Coding Agents

AI coding agents are the fastest way to go from idea to working Bkper integration. They can scaffold projects, write SDK code, debug issues, and iterate with you in real time — as long as they have the right context about the platform.

Use whichever agent harness you’re most comfortable with. They all work well. But we have a recommended starting point.

New to AI?

If you’re new to how LLMs, context, and agents work, read AI Fundamentals first. It covers the mental model you need before building with AI.

Security

Coding agents run with your permissions. Before using them with real Bkper data, read Agent Security to understand container isolation, credential protection, and permission scoping.

Bkper CLI Agent (recommended)

The Bkper CLI ships with a built-in coding agent. It’s powered by Pi Agent with a custom system prompt that gives it deep knowledge of Bkper — core concepts, the SDK, CLI commands, and the accounting model. It’s what the Bkper team uses every day, and the choice of Pi as the engine was deliberate.

Why Pi

We spent over a year working heavily with coding agents — Claude Code, Cursor, OpenCode, Codex — before settling on Pi as the foundation for Bkper CLI Agent. The reasoning comes down to three things:

• Distribution — One install gets you everything. The Bkper CLI bundles the Pi agent runtime and all Bkper domain context, so bkper agent works the moment the CLI is installed. You don’t need a separate agent harness, a skill download, or manual context setup. One package, one command, and the from-to model, SDK types, and CLI references are already loaded.

• Philosophy — Pi is built around a minimal, well-done core that you extend through TypeScript extensions, skills, and packages instead of fighting an opinionated black box. That mirrors how we think about Bkper: a simple, rigorous accounting engine with powerful hooks for apps, bots, and automations. Embedding Bkper’s domain into Pi felt natural because both systems share the same shape — get the core right, then get out of the way.

• Model freedom — 15+ providers (Anthropic, OpenAI, Google, and more), including low-cost hosts for open-weight models. Use Claude or GPT when the task demands peak capability, and switch to affordable APIs serving Kimi, Qwen, or DeepSeek when it doesn’t. Run models locally too if you prefer. The freedom to choose the best model for the task also means the freedom to choose the most cost-effective option. AI assistance should be available to everyone, not just those with a premium API subscription.

We’re still early

Nobody knows what the ideal AI coding workflow looks like yet. Pi’s bet — give developers a minimal, hackable foundation instead of an opinionated black box — matches how we think about developer tools at Bkper. Lucas Meijer’s talk captures this mindset well.

Further watching

• Lucas Meijer on Pi’s minimal, hackable design philosophy
• Pi Agent overview and capabilities
• Pi Agent in action

Make sure the CLI is installed and authenticated, then start the agent:

bkper agent

Windows users

Run the agent inside WSL for the best experience. Terminal agents rely on Linux tooling that works more reliably under WSL than native Windows. Clone your project into the WSL filesystem (e.g. ~/code/) for best performance — Windows files are also accessible via /mnt/c/.

Connect a model provider

The agent needs access to a language model. On first launch, type /login and select a provider. We recommend OpenCode Go for low-cost open-weight models. For frontier model access, use a Codex subscription — one of the most affordable ways to use a frontier models in coding agents.

Once logged in, you’re in an interactive session. The agent can read your project files, run CLI commands, write code, and iterate with you — all with Bkper context already loaded.

Start asking questions

Just talk to it. Good starting prompts:

• What are the main account types in Bkper?
• How do I query transactions using the CLI?
• What files are in this project?
• Help me create a script that lists all accounts in my book

The agent reads your project’s AGENTS.md and nearby files automatically, so it already has project-specific context. Since Bkper CLI Agent wraps Pi Agent, you can pass any Pi flag through — --model, --continue, @file references, and so on.

Other coding agents

Any coding agent can build effectively with Bkper when given the right context. Here are the ones we’ve used and recommend:

Agent	Type	Models	What it is
Pi Agent	Terminal	15+ providers — Anthropic, OpenAI, Google, and more	Minimal, extensible harness — the engine behind Bkper CLI Agent
Claude Code	Terminal, Desktop, IDE	Claude models	Anthropic’s full-featured agent across all surfaces
OpenCode	Terminal, Desktop, IDE	75+ providers — free models included, works with Copilot and ChatGPT subscriptions	Open-source agent with the largest provider ecosystem
Codex	Terminal, Desktop, IDE	OpenAI — works with your ChatGPT plan	OpenAI’s open-source coding agent
AMP	Terminal	Multiple providers — works with Claude, OpenAI, and more	Open-source coding agent built for AI-native development workflows
Cursor	Terminal, Desktop, IDE	Multiple providers built in	AI-native code editor with a terminal agent

Each tool has its own way of loading project context. The next section explains how to provide Bkper knowledge to any of them.

Giving your agent context

Bkper CLI Agent has context built in. For other agents, you need to provide it.

Install the Bkper skill (recommended)

For agents that support the Agent Skills standard, install the Bkper skill:

npx skills add bkper/skills --skill bkper

This bundles everything the Bkper Agent knows — core concepts, CLI reference, SDK types, and financial reporting workflows — into a single skill the agent loads automatically when working on Bkper tasks. Source on GitHub.

Direct Markdown access

If your agent doesn’t support skills, you can load context manually. Every page on bkper.com is available as clean Markdown — append .md to any URL:

https://bkper.com/docs/core-concepts.md
https://bkper.com/docs/api/bkper-js.md
https://bkper.com/docs/build.md

This strips navigation chrome and reduces token usage. See AI Tooling for all access methods.

Three URLs cover most Bkper development needs:

URL	What it covers
/platform/agents.md	Technical instincts, quality standards, domain sensibilities
/docs/core-concepts.md	The from-to model, account types, transactions, groups, queries
/docs/api/bkper-js.md	Full bkper-js SDK reference with TypeScript types

Use whichever combination your project needs.

Project-level context files

For project-specific knowledge — which book you’re working with, what accounts matter, what tags to use — add it to your agent’s context file (AGENTS.md, CLAUDE.md, or equivalent):

## Project context

- Book ID: abc123-def456
- Key accounts: Checking, Sales, Accounts Receivable
- Common tags: #invoice, #payment, #reconciled

## Rules

- All automated transactions must be created as drafts
- Use the #sync tag on all imported transactions

This gives the agent project-specific knowledge that no published doc can provide.

Next steps

• Your First App — build and deploy a full Bkper app (a great task to pair with an AI agent)
• CLI Scripting & Piping — automate data workflows with CLI pipes
• Apps Overview — understand the Bkper Platform architecture