PolarDB-X Zero
100% MySQL Compatible, Unified Single-Node & Distributed Architecture
Get a disposable PolarDB-X distributed database in one API call. No sign-up, no config, start instantly.
Read https://zero.polardbx.com/SKILL.md and follow the instructions to create a database using PolarDB-X Zero.
Why PolarDB-X Zero?
Built on PolarDB-X's unified single-node and distributed architecture, 100% MySQL compatible, providing an instant database experience for AI-native workflows.
Unified Single & Distributed
One database engine, seamlessly switch between single-node and distributed clusters. Full distributed transactions, horizontal elastic scaling, no architectural compromise.
100% MySQL Compatible
Full MySQL protocol and syntax support. JOINs, transactions, stored procedures, triggers โ all compatible. Any MySQL client or driver works out of the box.
Disposable Instances
On-demand database instances. Perfect for CI tests, demos, Agent sessions, or short-term experiments. Automatically cleaned up on expiry.
Zero Configuration
No sign-up, no credit card, no config. One API call gets you a full distributed database.
Use Cases
A 100% MySQL-compatible distributed database, ready to use instantly. Here are typical applications for PolarDB-X Zero.
AI Agent Memory Database
Built-in vector indexing, natively compatible with Mem0 and other AI memory frameworks. Store Agent state, user preferences, and vector memories in a single MySQL database โ relational queries and semantic search in one place.
App Development with Instant Database
Got an idea? Start building immediately without setting up a database. One call gets you a full MySQL database โ focus on business logic, leave infrastructure to us.
Seamless Use in Qoder, Cursor & OpenClaw
Through MCP protocol or SKILL instructions, AI coding assistants can automatically create and connect to databases. Get a real MySQL environment anytime during coding, without leaving your IDE.
Ephemeral Database Workflows
Quickly create isolated sandboxes for tutorials, demos, benchmark frameworks, and short-term testing. Full MySQL compatibility, auto-cleanup when done.
MCP Server
Give any AI agent persistent MySQL databases through the Model Context Protocol. Install the MCP server and connect from your favorite IDE.
pip install polardbx-zero-mcpQoder / Cursor / Windsurf / Claude Desktop โ add to MCP config:
{
"mcpServers": {
"polardbx": {
"command": "polardbx-zero-mcp"
}
}
}CLI Quick Start
Three steps, under a minute.
curl -s -X POST https://zero.polardbx.com/api/v1/instances \
-H 'Content-Type: application/json' \
-d '{"tag":"quickstart","ttlMinutes":43200}' | tee pxz.json | jq .export MYSQL_URL="$(jq -r '.instance.connectionString' pxz.json)"eval $(jq -r '.instance.connection | "mysql -h \(.host) -P \(.port) -u \(.username) -p\(.password)"' pxz.json)
# Or use $MYSQL_URL directly in your codeAPI Reference
One endpoint, no auth required, get a database instantly.
/api/v1/instances{
"tag": "my-agent", // Optional, caller tag
"edition": "enterprise", // Optional, standard or enterprise, default standard
"ttlMinutes": 43200, // Optional, custom TTL (minutes), default 30 days, must be โค 43200 (30 days)
"whitelistIp": "1.2.3.4" // Optional, comma-separated IPv4/CIDR whitelist
}{
"instance": {
"id": "pxz_a1b2c3d4e5f6",
"connection": {
"host": "pxc-xxxxx.polardbx.rds.aliyuncs.com",
"port": 3306,
"username": "polardbx_root",
"password": "Px$9aB3cD7eF1gH",
"database": ""
},
"connectionString": "mysql://polardbx_root:...@pxc-xxxxx:3306",
"createdAt": "2026-03-20T10:30:00.000Z",
"expiresAt": "2026-03-27T10:30:00.000Z",
"ttlDays": 0,
"ttlMinutes": 43200,
"status": "ready"
}
}Vector Search in Action
PolarDB-X has built-in HNSW vector indexing, supporting up to 16,383 dimensions, enabling semantic search directly in standard SQL.
Send the following instruction to Qoder, OpenClaw, or other AI Agents to complete all steps automatically:
Read https://playbook.polardbx.com/vector.md and follow the instructions๐ง Mem0 Memory Storage
Built on PolarDB-X vector engine, providing out-of-the-box long-term memory storage for AI Agents.
Read https://zero.polardbx.com/MEM0_SKILL.md and follow the instructions to get a Mem0 memory storage instance using PolarDB-X Zero.
Why Mem0 on PolarDB-X Zero?
Built on PolarDB-X vector engine, providing out-of-the-box long-term memory storage for AI Agents.
AI Memory Storage
A memory layer designed for AI Agents. Store user preferences, conversation history, and context to give your Agent persistent memory.
mem0ai SDK Compatible
Fully compatible with the mem0ai Python SDK. No code changes needed โ pip install mem0ai and connect.
Semantic Search
Built-in vector indexing with semantic similarity search. Recall relevant memories using natural language queries.
