openclaude/docs/advanced-setup.md

OpenClaude Advanced Setup

This guide is for users who want source builds, Bun workflows, provider profiles, diagnostics, or more control over runtime behavior.

Install Options

OpenClaude requires Node.js >=22.0.0 for npm installs and runtime. Bun is only required when building or running from source.

Option A: npm

npm install -g @gitlawb/openclaude@latest

Option B: From source with Bun

Use Bun 1.3.13 or newer for source builds. Older Bun versions can fail during bun run build.

git clone https://github.com/Gitlawb/openclaude.git
cd openclaude

bun install
bun run build
npm link

Option C: Run directly with Bun

git clone https://github.com/Gitlawb/openclaude.git
cd openclaude

bun install
bun run dev

Provider Examples

OpenAI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=sk-...
export OPENAI_MODEL=gpt-4o

Codex via ChatGPT auth

codexplan maps to GPT-5.5 on the Codex backend with high reasoning. codexspark maps to GPT-5.3 Codex Spark for faster loops.

If you use the in-app provider wizard, choose Codex OAuth to open ChatGPT sign-in in your browser and let OpenClaude store Codex credentials securely.

If you already use the Codex CLI, OpenClaude reads ~/.codex/auth.json automatically. You can also point it elsewhere with CODEX_AUTH_JSON_PATH or override the token directly with CODEX_API_KEY.

If you set CODEX_API_KEY manually and are not relying on auth.json or stored Codex OAuth credentials, also set CHATGPT_ACCOUNT_ID (or CODEX_ACCOUNT_ID).

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_MODEL=codexplan

# optional if you do not already have ~/.codex/auth.json
export CODEX_API_KEY=...
export CHATGPT_ACCOUNT_ID=...

openclaude

DeepSeek

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=sk-...
export OPENAI_BASE_URL=https://api.deepseek.com/v1
export OPENAI_MODEL=deepseek-v4-flash

Use deepseek-v4-pro when you want the stronger model. deepseek-chat and deepseek-reasoner remain available as DeepSeek's legacy API aliases.

Google Gemini

export CLAUDE_CODE_USE_GEMINI=1
export GEMINI_API_KEY=...
export GEMINI_MODEL=gemini-3-flash-preview

Claude on Vertex AI

The Vertex route uses Anthropic's Claude-on-Vertex API. It is not a general Vertex AI Model Garden adapter for Gemini or arbitrary partner models; use the Gemini provider for Gemini models and OpenAI-compatible routes for compatible third-party gateways.

Authentication uses Google Application Default Credentials through google-auth-library. There is no OPENAI_API_KEY-style API key for this route. For global npm installs, install the auth package on demand (it is not bundled by default — see Optional provider packages):

npm i -g google-auth-library

Authenticate with either local Application Default Credentials (ADC) or a service-account key file:

# Option 1 — local ADC (interactive, uses your own Google account):
gcloud auth application-default login

# Option 2 — service-account key file (headless / CI):
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

Minimal setup:

export CLAUDE_CODE_USE_VERTEX=1
export ANTHROPIC_VERTEX_PROJECT_ID=my-gcp-project
export GOOGLE_CLOUD_PROJECT=my-gcp-project
export CLOUD_ML_REGION=us-east5

openclaude --model claude-sonnet-4-6

CLOUD_ML_REGION is optional and defaults to us-east5. Model-specific Vertex region override variables are also supported for Claude models; see src/utils/envUtils.ts for the current override names.

Gemini via OpenRouter

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=sk-or-...
export OPENAI_BASE_URL=https://openrouter.ai/api/v1
export OPENAI_MODEL=google/gemini-2.5-pro

OpenRouter model availability changes over time. If a model stops working, try another current OpenRouter model before assuming the integration is broken.

Ollama

ollama pull llama3.3:70b

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:11434/v1
export OPENAI_MODEL=llama3.3:70b

Ollama Context Length

OpenClaude sends the current conversation history to Ollama on each turn and uses Ollama's native chat API for Ollama endpoints. Native chat lets OpenClaude send options.num_ctx with each request, so Ollama receives a 32768-token context window by default instead of falling back to the smaller context often used by Ollama's OpenAI-compatible /v1/chat/completions shim.

