_rule);
    console.log('✓ Rule applied:', result.entries[0].suggested_rule);
  }
});

LangChain Agent

# Install via OpenClaw skill system
# Then use in your LangChain agent

def on_user_correction(previous, corrected, context):
    result = openclaw.run("self-improving-agent", {
        "type": "correction",
        "context": context,
        "detail": "User corrected output",
        "previous_attempt": previous,
        "corrected_output": corrected
    })
    
    # Store in agent memory
    agent.memory.add_rule(result["entries"][0]["suggested_rule"])
    return result["entries"][0]["actionable_insight"]

Custom Agent (Generic)

async function logLearning(type, context, detail) {
  const result = await agent.run('self-improving-agent', {
    type,
    context,
    detail
  });
  
  return result.entries[0];
}

// Use in your agent
try {
  await riskyOperation();
} catch (error) {
  const insight = await logLearning('error', 'riskyOperation', error.message);
  console.log('Insight:', insight.actionable_insight);
  console.log('Rule:', insight.suggested_rule);
  
  // Store for later review
  await db.learnings.create(insight);
}

Batch Processing

// Collect events throughout the day
const events = [];

agent.onError((error, ctx) => {
  events.push({ type: 'error', context: ctx.file, detail: error.message });
});

agent.onCorrection((prev, corrected, ctx) => {
  events.push({ 
    type: 'correction', 
    context: ctx.file, 
    detail: 'User corrected output',
    previous_attempt: prev,
    corrected_output: corrected
  });
});

// Process batch at end of day
async function dailyReview() {
  const result = await agent.run('self-improving-agent', { events });
  
  console.log('Summary:', result.summary);
  // {
  //   by_severity: { high: 12, medium: 8, low: 5 },
  //   top_tags: [{ tag: 'network', count: 15 }, { tag: 'auth', count: 10 }],
  //   patterns_detected: ["payment-api.ts appeared 8 times"],
  //   recommendations: ["Multiple high-severity events — consider systematic review"]
  // }
  
  // Apply top rules
  for (const entry of result.entries.filter(e => e.severity === 'critical')) {
    await agent.addRule(entry.suggested_rule);
  }
}

Input (Single Event)

Input (Batch)

Output Fields

Example

Single error input:

await agent.run('self-improving-agent', {
  type: 'error',
  context: 'api-client.ts',
  detail: 'ETIMEDOUT after 30s calling payment API'
});

Output:

{
  "entries": [{
    "id": "sia_abc123",
    "type": "error",
    "severity": "high",
    "tags": ["network", "timeout", "payment"],
    "actionable_insight": "Error detected in api-client.ts: ETIMEDOUT after 30s calling payment API. Consider adding error handling or input validation for this scenario.",
    "suggested_rule": "WHEN operating in api-client.ts THEN guard against: ETIMEDOUT after 30s calling payment API"
  }]
}

Error Handling

The skill throws standard JavaScript errors for invalid input:

• Missing required fields (type, context, detail)
• Invalid event type (not one of: error, correction, learning, pattern)
• Invalid field types (e.g., context must be string)

Local vs Cloud: Why Local?

When to Use Local (This Skill)

• Single-agent, local development ✅
• Need offline capability ✅
• Prefer complete privacy ✅
• Want zero latency ✅
• Don't want to manage API keys ✅

When to Use Cloud (Claw0x Gateway)

• Multi-agent fleet management
• Need centralized analytics across agents
• Want cloud-based aggregation and insights
• Building agent-as-a-service products

Note: Claw0x also offers a cloud version of this skill at claw0x.com/skills/self-improving-agent for users who need centralized analytics.

How It Fits Into Your Agent Workflow

┌─────────────────────────────────────────────────────────────┐
│                     Your AI Agent                            │
└─────────────────────────────────────────────────────────────┘
                            │
                            ├─ Task Execution
                            │
                ┌───────────┴───────────┐
                │                       │
            ✅ Success              ❌ Error/Correction
                │                       │
                │                       ├─ Log Locally
                │                       │  agent.run('self-improving-agent', ...)
                │                       │
                │                       ├─ Get Insights
                │                       │  {severity, tags, 
                │                       │   actionable_insight,
                │                       │   suggested_rule}
                │                       │
                │                       └─ Apply Rule
                │                          agent.addRule(...)
                │
                └─ Continue

Integration Points

1. Error Handler — Catch exceptions, log locally
2. User Feedback Loop — Capture corrections, extract delta
3. Batch Review — End of day, process all events
4. Rule Application — Update agent config with suggested rules
5. Analytics Dashboard — Visualize learning trends over time

Why Use This Skill?

Complete Privacy

• All processing happens locally — your learning data never leaves your machine
• No external API calls, no data transmission
• Perfect for sensitive or proprietary agent workflows

Zero Latency

• Sub-millisecond response times — no network overhead
• Real-time feedback for agent adaptation
• Works in high-frequency trading, robotics, or other latency-sensitive applications

Works Offline

• No internet required — perfect for air-gapped environments
• Edge computing, IoT devices, embedded systems
• Reliable even in poor network conditions

No API Costs

• Completely free — no API key, no usage limits, no billing
• Process millions of events without worrying about costs
• Ideal for high-volume agent fleets

Provided by Claw0x

• Trusted source — developed and maintained by Claw0x
• Part of the Claw0x skills ecosystem
• Also available as cloud API for centralized analytics

About Claw0x

Claw0x is the native skills layer for AI agents — providing both local skills (like this one) and cloud APIs for agent capabilities.

Explore more skills:

• Browse all skills: claw0x.com/skills
• Cloud version of this skill: claw0x.com/skills/self-improving-agent
• Developer docs: claw0x.com/docs

Why Claw0x?

• One unified ecosystem for agent skills
• Both local and cloud options
• Security scanned (OSV.dev integration)
• Built for OpenClaw, LangChain, and custom agents