Instant Setup
Get a Mem0 instance in one API call. No need to deploy Redis, Qdrant, or other dependencies โ works out of the box.
Three Steps to Start
From getting an instance to storing your first memory, in under a minute.
Get Instance
Through Agent instructions or API call, instantly get a Mem0 storage instance with API Key and connection info.
Connect SDK
Use the mem0ai Python SDK, configure API Key and Host to connect โ fully compatible with the official Mem0 platform.
Store & Search
Add memories and search related memories via SDK. Organize memories by user, agent, session, and more.
SDK Example
Use the mem0ai Python SDK to add long-term memory to your Agent in just a few lines of code.
from mem0 import MemoryClient
# Connect to your Mem0 instance on PolarDB-X Zero
client = MemoryClient(
api_key="your-api-key",
host="https://your-instance.zero.polardbx.com"
)
# Add a memory
client.add(
"I prefer dark mode and Python for coding",
user_id="alice"
)
# Search memories
results = client.search(
"What are alice's preferences?",
user_id="alice"
)Compatibility & Specs
Enterprise-grade memory storage service built on PolarDB-X vector engine.
Fully compatible with mem0ai Python SDK
Free to use, no sign-up required
Semantic search and structured queries in one
Context0 โ Your AI Context Workspace
Persistent context storage for AI Agents and humans โ knowledge management and intelligent retrieval in one place
Supported Agent Types
Read https://zero.polardbx.com/context0/SKILL.md and follow the instructions to install and use ctxdb0 with your Context0 workspace.
Nine Core Capabilities
From knowledge storage to intelligent retrieval โ full coverage of AI Agent context management
Knowledge Base & RAG
Vector + full-text hybrid retrieval
LLM Wiki Distillation
Auto-distill knowledge into structured Wiki
Knowledge Graph & GraphRAG
Entity-relation modeling + graph-enhanced retrieval
Multimodal Ingestion
Unified storage for text/code/image/audio
Memory Promotion (3-Tier)
Short-term โ Long-term โ Knowledge auto-promotion
Hybrid Recall (RRF)
Multi-path recall + reciprocal rank fusion
Token Compression
Intelligent summarization to reduce context cost
Programmable Pipelines
Custom memory processing pipelines
Enterprise Isolation
Workspace-level data & resource isolation
Use Cases
From coding assistants to enterprise knowledge โ Context0 powers diverse AI applications
AI Coding Assistant
Let Codex/Claude Code remember your repo context
Knowledge Management
Team knowledge accumulation, auto-organization, smart retrieval
Customer Service
Multi-turn memory, customer profiling, precise responses
Data Analysis
Insight accumulation, pattern memory, report generation
My Workspaces
Manage your provisioned Context0 workspaces
โก PolarDB-X BaaS
Get a BaaS instance in one click โ no sign-up required. Experience a Backend-as-a-Service (BaaS) platform built on PolarDB-X, with authentication, realtime subscriptions, RESTful API, file storage, edge functions and more.
Read https://zero.polardbx.com/BAAS-SKILL.md and follow the instructions to provision a BaaS instance using PolarDB-X Zero.
See every LLM call
PXFuse records every reasoning step, every token and every cent your agent spends. langfuse SDK compatible โ three lines of config to connect.
Supported Agent Types
Read https://zero.polardbx.com/pxfuse/SKILL.md and follow the instructions to set up observability for your LLM app with your PXFuse project.
This is what one agent call looks like in PXFuse
Three lines to connect
PXFuse speaks the langfuse protocol โ swap three environment variables and you are done
LANGFUSE_BASEURL=https://pxfuse.example.com
LANGFUSE_HOST=https://pxfuse.example.com
LANGFUSE_PUBLIC_KEY=pk-lf-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
LANGFUSE_SECRET_KEY=sk-lf-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxxSample values below. Claim a project and your real credentials appear here automatically.
My Projects
Manage your provisioned PXFuse projects
Capability Map
Trace ยท Measure ยท Evaluate ยท Govern โ four layers converging on production quality
Tracing
TracingFull-path traceability
- Full-path call tracing
- Nested span hierarchy
- Session context linking
- Error and retry annotation
Metrics
MetricsCalls quantified as metrics
- Throughput, latency, error rate
- Token usage and cost accounting
- Model, project, user drill-down
- Custom metric ingestion
Evaluation
EvaluationOutput quality made measurable
- Human annotation and scoring
- LLM-as-judge scoring
- Badcase-driven datasets
- Cross-version regression
Governance
GovernanceChanges controlled and reversible
- Prompt version control
- Staged rollout and rollback
- Project-level data isolation
- Per-project key authorization
From connect to operate
Four steps that turn LLM app quality into a loop you can keep iterating on
Connect
Connect
Drop in the host and key pair โ calls report automatically, no logic changes
Debug
Debug
Expand your agent's multi-step tool calls and see exactly where it stalled
Optimise
Optimise
Surface the slow, expensive calls, then go back and tune prompts and models
Operate
Operate
Sample, score, regress badcases โ let the data decide the next revision