Cline vs Continue vs OpenHands: Comparing Coding Agents for AI-Assisted Development

Cline delivers lightweight IDE-embedded refactoring through a local Go daemon, Continue enables version-controlled prompt automation via JSON rule files, and OpenHands operates as a full-stack autonomous platform with sandboxed execution and iterative testing loops.

The owainlewis/awesome-artificial-intelligence repository categorizes these three tools as essential open-source coding agents—CLI and IDE extensions that leverage large language models (LLMs) to write, refactor, and execute code. While they share the common goal of AI-assisted development, their architectures range from lightweight editor plugins to autonomous software engineering platforms.

Architecture and Design Philosophy

Each agent adopts a distinct architectural approach to LLM integration, from local daemons to containerized autonomous loops.

Cline: Editor-Centric Local Daemon

Cline functions as a lightweight IDE extension supported by a minimal Go-based daemon. According to the source analysis, the architecture consists of a frontend editor extension (VS Code or Neovim) communicating with a tiny backend process that forwards prompts to a single LLM endpoint. The model abstraction supports OpenAI, Anthropic, and other providers through optional custom adapters, making it feel like a native refactoring tool.

Continue: Rule-Driven Configuration Engine

Continue operates as an IDE-plus-CLI hybrid that stores interaction logic in source-controlled JSON or YAML files. The backend runs on Node.js and interprets a user-editable rule engine—typically defined in .continue.json—that maps intents (such as "refactor function") to specific LLM prompts and post-processing steps. This explicit configuration allows teams to standardize LLM behavior across repositories.

OpenHands: Autonomous Full-Stack Platform

OpenHands implements a persistent agent server built in Python that orchestrates multiple LLM calls within sandboxed containers. The architecture includes a React-based web UI with a terminal emulator, a backend server that manages iterative code generation, and a Git-compatible state store that tracks commits. Unlike the other two agents, OpenHands maintains a feedback loop using unit-test failures and lint warnings to drive subsequent iterations until task completion.

Workflow Comparison

Understanding how each agent handles typical coding tasks reveals their operational differences.

Cline Workflow: In-Place Editing

Cline optimizes for minimal latency and rapid feedback. The typical interaction follows this pattern:

1. Place cursor on target code within your editor
2. Press the default shortcut (Ctrl+Shift+L)
3. Select an action (e.g., "Refactor")
4. The Go daemon transmits the snippet and instruction to the LLM
5. Receive and apply edits in-place immediately

This workflow emphasizes single-shot transformations without project-wide context management.

Continue Workflow: Rule-Based Execution

Continue requires upfront configuration but offers reproducible automation:

1. Define tasks in .continue.json with specific prompts and models
2. Invoke the agent via CLI: continue run --file src/main.py --task "Add docstring"
3. The Node.js backend loads the rule set and processes the LLM response
4. Optional tool calls execute (e.g., git diff, npm install) based on rule definitions
5. Apply the generated diff to the codebase

This approach suits teams requiring auditable and version-controlled LLM interactions.

OpenHands Workflow: End-to-End Autonomy

OpenHands manages complete software engineering tasks through iterative loops:

1. Start the server: openhands serve (requires Docker)
2. Submit high-level tasks: openhands create-task --name "Implement a FastAPI CRUD service"
3. The Python server spawns a worker that writes files, runs tests, and analyzes failures
4. The agent iteratively rewrites code based on test feedback
5. Upon success, the system creates a commit with all changes

This workflow supports autonomous planning, coding, testing, and debugging without human intervention.

Implementation Examples

The following snippets demonstrate minimal command-line interactions for each agent, assuming installation from the repositories listed in awesome-artificial-intelligence (lines 1002-1004).

Cline Implementation

Trigger refactoring through the VS Code extension:

# Open VS Code, place the cursor on a function you want to refactor,

# then press the default shortcut (Ctrl+Shift+L).  

# The extension will open a quick-pick menu; choose "Refactor".

Programmatic access via the Go daemon (pseudo-code):

// Example of invoking Cline's daemon directly
import "github.com/cline/cline/client"

func main() {
    resp, _ := client.Edit(
        client.EditRequest{
            Model:   "anthropic/claude-2",
            Prompt:  "Add type hints to this Python function",
            Snippet: "def add(a,b): return a+b",
        })
    fmt.Println(resp.EditedSnippet)
}

Continue Implementation

Execute a configured task from the command line:

# Run the agent on the current file using the rule set in .continue.json

continue run --file src/main.py --task "Add docstring"

Example .continue.json configuration:

{
  "tasks": {
    "Add docstring": {
      "prompt": "Write a concise docstring for the following Python function:",
      "model": "openai/gpt-4o",
      "postprocess": "apply_patch"
    }
  }
}

OpenHands Implementation

Launch the autonomous server and create tasks:

# Start the OpenHands server (requires Docker)

openhands serve

# Submit a high-level task; the agent will generate a repo and iterate.

openhands create-task --name "Implement a FastAPI CRUD service"

Typical output from the autonomous loop:

[Step 1] Generated file app/main.py
[Step 2] Ran pytest – 3 failures
[Step 3] Updated app/models.py to fix schema
[Step 4] All tests passed – commit created

Key Repository References

Direct access to source implementations reveals the technical foundations of each agent:

• Cline Repository: https://github.com/cline/cline — Contains the Go daemon and editor-extension source; demonstrates the lightweight client-server model.
• Continue Rule File: https://github.com/continue-dev/continue/blob/main/.continue.json — Example of the JSON rule engine driving prompts and tool calls.
• OpenHands Server: https://github.com/all-hands-dev/openhands — Core Python server implementing autonomous loops, sandboxing, and multi-model orchestration.
• Awesome-AI README: https://github.com/owainlewis/awesome-artificial-intelligence/blob/master/README.md — The curated list that introduced these three agents (lines 1002-1004).

Summary

• Cline provides the lowest latency for single-shot edits via a local Go daemon, ideal for rapid in-editor refactoring but limited to simple transformations without project-wide feedback loops.
• Continue offers explicit, version-controlled configuration through JSON rule files, making it optimal for teams requiring reproducible LLM behavior and custom tool integrations, though it requires maintaining configuration overhead.
• OpenHands delivers full-stack autonomy with sandboxed execution and iterative testing, suitable for complex software engineering tasks, but demands greater resources (Docker containers, Python server) and longer startup times.

Frequently Asked Questions

Which coding agent is best for quick refactoring tasks?

Cline excels at rapid refactoring because its Go-based daemon runs locally with minimal latency, providing immediate in-editor edits that feel like native IDE commands. It requires no configuration files or container setup, making it the fastest option for single-shot transformations.

How does Continue's rule-based approach differ from Cline's single-provider model?

Continue uses explicit JSON configuration files (.continue.json) that version-control prompts, temperature settings, and post-processing steps, enabling team-wide consistency. Cline uses a simpler plug-in system focused on single LLM endpoints without persistent rule management, prioritizing speed over configurability.

Can OpenHands handle entire software engineering projects autonomously?

Yes, OpenHands operates as a full-stack platform that can plan, code, test, and debug without human intervention. Its Python server manages multi-step workflows in sandboxed containers, using test failures and lint warnings to iteratively improve code until tasks complete, though this autonomy requires significant computational resources.

What are the system requirements for running these coding agents?

Cline requires only a Go runtime and editor extension (VS Code/Neovim), making it the lightest. Continue needs Node.js and functions as both CLI and IDE extension. OpenHands demands Docker for containerized execution and a Python server environment, consuming the most resources but providing the highest level of autonomy.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}