Cline SDK: Build Custom AI Coding Agents & Multi-Agent Workflows

Cline SDK: How to Build Custom AI Coding Agents and Multi-Agent Workflows starts with a small TypeScript agent loop, then adds narrowly scoped tools, completion criteria, permission controls, and—only for decomposable work—a coordinator that delegates to specialists. The practical path is to use Agent for stateless or one-shot coding tasks and ClineCore when you need sessions, built-in capabilities, persistence, approvals, schedules, subagents, or durable teams.

The important mental model is that an AI coding agent is not a model with a longer prompt. It is a program that gives a model a goal, a constrained set of actions, feedback from those actions, and a stopping condition. Your engineering work is to make each of those pieces legible, testable, and safe.

What Is the Cline SDK?

The Cline SDK is a set of TypeScript packages for embedding an agent runtime in an application, automation, or integration. Its package boundaries are useful architecture choices: @cline/agents supplies a lightweight, browser-compatible stateless execution loop; @cline/core provides the fuller Node.js harness with sessions, tools, persistence, scheduling, and hub support; @cline/llms handles provider access; and @cline/sdk is the umbrella import. The documented runtime requires Node.js 22 or later.

Choose the smallest surface that can express the job. A branch-summary command, a CI review bot, or an internal command-line utility commonly starts with Agent. A persistent developer assistant, a scheduled maintenance service, or a task board with several collaborating agents is a ClineCore problem. Starting small matters: a simple single agent with good tools is often cheaper, faster, and easier to debug than a prematurely distributed “team.”

How Do You Build a Custom AI Coding Agent?

Start by writing down one concrete contract: input, allowed evidence, permitted actions, output shape, and definition of done. For example, “review the current git diff and return structured findings” is a better first product than “be a senior engineer.” It bounds the agent’s context, makes evaluation possible, and tells you which tools belong in the loop.

1. Define a narrow task: summarize a diff, generate a migration plan, triage a failing build, or review changed files.
2. Expose only the actions required for that task. Do not hand a read-only reviewer production deployment access “just in case.”
3. Use a system prompt to state process rules and explicitly name the required final action.
4. Subscribe to events so users and logs can observe text, tool calls, failures, and completion.
5. Make the agent return structured data through a completion tool, then validate and process it in ordinary application code.

Here is the skeleton of a coding-review agent. The point is not the exact review policy; it is that tool input and final output are typed, while the model decides when to call them.

import { Agent, createTool } from "@cline/sdk"
import { z } from "zod"

const findings: Array<{
  file: string
  line: number
  severity: "critical" | "warning" | "suggestion"
  comment: string
}> = []

const addFinding = createTool({
  name: "add_review_finding",
  description: "Record a concrete code-review finding for a changed file.",
  inputSchema: z.object({
    file: z.string().describe("Repository-relative file path"),
    line: z.number().describe("Relevant line number"),
    severity: z.enum(["critical", "warning", "suggestion"]),
    comment: z.string().describe("Specific explanation and suggested fix"),
  }),
  async execute(input) {
    findings.push(input)
    return { recorded: true, count: findings.length }
  },
})

const submitReview = createTool({
  name: "submit_review",
  description: "Submit the final review summary and end the run.",
  inputSchema: z.object({
    summary: z.string(),
    approve: z.boolean(),
  }),
  lifecycle: { completesRun: true },
  async execute(input) {
    return JSON.stringify(input)
  },
})

const agent = new Agent({
  providerId: "your-provider",
  modelId: "your-model",
  apiKey: process.env.LLM_API_KEY,
  tools: [addFinding, submitReview],
  systemPrompt: "Review the supplied diff. Record findings, then call submit_review.",
})

const result = await agent.run("Review this diff: ...")

How Should You Design Custom Agent Tools?

A tool is an API contract written partly for the model. In the SDK, it has a unique name, a description, an input schema, and an execute function. The schema protects your program from malformed arguments; the description helps the model select the correct action. Both matter. A vague tool named database that “handles data” forces the model to guess. A search_database tool that says it performs read-only SQL, returns JSON, limits results, and should be used for record lookup gives it a workable interface.

Prefer action-oriented names, concise descriptions, explicit “when not to use it” constraints, and Zod descriptions on every input. Use enums instead of unconstrained strings for fixed categories such as severity, environment, or operation. Return structured JSON where possible; it is easier for both the next model turn and your post-run code to consume than prose. For long-running work, observe the supplied abort signal. For recoverable failures—an unavailable API or invalid query—return structured error output rather than throwing, so the agent can adapt.

Completion is a tool-design concern, too. Marking a final tool with lifecycle: { completesRun: true } gives the loop an intentional end state. Without it, an agent may continue until its iteration budget is exhausted, even after it has effectively finished. This pattern also creates a clean boundary: the model proposes validated structured results, and deterministic code decides whether to post a review, open a ticket, or fail a CI job.