To choose a different request-level context size, set OPENCLAUDE_OLLAMA_NUM_CTX before launching OpenClaude:

export OPENCLAUDE_OLLAMA_NUM_CTX=65536

You can also start Ollama with a global context length:

macOS / Linux:

# Stop any existing Ollama app/server first, then run:
OLLAMA_CONTEXT_LENGTH=32768 ollama serve

Windows PowerShell:

# Quit any existing Ollama app/server first, then run:
$env:OLLAMA_CONTEXT_LENGTH="32768"
ollama serve

After a chat request, verify the loaded model is using the requested context:

ollama ps

Check the CONTEXT column. If it still shows a small value such as 4K after a new OpenClaude request, stop the existing Ollama app/server, start it again, and retry the request.

Use a concrete recall test after changing the setting, such as asking the model to repeat the first topic from the current chat. Questions like "do you remember our conversation?" can trigger generic local-model disclaimers even when history is present.

Atomic Chat (local, Apple Silicon)

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://127.0.0.1:1337/v1
export OPENAI_MODEL=your-model-name

No API key is needed for Atomic Chat local models.

Or use the profile launcher:

bun run dev:atomic-chat

Download Atomic Chat from atomic.chat. The app must be running with a model loaded before launching.

LM Studio

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=http://localhost:1234/v1
export OPENAI_MODEL=your-model-name

Together AI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=...
export OPENAI_BASE_URL=https://api.together.xyz/v1
export OPENAI_MODEL=meta-llama/Llama-3.3-70B-Instruct-Turbo

Groq

export CLAUDE_CODE_USE_OPENAI=1
export GROQ_API_KEY=gsk_...
export OPENAI_BASE_URL=https://api.groq.com/openai/v1
export OPENAI_MODEL=llama-3.3-70b-versatile

GROQ_API_KEY matches the built-in Groq gateway preset. OPENAI_API_KEY also works as a fallback on the generic OpenAI-compatible path, but GROQ_API_KEY is the preferred variable for Groq-specific setup.

OpenCode Zen (pay-as-you-go)

export CLAUDE_CODE_USE_OPENAI=1
export OPENCODE_API_KEY=...
export OPENAI_BASE_URL=https://opencode.ai/zen/v1
export OPENAI_MODEL=gpt-5.4

openclaude

OpenCode Zen is a pay-as-you-go AI gateway with 48 models (GPT, Claude, Gemini, Qwen, MiniMax, GLM, Kimi, Grok, Big Pickle, DeepSeek, Nemotron). Uses the same OPENCODE_API_KEY as OpenCode Go. Get your key from https://opencode.ai.

OpenCode Go (subscription)

export CLAUDE_CODE_USE_OPENAI=1
export OPENCODE_API_KEY=...
export OPENAI_BASE_URL=https://opencode.ai/zen/go/v1
export OPENAI_MODEL=glm-5.1

openclaude

OpenCode Go is a $10/mo subscription for 13 open models (GLM, Kimi, DeepSeek, MiMo, MiniMax, Qwen). Uses the same OPENCODE_API_KEY as OpenCode Zen.

Gitlawb Opengateway

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_BASE_URL=https://opengateway.gitlawb.com/v1
export OPENGATEWAY_API_KEY=ogw_live_...
export OPENAI_MODEL=mimo-v2.5-pro

The Opengateway route is the fresh-install startup default and requires an API key from https://gitlawb.com/opengateway/keys. Keep the base URL at /v1 and switch models with /model or OPENAI_MODEL. Current partner models include:

• mimo-v2.5-pro
• google/gemini-3.1-flash-lite-preview

Xiaomi MiMo

export CLAUDE_CODE_USE_OPENAI=1
export MIMO_API_KEY=...
export OPENAI_BASE_URL=https://api.xiaomimimo.com/v1
export OPENAI_MODEL=mimo-v2.5-pro

The /provider Xiaomi MiMo preset uses the same endpoint and stores the key as MIMO_API_KEY. OPENAI_API_KEY also works as a compatibility fallback, but MIMO_API_KEY keeps the profile tied to the MiMo route.

NEAR AI

export CLAUDE_CODE_USE_OPENAI=1
export NEARAI_API_KEY=...
export OPENAI_BASE_URL=https://cloud-api.near.ai/v1
export OPENAI_MODEL=anthropic/claude-sonnet-4-6

openclaude

NEAR AI is a unified OpenAI-compatible gateway that proxies Anthropic, OpenAI, and Google models alongside TEE-hosted open models (GLM 5.1, Qwen3.5, Kimi K2.6). All models are accessible from a single endpoint with one API key. Get your key from https://cloud.near.ai/dashboard/organizations.

