park-2023-generative-agents

license	Apache-2.0
name	park-2023-generative-agents
description	Simulation of believable human behavior using LLM-powered generative agents with memory and social interaction
metadata	{"category":"Research & Academic","tags":["generative-agents","simulation","llm-agents","social-behavior","memory"],"io-contract":{"kind":"deliverable","produces":["[Truncated]","[Truncated]","[Truncated]"]}}
allowed-tools	Read,Write,Edit,Glob,Grep

SKILL: Generative Agents Architecture

When to Use This Skill

Activate when designing AI agents that maintain coherent behavior over extended periods (hours/days/weeks) with accumulated experience. Essential for multi-agent simulations, long-running assistants, and believable AI characters.

NOT for: Single-turn responses, prompt engineering, task-specific tools, or centrally coordinated systems.

Decision Points

Memory System Design Decision Tree

Is agent behavior incoherent with past actions?
├─ YES: Audit retrieval function
│   ├─ Agent seems amnesic about important events?
│   │   └─ → Increase importance weight (0.1→0.3) or lower threshold (5→3)
│   ├─ Agent retrieves irrelevant memories?
│   │   └─ → Increase relevance weight (0.5→0.7) or improve embeddings
│   └─ Agent over-focuses on recent trivial events?
│       └─ → Decrease recency weight (0.99→0.95 decay factor)
└─ NO: Memory system functioning, check other components

Reflection Triggering Decision Matrix

Importance Sum	Time Since Last	Action
>150 points	Any	Trigger reflection immediately
100-150	>2 hours	Trigger reflection
50-100	>6 hours	Trigger reflection
<50	Any	Wait for more observations

Planning Replan Threshold

Observation conflicts with current plan?
├─ Minor conflict (efficiency impact only)
│   └─ → Continue with plan
├─ Moderate conflict (plan becomes suboptimal)
│   ├─ High commitment context (public promises, deadlines)?
│   │   └─ → Continue plan, note conflict for future planning
│   └─ Low commitment context?
│       └─ → Replan affected time blocks only
└─ Major conflict (plan becomes impossible/harmful)
    └─ → Full replan from current moment

Multi-Agent Information Diffusion

Agent receives socially significant information?
├─ Information affects other known agents?
│   ├─ Strong relationship exists?
│   │   └─ → High probability (0.8+) to share in next interaction
│   └─ Weak relationship?
│       └─ → Moderate probability (0.4) if contextually relevant
└─ Information is private/personal?
    └─ → Share only if directly asked or high trust relationship

Failure Modes

1. Retrieval Cascade Failure

Detection: Agent denies knowledge of information they previously demonstrated knowing Symptom: "I don't know about X" when agent stored observations about X Diagnosis: Retrieval function weights are mistuned, causing relevant memories to score below threshold Fix: Increase importance scoring for similar event types OR lower retrieval threshold temporarily OR retune relevance embeddings

2. Reflection Vacuum

Detection: Agent repeats same mistakes despite having multiple similar experiences Symptom: No behavioral learning from patterns (e.g., always late to meetings despite noting lateness) Diagnosis: Reflection not triggering on significant patterns OR reflections not being stored with sufficient importance Fix: Lower reflection threshold (150→100 importance points) OR increase importance scoring for reflection outputs (auto-score reflections as 8+ importance)

3. Plan Rigidity Lock

Detection: Agent continues obviously suboptimal plans when context changes Symptom: Walking to closed locations, pursuing obsolete goals, ignoring environmental changes Diagnosis: Replanning thresholds too high OR commitment override too strong Fix: Lower conflict threshold for replanning OR add forced replan checks at major time boundaries (hourly)

4. Social Isolation Spiral

Detection: Agents stop interacting despite being in proximity and having social motivations Symptom: Multiple agents in same location but no conversation or coordination Diagnosis: Social observations scoring too low in importance OR reflection not synthesizing social patterns Fix: Boost importance scoring for social events (conversations, relationships) OR add social-specific reflection triggers

5. Memory Importance Inflation

Detection: Agent treats mundane events as highly significant, drowning out actual important events Symptom: Reflection on trivial activities, treating routine tasks as major life events Diagnosis: Importance scoring model lacks calibration OR no relative scoring mechanism Fix: Implement comparative importance scoring (rate events relative to recent history) OR add importance decay over time

Worked Examples

Example 1: Multi-Day Party Planning Coordination

Scenario: Isabella (artist) wants to throw Valentine's Day party, needs to coordinate with multiple agents over 3 days.

