subs

substructure.ai

Pre-1.0: APIs and the wire protocol can change between releases.

subs is an agent harness for the cloud.

It runs an unprivileged agent loop with no system access. It uses MCP servers for tools. It runs locally or as a client and a server.

Declare your agents in a config file. To customize the loop, point an agent at an HTTP endpoint and answer a webhook.

subs handles durability, retries, timeouts, MCP connection management, session state, session branching, AG-UI, Slack connection, LLM calls, subagents, interrupts and more.

To turn a sandbox into an MCP server, see mcpd.

Get started

Install the CLI

curl -fsSL https://subs.dev/cli.sh | bash

The CLI is also the engine.

Run an agent on your machine

Create a subs.toml.

name = "example"

[llm.openrouter]
type = "openrouter"

[agent.teammate]
llm = "openrouter"
model = "deepseek/deepseek-v4-flash-0731"
system = "You are a helpful teammate."

Set your provider key and talk to the agent.

export OPENROUTER_API_KEY=sk-or-...
subs chat teammate -c subs.toml

Run the engine as a server

Add a [serve] section and a [remote] that points at it.

[serve]
port = 9999
auth = false

[remote]
url = "http://localhost:9999"

Start the server.

subs serve -c subs.toml

In another terminal, the same chat command now talks to it.

subs chat teammate -c subs.toml

Run the agent on substructure.ai

Point [remote] at the hosted engine instead of your own.

[remote]
url = "https://api.substructure.ai"

Create the project from the file, then upload your LLM key.

subs apply
subs auth llm.openrouter

The same chat command now runs the turn on the hosted engine.

subs chat teammate -c subs.toml

Put the agent in Slack

Say which agent takes a DM and which one answers a mention.

[slack]
dm = "teammate"
mentions = "teammate"

Apply the file again, then connect your workspace.

subs apply
subs slack connect

Mention the bot in a channel and it answers in the thread.

Connect the agent to an MCP server

Declare the server and give it to an agent. Every user of the agent shares one credential.

[mcp.sentry]
url = "https://mcp.sentry.dev/mcp"

[agent.teammate]
llm = "openrouter"
model = "deepseek/deepseek-v4-flash-0731"
system = "You are a helpful teammate."
mcp = ["mcp.sentry"]

Authorize the connection.

subs auth mcp.sentry

Give each user their own MCP connection

Set credential = "user" and each user connects their own account. A user-scoped connection works only in a one-on-one chat between the agent and that user.

[mcp.linear]
url = "https://mcp.linear.app/mcp"
credential = "user"

[agent.personal]
llm = "openrouter"
model = "deepseek/deepseek-v4-flash-0731"
system = "Help me with my Linear issues."
mcp = ["mcp.linear"]

[slack]
dm = "personal"
mentions = "teammate"

Control the agent loop with a webhook

Point an agent at a URL and the engine sends every decision for that agent to your code. This gives you full control of the loop, including how the agent behaves in Slack.

[agent.teammate]
llm = "openrouter"
model = "deepseek/deepseek-v4-flash-0731"
system = "You are a helpful teammate."
mcp = ["mcp.sentry"]
worker = "https://example.com/agent"

Your endpoint reads the engine's proposal and returns it, changing only the steps you care about. There is no SDK to install.

import { serve } from "@hono/node-server";
import { Hono } from "hono";
import type { DecisionRequest, DecisionResponse } from "./protocol.ts";

function decide({ trigger, proposed }: DecisionRequest): DecisionResponse {
    if (trigger.type === "session.start") {
        return {
            agent: {
                ...proposed.agent,
                tools: [{ name: "current_time", description: "Get the current time" }]
            }
        };
    }

    // Run our tool when the model calls it.
    if (trigger.type === "tool.execute" && trigger.name === "current_time") {
        return { actions: [{ type: "tool.result", result: new Date().toISOString() }] };
    }

    // Accept the engine's proposal for everything else.
    return proposed;
}

const app = new Hono();
app.post("/", async (c) => c.json(decide(await c.req.json())));

serve({ fetch: app.fetch, port: 4444 });

Only the agents that name a worker use one. The rest stay with the engine, in the same project and the same file.

