commerce-agents README

The project's README from github.com/anthropics/commerce-agents. As one markdown file: README.md.

Claude Commerce Agents

Two commerce agents built on Claude: a shopping agent a business embeds in its app for customers, and a merchant agent its staff use to run the back office. Each is defined once (prompt, skills, tool contracts, gates) and runs on the Messages API, the Claude Agent SDK, and Managed Agents; four runnable verticals show both over the same libraries.

[!NOTE] Every company, brand, product, and person here is fictional; the only company is ACME. Nothing places an order, charges a card, or changes a live listing: checkout renders the cart for the host to complete, and every merchant write is staged until a person approves it. Business rules, authorization, and compliance are the deployment's.

Quick start: run the demos

Python 3.11+ and Node 22. Clone, install, add a key, run a vertical:

git clone https://github.com/anthropics/commerce-agents.git && cd commerce-agents
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt       # the seven packages and their pinned dependencies
cp .env.example .env                  # add ANTHROPIC_API_KEY
(cd examples && npm ci)               # the eight web apps share one workspace
python scripts/run_demo.py retail     # API :8000 + storefront :3000

--merchant starts the portal instead of the storefront and --all starts both. The verticals are retail (:3000, portal :3100), travel (:3001, :3101), telecom (:3002, :3102), and entertainment (:3003, :3103); each README lists prompts to try on both surfaces.

Quick start: build your own

The Claude Code plugin scaffolds an agent on these packages against your systems, or reviews one you have. With the repo cloned as above (the plugin reads it as the reference):

claude plugin marketplace add anthropics/commerce-agents
claude plugin install commerce-builder@claude-commerce-agents
claude
/scaffold-commerce-agent a shopping assistant for our store

The command asks about your stack, plays the plan back, and builds the project; /add-commerce-flow and /author-commerce-evals continue from there, and /review-commerce-agent starts from an agent that already exists (plugins/commerce-builder/). Each command also runs when a request matches its description, so naming it is optional.

The two agents

The shopping agent searches, compares, plans, fills the cart, answers order and policy questions, and remembers what a customer tells it. Its five flows are the skills in shopping-agent/skills/; a deployment implements StorefrontBackend over its catalog, cart, order, and policy systems.

The merchant agent explains performance, maintains listings, acts on inventory and order alerts, prices and promotes, and drafts campaigns; every write is a staged change the host's approval surface applies. Its five flows are the skills in merchant-agent/skills/; a deployment implements MerchantBackend over its analytics, catalog, inventory, pricing, and campaign systems.

Layout

Directory Contents pip package, import name
commerce-common/ What both roles share: config, fencing, memory, skills, grounding, presentation, executor frame, events commerce-common, commerce_common
shopping-agent/core/ Shopping types, StorefrontBackend, prompt, tool contracts, gates, executor shopping-agent-core, shopping_agent
shopping-agent/runtime-messages-api/ ShoppingAgent, the turn loop on the Messages API shopping-agent-runtime, shopping_agent_runtime
shopping-agent/runtime-agent-sdk/ The shopping agent on the Agent SDK, with a console shopping-agent-sdk, shopping_agent_sdk
shopping-agent/managed-agents/ Manifest and storefront MCP server for Managed Agents —
merchant-agent/core/ Merchant types, MerchantBackend, prompt, tool contracts, change guardrails, gates, executor merchant-agent-core, merchant_agent
merchant-agent/runtime-messages-api/ MerchantAgent and the analysis delegate on the Messages API merchant-agent-runtime, merchant_agent_runtime
merchant-agent/runtime-agent-sdk/ The merchant agent on the Agent SDK, with an approving console merchant-agent-sdk, merchant_agent_sdk
merchant-agent/managed-agents/ Manifest, merchant MCP server, scheduled digest for Managed Agents —
examples/ Four verticals, shared host code (demo_common/), shared web code (web-shared/) —
plugins/commerce-builder/ The Claude Code plugin —
docs/ safety.md (enforced rules), backends.md (mapping your systems), deployment.md (other platforms) —
tests/ Cross-package suites; each package also has its own tests/ —
scripts/ install.sh, run_demo.py, smoke_chat.py, screenshot_tour.py, check.py, deploy_managed_agent.sh, verify_all.py —

Three ways to run an agent

Messages API. The reference loop; the examples are host applications around it:

from pathlib import Path

from shopping_agent import ShoppingAgentConfig
from shopping_agent_runtime import ShoppingAgent

agent = ShoppingAgent(backend=your_backend, skills_dir=Path("shopping-agent/skills"),
                      config=ShoppingAgentConfig(brand_name="Your Store"))
async for event in agent.stream_turn(messages, session, state):
    ...   # text_delta, tool_call, ui, cart_update (change_update on the merchant side), turn_complete
await agent.update_memory(messages, session)   # memory extraction; this path only

The example hosts take the session id in an X-Session-Id header.

Agent SDK. The same prompt, skills, and tools, with the SDK running the loop; the host prefetches grounding reads, and nothing runs after the turn:

python shopping-agent/runtime-agent-sdk/main.py --once "a two-person tent under $250"
python merchant-agent/runtime-agent-sdk/main.py          # approves staged changes with y/N

Managed Agents. A hosted agent over the same skills and contracts, calling your MCP server:

scripts/deploy_managed_agent.sh shopping-agent/managed-agents/shopping-agent   # or merchant-agent/...; --live deploys

Safety

Fencing, provenance gates, caps, memory validation, and the merchant approval gate run inside the tool call and hold on all three paths; grounding, the analysis budgets, and memory extraction are runtime features. docs/safety.md lists each rule with its module and paths, and what a deployment adds first; the examples have no authentication and the MCP servers bind to loopback.

