2025-11-25 19:28:26 +01:00

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Think

Generate personality-aware responses using retrieved memories.

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

Basic Usage

from hindsight_client import Hindsight

client = Hindsight(base_url="http://localhost:8888")

answer = client.think(
    agent_id="my-agent",
    query="What should I know about Alice?"
)

print(answer["text"])

import { OpenAPI, ReasoningService } from '@hindsight/client';

OpenAPI.BASE = 'http://localhost:8888';

const response = await ReasoningService.thinkApiThinkPost({
    agent_id: 'my-agent',
    query: 'What should I know about Alice?'
});

console.log(response.text);

hindsight memory think my-agent "What should I know about Alice?"

# Verbose output shows reasoning and sources
hindsight memory think my-agent "What should I know about Alice?" -v

Response Format

{
    "text": "Alice is a software engineer at Google who joined last year...",
    "based_on": {
        "world": [
            {"text": "Alice works at Google", "weight": 0.95, "id": "..."}
        ],
        "agent": [],
        "opinion": [
            {"text": "Alice is very competent", "weight": 0.82, "id": "..."}
        ]
    },
    "new_opinions": [
        {"text": "Alice would be good for the ML project", "confidence": 0.75}
    ]
}

Field	Description
`text`	Generated response
`based_on`	Memories used, grouped by type
`new_opinions`	New opinions formed during reasoning

Parameters

Parameter	Type	Default	Description
`query`	string	required	Question or prompt
`thinking_budget`	int	100	Tokens for retrieval + reasoning
`top_k`	int	10	Max memories to retrieve

answer = client.think(
    agent_id="my-agent",
    query="What do you think about remote work?",
    thinking_budget=150,
    top_k=20
)

What Think Does

sequenceDiagram
    participant C as Client
    participant A as Hindsight API
    participant M as Memory Store
    participant L as LLM

    C->>A: think("What about Alice?")
    A->>M: Search all networks
    M-->>A: World + Agent + Opinion facts
    A->>A: Load agent personality
    A->>L: Generate with personality context
    L-->>A: Response + new opinions
    A->>M: Store new opinions
    A-->>C: Response + sources + new opinions

Retrieves relevant memories from all three networks
Loads agent personality (Big Five traits + background)
Generates response influenced by personality
Forms opinions if the query warrants it
Returns response with sources and any new opinions

Opinion Formation

Think can form new opinions based on evidence:

answer = client.think(
    agent_id="my-agent",
    query="What do you think about Python vs JavaScript for data science?"
)

# Response might include:
# text: "Based on what I know about data science workflows..."
# new_opinions: [
#     {"text": "Python is better for data science", "confidence": 0.85}
# ]

New opinions are automatically stored and influence future responses.

Personality Influence

The agent's personality affects Think responses:

Trait	Effect on Think
High Openness	More willing to consider new ideas
High Conscientiousness	More structured, methodical responses
High Extraversion	More collaborative suggestions
High Agreeableness	More diplomatic, harmony-seeking
High Neuroticism	More risk-aware, cautious

# Create an agent with specific personality
client.create_agent(
    agent_id="cautious-advisor",
    background="I am a risk-aware financial advisor",
    personality={
        "openness": 0.3,
        "conscientiousness": 0.9,
        "neuroticism": 0.8,
        "bias_strength": 0.7
    }
)

# Think responses will reflect this personality
answer = client.think(
    agent_id="cautious-advisor",
    query="Should I invest in crypto?"
)
# Response will likely emphasize risks and caution

Using Sources

The based_on field shows which memories informed the response:

answer = client.think(agent_id="my-agent", query="Tell me about Alice")

print("Response:", answer["text"])
print("\nBased on:")
for fact in answer["based_on"]["world"]:
    print(f"  - {fact['text']} (relevance: {fact['weight']:.2f})")

This enables:

Transparency — users see why the agent said something
Verification — check if the response is grounded in facts
Debugging — understand retrieval quality

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