Cline vs Hexis: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Cline and Hexis — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Cline
Cline Bot Inc
Open-source coding agent runtime that runs in your IDE, your terminal or embedded via SDK, works with any model, and asks approval on every step.
Key features
- One runtime, three surfaces: The same agent runs as a VS Code extension, a terminal CLI, or embedded in your own product through the SDK
- Model agnostic: Works with Claude, GPT, Gemini, local Ollama or LM Studio models and any OpenAI-compatible endpoint, using your own key or weights
- Plan then Act: Align on a strategy in Plan mode before execution, then approve each step in Act mode or flip on auto-approve
- Multi-file edits with undo: Coordinated changes across a project with linter-aware fixes, diffs, checkpoints and one-click undo on every step
- Live terminal execution: Runs bash commands and reacts to output as it appears, handling dev servers, test runs and deploys
- Rules and Skills: Ship .clinerules with the repo so the agent follows your coding standards, architecture and deployment conventions
- Multi-agent teams: Coordinator agents delegate to specialists with their own tools and context, and can run on cron for recurring automation
- MCP and integrations: Register MCP servers and custom tools, chat from Slack, Discord, Telegram or Linear, and run headlessly in GitHub Actions or GitLab
Best for
- A developer onboarding to an unfamiliar codebase and asking the agent how files, dependencies and behaviour fit together
- Refactoring across a large repository while keeping imports, types and behaviour consistent
- Running recurring maintenance — dependency bumps, lint sweeps, scheduled checks — from cron or a CI pipeline
- A team that must keep code on self-hosted or local models for compliance reasons, pointing the agent at its own endpoint
- Embedding an agent loop inside an internal developer platform via the SDK instead of building one from scratch
- Encoding team conventions in .clinerules so every engineer's agent produces consistent, review-ready changes
- Triggering a coding task from Slack or Linear and having the agent open the resulting change
H
Hexis
Bevelites GmbH
Open-source git-backed control plane for enterprise AI agents — context, skills, tools and permissions as files in your own repo, served over MCP.
Key features
- Git-backed source of truth: Context, skills, tools, permissions, and agent identities live as Markdown/YAML files in your own repository — reviewed, branched, and diffed like any other code.
- Typed knowledge with provenance: Every fact is a typed node with source, owner, and last verification date, compiled into a graph you can traverse, mass-update, and dashboard.
- Markdown skills, not prompt fragments: Procedures live as readable Markdown files that domain owners can review, instead of prompt snippets buried in a vendor config.
- Tool manifests with vaulted secrets: Declare each tool once, hold secrets in a vault, and set file-level access rules that say which agent may read which file and call which endpoint.
- Per-agent identity: Each agent is a named actor with its own credentials and scope — no shared service accounts, and every action is attributable to a specific agent.
- Any runtime via MCP or UTCP: Serves the same governed surface to Claude Code, Cursor, ChatGPT, opencode, background agents, in-platform agents, and self-hosted models.
- Open-source Apache-friendly release: Hexis is the OSS core; Bevel (paid) adds hosted services, integrations, and enterprise support on top.
Best for
- Enterprises running AI agents across multiple vendors (Claude, Cursor, ChatGPT) who want context and skills defined once in their own infrastructure instead of re-uploaded per vendor.
- Procurement, GTM, and RFI teams building specialised agents (e.g., tender desks, campaign agents) on top of a shared reusable knowledge layer with provenance.
- Platform teams that need per-agent identities and file-level access controls so every tool call is attributable and destructive endpoints are gated by review.
- Companies wary of vendor lock-in who want a portable spec (MCP/UTCP) so they can switch agent runtimes without rebuilding context, skills, and tool wiring.
- Engineering leaders who want the AI 'operating manual' reviewed in git diffs and change requests instead of edited inside a black-box vendor console.
- Open-source-first teams evaluating a control plane locally with Hexis before committing to Bevel's hosted platform for larger deployments.