Reflect

Generate a grounded, disposition-aware response using an agentic reasoning loop.

When you call reflect, Hindsight runs an agentic loop that autonomously searches the memory bank using multiple retrieval tools, applies the bank's disposition traits to shape the reasoning style, and produces a final answer grounded in what it found. Unlike recall — which returns raw facts — reflect returns a synthesized response written by the LLM.

How Reflect Works

Learn about disposition-driven reasoning in the Reflect Architecture guide.

Prerequisites

Make sure you've completed the Quick Start to install the client and start the server.

Basic Usage

Python

client.reflect(bank_id="my-bank", query="What should I know about Alice?")

Node.js

await client.reflect('my-bank', 'What should I know about Alice?');

CLI

hindsight memory reflect my-bank "What do you know about Alice?"

Go

client.MemoryAPI.Reflect(ctx, "my-bank").
	ReflectRequest(hindsight.ReflectRequest{
		Query: "What should I know about Alice?",
	}).Execute()

---

Parameters

query

The question or prompt to reflect on. This is the only required field. If you have situational context that should influence the answer, include it directly in the query rather than as a separate field.

budget

Controls how thoroughly the agent explores the memory bank before answering. Accepted values are low (default), mid, and high. At low, the agent does a shallow search optimized for speed. At mid, it checks multiple sources when the question warrants it. At high, it performs deep exploration across all knowledge levels and may use multiple query variations to find indirect connections. Use high for complex questions that require synthesizing information from many sources.

Python

response = client.reflect(
    bank_id="my-bank",
    query="We're considering a hybrid work policy. What do you think about remote work?",
    budget="mid",
)

Node.js

const response = await client.reflect('my-bank', 'What do you think about remote work?', {
    budget: 'mid',
    context: "We're considering a hybrid work policy"
});

CLI

hindsight memory reflect my-bank "Summarize my week" --budget high --max-tokens 8192

Go

budgetMid := hindsight.MID
client.MemoryAPI.Reflect(ctx, "my-bank").
	ReflectRequest(hindsight.ReflectRequest{
		Query:  "We're considering a hybrid work policy. What do you think about remote work?",
		Budget: &budgetMid,
	}).Execute()

max_tokens

Limits the length of the final generated response. Defaults to 4096. This does not affect how much the agent can retrieve during the agentic loop — only the final answer length.

response_schema

An optional JSON Schema object with a non-empty properties map (nested objects and arrays are supported). When provided, the response includes a structured_output field in addition to the markdown text: the agent reasons to its answer, then a second pass extracts that answer into JSON matching your schema. structured_output is therefore a faithful projection of text — you get the readable answer and a typed object to program against, never one instead of the other. Invalid schemas (not an object, or no properties) are rejected before the call runs.

Python

from pydantic import BaseModel

# Define your response structure with Pydantic
class HiringRecommendation(BaseModel):
    recommendation: str
    confidence: str  # "low", "medium", "high"
    key_factors: list[str]
    risks: list[str] = []

response = client.reflect(
    bank_id="hiring-team",
    query="Should we hire Alice for the ML team lead position?",
    response_schema=HiringRecommendation.model_json_schema(),
)

# Parse structured output into Pydantic model
result = HiringRecommendation.model_validate(response.structured_output)
print(f"Recommendation: {result.recommendation}")
print(f"Confidence: {result.confidence}")
print(f"Key factors: {result.key_factors}")

Node.js

// Define JSON schema directly
const responseSchema = {
    type: 'object',
    properties: {
        recommendation: { type: 'string' },
        confidence: { type: 'string', enum: ['low', 'medium', 'high'] },
        key_factors: { type: 'array', items: { type: 'string' } },
        risks: { type: 'array', items: { type: 'string' } },
    },
    required: ['recommendation', 'confidence', 'key_factors'],
};

const structuredResponse = await client.reflect('my-bank', 'What do you know about Alice and her career?', {
    responseSchema: responseSchema,
});

// Structured output (if returned)
if (structuredResponse.structuredOutput) {
    console.log('Recommendation:', structuredResponse.structuredOutput.recommendation || 'N/A');
    console.log('Key factors:', structuredResponse.structuredOutput.key_factors || []);
}

