calebwin/pgclaw

README

pgclaw

"What if I had an AI assistant in every row of my Postgres table?"

I asked myself this a few weeks back and pgclaw is what I came up with: an open-source Postgres extension that introduces a claw data type to instantiate an AI agent - either a simple LLM or an "OpenClaw" agent - as a Postgres column.

With pgclaw, store your AI agents with the rest of your data. Supports:

Simple LLM agents and more stateful "OpenClaw" agents
Any LLM provider via rig: Anthropic, OpenAI, Ollama, Gemini, Groq, Together, DeepSeek, Mistral, Perplexity, Cohere, xAI, OpenRouter, and more
A "Claude Code" in each row via claude-agent-sdk: agents that can read/write files, run code, and use tools
ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres
any language with a Postgres client

Quick Start

Prerequisites: Rust toolchain and PostgreSQL 17 dev headers.

cargo install cargo-pgrx
cargo pgrx init --pg17 download
git clone https://github.com/calebwin/pgclaw.git
cd pgclaw
cargo pgrx install

Then in postgresql.conf:

shared_preload_libraries = 'pgclaw'
pgclaw.api_key = 'sk-ant-...'
pgclaw.database = 'mydb'

Try it

CREATE EXTENSION pgclaw;

CREATE TABLE tickets (
    id       serial PRIMARY KEY,
    title    text,
    body     text,
    priority text,
    agent    claw DEFAULT claw('Set priority to low/medium/high/critical based on urgency.')
);
SELECT claw_watch('tickets');

INSERT INTO tickets (title, body) VALUES ('Login broken', '500 error on login page');
-- ~1-2s later: priority = 'critical'

The `claw` Data Type

A claw value binds an LLM agent to a row. Two modes:

Inline — just a prompt:

claw('Summarize this ticket in one sentence.')
claw('Classify sentiment.', model => 'claude-sonnet')

Agent reference — points to a reusable agent definition in claw.agents:

claw(agent => 'ticket-triage')
claw(agent => 'ticket-triage', model => 'claude-opus')

Stateful Agents

Define reusable agents with identity, instructions, and memory:

INSERT INTO claw.agents (id, soul, instructions, memory) VALUES (
    'ticket-triage',
    'You are a meticulous support ticket classifier.',
    'Read the ticket. Set priority (low/medium/high/critical) and summary.
     If unsure, set medium and explain in summary.',
    '{"patterns": []}'
);

CREATE TABLE tickets (
    id       serial PRIMARY KEY,
    title    text,
    body     text,
    priority text,
    summary  text,
    agent    claw DEFAULT claw(agent => 'ticket-triage')
);
SELECT claw_watch('tickets');

The agent can update its own memory by including {"__memory": {...}} in its response.

Agents with Workspaces (Claude Code)

Agents with a workspace field get their own filesystem directory and run via Claude Code:

INSERT INTO claw.agents (id, soul, instructions, workspace) VALUES (
    'engineer',
    'You are a senior engineer. You write clean, tested code.',
    'Implement the change. Run tests. Update status to pass/fail.',
    'auto'
);

CREATE TABLE tasks (
    id serial PRIMARY KEY, task text, status text DEFAULT 'pending', diff text,
    agent claw DEFAULT claw(agent => 'engineer')
);
SELECT claw_watch('tasks');

Requires Claude Code CLI (npm install -g @anthropic-ai/claude-code). Workspace paths: 'auto' uses {pgclaw.workspace_dir}/{agent}/{table}/{pk}/, or use a custom template like '/data/{agent}/{table}/{pk}'.

