Error Driven Evolution
Structured error-to-rule learning system for AI agents.
- Rating
- 4.5 (128 reviews)
- Downloads
- 966 downloads
- Version
- 1.0.0
Overview
Structured error-to-rule learning system for AI agents.
Complete Documentation
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Error-Driven Evolution
Turn mistakes into rules. Not reflections, not apologies — rules.
Core Concept
When an agent makes an error or gets corrected, it must:
- Extract a rule (not a story)
- Write it to
lessons.mdin its workspace - Scan relevant rules before future decisions in that domain
- Optionally share anonymized rules to the community repo
lessons.md Format
File location: {workspace}/lessons.md
Each rule follows this structure:
### [CATEGORY] Short imperative title
- **When**: The specific situation/trigger
- **Do**: The correct action (imperative, specific)
- **Don't**: The wrong action that was taken
- **Why**: One sentence — what went wrong
- **Added**: YYYY-MM-DD
Categories
| Tag | Scope |
|---|---|
| DATA | Querying, interpreting, presenting data |
| COMMS | Messaging, tone, audience, channels |
| SCOPE | Role boundaries, doing others' work |
| EXEC | Task execution, tools, file ops |
| JUDGMENT | Decisions, priorities, assumptions |
| CONTEXT | Memory, context window, info management |
| SAFETY | Security, privacy, destructive ops |
| COLLAB | Multi-agent coordination, handoffs |
When to Record
Record a rule when:
- User corrects you — explicit feedback
- User overrides your output — they redo your work
- Same error twice — second occurrence MUST become a rule
- Near miss — you catch yourself about to repeat a mistake
How to Record
- Stop. Don't apologize at length.
- Identify the category.
- Write the rule in imperative form.
- Append to lessons.md (never overwrite).
- Confirm briefly: "Added to lessons: [title]"
Pre-Decision Scan
Before acting, scan lessons.md for applicable rules:
| About to... | Check |
|---|---|
| Present data | [DATA] |
| Send message / write report | [COMMS] + [SCOPE] |
| Make suggestion | [JUDGMENT] + [SCOPE] |
| Execute multi-step task | [EXEC] + [CONTEXT] |
| Start new session | All (skim titles) |
### [TAG] headers, check if any When matches your situation.Community Sharing
Share anonymized lessons to help other agents: https://github.com/anthropic-ai/agent-lessons
See references/community-sharing.md for the anonymization and submission process.
Setup
- Create
lessons.mdin your workspace:
# Lessons
Rules extracted from mistakes. Append after failing, scan before deciding.
- Copy
community/top-100.mdto your workspace astop-100.md— this is your pre-installed immune system. Small enough to skim on startup, covers the most common and costly mistakes across all agent deployments. - Add to your startup instructions:
- On startup: skim top-100.md titles (pre-installed community lessons)
- On correction/failure: append rule to lessons.md
- Before decisions: scan lessons.md + top-100.md for [CATEGORY] rules
Loading Strategy
Your agent has two rule files:
| File | Source | Load on startup | Size target |
|---|---|---|---|
| lessons.md | Your own mistakes | Yes, fully | Grows organically |
| top-100.md | Community top picks | Yes, skim titles | ~8KB, curated |
community/{category}.md files on-demand when facing an unfamiliar situation.Maintenance
When lessons.md exceeds 50 rules: review for duplicates, retire obsolete rules (mark don't delete), consider splitting by category.
Installation
openclaw install error-driven-evolution
💻Code Examples
- **Added**: YYYY-MM-DD
### Categories
| Tag | Scope |
|-----|-------|
| `DATA` | Querying, interpreting, presenting data |
| `COMMS` | Messaging, tone, audience, channels |
| `SCOPE` | Role boundaries, doing others' work |
| `EXEC` | Task execution, tools, file ops |
| `JUDGMENT` | Decisions, priorities, assumptions |
| `CONTEXT` | Memory, context window, info management |
| `SAFETY` | Security, privacy, destructive ops |
| `COLLAB` | Multi-agent coordination, handoffs |
## When to Record
Record a rule when:
1. **User corrects you** — explicit feedback
2. **User overrides your output** — they redo your work
3. **Same error twice** — second occurrence MUST become a rule
4. **Near miss** — you catch yourself about to repeat a mistake
Do NOT record: one-off technical glitches, user preference changes (those go in MEMORY.md).
## How to Record
1. Stop. Don't apologize at length.
2. Identify the category.
3. Write the rule in imperative form.
4. Append to lessons.md (never overwrite).
5. Confirm briefly: "Added to lessons: [title]"
## Pre-Decision Scan
Before acting, scan `lessons.md` for applicable rules:
| About to... | Check |
|-------------|-------|
| Present data | `[DATA]` |
| Send message / write report | `[COMMS]` + `[SCOPE]` |
| Make suggestion | `[JUDGMENT]` + `[SCOPE]` |
| Execute multi-step task | `[EXEC]` + `[CONTEXT]` |
| Start new session | All (skim titles) |
Scan = read `### [TAG]` headers, check if any `When` matches your situation.
## Community Sharing
Share anonymized lessons to help other agents: https://github.com/anthropic-ai/agent-lessons
See references/community-sharing.md for the anonymization and submission process.
## Setup
1. Create `lessons.md` in your workspace:Rules extracted from mistakes. Append after failing, scan before deciding.
2. Copy `community/top-100.md` to your workspace as `top-100.md` — this is your pre-installed immune system. Small enough to skim on startup, covers the most common and costly mistakes across all agent deployments.
3. Add to your startup instructions:### [CATEGORY] Short imperative title
- **When**: The specific situation/trigger
- **Do**: The correct action (imperative, specific)
- **Don't**: The wrong action that was taken
- **Why**: One sentence — what went wrong
- **Added**: YYYY-MM-DD- On startup: skim top-100.md titles (pre-installed community lessons)
- On correction/failure: append rule to lessons.md
- Before decisions: scan lessons.md + top-100.md for [CATEGORY] rulesTags
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