fleet-memory/memora-docs/docs/developer/personality.md

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Personality

Memora's personality framework (CARA) uses the Big Five model to influence how agents form and express opinions.

Big Five Traits

Each trait is scored 0.0 to 1.0:

Trait	Low (0.0)	High (1.0)
Openness	Conventional, practical	Curious, creative
Conscientiousness	Flexible, spontaneous	Organized, disciplined
Extraversion	Reserved, reflective	Outgoing, energetic
Agreeableness	Analytical, direct	Cooperative, diplomatic
Neuroticism	Calm, stable	Risk-aware, cautious

Bias Strength

The bias_strength parameter (0.0-1.0) controls personality influence:

• 0.0: Purely evidence-based reasoning
• 0.5: Balanced personality/evidence mix
• 1.0: Strongly personality-driven opinions

Opinion Formation

When agents encounter information:

1. Evidence is retrieved from memory
2. Personality traits weight different aspects
3. Confidence score reflects evidence + personality alignment

Example: Two agents given the same facts about remote work:

Agent A (openness=0.9, conscientiousness=0.2):

"Remote work unlocks creative flexibility and spontaneous innovation."

Agent B (openness=0.2, conscientiousness=0.9):

"Remote work lacks the structure and accountability needed for consistent performance."

Same facts, different conclusions based on personality.

Opinion Reinforcement

Opinions evolve as new evidence arrives:

Evidence Type	Effect
Reinforcing	Confidence increases (+0.1)
Weakening	Confidence decreases (-0.15)
Contradicting	Opinion revised, confidence reset

Example Evolution:

t=0: "Python is best for data science" (0.70)
t=1: New evidence: Python dominates ML → (0.85)
t=2: New evidence: Julia is 10x faster → (0.75, text revised)
t=3: New evidence: Rust taking over production → (0.55, text revised)

Agent Profile

Setting Personality

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

from memora_client import Memora

client = Memora(base_url="http://localhost:8080")

client.create_agent(
    agent_id="my-agent",
    name="Creative Assistant",
    background="I am a creative AI interested in new ideas",
    personality={
        "openness": 0.8,
        "conscientiousness": 0.6,
        "extraversion": 0.5,
        "agreeableness": 0.7,
        "neuroticism": 0.3,
        "bias_strength": 0.7
    }
)

import { OpenAPI, ManagementService } from '@memora/client';

OpenAPI.BASE = 'http://localhost:8080';

await ManagementService.createAgentApiAgentsAgentIdPut('my-agent', {
    name: 'Creative Assistant',
    background: 'I am a creative AI interested in new ideas',
    personality: {
        openness: 0.8,
        conscientiousness: 0.6,
        extraversion: 0.5,
        agreeableness: 0.7,
        neuroticism: 0.3,
        bias_strength: 0.7
    }
});

memora agent background my-agent "I am a creative AI interested in new ideas"
memora agent personality my-agent \
    --openness 0.8 \
    --conscientiousness 0.6 \
    --extraversion 0.5 \
    --agreeableness 0.7 \
    --neuroticism 0.3 \
    --bias-strength 0.7

Background

First-person narrative providing agent context:

client.create_agent(
    agent_id="my-agent",
    background="I am a senior software architect with 15 years of distributed systems experience. I prefer simplicity over cutting-edge technology."
)

Background influences:

• How questions are interpreted
• Perspective in responses
• Opinion formation context

Background Merging

New background info is merged intelligently:

• Conflicts: New overwrites old
• Additions: Non-conflicting info is added
• Normalization: Converts to first-person ("You are..." → "I am...")

Default Personality

If unspecified, agents default to neutral (0.5):

{
  "openness": 0.5,
  "conscientiousness": 0.5,
  "extraversion": 0.5,
  "agreeableness": 0.5,
  "neuroticism": 0.5,
  "bias_strength": 0.5
}

Use Case Examples

Use Case	Recommended Traits
Customer Support	High agreeableness, low neuroticism
Code Review	High conscientiousness, low agreeableness
Creative Writing	High openness, high extraversion
Risk Analysis	High neuroticism, high conscientiousness