Channels, Sessions, Heartbeats

INSERT INTO claw.agents (id, soul, instructions) VALUES (
    'support-bot', 'You are the support bot for Acme Inc.', 'Help users with orders.'
);
INSERT INTO claw.bindings (channel, agent_id) VALUES ('telegram', 'support-bot');
INSERT INTO claw.heartbeats (agent_id, every) VALUES ('support-bot', '30 minutes');

SELECT claw.route('telegram', '*', 'user123', NULL, 'My order is late');
-- Pick up responses from claw.outbox

Configuration

# Required
pgclaw.api_key = 'sk-ant-...'                      # API key for your provider
pgclaw.database = 'mydb'                            # Database for the background worker

# Optional
pgclaw.api_provider = 'anthropic'                   # see supported providers below
pgclaw.api_url = ''                                 # override API endpoint
pgclaw.default_model = 'claude-sonnet-4-5-20250929' # default model for inline claw values
pgclaw.enabled = true                               # enable/disable processing
pgclaw.workspace_dir = '/tmp/pgclaw/workspaces'     # base dir for Claude Code workspaces

Supported providers: anthropic (default), openai, ollama, gemini, groq, together, deepseek, perplexity, cohere, mistral, moonshot, openrouter, xai, hyperbolic, mira, galadriel

# OpenAI
pgclaw.api_provider = 'openai'
pgclaw.api_key = 'sk-...'
pgclaw.default_model = 'gpt-4o'

# Ollama (local, no API key needed)
pgclaw.api_provider = 'ollama'
pgclaw.default_model = 'llama3.1'

# OpenAI-compatible (vLLM, LM Studio, etc.)
pgclaw.api_provider = 'openai'
pgclaw.api_url = 'http://localhost:8000/v1'

How It Works

You add a claw column to a table and call claw_watch()
On INSERT/UPDATE, a Postgres trigger enqueues the row to claw.queue
A Postgres background worker polls the queue:
- LLM agents (no workspace): builds prompt from agent config + row data, calls LLM via rig
- Claude Code agents (with workspace): creates a workspace directory with SOUL.md, AGENTS.md, context.json, spawns Claude Code via claude-agent-sdk
The response is parsed for column updates (JSON {"column": "value"}) and applied back to the row
Conversation history is preserved in claw.history for multi-turn interactions

License

MIT of course!

README

pgclaw

"What if I had an AI assistant in every row of my Postgres table?"

With pgclaw, store your AI agents with the rest of your data. Supports:

Simple LLM agents and more stateful "OpenClaw" agents
Any LLM provider via rig: Anthropic, OpenAI, Ollama, Gemini, Groq, Together, DeepSeek, Mistral, Perplexity, Cohere, xAI, OpenRouter, and more
A "Claude Code" in each row via claude-agent-sdk: agents that can read/write files, run code, and use tools
ACID compliance, point-in-time recovery, JOINs, and all of the other great features of Postgres
any language with a Postgres client

Quick Start

Prerequisites: Rust toolchain and PostgreSQL 17 dev headers.

cargo install cargo-pgrx
cargo pgrx init --pg17 download
git clone https://github.com/calebwin/pgclaw.git
cd pgclaw
cargo pgrx install

Then in postgresql.conf:

shared_preload_libraries = 'pgclaw'
pgclaw.api_key = 'sk-ant-...'
pgclaw.database = 'mydb'

Try it

CREATE EXTENSION pgclaw;

CREATE TABLE tickets (
    id       serial PRIMARY KEY,
    title    text,
    body     text,
    priority text,
    agent    claw DEFAULT claw('Set priority to low/medium/high/critical based on urgency.')
);
SELECT claw_watch('tickets');

INSERT INTO tickets (title, body) VALUES ('Login broken', '500 error on login page');
-- ~1-2s later: priority = 'critical'

The `claw` Data Type

A claw value binds an LLM agent to a row. Two modes:

Inline — just a prompt:

claw('Summarize this ticket in one sentence.')
claw('Classify sentiment.', model => 'claude-sonnet')

Agent reference — points to a reusable agent definition in claw.agents:

claw(agent => 'ticket-triage')
claw(agent => 'ticket-triage', model => 'claude-opus')

Stateful Agents

Define reusable agents with identity, instructions, and memory:

INSERT INTO claw.agents (id, soul, instructions, memory) VALUES (
    'ticket-triage',
    'You are a meticulous support ticket classifier.',
    'Read the ticket. Set priority (low/medium/high/critical) and summary.
     If unsure, set medium and explain in summary.',
    '{"patterns": []}'
);

CREATE TABLE tickets (
    id       serial PRIMARY KEY,
    title    text,
    body     text,
    priority text,
    summary  text,
    agent    claw DEFAULT claw(agent => 'ticket-triage')
);
SELECT claw_watch('tickets');

The agent can update its own memory by including {"__memory": {...}} in its response.

