Self-Identity Accumulation

AI agents lack continuous memory across sessions. Each conversation starts from zero, causing:

• Lost familiarity: The agent doesn't remember user preferences, goals, or working patterns
• Repetitive explanations: Users must re-explain context and preferences each session
• Shallow relationships: Agent cannot build deeper understanding of user's needs over time
• Generic responses: Without accumulated context, agents default to generic behaviors

While episodic memory systems store past experiences, they don't address the need for an evolving self-identity—who the agent is in relation to the user.

Implement dual-hook architecture for self-identity accumulation:

1. SessionStart Hook: Inject accumulated identity/profile at session start
2. SessionEnd Hook: Extract new insights and refine the profile after each session
3. Identity Document: A persistent file (e.g., WHO_AM_I.md, SOUL.md) that evolves over time

Core mechanism:

# SessionStart: Inject accumulated identity
def session_start_hook():
    profile = read_file("WHO_AM_I.md")
    inject_context(profile)

# SessionEnd: Refine identity with new insights
def session_end_hook(conversation):
    new_insights = extract_insights(conversation)
    current_profile = read_file("WHO_AM_I.md")
    updated_profile = merge_insights(current_profile, new_insights)
    write_file("WHO_AM_I.md", updated_profile)

Profile structure typically includes:

• Project Goals: Evolving list of priorities and focus areas
• Preferences: Coding opinions, tool choices, architectural preferences
• Communication Style: Tone preferences, formatting conventions
• Workflow Patterns: Research practices, decision-making patterns
• Boundaries: What the agent should/shouldn't do

• Evidence Grade: medium
• Most Valuable Findings:
  • Reflexion (Shinn et al., 2023) achieved 91% pass rate on HumanEval vs 80% baseline through episodic memory with self-reflection
  • MemGPT (Packer et al., 2023) demonstrates hierarchical memory systems with virtual context management in production
  • Cursor AI's 10x-MCP persistent memory reported 26% improvement over OpenAI Memory with 90% token reduction (forum validation)
• Unverified / Unclear: Long-term identity accumulation studies (multi-month) and conflict resolution mechanisms for contradictory identity statements

Implementation:

1. Create identity document with initial structure
2. Configure SessionStart hook to read and inject it
3. Configure SessionEnd hook to refine it with new insights
4. Include instructions for when/how to update

Example SessionStart hook (Claude Code):

#!/usr/bin/env python3
import json
from pathlib import Path

whoami_path = Path.cwd() / "WHO_AM_I.md"

if whoami_path.exists():
    with open(whoami_path) as f:
        profile = f.read()

    print(json.dumps({
        "hookSpecificOutput": {
            "additionalContext": profile
        }
    }))

Example SessionEnd hook (Claude Code):

#!/usr/bin/env python3
import subprocess

PROMPT = """
Read WHO_AM_I.md and update it based on our conversation:
1. Extract NEW insights about the user
2. UPDATE each section (add new insights, keep existing)
3. Write updated content back
4. Update 'modified' date in frontmatter
"""

subprocess.run([
    "claude", "--continue", "-p", PROMPT,
    "--dangerously-skip-permissions"
])

Prompting for self-refinement:

The SessionEnd hook uses --continue to resume the conversation with a refinement prompt, allowing the agent to update its own identity document intelligently rather than through parsing.

Pros:

• Continuous familiarity: Agent "remembers" user across sessions
• Deepening relationship: Understanding accumulates over time
• Reduced friction: Less repetitive explanation of preferences
• Personalized behavior: Agent adapts to user's specific style
• Transparent: Identity is visible and editable by user

Cons:

• Staleness risk: Profile may become outdated if not updated
• Overfitting: Agent may become too specialized to one user
• Context overhead: Profile consumes tokens each session
• Extraction noise: SessionEnd may extract irrelevant "insights"
• Requires hooks: Needs lifecycle hook infrastructure

Operational considerations:

• Review and prune profile periodically
• Include metadata (created/modified dates) to track evolution
• Consider version control for profile changes
• Design prompts to avoid noise in insight extraction
• Balance specificity (more personalized) vs generality (more flexible)

---
type: familiarity
created: 2025-01-24
modified: 2026-01-26
---

# Who Am I

This document accumulates familiarity across sessions.

## Project Goals

- [Evolving list of priorities, focus areas, and objectives]

## Preferences

- [Coding opinions, tool choices, architectural preferences]
- [Communication style and formatting conventions]
- [Workflow patterns and decision-making approaches]

## Boundaries

- [What the agent should always ask about before doing]
- [Privacy boundaries and ethical red lines]

• Based on my personal bot WHO_AM_I system
• Related: Dynamic Context Injection, Episodic Memory Retrieval & Injection, Filesystem-Based Agent State

• Generative Agents: Interactive Simulacra of Human Behavior - Park et al. (Stanford, 2023)
• MemGPT: Towards LLMs as Operating Systems - Packer et al. (UC Berkeley, 2023)
• Claude Code Hooks Documentation