Search Memories

curl -X POST "https://api.memchat.io/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": true
  }'

import requests

headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": True
}

response = requests.post("https://api.memchat.io/v1/memories/search",
                        headers=headers, json=data)
result = response.json()

print(f"User Bio: {result['user_bio']}")
for memory in result['memories']:
    print(f"- {memory['memory']} (relevance: {memory['vector_distance']:.2f})")

const response = await fetch('https://api.memchat.io/v1/memories/search', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: "What does the user do for work?",
    limit: 5,
    categories: ["career"],
    rerank: true
  })
});

const result = await response.json();
console.log("User Bio:", result.user_bio);
result.memories.forEach(memory => {
  console.log(`- ${memory.memory} (relevance: ${memory.vector_distance.toFixed(2)})`);
});

{
  "user_bio": "Senior Software Engineer at Google specializing in machine learning infrastructure with 8+ years of experience. Passionate about hiking, photography, and sustainable living.",
  "memories": [
    {
      "id": "mem_abc123",
      "memory": "Works as a Senior Software Engineer at Google on ML infrastructure team",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.15,
      "rerank_score": 0.95,
      "source": "chat",
      "created_at": "2024-03-20T10:00:00Z",
      "updated_at": "2024-03-20T10:00:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_def456",
      "memory": "Recently promoted to Senior Software Engineer, leads team of 5 engineers",
      "categories": ["career"],
      "type": "episodic",
      "vector_distance": 0.22,
      "rerank_score": 0.88,
      "source": "chat",
      "created_at": "2024-03-20T09:30:00Z", 
      "updated_at": "2024-03-20T09:30:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_ghi789",
      "memory": "Has 8+ years of experience in software engineering",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.28,
      "rerank_score": 0.82,
      "source": "integration",
      "created_at": "2024-03-19T15:20:00Z",
      "updated_at": "2024-03-19T15:20:00Z",
      "agent_id": "linkedin-integration",
      "thread_id": "integration-linkedin-001"
    }
  ]
}

{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid request format",
    "details": {
      "field": "limit",
      "reason": "Must be between 1 and 100"
    }
  },
  "status": "error",
  "timestamp": "2024-03-20T10:00:00Z"
}

POST

memories

curl -X POST "https://api.memchat.io/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": true
  }'

import requests

headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": True
}

response = requests.post("https://api.memchat.io/v1/memories/search",
                        headers=headers, json=data)
result = response.json()

print(f"User Bio: {result['user_bio']}")
for memory in result['memories']:
    print(f"- {memory['memory']} (relevance: {memory['vector_distance']:.2f})")

const response = await fetch('https://api.memchat.io/v1/memories/search', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: "What does the user do for work?",
    limit: 5,
    categories: ["career"],
    rerank: true
  })
});

const result = await response.json();
console.log("User Bio:", result.user_bio);
result.memories.forEach(memory => {
  console.log(`- ${memory.memory} (relevance: ${memory.vector_distance.toFixed(2)})`);
});

{
  "user_bio": "Senior Software Engineer at Google specializing in machine learning infrastructure with 8+ years of experience. Passionate about hiking, photography, and sustainable living.",
  "memories": [
    {
      "id": "mem_abc123",
      "memory": "Works as a Senior Software Engineer at Google on ML infrastructure team",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.15,
      "rerank_score": 0.95,
      "source": "chat",
      "created_at": "2024-03-20T10:00:00Z",
      "updated_at": "2024-03-20T10:00:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_def456",
      "memory": "Recently promoted to Senior Software Engineer, leads team of 5 engineers",
      "categories": ["career"],
      "type": "episodic",
      "vector_distance": 0.22,
      "rerank_score": 0.88,
      "source": "chat",
      "created_at": "2024-03-20T09:30:00Z", 
      "updated_at": "2024-03-20T09:30:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_ghi789",
      "memory": "Has 8+ years of experience in software engineering",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.28,
      "rerank_score": 0.82,
      "source": "integration",
      "created_at": "2024-03-19T15:20:00Z",
      "updated_at": "2024-03-19T15:20:00Z",
      "agent_id": "linkedin-integration",
      "thread_id": "integration-linkedin-001"
    }
  ]
}

{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid request format",
    "details": {
      "field": "limit",
      "reason": "Must be between 1 and 100"
    }
  },
  "status": "error",
  "timestamp": "2024-03-20T10:00:00Z"
}

Search for relevant memories using natural language queries. MemSync uses semantic search with vector embeddings to find memories that match the meaning and context of your query, not just keywords.

