CAMEL - Build Multi-Agent AI Systems

What Are Storages in CAMEL-AI?

The Storage module in CAMEL-AI gives you a unified interface for saving, searching, and managing your data from simple key-value records to high-performance vector databases and modern graph engines. It’s your plug-and-play toolkit for building robust, AI-ready storage layers.

Types of Storages

Key-Value Storages

BaseKeyValueStorage

Abstract base for all key-value storage backends.
Standardizes: Save, load, clear operations.
Interface: Python dicts.
Use cases:
- JSON file storage
- NoSQL (MongoDB, Redis)
- In-memory caches

InMemoryKeyValueStorage

Fast, simple, not persistent (resets on restart)
Ideal for caching, development, or quick prototyping

JsonStorage

Human-readable, portable JSON file storage
Supports custom JSON encoder (for Enums, etc)
Good for configs, small persistent datasets, export/import flows

VectorDB Storages

BaseVectorStorage

Abstract base for vector database backends
Core operations: Add/query/delete vectors, check DB status
Customizable: Vector dimensions, collections, distance metrics

MilvusStorage

For Milvus (cloud-native vector search engine)
High scalability, real-time search

TiDBStorage

For TiDB (hybrid vector/relational database)
Handles embeddings, knowledge graphs, ops data

QdrantStorage

For Qdrant (open-source vector DB)
Fast similarity search for AI/ML

OceanBaseStorage

For OceanBase (cloud and on-prem vector DB)
Supports large-scale, distributed deployments

WeaviateStorage

For Weaviate (open-source vector engine)
Schema-based, semantic search, hybrid queries

ChromaStorage

For ChromaDB (AI-native open-source embedding database)
Simple API, scales from notebook to production

SurrealStorage

For SurrealDB (scalable, distributed database with WebSocket support)
Efficient vector storage and similarity search with real-time updates

PgVectorStorage

For PostgreSQL with pgvector (open-source vector engine)
Leverages PostgreSQL for vector search

Graph Storages

BaseGraphStorage

Abstract base for graph database integrations
Supports:
- Schema queries and refresh
- Adding/deleting/querying triplets

NebulaGraph

For NebulaGraph (distributed, high-performance graph DB)
Scalable, open source

Neo4jGraph

For Neo4jGraph (most popular enterprise graph DB)
Widely used for graph analytics, recommendations

Get Started

Here are practical usage patterns for each storage type—pick the ones you need and mix them as you like.

In-Memory Key-Value Storage

Use for: Fast, temporary storage. Data is lost when your program exits. Perfect for: Prototyping, testing, in-memory caching.

Python Example
Output

from camel.storages.key_value_storages import InMemoryKeyValueStorage

memory_storage = InMemoryKeyValueStorage()
memory_storage.save([{'key1': 'value1'}, {'key2': 'value2'}])
records = memory_storage.load()
print(records)
memory_storage.clear()

>>> [{'key1': 'value1'}, {'key2': 'value2'}]

JSON File Storage

Use for: Persistent, human-readable storage on disk. Perfect for: Logs, local settings, configs, or sharing small data sets.

Python Example
Output

from camel.storages.key_value_storages import JsonStorage
from pathlib import Path

json_storage = JsonStorage(Path("my_data.json"))
json_storage.save([{'key1': 'value1'}, {'key2': 'value2'}])
records = json_storage.load()
print(records)
json_storage.clear()

>>> [{'key1': 'value1'}, {'key2': 'value2'}]

Milvus Vector Storage

Use for: Scalable, high-performance vector search (RAG, embeddings). Perfect for: Semantic search and production AI retrieval.

Python Example
Output

from camel.storages import MilvusStorage, VectorDBQuery, VectorRecord

milvus_storage = MilvusStorage(
    url_and_api_key=("Your Milvus URI","Your Milvus Token"),
    vector_dim=4,
    collection_name="my_collection"
)
milvus_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])
milvus_storage.load()
query_results = milvus_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)
milvus_storage.clear()

>>> {'key2': 'value2'} 0.5669466853141785

TiDB Vector Storage

Use for: Hybrid cloud-native storage, vectors + SQL in one. Perfect for: Combining AI retrieval with your business database.