Verticals

Example Storefront Portal
examples/retail/ ACME Search, comparison, plans, cart, checkout, memory over the built-in components Digest, staged restocks and listing fixes, analysis delegate over a SQL view
examples/travel/ ACME Travel Date-bound inventory and a present_itinerary extension Occupancy calendar and date-window rate moves
examples/telecom/ ACME Mobile Account context, plan matrix, server-authored fee disclosures Plan mix, price moves that state the lines affected, protected regulated fees
examples/entertainment/ ACME Tickets Timed holds, waitlists, transfers, venue map, all-in fee disclosures Event pacing, hold releases that add real capacity, fee-preserving price moves

Each example's README has a Try section: the turns scripts/smoke_chat.py runs, and single prompts with what a good answer does.

Verify

ruff check . && ruff format --check . && pytest && python scripts/check.py
python scripts/verify_all.py                        # the line above plus deploy dry runs and web builds
python scripts/smoke_chat.py --vertical travel      # one live conversation; needs a key

requirements-dev.txt adds pytest and ruff. CI installs from it on two Python versions, builds the eight web apps, and checks that the package names stay unregistered on the public index (the pin files install them from their directories, never from the index). To confirm caching, read cache_read_input_tokens from turn_complete, or the line each model call logs on its runtime's logger: zero on a second turn means the prefix changed.

Deploying elsewhere

The runtimes take any anthropic client as client= and the SDK runtimes take the platform from the CLI environment; docs/deployment.md covers GCP Vertex AI, AWS Bedrock, Microsoft Foundry, and gateways.

MCP connectors

None ship; both agents reach your systems through the backend interfaces. Where an official connector is the source of record, it is the integration target: analytics warehouses (Snowflake, BigQuery, Databricks, Amplitude), finance (Stripe, Square, PayPal, QuickBooks), delivery (Slack, Google Drive, Gmail). A commerce platform's own MCP server for catalog, cart, or checkout is called from a backend method server-side; on Managed Agents the manifest mounts it beside the role's server, and the provenance gates stay in front of every write.

Making it yours

License

Copyright 2026 Anthropic PBC. Licensed under the Apache License 2.0. This is a reference implementation; it is not maintained and does not accept contributions.

commerce-agents (anthropics/commerce-agents)

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3.3K ·GitHub
Commerce-agents
What is this?A reference blueprint from Anthropic for building two kinds of Claude-powered commerce agents: a shopping agent a business embeds in its app for customers, and a merchant agent staff use to run the back office. It ships four runnable example verticals (retail, travel, telecom, entertainment).
Is this a ready-to-use product or something I adapt into my own systems?It's a reference implementation, not a product: the shopping and merchant agents are defined once (prompt, skills, tool contracts, gates) and can run on the Messages API, the Claude Agent SDK, or Managed Agents, with the four verticals as demo hosts you adapt to your own backend.
How do I get the demos running?Clone the repo, install dependencies, add an API key, and run a chosen vertical; --merchant starts the staff portal and --all starts both portal and storefront. There is also a Claude Code plugin that scaffolds a new agent against your own stack.
What do I need installed before trying this?Python 3.11+ and Node 22 to run the demos, plus an Anthropic API key for the model calls. The Claude Code plugin additionally requires Claude Code itself.
Does running a demo actually place real orders or charge cards?No. Every brand and company in the examples is fictional, checkout only renders a cart for the host to complete, and every merchant-side write is staged until a human approves it. The repo is also explicitly unmaintained and not accepting contributions.
Who is this meant for?Developers at a commerce, travel, telecom, or entertainment business who want to wire a shopping or merchant agent into their own storefront, order, inventory, and pricing systems rather than build the agent logic from scratch.
Can I run the demos on my own machine regardless of OS?Yes, it's plain Python/Node code with no OS-specific requirements; the example verticals run as local web servers (storefront and portal on separate ports) you access in a browser.
What are the three ways an agent can actually run?On the raw Messages API (the reference turn loop), on the Claude Agent SDK (same prompt/skills/tools with the SDK running the loop), or as a Managed Agent (a hosted agent that calls your own MCP server).
What does the Claude Code plugin add beyond the example code?It scaffolds a brand-new commerce agent against your own systems (or reviews an existing one), asking about your stack and building the project; follow-up commands like /add-commerce-flow and /author-commerce-evals continue from there.
How is the shopping agent different from the merchant agent?The shopping agent is customer-facing: it searches, compares, fills the cart, and answers order/policy questions. The merchant agent is for staff: it explains performance, maintains listings, and acts on inventory, pricing, and campaigns, with every write staged for human approval.
Does this ship any ready-made integrations to platforms like Stripe or Snowflake?No connectors ship in the repo; both agents reach real systems only through backend interfaces you implement, though the README lists target platforms like Snowflake, Stripe, and Slack as integration points.
What are similar projects?ruflo, turnstone, open-multi-agent, qwen-code, headroom, wigolo, SuperClaude_Framework, openpencil
Which topics is it in?ecommerce, agent-framework, ci, model-context-protocol, anthropic, sdk, multi-agent, mcp-server, claude, ai-agent, framework, claude-code
Creator
anthropics
Size
1.5 MB
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3.3K
Updated
CommitAug 31
Age
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Commits · 90 Days