---
title: Configuration Reference
description: Complete field reference for environment variables, Project config, Agent config, the Vault, and Schedules.
---

PenguinHarness configuration has three layers: environment variables shape the deployment, the Project config manages models and credentials, and the Agent config defines a single Agent's behavior. Each Agent additionally has two kinds of state files: the Vault (private environment variables) and Schedules (timed tasks).

## Environment variables

The CLI and the server automatically load a `.env` file from the working directory on startup.

| Variable | Description | Default |
| --- | --- | --- |
| `PENGUIN_HOME` | Data root directory | `~/.penguin/data` |
| `PORT` | Web service listen port | `7364` |
| `HOST` | Web service listen address | `127.0.0.1` |
| `PENGUIN_WEB_DB` | Server SQLite database path | `<root>/web.db` |
| `PENGUIN_WEB_DIST` | Front-end static assets directory | the npm server package falls back to its bundled web-dist |
| `PENGUIN_LANG` | CLI language (`en` / `zh`), set via `penguin config lang` | `en` |

### Provider credential variables

When a model entry has no inline `api_key`, the AgentHub gateway falls back to the provider's environment variable; the `*_BASE_URL` variants override the base URL the same way:

| Provider | API key | Base URL |
| --- | --- | --- |
| deepseek | `DEEPSEEK_API_KEY` | `DEEPSEEK_BASE_URL` |
| anthropic | `ANTHROPIC_API_KEY` | `ANTHROPIC_BASE_URL` |
| openai, openrouter, siliconflow, custom | `OPENAI_API_KEY` | `OPENAI_BASE_URL` |
| google | `GEMINI_API_KEY` | `GEMINI_BASE_URL` |
| zhipu | `ZAI_API_KEY` | `ZAI_BASE_URL` |
| moonshot | `MOONSHOT_API_KEY` | `MOONSHOT_BASE_URL` |

The openrouter, siliconflow, and custom groups speak the OpenAI-compatible protocol, hence the shared `OPENAI_*` variables. Provider groups and the built-in model catalog are covered in [Models & Providers](/models).

## Project config

`<root>/<project>/.project_config.toml` is the Project's single config file: a hidden file written with mode 0600, with credentials inlined on the model entries. Model identity is always the `(provider, model_id)` pair — string concatenation is forbidden everywhere, and every reference into this file carries both halves: the provider is never inferred from a bare `model_id`.

| Field | Description |
| --- | --- |
| `name` | Project display name (the id is shown when unset) |
| `default_model` | Paired reference `{ provider, model_id }` to the default model; must point to an entry in `models` |
| `vision_model` | The vision model that reads images on behalf of text-only models (used by `describe_image`); a paired reference |
| `[[models]]` | The list of available model entries |

Model entry (`[[models]]`) fields:

| Field | Description |
| --- | --- |
| `provider` | Provider group; together with `model_id` forms the entry's unique key |
| `model_id` | Upstream request id, sent to AgentHub unchanged |
| `context_window` | Context window size |
| `client_type` | AgentHub client protocol; inferred from `model_id` by default — third-party OpenAI-compatible models should set `openai` |
| `display_name` | Display name; persisted only when it differs from the built-in catalog |
| `vision` | Whether image input is supported; defaults to supported |
| `pricing` | Three price buckets `cache_read` / `cache_write` / `output`, in USD per million Tokens (`unit = "usd_per_mtok"`) |
| `api_key` | Inline credential; when empty, falls back to the provider environment variable |
| `base_url` | Custom base URL; preset for gateway models |
| `created_at` | Write timestamp of `api_key` (ISO 8601; a display field maintained by the interface layer) |

```toml
default_model = { provider = "deepseek", model_id = "deepseek-v4-pro" }

[[models]]
provider = "deepseek"
model_id = "deepseek-v4-pro"
context_window = 1000000
vision = false
api_key = "sk-..."

[models.pricing]
unit = "usd_per_mtok"
cache_read = 0.003571
cache_write = 0.428571
output = 0.857143
```

`pricing.unit` is currently always `usd_per_mtok` (USD per million tokens); the three buckets map onto `token_usage`'s three counters.

Edit this file via the CLI (`penguin config model …`) or the Web Models page — never by hand while the service is running, and never by the model itself, which has no right to read or write it.