Authentication

string

required

Your MemSync API key for authentication

Request Body

string

required

Natural language search query (e.g., “What does the user do for work?”)

integer

default:"10"

Maximum number of memories to return (1-100)

array

Filter results to specific memory categories

Show Available Categories

identity - Personal information and background
career - Work, profession, and career goals
interests - Hobbies and recreational activities
relationships - Social connections and family
health - Wellness, fitness, and medical information
finance - Financial goals, budgets, and investments
learning - Education, skills, and knowledge acquisition
travel - Travel experiences and plans
productivity - Work habits and organizational systems
private - Sensitive personal information

boolean

default:"false"

Enable reranking for improved search quality (recommended for complex queries)

boolean

default:"true"

Include user bio in the response

string

Filter results to memories from a specific agent

string

Filter results to memories from a specific conversation thread

Response

string

Auto-generated biographical summary of the user

array

Array of relevant memories matching the search query

Show Memory Object

string

Unique identifier for the memory

string

The extracted memory content

array

Categories assigned to this memory

string

Memory type: “semantic” (lasting facts) or “episodic” (time-bound events)

number

Semantic similarity score (lower = more similar, 0.0-1.0)

number

Reranking relevance score when reranking is enabled (higher = more relevant, 0.0-1.0)

string

Source of the memory (e.g., “chat”, “integration”)

string

ISO 8601 timestamp when the memory was created

string

ISO 8601 timestamp when the memory was last updated

string

Identifier of the agent that created this memory

string

Conversation thread where this memory originated

curl -X POST "https://api.memchat.io/v1/memories/search" \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": true
  }'

import requests

headers = {
    "X-API-Key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {
    "query": "What does the user do for work?",
    "limit": 5,
    "categories": ["career"],
    "rerank": True
}

response = requests.post("https://api.memchat.io/v1/memories/search",
                        headers=headers, json=data)
result = response.json()

print(f"User Bio: {result['user_bio']}")
for memory in result['memories']:
    print(f"- {memory['memory']} (relevance: {memory['vector_distance']:.2f})")

const response = await fetch('https://api.memchat.io/v1/memories/search', {
  method: 'POST',
  headers: {
    'X-API-Key': 'YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: "What does the user do for work?",
    limit: 5,
    categories: ["career"],
    rerank: true
  })
});

const result = await response.json();
console.log("User Bio:", result.user_bio);
result.memories.forEach(memory => {
  console.log(`- ${memory.memory} (relevance: ${memory.vector_distance.toFixed(2)})`);
});

{
  "user_bio": "Senior Software Engineer at Google specializing in machine learning infrastructure with 8+ years of experience. Passionate about hiking, photography, and sustainable living.",
  "memories": [
    {
      "id": "mem_abc123",
      "memory": "Works as a Senior Software Engineer at Google on ML infrastructure team",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.15,
      "rerank_score": 0.95,
      "source": "chat",
      "created_at": "2024-03-20T10:00:00Z",
      "updated_at": "2024-03-20T10:00:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_def456",
      "memory": "Recently promoted to Senior Software Engineer, leads team of 5 engineers",
      "categories": ["career"],
      "type": "episodic",
      "vector_distance": 0.22,
      "rerank_score": 0.88,
      "source": "chat",
      "created_at": "2024-03-20T09:30:00Z", 
      "updated_at": "2024-03-20T09:30:00Z",
      "agent_id": "career-advisor-bot",
      "thread_id": "conversation-2024-001"
    },
    {
      "id": "mem_ghi789",
      "memory": "Has 8+ years of experience in software engineering",
      "categories": ["career"],
      "type": "semantic",
      "vector_distance": 0.28,
      "rerank_score": 0.82,
      "source": "integration",
      "created_at": "2024-03-19T15:20:00Z",
      "updated_at": "2024-03-19T15:20:00Z",
      "agent_id": "linkedin-integration",
      "thread_id": "integration-linkedin-001"
    }
  ]
}

{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Invalid request format",
    "details": {
      "field": "limit",
      "reason": "Must be between 1 and 100"
    }
  },
  "status": "error",
  "timestamp": "2024-03-20T10:00:00Z"
}

Query Examples

Basic Queries

# Find career and work-related information
response = requests.post(url, headers=headers, json={
    "query": "What does the user do for work?",
    "categories": ["career"],
    "limit": 10
})

# Find hobbies and interests
response = requests.post(url, headers=headers, json={
    "query": "What are the user's hobbies and interests?",
    "categories": ["interests"],
    "limit": 8
})

# Find current activities and projects
response = requests.post(url, headers=headers, json={
    "query": "What is the user currently working on?",
    "categories": ["career", "learning"],
    "rerank": True,
    "limit": 5
})

# Get broad understanding of the user
response = requests.post(url, headers=headers, json={
    "query": "Tell me about this user",
    "rerank": True,
    "limit": 15
})

Advanced Queries

# Search with conversation context
response = requests.post(url, headers=headers, json={
    "query": "What would help the user with their career goals?",
    "categories": ["career", "learning"],
    "rerank": True,
    "limit": 10
})

# Find preferences and dislikes
response = requests.post(url, headers=headers, json={
    "query": "What does the user like and dislike?",
    "categories": ["interests", "productivity"],
    "limit": 12
})