Python Example
Output

import os
from camel.storages import TiDBStorage, VectorDBQuery, VectorRecord

os.environ["TIDB_DATABASE_URL"] = "The database url of your TiDB cluster."
tidb_storage = TiDBStorage(
    url_and_api_key=(os.getenv("DATABASE_URL"), ''),
    vector_dim=4,
    collection_name="my_collection"
)
tidb_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])
tidb_storage.load()
query_results = tidb_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)
tidb_storage.clear()

>>> {'key2': 'value2'} 0.5669466755703252

Qdrant Vector Storage

Use for: Fast, scalable open-source vector search. Perfect for: RAG, document search, and high-scale retrieval tasks.

Python Example
Output

from camel.storages import QdrantStorage, VectorDBQuery, VectorRecord

# Create an instance of QdrantStorage with dimension = 4
qdrant_storage = QdrantStorage(vector_dim=4, collection_name="my_collection")

# Add two vector records
qdrant_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])

# Query similar vectors
query_results = qdrant_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)

# Clear all vectors
qdrant_storage.clear()

>>> {'key2': 'value2'} 0.5669467095138407

ChromaDB Vector Storage

Use for: Fastest way to build LLM apps with memory and embeddings. Perfect for: From prototyping in notebooks to production clusters with the same simple API.

Python Example
Output

from camel.storages import ChromaStorage, VectorDBQuery, VectorRecord
from camel.types import VectorDistance

# Create ChromaStorage instance with ephemeral (in-memory) client
chroma_storage = ChromaStorage(
    vector_dim=4,
    collection_name="camel_example_vectors",
    client_type="ephemeral",  # or "persistent", "http", "cloud"
    distance=VectorDistance.COSINE,
)

# Add vector records
chroma_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])

# Query similar vectors
query_results = chroma_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)

# Clear all vectors
chroma_storage.clear()

>>> {'key2': 'value2'} 0.7834733426570892

SurrealDB Vector Storage

Use for: Scalable, distributed vector storage with WebSocket support. Perfect for: Real-time vector search with distributed deployments and SQL-like querying.

Python Example
Output

import os
from camel.storages import SurrealStorage, VectorDBQuery, VectorRecord

# Set environment variables for SurrealDB connection
os.environ["SURREAL_URL"] = "ws://localhost:8000/rpc"
os.environ["SURREAL_PASSWORD"] = "your_password"

# Create SurrealStorage instance with WebSocket connection
surreal_storage = SurrealStorage(
    url=os.getenv("SURREAL_URL"),
    table="camel_vectors",
    namespace="ns",
    database="db",
    user="root",
    password=os.getenv("SURREAL_PASSWORD"),
    vector_dim=4,
)

# Add vector records
surreal_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])

# Query similar vectors
query_results = surreal_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)

# Clear all vectors
surreal_storage.clear()

>>> {'key2': 'value2'} 0.5669467095138407

OceanBase Vector Storage

Use for: Massive vector storage with advanced analytics. Perfect for: Batch operations, cloud or on-prem setups, and high-throughput search.

Python Example
Output

import random

from camel.storages.vectordb_storages import (
    OceanBaseStorage,
    VectorDBQuery,
    VectorRecord,
)

# Replace these with your OceanBase connection parameters
OB_URI = "127.0.0.1:2881"
OB_USER = "root@sys"
OB_PASSWORD = ""
OB_DB_NAME = "oceanbase"

def main():
    ob_storage = OceanBaseStorage(
        vector_dim=4,
        table_name="my_ob_vector_table",
        uri=OB_URI,
        user=OB_USER,
        password=OB_PASSWORD,
        db_name=OB_DB_NAME,
        distance="cosine",
    )

    status = ob_storage.status()
    print(f"Vector dimension: {status.vector_dim}")
    print(f"Initial vector count: {status.vector_count}")

    random.seed(20241023)
    large_batch = []
    for i in range(1000):
        large_batch.append(
            VectorRecord(
                vector=[random.uniform(-1, 1) for _ in range(4)],
                payload={'idx': i, 'batch': 'example'},
            )
        )
    ob_storage.add(large_batch, batch_size=100)
    status = ob_storage.status()
    print(f"Vector count after adding batch: {status.vector_count}")

    query_vector = [random.uniform(-1, 1) for _ in range(4)]
    query_results = ob_storage.query(
        VectorDBQuery(query_vector=query_vector, top_k=5)
    )
    for i, result in enumerate(query_results):
        print(f"Result {i+1}:")
        print(f"  ID: {result.record.id}")
        print(f"  Payload: {result.record.payload}")
        print(f"  Similarity: {result.similarity}")

    ob_storage.clear()
    status = ob_storage.status()
    print(f"Vector count after clearing: {status.vector_count}")

if __name__ == "__main__":
    main()