CLI

# First, create a JSON schema file schema.json:
cat > schema.json << 'EOF'
{
  "type": "object",
  "properties": {
    "recommendation": {"type": "string"},
    "confidence": {"type": "string", "enum": ["low", "medium", "high"]},
    "key_factors": {"type": "array", "items": {"type": "string"}}
  },
  "required": ["recommendation", "confidence", "key_factors"]
}
EOF

# Then use the --schema flag:
hindsight memory reflect hiring-team \
  "Should we hire Alice for the ML team lead position?" \
  --schema schema.json

# Cleanup the temporary schema file
rm -f schema.json

Go

// Define JSON schema for structured output
responseSchema := map[string]interface{}{
	"type": "object",
	"properties": map[string]interface{}{
		"recommendation": map[string]interface{}{"type": "string"},
		"confidence":     map[string]interface{}{"type": "string", "enum": []string{"low", "medium", "high"}},
		"key_factors":    map[string]interface{}{"type": "array", "items": map[string]interface{}{"type": "string"}},
		"risks":          map[string]interface{}{"type": "array", "items": map[string]interface{}{"type": "string"}},
	},
	"required": []string{"recommendation", "confidence", "key_factors"},
}

structuredResponse, _, _ := client.MemoryAPI.Reflect(ctx, "my-bank").
	ReflectRequest(hindsight.ReflectRequest{
		Query:          "Should we hire Alice for the ML team lead position?",
		ResponseSchema: responseSchema,
	}).Execute()

// Access structured output
if out := structuredResponse.GetStructuredOutput(); out != nil {
	fmt.Println("Recommendation:", out["recommendation"])
	fmt.Println("Key factors:", out["key_factors"])
}

tags

Defines the visibility scope used throughout the reflect agent. It filters raw facts, observations, and mental models that the agent can retrieve. The same tags and tags_match values also select which tagged directives are injected into the reflect prompt.

tags defaults to null, tags_match defaults to any, and tag_groups defaults to null. For non-empty tags, raw facts, observations, and mental models use the same matching modes as recall tags. Directives have one additional rule: untagged directives are global and remain eligible whenever a tag scope is supplied, including with a strict or exact match.

Reflect configuration	Raw facts and observations	Mental models	Active directives
Omit tags, tags_match, and tag_groups	All tagged and untagged data	All tagged and untagged models	Untagged/global directives only
tags: [], default tags_match: "any"	All tagged and untagged data	All tagged and untagged models	Untagged/global directives only
No tags, tags_match: "exact"	Untagged/global data only	Untagged/global models only	Untagged/global directives only
Non-empty tags, any or all	Matching tagged data plus untagged/global data	Matching models plus untagged/global models	Matching tagged directives plus untagged/global directives
Non-empty tags, any_strict or all_strict	Matching tagged data only	Matching tagged models only	Matching tagged directives plus untagged/global directives
Non-empty tags, exact	Data with exactly the requested tag set	Models with exactly the requested tag set	Exactly matching tagged directives plus untagged/global directives
Non-empty tag_groups, default top-level tags_match	Data matching the compound expression	Models matching the compound expression	Matching tagged directives plus untagged/global directives

The first row is intentionally asymmetric: an unscoped reflect can search all memories, but it does not load tagged directives. To create a directive that applies to every reflect call, leave its tags empty. To create a scoped directive, assign tags and pass a matching scope to reflect.

isolation_mode

isolation_mode is an internal list_directives option, not a public reflect request parameter. Reflect always enables it. When neither tags nor tag_groups is supplied, it limits directive loading to untagged directives. There is currently no per-request switch to disable it.

MCP omitted tags

The MCP reflect tool forwards tags_match only when tags is present. To request the empty exact scope through MCP, pass tags: [] together with tags_match: "exact".

Python

# Filter reflection to only consider memories for a specific user
response = client.reflect(
    bank_id="my-bank",
    query="What does this user think about our product?",
    tags=["user:alice"],
    tags_match="any_strict"  # Only use memories tagged for this user
)

Node.js

// Filter reflect to only use memories tagged for a specific user
await client.reflect('my-bank', 'What feedback did the user give?', {
    tags: ['user:alice'],
    tagsMatch: 'any_strict'
});

CLI

hindsight memory reflect my-bank "What feedback did the user give?" \
  --tags "user:alice" --tags-match any_strict

