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Postgres but for agents_

Query it. Or cat it._

Create, manage, and query serverless PostgreSQL databases from your
terminal. Branching, migrations, observability, type generation -- all
built in.

Full PostgreSQL and a cloud filesystem for AI agents. SQL when you
need power. File ops when you need simplicity. One database, unified
interfaces.

CLI
$ curl -fsSL https://db9.ai/install | sh

macOS / Linux (x86_64, arm64)

AI Agents
Read https://db9.ai/skill.md and follow instructions

Your agent learns to install, auth, and use db9 autonomously

DB9 works with any agent and stackany agent and stack
Claude Code OpenAI Codex Cursor Cline VS Code OpenCode OpenClaw 
Vercel m9mem9 Claude Code OpenAI Codex Cursor Cline VS Code OpenCode 
OpenClaw Vercel m9mem9
Memory Knowledge Outputs

Personal assistants & customer copilots

Memory in tables. Context in files.

Structured state lives in Postgres. Raw context, transcripts, and
session snapshots stay as files -- all in one workspace.

db9
[?]memoriesPostgres

agent_id  kind         file_path        topic
a1       session /a1/sessions/mar09.md pricing
a1       pref    /a1/prefs.json        tone
a1       task    /a1/tasks/launch.md   launch

[?]/a1/sessions/Filesystem
mar09.md
mar08.md
[?]Query result
query topic = 'pricing' AND agent_id = 'a1'
returned 1 row
file_path /a1/sessions/mar09.md

Research, docs & coding agents

Knowledge from files. Retrieval from SQL.

Source documents stay in the filesystem. Chunks, metadata, and
vectors live in Postgres -- agents retrieve grounded context from one
backend.

db9
[?]doc_chunksPostgres

    title         file_path     topic
Auth overview /kb/auth.md       auth
Error codes   /kb/api/errors.md auth

[?]/kb/Filesystem
auth.md
api/
errors.md
[?]Query result
query WHERE topic = 'auth'
returned 2 rows
source /kb/auth.md
source /kb/api/errors.md

Automation, reports & multi-agent runs

Outputs in files. History in Postgres.

Reports, traces, and artifacts persist as files. Run history, status,
and metadata live in Postgres -- one workspace per agent run.

db9
[?]runsPostgres

id   agent     status         report
42 researcher completed /runs/42/report.md
41 browser    failed    /runs/41/report.md
40 planner    completed /runs/40/summary.md

[?]/runs/42/Filesystem
report.md
sources.csv
screenshot.png
run.log
[?]Query result
query WHERE id = 42
agent researcher
status completed
report /runs/42/report.md

Everything your agent needs

Auto-embeddings, vector search, environment branching, file storage,
cron -- built in, not bolted on.

AI, built in

Auto-embeddings, vector search, and HTTP -- all in SQL.

Call embedding() in a query and get vectors back -- no external
pipeline, no API keys in application code. Similarity search and
outbound HTTP are native too.

replaces vector DB replaces embedding pipeline replaces HTTP glue
db9:myapp=>

-- similarity search with built-in embeddings
SELECT title, content
FROM docs
ORDER BY vec <-> embedding('deploy to production')
LIMIT 5;
-- generate embeddings inline
UPDATE docs SET vec = embedding(content)
WHERE vec IS NULL;
-- call external APIs from SQL
SELECT body::json>>'status'
FROM http_get('https://api.example.com/health');

Branch everything

Clone your entire environment. Not just tables.

One command creates an isolated copy -- data, files, cron jobs, and
user permissions. Test against real conditions, then delete it.

terminal

$ db9 branch create myapp --name staging
  Branch 'staging' created from database myapp.
  Name        staging
  State       ready
  Admin User  admin

Tables & rows Files & uploads Cron jobs User permissions 
Extensions
Built-in file storage

Upload, download, and mount files alongside your data. No S3 buckets
to configure.

$ db9 fs cp ./data.csv myapp:/imports/
  data.csv -> /imports/data.csv

$ db9 fs mount myapp ~/local
  Mounted myapp at ~/local

Cron that never sleeps

Distributed job scheduling from SQL or CLI. No idle timeouts, no
missed runs.

$ db9 db cron myapp create \
  '*/5 * * * *' \
  'SELECT sync_embeddings()'

  Cron job created.
  jobid: 1

Full Postgres. Fully typed.

Zero setup, 600+ ORM tests passing. One command to generate
TypeScript or Python types.

$ db9 create
  Name  brave-tiger-42

$ db9 gen types brave-tiger-42
interface Document {
  id: number;
}

Start building in seconds

One command to install. One command to create a database. Zero
config.

$ curl -fsSL https://db9.ai/install | sh

Native to your agent

db9-bootstrap

CLI

Serverless Postgres for AI agents. Create, query, branch, and manage
databases from the terminal -- zero config. Built-in JSONB, vector
search, HTTP extension, filesystem queries, cron jobs, and full-text
search.

Read https://db9.ai/skill.md and follow the instructions

my-claw-dash

OpenClaw

Unlock deep insights and enterprise-grade observability. Stream
OpenClaw events into db9 and secure runtime events as immutable JSONL
audit logs.

Read https://db9.ai/plugins/my-claw-dash.md and follow instructions
to use my-claw-dash
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