## Agent config

`agent_state/system_config.yaml` defines a single Agent's behavior (YAML; comments are preserved when edited via the Web UI):

| Field | Default | Description |
| --- | --- | --- |
| `name` | — | Agent display name (falls back to the id) |
| `description` | — | Agent description |
| `version` | `1` | Agent State version (a natural number), incremented on each successful optimization |
| `system_prompt` | built-in template | Required; the only template with placeholder substitution |
| `max_turns` | `100` | Maximum LLM turns per Task |
| `model.max_tokens` | `32000` | Output Token limit per Request |
| `model.thinking_level` | `medium` | `none` / `low` / `medium` / `high` / `xhigh` |
| `model.timeoutMs` | `120000` | Per-Request timeout (milliseconds) |
| `compaction.max_context_length` | `128000` | Context Token threshold that triggers compaction |
| `compaction.max_session_turns` | `-1` | Cumulative Session turn threshold (`-1` = unlimited) |
| `compaction.mode` | `summarize` | `summarize` / `discard` |
| `compaction.prompt` | built-in template | Prompt used for summarize compaction |
| `tools.builtin` | full default toolset when omitted | Tool entries: `name` / `description` / `parameters` / `permission` (`r` or `rw`) / `forModel` / `timeoutMs` / `maxOutputLength`; once written it replaces the default list wholesale |
| `tools.mcpServers` | `[]` | MCP Server configuration (`name` + `config`); reserved for the MCP adapter layer |

Tool permissions and approval semantics are covered in [Tools & Approval](/tools).

A partial-override example (edit the file the init step generated). Note that this file is **not deep-merged with the defaults**: a key you write out takes effect wholesale, and only omitted keys fall back to the defaults above at their use sites; `system_prompt` is required (loading refuses without it), so keep the full generated template when editing other fields:

```yaml
name: default_agent
description: General-purpose agent
version: 3

# Required: keep the full generated default template ({{AGENTS_MD}} and friends; elided here).
system_prompt: |
  …

max_turns: 100

model:
  max_tokens: 32000
  thinking_level: medium
  timeoutMs: 120000

compaction:
  max_context_length: 128000
  max_session_turns: -1
  mode: summarize

# Omitting the whole tools section = the full default toolset. Writing tools.builtin
# REPLACES the default list wholesale: carry the complete definition (including the
# parameters JSON Schema) for every tool you keep — see Tools & Approval.
```

### System prompt placeholders

`system_prompt` is the only template with placeholder substitution. Available placeholders:

| Placeholder | Injected content |
| --- | --- |
| `{{AGENTS_MD}}` | Full text of `AGENTS.md` |
| `{{VAULT_KEYS}}` | List of Vault key names (names only) |
| `{{SKILL_METADATA}}` | Metadata of installed Skills |
| `{{PLATFORM}}` | Runtime platform |
| `{{OS_VERSION}}` | Operating system version |
| `{{DATE}}` | Current date |
| `{{PROJECT_DIR}}` | Project directory |
| `{{AGENT_ID}}` | Agent id |
| `{{CWD}}` | Workspace path |
| `{{PROVIDER}}` | Model provider group |
| `{{MODEL_ID}}` | Upstream model id |
| `{{SESSION_ID}}` | Session id |

`agent_state/AGENTS.md` is the developer-editable instruction file, injected via `{{AGENTS_MD}}` and empty by default — it is also the file an optimizer edits most (see [Self-Improvement](/self-improvement)).

## Vault

`agent_state/.vault.toml` is the Agent-level environment-variable vault: a hidden file written with mode 0600.

- Key names must match `^[A-Za-z_][A-Za-z0-9_]*$` (shell environment variable naming rules);
- Values are injected only into tool subprocess environments and never enter the model context or the Trace;
- Only key names are disclosed in the system prompt via `{{VAULT_KEYS}}`;
- Saving through the Web/API invalidates the Agent's cached Session runtimes: the next Task on any of its Sessions re-resumes and runs with the new values; a Task already in flight keeps the values it started with (a direct CLI file edit reaches a running server only when a Session is next created or resumed);
- Managed via `penguin config vault set/list/remove` or the Web Vault tab.

## Schedules

Each file `agent_state/schedule/<name>.toml` describes one scheduled task (the filename is its identity) that sends a preset Prompt to the Agent on a cadence. Schedules execute only while the Web service (the server runtime) is running, and are managed in the Web Agent settings → Schedule tab.

| Field | Required | Description |
| --- | --- | --- |
| `prompt` | yes | The Prompt sent on each trigger |
| `enabled` | no | Enabled switch; defaults to `false` |
| `start_at` | yes | First trigger time (ISO 8601) |
| `period` | no | Cadence such as `30m` / `12h` / `7d`, minimum 5 minutes; omitted means a one-shot task |
| `end_at` | no | End time; must be later than `start_at` |
| `session_id` | no | Bind to an existing Session; mutually exclusive with the three fields below |
| `workspace` | no | Workspace for new-Session mode |
| `provider` / `model_id` | no | Paired model reference for new-Session mode; write both or neither — a lone `model_id` is rejected, and with neither the Project's default model is used |

```toml
prompt = "Check yesterday's builds and summarize the failures"
enabled = true
start_at = 2026-08-01T09:00:00Z
period = "12h"
```

## Design principle

An Agent's behavior lives entirely in editable files on disk — prompts, Skills, and configuration are data, not code. That is what makes Agents improvable by Agents: an optimizer edits exactly the same files you edit by hand. See [Self-Improvement](/self-improvement) and the [CLI Reference](/cli).