# Find social and relationship information
response = requests.post(url, headers=headers, json={
    "query": "Who is important to the user?",
    "categories": ["relationships"],
    "limit": 8
})

Understanding Search Results

Vector Distance

The vector_distance indicates semantic similarity:

0.0-0.3: Highly relevant and closely related
0.3-0.6: Moderately relevant
0.6-1.0: Less relevant, may be tangentially related

Rerank Score

When rerank: true is enabled, the rerank_score provides enhanced relevance:

0.8-1.0: Excellent match for the query
0.6-0.8: Good match with clear relevance
0.4-0.6: Fair match, some relevance
0.0-0.4: Weak match, limited relevance

Result Ordering

Results are ordered by relevance:

With reranking: Ordered by rerank_score (descending)
Without reranking: Ordered by vector_distance (ascending)

Best Practices

Query Optimization

Use Natural Language

Write queries as natural questions rather than keywords

# ✅ Good
"What technical skills does the user have?"

# ❌ Less effective  
"skills technical programming"

Be Specific

Specific queries often yield better results than generic ones

# ✅ Better
"What programming languages is the user learning?"

# ❌ Too generic
"user learning"

Use Categories

Filter by categories for more targeted results

# For work-related queries
{"query": "user responsibilities", "categories": ["career"]}

# For personal interests
{"query": "user hobbies", "categories": ["interests"]}

Enable Reranking

Use reranking for complex or important queries

# For nuanced queries
{
    "query": "How does the user balance work and personal life?",
    "rerank": True,
    "categories": ["career", "health", "relationships"]
}

Performance Tips

Appropriate Limits: Use reasonable limits (5-15 for most use cases)
Category Filtering: Reduce search space with relevant categories
Caching: Cache frequent queries to improve response times
Batch Processing: Group related searches when possible

Common Use Cases

Personalized Responses

def get_context_for_response(user_message):
    context_search = {
        "query": f"What's relevant to helping with: {user_message}",
        "limit": 8,
        "rerank": True
    }
    
    response = requests.post(search_url, headers=headers, json=context_search)
    memories = response.json()['memories']
    
    return [m['memory'] for m in memories]

User Analysis

def analyze_user_interests():
    searches = [
        {"query": "What is the user passionate about?", "categories": ["interests"]},
        {"query": "What is the user learning?", "categories": ["learning"]},
        {"query": "What are the user's goals?", "categories": ["career", "health"]}
    ]
    
    analysis = {}
    for search in searches:
        response = requests.post(search_url, headers=headers, json=search)
        analysis[search['query']] = response.json()['memories']
    
    return analysis

Content Recommendations

def get_content_recommendations():
    interests_search = {
        "query": "What topics interest the user?",
        "categories": ["interests", "learning"],
        "limit": 10
    }
    
    response = requests.post(search_url, headers=headers, json=interests_search)
    memories = response.json()['memories']
    
    # Extract topics for content recommendation
    topics = extract_topics_from_memories(memories)
    return generate_content_suggestions(topics)

Rate Limiting

Search endpoints have enhanced rate limiting:

50 requests per minute per authenticated user
Complex queries with reranking may take slightly longer
Consider implementing client-side caching for frequent queries

Error Codes

Error Code	Description
`VALIDATION_ERROR`	Invalid request format or parameters
`AUTHENTICATION_ERROR`	Invalid or missing API key
`RATE_LIMIT_EXCEEDED`	Too many search requests
`NO_MEMORIES_FOUND`	No memories exist for the user

Next Steps

Store Memories

Learn how to add memories to search from

User Profiles

Get comprehensive user profiles and insights

Memory Categories

Understand how to use categories effectively

Search Best Practices

Learn advanced search techniques and strategies

Getting Started

Memory Management

User Profiles

Integrations

API Keys

Authentication

Request Body

Response

Query Examples

Basic Queries

Advanced Queries

Understanding Search Results

Vector Distance

Rerank Score

Result Ordering

Best Practices

Query Optimization

Performance Tips

Common Use Cases

Personalized Responses

User Analysis

Content Recommendations

Rate Limiting

Error Codes

Next Steps

Store Memories

User Profiles

Memory Categories

Search Best Practices

​Authentication

​Request Body

​Response

​Query Examples

​Basic Queries

​Advanced Queries

​Understanding Search Results

​Vector Distance

​Rerank Score

​Result Ordering

​Best Practices

​Query Optimization

​Performance Tips

​Common Use Cases

​Personalized Responses

​User Analysis

​Content Recommendations

​Rate Limiting

​Error Codes

​Next Steps

Store Memories

User Profiles

Memory Categories

Search Best Practices

Authentication

Request Body

Response

Query Examples

Basic Queries

Advanced Queries

Understanding Search Results

Vector Distance

Rerank Score

Result Ordering

Best Practices

Query Optimization

Performance Tips

Common Use Cases

Personalized Responses

User Analysis

Content Recommendations

Rate Limiting

Error Codes

Next Steps