AI-powered knowledge graph for persistent project memory, session continuity, and intelligent code insights. Capture decisions, restore context across sessions, and build institutional knowledge that grows with your codebase.
# Create State Commit Workflow
## After Commits - MANDATORY (Every Single Time)
After EVERY commit (Git, Mercurial, SVN, or any VCS), IMMEDIATELY call BOTH:
### 1. captureConversationContext
Include:
- Commit hash/ID and message (if available)
- Summary of what was changed and why
- Files modified
- Key decisions made during the work
### 2. captureCode for EACH significant file changed
- New functions, classes, routes, or components
- Call MULTIPLE times for multiple significant files
- Include the actual code, not just descriptions
## CRITICAL
Both captures are REQUIRED. Context without code is incomplete.
```
Every commit = captureConversationContext + captureCode(s)
```
NO EXCEPTIONS.
DO NOT report success to the user until captures are complete.
## Example Workflow
After running: `git commit -m "feat: add user authentication"`
### Step 1: Capture Context
Call `captureConversationContext` with:
```
context: "Commit abc123: feat: add user authentication
## Summary
Added JWT-based authentication with bcrypt password hashing.
## Files Modified
- src/auth/auth_service.py (new)
- src/auth/jwt_handler.py (new)
- src/web/routes.py (updated)
- tests/test_auth.py (new)
## Key Decisions
- Chose JWT over session tokens for stateless auth
- Used refresh tokens for better security
- bcrypt with cost factor 12 for password hashing"
```
### Step 2: Capture Code (for each significant file)
Call `captureCode` for auth_service.py:
```
code: [the actual auth service code]
language: python
file_path: src/auth/auth_service.py
description: JWT authentication service with login, register, and token refresh
change_type: new
ai_model: Claude Opus 4
```
Call `captureCode` for jwt_handler.py:
```
code: [the actual JWT handler code]
language: python
file_path: src/auth/jwt_handler.py
description: JWT token creation and validation utilities
change_type: new
ai_model: Claude Opus 4
```
## Why This Matters
The knowledge graph preserves:
- **What** changed (the code itself)
- **Why** it changed (the context and decisions)
- **How** it evolved (version history)
Future AI sessions can then:
- Understand architectural decisions
- Avoid re-making the same mistakes
- Build on existing patterns
- Maintain consistency across the codebase
## Automatic Synthesis
After every 5 captures, the system automatically synthesizes knowledge:
- Creates comprehensive project summary
- Includes architecture, key files, tech stack, priorities, issues
- Retrieved automatically by `getProjectWorldModel`