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Copy file name to clipboardExpand all lines: README.md
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# Keiko
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Keiko is a local enterprise coding assistant for regulated engineering teams. It helps inspect a repository, chat with configured language models, generate reviewable unit tests, investigate bugs, run verification, and keep redacted evidence for human review.
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Keiko is a governed agentic workspace for knowledge work that learns from experience.
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Keiko is developer-controlled by design. It does not commit, push, open pull requests, merge code, or apply changes without an explicit local action. The manifest-producing surfaces emit redacted evidence for audit.
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The current npm release starts with local developer-assist workflows for regulated engineering teams. Keiko helps inspect a repository, chat with configured language models, generate reviewable unit tests, investigate bugs, run verification, and keep redacted evidence for human review.
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Keiko is human-controlled by design. It does not commit, push, open pull requests, merge code, or apply changes without an explicit local action. The manifest-producing surfaces emit redacted evidence for audit.
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## Vision
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Keiko's long-term direction is a governed workspace where people can delegate knowledge work to learning agents without giving up control, oversight, or accountability.
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-**Governed delegation:** agents start with a task, not standing rights.
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-**Harness-first control:** agent actions, tool calls, connector access, approvals, failures, and outcomes flow through one observable control layer.
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-**Keiko Twin:** a governed work representative that can build controlled memory about user preferences, project routines, accepted outcomes, and recurring corrections.
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-**Learning from experience:** Keiko should improve future tool selection, escalation, policy suggestions, and workflow quality from structured evidence and feedback.
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-**Enterprise boundaries:** learning can improve suggestions and routines, but it must never grant itself authority or bypass human and organizational policy.
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Software engineering is the first use case because repositories, tests, reviews, and tool calls create hard evidence. The product direction is broader: a controlled agentic workspace for enterprise knowledge work.
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## Requirements
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- Base URL, for example `https://llm-gateway.example.com/v1`
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- API token
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- Optional API-key header, only when your gateway admin provides a custom header
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- Deployment names, only when the gateway cannot expose a reliable model list
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Keiko calls the gateway model list endpoint, tests discovered chat models with a small chat-completions request, and stores only callable chat models in the local runtime configuration. LiteLLM-compatible gateways can also provide model metadata that lets Keiko filter non-chat models before testing. Credentials stay on the local machine and are not returned to the browser.
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Keiko calls the gateway model list endpoint, tests discovered chat models with a small chat-completions request, and stores only callable chat models in the local runtime configuration. Credentials stay on the local machine and are not returned to the browser.
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For OpenAI-compatible gateways such as LiteLLM, usually leave deployment names empty. For Azure AI Foundry, paste the deployment names you want Keiko to offer in the UI.
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The UI runs on loopback only. The `--host` option can validate a loopback host value; the server always binds `127.0.0.1`.
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Supported credential headers are `authorization`, `x-litellm-key`, `x-api-key`, and `api-key`.
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Do not commit gateway config files, API tokens, `.keiko/`, or evidence that contains project-specific review material unless your process explicitly requires it.
Copy file name to clipboardExpand all lines: docs/ui-runbook.md
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- Base URL, for example `https://llm-gateway.example.com/v1`
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- API token
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- Optional API-key header, only when your gateway admin provides a custom header
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- Deployment names, only when the gateway cannot expose a reliable model list
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Keiko tests the credentials immediately. It lists available models from the gateway, performs a small chat-completions smoke test, and saves only callable chat models in the local runtime configuration.
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Supported credential headers are `authorization`, `x-litellm-key`, `x-api-key`, and `api-key`.
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Keiko tests the credentials immediately. It lists available models from the gateway, uses LiteLLM-style model metadata when available, performs a small chat-completions smoke test, and saves only callable chat models in the local runtime configuration.
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For OpenAI-compatible gateways such as LiteLLM, usually leave deployment names empty. For Azure AI Foundry, paste the deployment names you want Keiko to offer in the UI.
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Environment variables are still supported for scripted starts. They are not a standalone UI configuration source when the UI needs to discover models for a first-time user.
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| Port conflict | Stop the conflicting process or start Keiko with another port. |
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| No model appears | Re-run Settings credential test and confirm the gateway exposes chat models. |
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| Credential test fails | Confirm the base URL points to an OpenAI-compatible API and that the token is valid. |
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| Custom proxy key fails | Confirm whether your gateway expects `Authorization` or a custom API-key header. |
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| Stop does not clean up | Ensure the process is gone, then remove `.keiko/ui.pid` and start again. |
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