Scale Your AI Agents Without Compromise

-- One query. Vectors + transactions.
-- No separate vector DB needed.
SELECT
 a.agent_id, a.goal, a.status,
 m_top.relevance
FROM agent_tasks a
JOIN (
 SELECT id,
 vec_cosine_distance(
 embedding, '[0.12, 0.85, ...]'
 ) AS relevance
 FROM memory_store
 ORDER BY vec_cosine_distance(
 embedding, '[0.12, 0.85, ...]')
 LIMIT 100
) m_top ON a.context_id = m_top.id
WHERE a.status = 'active'
ORDER BY m_top.relevance
LIMIT 10;

-- Agent step 3: update, then read back.
-- The read ALWAYS reflects the write.
BEGIN;
UPDATE agent_state SET
 step = 3,
 plan = JSON_SET(plan,
 '$.next_action', 'execute')
WHERE agent_id = 'a-7f3e'
 AND step = 2; -- compare-and-swap
-- Immediate read: no stale state.
SELECT step, plan->>>'$.next_action'
 FROM agent_state
 WHERE agent_id = 'a-7f3e';
-- Returns: step=3, next_action='execute'
-- Even across distributed nodes.
COMMIT;

// Connect any AI agent to TiDB
// via the MCP server.
{
 "mcpServers": {
  "TiDB Cloud": {
   "command": "npx",
   "args": [
    "-y", "mcp-remote",
    "https://tidb.link/mcp"
   ]
  }
 }
}
// Now your agent can:
// "Create a branch from prod"
// "Run SELECT * FROM agents"
// "Show me slow queries"

Expand instantly to match demand, then scale to zero when idle

• Autoscaling adapts to agent workloads automatically, from zero to peak
• Instant branching creates isolated environments in milliseconds
• Compute and storage scale independently, no capacity planning required

Query vectors, transactions, and analytics in a single call

• Join vector similarity results directly against transactional data
• One engine replaces your vector store, warehouse, and transaction layer
• No ETL, no middleware, fewer systems to build and maintain

Give every agent action a reliable, up-to-date view of the data

• Agent state stays ACID-consistent from one workflow step to the next
• Concurrent agents always read identical state, no matter which node they hit
• Multi-step workflows survive failover without losing progress or replaying actions

Run concurrent AI agents without interference or compromise

• Each agent gets dedicated throughput that other workloads can’t touch
• Tenant-scoped data separation keeps every agent’s state private
• No shared resources, cross-tenant contention, or coordination overhead

Build with the tools and syntax your agents and developers already know

• Standard SQL means agents and developers start productive on day one
• MCP, pytidb, LangChain, and LlamaIndex integrations out of the box
• Online DDLs and zero-downtime schema changes so shipping never stops