Software Factory
Your whole Software Factory, run by governed agents.
Kimchi turns plan, code, review, test, deploy, and monitor into one continuous loop. Agents do the work. Your rules, models, and budgets stay in charge.
ticketKIM-482 Add rate limiting to /v1/chatokplanSpec drafted, 3 files, approved by @danaokcodeAgent on GLM wrote the middleware and configoktest12 tests added, all passingokreviewPR #918 reviewed: score 92, 1 warning fixedokdeployMerged, released through CIokmonitor$0.84 spent, within team budgetok
The loop
One loop, ticket to production and back.
Pick a stage to see what Kimchi does there.
Tickets become specs
Agents read the issue, the codebase, and past decisions, then draft a plan your team can approve before a line is written.
Kimchi CodingThe Kimchi stack
Every surface, every agent, one governed platform.
Work from the CLI, your editor, the browser or the desktop app. Kimchi's gateway, workspaces and inference run as SaaS or in your own infrastructure.
01 Surfaces- CLI
- VS Code plugin
- Web IDE
- Kimchi desktop app
02 Agents- Coding agents
- Review agents
- Test agents
- Your custom agents
- Sales agents
- Marketing agents
- Support agents
- Ops agents
03 Kimchi platformKimchi
GatewayControl plane
- Auto-router
- Policies
- Budgets
- Secrets
- MCP Gateway
- Audit trail
Runs in Kimchi cloud, EU region
WorkspacesRemote workspaces
- Persistent cloud dev environments
- Teleport sessions
- Remote plan execution
- Secrets brokering
- In-region isolation
- GPU workspacesSoon
Runs in Kimchi cloud, in your region
InferenceInference
- GLM, MiniMax, Kimi
- Dedicated GPUs
- EU hosting
- OpenAI-compatible API
- No training on your data
- Bring your own providerSoon
Runs on Kimchi GPUs in Paris
Each component deploys on its own terms. For example, self-host the gateway and keep inference on Kimchi.
04 Your tools- Git
- CI / CD
- Jira and Slack
- CRM
- Your VPC
Capabilities
What you get, stage by stage.
From the agent and spend control to the remote, reviewed, secured layer teams need to run agents at scale.
Capability | What it is |
|---|---|
Work anywhere | |
| Kimchi agent (harness) | Plans, edits and runs code, and handles long multi-step work on any model your admin allows. |
| CLI | The full Kimchi agent in your terminal, with live cost and status. |
| IDE: VS Code, JetBrains, Zed | Use Kimchi in the editor you already have. |
| Kimchi Studio | Desktop app that creates docs, decks and sheets, and connects to Slack, Atlassian and any MCP server. |
Models and spend control | |
| Kimchi AI Gateway | One endpoint for Kimchi's open-weight models plus your Anthropic, OpenAI or Copilot accounts. |
| Open-weight models hosted in the EU | GLM, Kimi, DeepSeek and others on dedicated GPUs in Paris, with no third-party API in the path. |
| Auto-router | Picks the best model for each task automatically. |
| Budgets and model access control | Budget caps per user, team and org, plus control over which models each team can use. |
| Cross-tool spend visibility | AI spend by user, team, model and project across every tool on the gateway. |
| Admin policy enforcement | Enforce permission modes (for example block yolo mode) across CLI, Studio and IDEs. |
Scale and secure | |
| Secure, in-region remote workspaces | Persistent cloud dev environments for developers and agents. |
| Teleport and remote execution | Move a session to the cloud, or run an approved plan remotely. |
| Code Reviewer | AI review on every merge request or pull request. |
| MCP Gateway | One governed entry point for every MCP server your agents use, with access policies, secrets and audit logs. |
| Secrets brokering | Agents use your credentials without secrets ever being placed in the workspace. |
| Cost per PR and ROI reporting | Prove the return on AI spend, PR by PR. |
| PII redaction | Detect and redact personal data in prompts before it reaches a model. |
| Self-hosted Kimchi | Run Kimchi in your own cloud or data center, with an air-gapped option. |
| Priority support | Dedicated support channel and faster response times. |
| Coming soon | |
| Scheduled and event-triggered agents | Agents that start on a schedule or from Jira, GitHub or Slack events. |
| GPU workspaces | Remote workspaces with GPUs for ML and inference work. |
| Bring your own model provider | Connect your own OpenAI-compatible inference endpoint. |
| Managed MCP connectors in Studio | Ready-made connectors for common work apps, managed centrally. |
| SSO, SCIM and audit logs | Single sign-on, user provisioning and audit trails for enterprise identity. |
Governed by default
Automation you can actually sign off on.
Open models
GLM, MiniMax, Kimi, routed per task.
Your infrastructure
Run in your VPC. Your code is never training data.
Spend limits
Budgets per team, agent, and run. Hard caps, not alerts.
Audit trail
Every agent action and model call recorded.