Day 1 - Initial Planning:

• Isabella reflects on recent loneliness observations → forms goal to host party
• Retrieval surfaces memories of past parties, friend relationships
• Plans: "Ask Maria and Tom about Valentine's party this week"
• Memory stores: [Observation: "Decided to host Valentine's party", Importance: 9]

Day 2 - Information Spreading:

• Isabella tells Maria about party → Maria stores [Observation: "Isabella planning Valentine's party, invited me", Importance: 7]
• Maria's next reflection synthesizes: "Isabella values our friendship, I should help with party"
• Maria plans: "Offer to help Isabella with decorations"
• Tom overhears Isabella-Maria conversation → stores social observation, plans to ask about invitation

Day 3 - Emergent Coordination:

• Multiple agents now have party-related memories with high importance scores
• Retrieval surfaces party context in multiple conversations
• Klaus (who wasn't directly invited) learns through Tom, plans to create artwork for party
• Coordination emerges: no central planner, but multiple agents converge on party preparation

Memory Retrieval Trade-offs Demonstrated:

• High relevance weight ensures party-related memories surface in social contexts
• Importance decay prevents Day 1 memories from dominating Day 3 conversations
• Recency bias helps coordinate immediate actions while importance preserves long-term goals

Example 2: Conflicting Plans Resolution

Scenario: Tom has standing plan to work on novel 2-4pm, but Maya asks him to coffee at 3pm.

Decision Process:

1. Observation: "Maya invited me to coffee at 3pm" [Importance: 6]
2. Retrieval surfaces: current plan [Recent], Maya relationship memories [Relevant], past coffee meetings [Similar]
3. Conflict detection: overlap between 3-4pm work block and coffee invitation
4. Reflection synthesis: "Maya is a good friend, but I've been inconsistent with writing schedule"
5. Planning decision: Moderate conflict + relationship importance → replan work to 1-3pm, accept coffee

What novice would miss: Treating this as binary choice (work OR coffee) instead of temporal reoptimization What expert catches: Relationship maintenance has long-term importance, schedule flexibility enables both goals

Reference Files

• references/memory-retrieval-as-attention-mechanism.md — Three-factor model (recency, importance, relevance) for tuning memory retrieval weights. Read when agent exhibits incoherent behavior or retrieves irrelevant memories.
• references/reflection-as-hierarchical-synthesis.md — Hierarchical synthesis pattern (leaf observations → patterns → identity insights). Read when agent repeats mistakes despite accumulated experience.
• references/planning-as-recursive-decomposition.md — Multi-timescale planning decomposition (day/hour/minute intentions). Read when agent lacks long-term coherence or replans too frequently.
• references/grounding-language-models-in-structured-environments.md — Tree representation pattern for bridging language models to structured environments. Read when designing agent perception of spatial/hierarchical worlds.
• references/emergent-coordination-without-central-control.md — Distributed multi-agent synchronization without central planner. Read when designing information diffusion or social interaction protocols.
• references/failure-modes-and-boundary-conditions.md — Documented failure modes and erratic behavior patterns from original research. Read when diagnosing unexpected agent behavior or planning robustness improvements.
• references/prompt-engineering-as-cognitive-architecture.md — Prompt design as reasoning architecture component. Read when tuning agent decision-making or reflection triggers.
• diagrams/01_flowchart_agent_coherence_decision_tree.md — Decision tree for diagnosing incoherent behavior and selecting remediation. Read when troubleshooting agent failures.
• diagrams/02_stateDiagram-v2_agent_behavior_loop:_memory-re.md — State machine of memory-reflection-planning cycle. Read when understanding agent execution flow or timing.
• diagrams/03_timeline_multi-timescale_planning_decom.md — Timeline visualization of hierarchical planning across timescales. Read when designing multi-level intention structures.

Quality Gates

• Memory retrieval returns 3-8 relevant memories for typical queries (not 0, not 50+)
• Agent behavior remains consistent with established personality traits across >24 hour periods
• Reflection triggers produce insights that influence future behavior (testable through repeated scenarios)
• Plans adapt appropriately to environmental changes without complete goal abandonment
• Multi-agent information spreads through social networks without telepathic coordination
• Agent can reference specific past events when asked, not just general patterns
• Social relationships strengthen/weaken based on interaction history and outcomes
• Importance scoring distinguishes between routine and significant events (10:1+ ratio difference)
• Temporal coherence maintained: actions reference appropriate past context for current situation
• Performance scales: 100+ stored memories don't degrade response quality or speed significantly

NOT-FOR Boundaries

Don't use this architecture for:

• Single-turn Q&A: Use standard prompt engineering instead
• Task automation: For IFTTT-style workflows, use [workflow-automation] skill
• Real-time coordination: For <1 second response requirements, use [reactive-systems] skill
• Factual knowledge queries: For information retrieval, use [knowledge-base] skill
• Mathematical reasoning: For computation-heavy problems, use [symbolic-reasoning] skill

Delegate when:

• Memory requirements exceed computational budget → Use [stateless-agents] skill
• Behavior must be completely predictable → Use [rule-based-systems] skill
• Privacy cannot tolerate memory persistence → Use [ephemeral-agents] skill
• Environment changes faster than agent can observe → Use [reactive-control] skill