rushdb 2.3.0

RushDB Python SDK — memory layer for AI agents and modern apps

RushDB — Python SDK

The memory layer for AI agents and apps.

Push any JSON. Get graph relationships and vector search — automatically. No schema. No pipeline. No glue code.

📖 Documentation • 🌐 Website • ☁️ Cloud

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Why RushDB

Agents need memory. Apps need connected data. The standard answer involves multiple databases, schema design, and an embedding pipeline before you write a single useful line of business logic.

RushDB skips all of that. Push any JSON — nested structure becomes a traversable graph, string properties become semantically searchable, type inference happens automatically.

Works with LangChain, CrewAI, AutoGen, or any Python AI framework.

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Installation

pip install rushdb

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Agent memory in 3 lines

Get an API key at app.rushdb.com.

from rushdb import RushDB

db = RushDB('RUSHDB_API_KEY')

# Store an agent action — graph links sessions and context automatically
db.records.create(
    label='MEMORY',
    data={
        'agent_id': 'agent-42',
        'session_id': 'sess-001',
        'action': 'summarized',
        'topic': 'Q4 results',
        'output': summary_text,
    },
)

# Recall — traverse relationships, filter by properties
results = db.records.find({
    'labels': ['MEMORY'],
    'where': {
        'agent_id': 'agent-42',
        'topic': {'$contains': 'Q4'},
    },
    'limit': 10,
})

for memory in results:
    print(memory.get('output'))

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Graph traversal

# Push nested JSON — relationships created automatically
db.records.create_many('COMPANY', {
    'name': 'Acme Corp',
    'DEPARTMENT': [{
        'name': 'Engineering',
        'EMPLOYEE': [{
            'name': 'Alice',
            'role': 'Staff Engineer',
        }]
    }]
})

# Traverse the auto-created graph
engineers = db.records.find({
    'labels': ['EMPLOYEE'],
    'where': {
        'role': {'$contains': 'Engineer'},
        'DEPARTMENT': {'COMPANY': {'name': 'Acme Corp'}},
    },
})

# Constrain by relationship type and direction
authored_posts = db.records.find({
    'labels': ['USER'],
    'where': {
        'POST': {
            '$relation': {'type': 'AUTHORED', 'direction': 'out'},
            'title': {'$contains': 'graph'},
        }
    },
    'limit': 10,
})

# Manage relationships explicitly
user = db.records.find_uniq({'labels': ['USER'], 'where': {'name': 'Alice'}})
company = db.records.find_uniq({'labels': ['COMPANY'], 'where': {'name': 'Acme Corp'}})
user.attach(
    target=company,
    options={'type': 'WORKS_AT', 'direction': 'out', 'properties': {'source': 'profile'}},
)

# Relationship search: where filters edge type/properties, source/target filter endpoint records
relationships = db.relationships.find({
    'source': {'labels': ['USER'], 'where': {'name': 'Alice'}},
    'target': {'labels': ['COMPANY']},
    'where': {'type': 'WORKS_AT', 'source': 'profile'},
})

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Importing CSV

csv_data = "name,email,age\nJohn,[e-mail],30\nJane,[e-mail],25"

db.records.import_csv(
    label='USER',
    data=csv_data,
    options={'returnResult': True, 'suggestTypes': True},
    parse_config={'header': True, 'skipEmptyLines': True, 'dynamicTyping': True},
)

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SearchResult

db.records.find() returns a SearchResult — a list-like container with pagination metadata.

result = db.records.find({
    'where': {'status': 'active'},
    'limit': 10,
    'skip': 0,
})

# List-like usage
print(f"Loaded {len(result)} of {result.total} total")
print(f"Has more: {result.has_more}")

for record in result:
    print(record.get('name'))

# Indexing and slicing
first = result[0]
top_five = result[:5]

# Boolean check
if result:
    process(result[0])

Property	Type	Description
data	List[Record]	The result items
total	int	Total matching records in the database
has_more	bool	Whether more records exist beyond this page
search_query	dict	The query that produced this result

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Record API

user = db.records.create('USER', {
    'name': 'Alice',
    'email': '[e-mail]',
})

# Safe field access
name = user.get('name')                  # 'Alice'
phone = user.get('phone', 'N/A')         # 'N/A'

# Clean data (excludes internal __id, __label fields)
data = user.get_data()                   # {'name': 'Alice', 'email': '...'}
full = user.get_data(exclude_internal=False)  # includes __id, __label, etc.

# Existence check (no exception if record was deleted)
if user.exists:
    user.update({'status': 'active'})

# String representations
repr(user)   # Record(id='abc-123', label='USER')
str(user)    # USER: Alice

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Transactions

with db.transactions.begin() as tx:
    record_a = db.records.create('NODE', {'value': 1}, transaction=tx)
    record_b = db.records.create('NODE', {'value': 2}, transaction=tx)
    record_a.attach(target=record_b, options={'type': 'LINKED'}, transaction=tx)
# auto-committed on exit, rolled back on exception

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Configuration

from rushdb import RushDB

db = RushDB(
    'RUSHDB_API_KEY',
    url='http://your-rushdb-server.com/api/v1',  # default: https://api.rushdb.com/api/v1
    timeout=30,
)

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Documentation

docs.rushdb.com/python-sdk — full API reference, vector search, aggregations, and more.

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Support

• GitHub Issues — bug reports and feature requests
• Email — direct support

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Contributing

See CONTRIBUTING.md. Issues and PRs welcome.