EGC vs Kilo

EGC and Kilo are both coding assistants tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

EGC

EGC is a local-first MCP runtime that persists memory across sessions and across AI tools, so agents pick up exactly where the last session stopped. The repo structure shows explicit support for Cursor, Codex, Gemini, Kiro, Trae, and OpenCode, meaning the memory layer sits beneath whichever assistant you switch to. The system tracks completed tasks, failures, and next steps automatically — you do not write the handoff notes. The wall appears when you need a hosted or API-accessible version: the vendor describes no hosted runtime, no remote API, and no paid tier, so teams requiring cloud-accessible memory or multi-user session state have nowhere to go within this tool.

Kilo

Kilo Code is an open-source (Apache 2.0) coding agent that runs inside VS Code, JetBrains IDEs, and the CLI, with cloud agent and Slack options on top. It ships five specialized modes — Code, Architect, Debug, Ask, and Custom — so you're not forcing a general-purpose chat model to plan a feature and then write it in the same session. The 500+ model catalog routes through Kilo Gateway at zero markup, which means your token bill reflects actual model pricing. That architecture holds up well for single-developer workflows and small teams. Where it gets complicated is at the org level: team-wide parallel workflows using isolated agent worktrees are a newer surface, and community reports suggest the tooling around coordinating those agents is still maturing.

Attribute	EGC	Kilo
Pricing	Free	Paid
Price	—	Free (extension); Kilo Pass $19–$199/month (credits); KiloClaw $55/month (cloud agent)
Free trial	No	14 days
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	Local / desktop	VS Code, JetBrains (IntelliJ, PyCharm, WebStorm), CLI, Cloud Agents, Slack, Cursor, Windsurf
Released	—	2025-03
Pros	Zero-prompt memory restoration on session start, which means developers stop spending the first part of every session re-explaining project state to an agent that forgot everything. Single memory layer spanning multiple AI coding assistants — Cursor, Codex, Gemini, Kiro, Trae, and OpenCode are all covered — so switching tools mid-project does not fragment your context into incompatible silos. Automatic tracking of completed tasks, failures, and next steps, which means the handoff document you never wrote still exists when you return after two weeks away. MIT license with a local-first runtime and self-hosted option, so your project memory never touches an external server and the tool cannot be deprecated behind a paywall. Install scripts and pre-built agent configuration files ship with the repo, which means the integration surface for supported tools is a config file change rather than a custom integration build.	Zero-markup model routing across 500+ providers, so your token cost reflects actual model pricing and switching models when costs spike is a config change rather than a platform migration. Five specialized agent modes (Code, Architect, Debug, Ask, Custom) split planning from execution, so you're not asking the same agent session to design an architecture and then write the implementation — context stays focused. Apache 2.0 core with self-hosted and air-gap deployment options, which means organizations with data residency requirements can run the agent without sending code to external infrastructure. BYOK support across 20+ providers according to the docs, so teams with existing enterprise model agreements don't pay a second time through the platform. KiloClaw managed cloud agents deploy without SSH, Docker, or yaml configuration, so teams that want 24/7 autonomous task execution don't need to maintain that infrastructure themselves.
Cons	No hosted runtime and no API surface: any workflow that requires memory to be accessible from a remote server, a CI pipeline, or a second developer's machine has no path forward inside EGC. Teams needing shared or cloud-accessible session state have to build their own persistence layer or switch to a tool that offers one. With 17 open GitHub issues and no paid support tier, production bugs in edge cases — unsupported assistant versions, memory corruption on interrupted sessions, schema mismatches after updates — land entirely on the team to diagnose and fix. Teams that cannot absorb that maintenance overhead typically move to a commercially supported memory layer. Coverage is limited to the AI coding assistants explicitly wired into the repo. If your tool of choice lacks a configuration directory in the project, memory persistence does not apply to it, and adding support requires contributing to the project or maintaining a fork.	Multi-agent parallel workflows using isolated worktrees are documented as a feature, but the tooling for coordinating agents across a shared codebase is less mature than the single-developer IDE flow — teams hitting this at scale report needing to build their own coordination layer on top. The five-mode system requires you to consciously switch contexts between planning and execution. Teams that want a single agent to move fluidly from architecture to implementation without manual mode switching find this model adds friction, and at that point tools with a more unified agent loop become the alternative they evaluate. KiloClaw (the managed cloud agent layer) is a paid-only feature, meaning teams that want the 'deploy in 60 seconds, no infrastructure' path are outside the free tier — the self-hosted option requires enough DevOps capacity to stand it up.

Bottom line

EGC is free while Kilo is paid; EGC is open source; only Kilo exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between EGC and Kilo?

EGC is Free and open source, while Kilo is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is EGC better than Kilo?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.

EGC vs Kilo: which should I pick?

Pick EGC if its pricing model, openness, or platform fit matches your constraints; pick Kilo otherwise. Check free-trial availability on each listing if you want to test before committing.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.