Agents with Workspaces (Claude Code)

Agents with a workspace field get their own filesystem directory and run via Claude Code:

INSERT INTO claw.agents (id, soul, instructions, workspace) VALUES (
    'engineer',
    'You are a senior engineer. You write clean, tested code.',
    'Implement the change. Run tests. Update status to pass/fail.',
    'auto'
);

CREATE TABLE tasks (
    id serial PRIMARY KEY, task text, status text DEFAULT 'pending', diff text,
    agent claw DEFAULT claw(agent => 'engineer')
);
SELECT claw_watch('tasks');

Channels, Sessions, Heartbeats

INSERT INTO claw.agents (id, soul, instructions) VALUES (
    'support-bot', 'You are the support bot for Acme Inc.', 'Help users with orders.'
);
INSERT INTO claw.bindings (channel, agent_id) VALUES ('telegram', 'support-bot');
INSERT INTO claw.heartbeats (agent_id, every) VALUES ('support-bot', '30 minutes');

SELECT claw.route('telegram', '*', 'user123', NULL, 'My order is late');
-- Pick up responses from claw.outbox

Configuration

# Required
pgclaw.api_key = 'sk-ant-...'                      # API key for your provider
pgclaw.database = 'mydb'                            # Database for the background worker

# Optional
pgclaw.api_provider = 'anthropic'                   # see supported providers below
pgclaw.api_url = ''                                 # override API endpoint
pgclaw.default_model = 'claude-sonnet-4-5-20250929' # default model for inline claw values
pgclaw.enabled = true                               # enable/disable processing
pgclaw.workspace_dir = '/tmp/pgclaw/workspaces'     # base dir for Claude Code workspaces

# OpenAI
pgclaw.api_provider = 'openai'
pgclaw.api_key = 'sk-...'
pgclaw.default_model = 'gpt-4o'

# Ollama (local, no API key needed)
pgclaw.api_provider = 'ollama'
pgclaw.default_model = 'llama3.1'

# OpenAI-compatible (vLLM, LM Studio, etc.)
pgclaw.api_provider = 'openai'
pgclaw.api_url = 'http://localhost:8000/v1'

How It Works

You add a claw column to a table and call claw_watch()
On INSERT/UPDATE, a Postgres trigger enqueues the row to claw.queue
A Postgres background worker polls the queue:
- LLM agents (no workspace): builds prompt from agent config + row data, calls LLM via rig
- Claude Code agents (with workspace): creates a workspace directory with SOUL.md, AGENTS.md, context.json, spawns Claude Code via claude-agent-sdk
The response is parsed for column updates (JSON {"column": "value"}) and applied back to the row
Conversation history is preserved in claw.history for multi-turn interactions

License

MIT of course!

gem-hunter

calebwin/pgclaw（新しいタブで開きます）

README

pgclaw

Quick Start

Try it

The `claw` Data Type

Stateful Agents

Agents with Workspaces (Claude Code)

Channels, Sessions, Heartbeats

Configuration

How It Works

License

README

pgclaw

Quick Start

Try it

The `claw` Data Type

Stateful Agents

Agents with Workspaces (Claude Code)

Channels, Sessions, Heartbeats

Configuration

How It Works

License

calebwin/pgclaw（新しいタブで開きます）

README

pgclaw

Quick Start

Try it

The claw Data Type

Stateful Agents

Agents with Workspaces (Claude Code)

Channels, Sessions, Heartbeats

Configuration

How It Works

License

README

pgclaw

Quick Start

Try it

The claw Data Type

Stateful Agents

Agents with Workspaces (Claude Code)

Channels, Sessions, Heartbeats

Configuration

How It Works

License

The `claw` Data Type

The `claw` Data Type