Full walkthrough in the quick start. Docs: Workers, Connectors, Slack, Local development

Features

Slack

Mention the bot or DM it. The thread is the session. Route different channels to different agents.

Docs: Slack

Terminal chat

subs chat holds one session open, streams the reply as it is written, and turns an approval prompt into a picker. The session is the same kind a Slack thread is.

Docs: Chat

Examples: no-code-chat

Custom decisions

At each step the engine tells your code what it plans to do next. Accept the plan or do something else. A working agent is a few lines.

Docs: How it works

Examples: Node, Python

Any language

Your agent is an HTTP endpoint. Generate typed bindings from the published JSON schema.

Docs: Typed bindings

Examples: Go, Python, TypeScript, Elixir

MCP connectors

Declare an MCP server and the engine handles the authorization, reads the tools it offers, and runs every call. Your code never holds a token.

Docs: Connectors

Examples: Node

Plugins

Point an agent at an agent-plugins directory and it gets that plugin's skills and MCP servers.

Docs: Plugins

Any LLM

The engine calls Anthropic, OpenAI, or OpenRouter with your key. Or your worker makes the call and the engine never sees a key.

Docs: LLMs

Examples: Anthropic, OpenAI, OpenRouter

Crash recovery

Every step is saved before it runs. A run continues from where it stopped. The same message submitted twice runs once.

Docs: Durability

Human approval

An agent can stop and wait for a person to approve, then continue. A waiting agent uses no compute. In Slack this is a button.

Docs: Interrupts

Examples: Node, No code

Tools that take hours

A tool does not have to answer immediately. Accept the call, do the work on your own schedule, and report the result later.

Docs: Async tools

Chat history, editing, and branching

History, editing, regeneration, and branching belong to the engine. A user can edit an earlier message and go a new direction. The original branch stays.

Docs: Conversations

Examples: Node, Python

Existing chat UIs

The engine streams AG-UI events, so assistant-ui and CopilotKit connect to it directly.

Docs: AG-UI

Examples: assistant-ui, CopilotKit

Tools that run in the browser

A tool can run in the user's browser instead of on your server. The run waits for the browser, then continues.

Docs: Client-side tools

Examples: Node

Agent state without a database

The engine stores your agent's state with the conversation. Your code gets it on every request and writes changes back.

Docs: Agent state

Examples: Node, Python

Subagents

An agent can give work to other agents. Each child runs in its own session. The parent's totals include each child's cost and token use.

Docs: Subagents

Examples: Node, Python

Validated tool calls

Give a tool an input and output schema. The engine checks every call against it.

Docs: Tool calls

Examples: Node, Python, Go

Retries and timeouts

Set a policy on any tool or model call. The engine applies it, and keeps applying it after a restart.

Docs: Retries and timeouts

Self-hosting

Run the engine on your own servers and hold every credential.

Docs: Self-hosting

Parts of the system

• Engine. Runs the agent loop, in Rust. It calls the model, runs tools, saves each step, retries failures, streams events, and supervises subagents. Use the hosted version at app.substructure.ai, run it from the CLI, or embed it in your process.
• Workers. Your agent code. It receives a trigger and returns actions. It runs in your codebase with your dependencies.
• Clients. They send work and stream events back, from your backend or from the browser. Slack and AG-UI are clients.
• CLI. Set up, deploy, watch, and debug from the terminal. It also runs the engine locally.

Install

curl -fsSL https://subs.dev/cli.sh | bash

The script verifies the release checksum and installs to ~/.local/bin. Set SUBS_INSTALL_DIR to install elsewhere and SUBS_VERSION to pin a release. Or install from npm:

npm i -g @substructure.ai/cli

Docs

Full documentation in docs/.

License

MIT