'''
===============================================================================
Vector dimension: 4
Initial vector count: 0
Adding vectors in batches...
Vector count after adding batch: 1000
Querying similar vectors...
Result 1:
  ID: f33f008d-688a-468a-9a2d-e005d27ad9d9
  Payload: {'idx': 496, 'batch': 'example'}
  Similarity: 0.9847431876706196
Result 2:
  ID: 0946ca4a-9129-4343-b339-b2f13a64c827
  Payload: {'idx': 306, 'batch': 'example'}
  Similarity: 0.9598140307734809
...
Clearing vectors...
Vector count after clearing: 0
===============================================================================
'''

Weaviate Vector Storage

Use for: Vector search with hybrid (vector + keyword) capabilities. Perfect for: Document retrieval and multimodal AI apps.

Python Example
Output

from camel.storages import WeaviateStorage, VectorDBQuery, VectorRecord

# Create WeaviateStorage instance with dimension = 4 using Weaviate Cloud
weaviate_storage = WeaviateStorage(
    vector_dim=4,
    collection_name="camel_example_vectors",
    connection_type="cloud",
    wcd_cluster_url="your-weaviate-cloud-url",
    wcd_api_key="your-weaviate-api-key",
    vector_index_type="hnsw",
    distance_metric="cosine",
)

weaviate_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])

query_results = weaviate_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)

weaviate_storage.clear()

>>> {'key2': 'value2'} 0.7834733128547668

NebulaGraph Storage

Use for: Open-source, distributed graph storage and querying. Perfect for: Knowledge graphs, relationships, and fast distributed queries.

from camel.storages.graph_storages import NebulaGraph

nebula_graph = NebulaGraph("your_host", "your_username", "your_password", "your_space")

# Show existing tags
query = 'SHOW TAGS;'
print(nebula_graph.query(query))

Neo4j Graph Storage

Use for: Industry-standard graph database for large-scale relationships. Perfect for: Enterprise graph workloads, Cypher queries, analytics.

from camel.storages import Neo4jGraph

neo4j_graph = Neo4jGraph(
    url="your_url",
    username="your_username",
    password="your_password",
)

query = "MATCH (n) DETACH DELETE n"
print(neo4j_graph.query(query))

PgVectorStorage Vector Storage

Use for: Storing and querying vectors in PostgreSQL. Perfect for: Leveraging an existing PostgreSQL database for vector search.

Python Example
Output

from camel.storages import PgVectorStorage, VectorDBQuery, VectorRecord

# Replace with your PostgreSQL connection details
PG_CONN_INFO = {
    "host": "127.0.0.1",
    "port": 5432,
    "user": "postgres",
    "password": "postgres",
    "dbname": "postgres",
}

# Create PgVectorStorage instance
pg_storage = PgVectorStorage(
    vector_dim=4,
    conn_info=PG_CONN_INFO,
    table_name="camel_example_vectors",
)

# Add vector records
pg_storage.add([
    VectorRecord(vector=[-0.1, 0.1, -0.1, 0.1], payload={'key1': 'value1'}),
    VectorRecord(vector=[-0.1, 0.1, 0.1, 0.1], payload={'key2': 'value2'}),
])

# Query similar vectors
query_results = pg_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, 0.1, 0.1], top_k=1))
for result in query_results:
    print(result.record.payload, result.similarity)

# Clear all vectors
pg_storage.clear()

>>> {'key2': 'value2'} 0.5669467

Get Started

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Storages

What Are Storages in CAMEL-AI?

Types of Storages

Get Started

In-Memory Key-Value Storage

JSON File Storage

Milvus Vector Storage

TiDB Vector Storage

Qdrant Vector Storage

ChromaDB Vector Storage

SurrealDB Vector Storage

OceanBase Vector Storage

Weaviate Vector Storage

NebulaGraph Storage

Neo4j Graph Storage

PgVectorStorage Vector Storage

​What Are Storages in CAMEL-AI?

​Types of Storages

​Get Started

In-Memory Key-Value Storage

JSON File Storage

Milvus Vector Storage

TiDB Vector Storage

Qdrant Vector Storage

ChromaDB Vector Storage

SurrealDB Vector Storage

OceanBase Vector Storage

Weaviate Vector Storage

NebulaGraph Storage

Neo4j Graph Storage

PgVectorStorage Vector Storage

What Are Storages in CAMEL-AI?

Types of Storages

Get Started