How Do You Make Coding Agents Safe Enough to Run?

Do not treat tool availability as a convenience setting. Treat it as an authorization system. A useful default is tiered permissions: auto-approve read-only operations such as file reads and code search, while requiring approval for writes, shell commands, network mutation, deployments, or access to sensitive systems. Explicitly configure policies for every tool that deserves review—the documented default for tools without a policy is enabled and auto-approved.

Headless automation deserves even stricter environmental design. If CI must let an agent run commands and edit files without a person clicking approve, isolate it: use a short-lived checkout or container, minimally scoped credentials, protected branches, output limits, timeouts, and an artifact containing the diff and event log. Autonomy should be earned by constraints and repeatable evaluation, not granted by a confident prompt.

When Should You Use Multi-Agent Workflows?

Use multi-agent workflows when work can be divided into genuinely independent streams, when specialists need different toolsets or instructions, or when a project spans sessions and needs a record of ownership and status. Do not use them merely because a task is difficult. Coordination adds model calls, latency, context-transfer loss, merge conflicts, and a new failure mode: agents can report progress without producing an integrated result.

The SDK distinguishes lightweight subagents from persistent teams. A subagent is parent-child delegation within one session: the parent asks for research or a bounded implementation, waits for the result, and continues. A team is intended for longer-running work: a coordinator assigns tasks to peers through a shared task board, retrieves results, and merges them; the team’s task board, mailbox, and mission history can persist across sessions.

How Do You Build a Multi-Agent Workflow That Actually Converges?

Make the coordinator responsible for integration, not just delegation. Give it the overall acceptance criteria, the authority to reject incomplete outputs, and a final validation step such as running tests, comparing the final diff to the plan, or invoking a dedicated review tool. Give each specialist a narrow charter and an output contract. “Investigate authentication” is ambiguous; “identify the existing session boundary, list affected modules, and return a migration plan with file paths and risks” is reviewable.

import { ClineCore } from "@cline/sdk"

const cline = await ClineCore.create({ clientName: "auth-workflow" })

const session = await cline.start({
  prompt: "Plan and implement authentication with tests.",
  config: {
    providerId: "your-provider",
    modelId: "your-model",
    apiKey: process.env.LLM_API_KEY,
    cwd: process.cwd(),
    workspaceRoot: process.cwd(),
    systemPrompt: [
      "You are the coordinator.",
      "Delegate repository research and test planning before edits.",
      "Integrate results, run validation, and report unresolved risks.",
    ].join(" "),
    enableTools: true,
    enableSpawnAgent: true,
    enableAgentTeams: true,
    teamName: "auth-sprint",
  },
})

This configuration enables the coordinator’s team tools for creating teammates, delegating work, checking task status, and retrieving completed results. In production, constrain who may spawn agents, cap concurrency and iterations, put a budget around each session, and persist enough telemetry to answer mundane but essential questions: which tool was called, with which input, what did it return, how much did it cost, and why did the coordinator accept the result?

How Do You Test and Evaluate an Agent Workflow?

Test the deterministic layers first. Unit-test each tool’s schema validation, authorization checks, happy path, error output, cancellation behavior, and side effects. Then create a small evaluation suite of representative repository states: a clean diff, a deliberately insecure change, a failing test, an unavailable dependency, and a request that should be refused. Assert on outcomes that matter to software engineering—valid structured output, test status, expected files changed, no forbidden command—not whether the agent’s prose sounds persuasive.

For multi-agent flows, evaluate the whole graph. Track whether the coordinator chose a sensible decomposition, whether specialist outputs were consumed, whether duplicate work occurred, and whether final verification caught conflicts. Start with a fixed model and prompt while you establish a baseline. Once the workflow is reliable, compare providers or models against that same task set rather than declaring success from a single impressive demo.

Build on the Cline SDK

For engineers who want this agent runtime rather than a blank orchestration loop, Cline describes its SDK as an open-source, free-to-use TypeScript runtime for embedding agents in applications, CI/CD, internal tools, and products. It includes modular packages, custom tools with Zod schemas, plugins and lifecycle hooks, model-provider flexibility, event streaming, MCP support, and the ClineCore facilities that make persistent sessions and multi-agent teams possible.

The broader Cline product is aimed at developers who want an AI coding agent in an editor, terminal, or their own software. The SDK itself is free and open source; model access can be supplied with your own provider credentials or local runtime. For developers who want Cline’s included open-weight model access instead, its optional ClinePass subscription is listed at $9.99 per month, with additional processing fees potentially applying. That separation is useful here: you can adopt the SDK’s workflow and safety primitives while choosing the model-access arrangement that fits your deployment.