Go

// Filter reflection to only consider memories for a specific user
tagsMatch := "any_strict"
client.MemoryAPI.Reflect(ctx, "my-bank").
	ReflectRequest(hindsight.ReflectRequest{
		Query:     "What does this user think about our product?",
		Tags:      []string{"user:alice"},
		TagsMatch: &tagsMatch,
	}).Execute()

Common scope examples

Unscoped reflect searches all memories but applies only global directives:

{
  "query": "Summarize the current project status"
}

A project scope includes global data and directives alongside matching project:a data and directives:

{
  "query": "Summarize the current project status",
  "tags": ["project:a"]
}

A strict project scope excludes untagged memories, observations, and mental models. Global directives still apply:

{
  "query": "Summarize the current project status",
  "tags": ["project:a"],
  "tags_match": "all_strict"
}

tag_groups

Provides compound tag filtering with recursive and, or, and not expressions. It affects the same reflect data sources and directive selection as flat tags. tag_groups and tags are mutually exclusive in the public REST request. Each leaf supplies its own matching mode and defaults to any_strict; the top-level groups are AND-ed. Normally leave the top-level tags_match at its default, any. Setting it to exact while using tag_groups additionally constrains facts, observations, and mental models to the global flat scope before applying the compound expression.

The MCP reflect tool currently exposes flat tags and tags_match, but not tag_groups.

include

Controls optional supplementary data returned alongside the main response.

include.facts

When enabled, the response includes a based_on object listing the memories, mental models, and directives the agent actually used to construct the answer. Only sources retrieved during the agent loop can appear here — citations are validated to prevent hallucinated references. Useful for transparency and verification.

Python

# include_facts=True enables the based_on field in the response
response = client.reflect(
    bank_id="my-bank",
    query="Tell me about Alice",
    include_facts=True,
)

print("Response:", response.text)
print("\nBased on:")
for fact in (response.based_on.memories if response.based_on else []):
    print(f"  - [{fact.type}] {fact.text}")

Node.js

const sourcesResponse = await client.reflect('my-bank', 'Tell me about Alice', {
    includeFacts: true
});

console.log('Response:', sourcesResponse.text);
console.log('\nBased on:');
for (const fact of (sourcesResponse.based_on?.memories || [])) {
    console.log(`  - [${fact.type}] ${fact.text}`);
}

CLI

hindsight memory reflect my-bank "Tell me about Alice" --include-facts

Go

// include.facts enables the based_on field in the response
sourcesResponse, _, _ := client.MemoryAPI.Reflect(ctx, "my-bank").
	ReflectRequest(hindsight.ReflectRequest{
		Query: "Tell me about Alice",
		Include: &hindsight.ReflectIncludeOptions{
			Facts: map[string]interface{}{}, // empty map enables fact inclusion
		},
	}).Execute()

fmt.Println("Response:", sourcesResponse.GetText())
fmt.Println("\nBased on:")
if basedOn := sourcesResponse.GetBasedOn(); basedOn.Memories != nil {
	for _, fact := range basedOn.GetMemories() {
		fmt.Printf("  - [%s] %s\n", fact.GetType(), fact.GetText())
	}
}

include.tool_calls

When enabled, the response includes a trace object with the full execution log of every tool call and LLM call made during the agentic loop, including inputs, outputs, and durations. Set output: false to include only tool inputs for a smaller payload. Useful for debugging why the agent reached a particular conclusion.

---

Response

text

The synthesized answer as a well-formatted markdown string. This is the primary output of reflect. Still returned when response_schema is provided — structured_output is derived from it, not a replacement for it.

structured_output

The LLM's response parsed according to the response_schema provided in the request. Only present when response_schema was set. null otherwise.

based_on

The sources the agent used to construct the answer. Only present when include.facts was enabled. Contains three fields:

• memories — a list of memory facts (world, experience, observation) that were retrieved and cited. Each item has id, text, type, context, occurred_start, and occurred_end.
• mental_models — a list of mental models that were used. Each item has id, text, and context.
• directives — a list of directives that were enforced during reasoning. Each item has id, name, and content.

usage

Token usage for all LLM calls made during the agentic loop: input_tokens, output_tokens, and total_tokens. Useful for cost tracking.

trace

The full execution log of the agentic loop. Only present when include.tool_calls was enabled. Contains:

• tool_calls — each tool invocation with tool name (lookup, recall, learn, expand), input, output (if output: true), duration_ms, and iteration number.
• llm_calls — each LLM call with scope (e.g., "agent_1", "final") and duration_ms.