Model IDs use provider/model-name format (e.g. anthropic/claude-opus-4-7, openai/gpt-5.5, google/gemini-3.5-flash, zai-org/GLM-5.1-FP8).

For direct TEE completions (lower latency, verifiable privacy):

export OPENAI_BASE_URL=https://qwen35-122b.completions.near.ai/v1

Cloudflare Workers AI

export CLAUDE_CODE_USE_OPENAI=1
export CLOUDFLARE_API_TOKEN=...
export OPENAI_BASE_URL=https://api.cloudflare.com/client/v4/accounts/<ACCOUNT_ID>/ai/v1
export OPENAI_MODEL=@cf/meta/llama-3.3-70b-instruct-fp8-fast

Replace <ACCOUNT_ID> with your Cloudflare account id (visible in the Cloudflare dashboard URL). OPENAI_API_KEY also works as a compatibility fallback, but CLOUDFLARE_API_TOKEN keeps the profile tied to the Cloudflare preset. The /provider Cloudflare Workers AI preset stores the token under CLOUDFLARE_API_TOKEN.

Mistral

export CLAUDE_CODE_USE_MISTRAL=1
export MISTRAL_API_KEY=...
export MISTRAL_MODEL=devstral-latest

Azure OpenAI

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=your-azure-key
export OPENAI_BASE_URL=https://your-resource.openai.azure.com/openai/deployments/your-deployment/v1
export OPENAI_MODEL=gpt-4o

Microsoft Foundry / Azure OpenAI (resource URL + deployment)

When your endpoint is the resource base URL (not the full .../deployments/.../v1 path), set OPENAI_MODEL to the deployment name and AZURE_OPENAI_API_VERSION to your API version. The OpenAI shim builds:

{base}/openai/deployments/{OPENAI_MODEL}/chat/completions?api-version={AZURE_OPENAI_API_VERSION}

and sends the key in the api-key header for Azure hosts.

export CLAUDE_CODE_USE_OPENAI=1
export OPENAI_API_KEY=your-azure-key
export OPENAI_BASE_URL=https://your-resource.openai.azure.com
export OPENAI_MODEL=your-deployment-name
export AZURE_OPENAI_API_VERSION=2024-12-01-preview

If your hostname is not detected as Azure (for example some inference endpoints), force Azure URL and header behavior:

export OPENAI_AZURE_STYLE=1

Fireworks AI

Fireworks AI provides a fully OpenAI-compatible endpoint. Model IDs use the full path format accounts/fireworks/models/<model-name>.

export CLAUDE_CODE_USE_OPENAI=1
export FIREWORKS_API_KEY=fw_your_key_here
export OPENAI_BASE_URL=https://api.fireworks.ai/inference/v1
export OPENAI_MODEL=accounts/fireworks/models/llama-v3p1-70b-instruct

The OpenClaude VS Code extension can store the key in Secret Storage and set these variables for you when you launch from the Control Center. See vscode-extension/openclaude-vscode/README.md.

Optional provider packages

To keep the default npm i -g @gitlawb/openclaude install small and warning-free, a few provider SDKs and the native image library are not bundled. They are loaded on demand, and the CLI prints an npm install <pkg> hint (add -g for the global CLI) if you enable a feature whose package is missing. Install only what you need:

Feature	Trigger	Install
AWS Bedrock	CLAUDE_CODE_USE_BEDROCK=1	npm i -g @anthropic-ai/bedrock-sdk. Profile-based auth (~/.aws/credentials) additionally needs @aws-sdk/credential-providers and @aws-sdk/client-sts; model listing needs @aws-sdk/client-bedrock. Proxy and skip-auth setups may also need @aws-sdk/credential-provider-node, @smithy/node-http-handler, or @smithy/core. The CLI prints the exact missing package if you hit one.
Azure Foundry	CLAUDE_CODE_USE_FOUNDRY=1	npm i -g @anthropic-ai/foundry-sdk @azure/identity
Claude on Vertex AI / Gemini ADC	CLAUDE_CODE_USE_VERTEX=1 / Gemini ADC auth	npm i -g google-auth-library
Reading/processing images	reading an image file	npm i -g sharp

When installing OpenClaude from source (bun install), all of these are already present as dev dependencies, so source/dev builds need no extra steps.

Environment Variables

Variable	Required	Description
CLAUDE_CODE_USE_OPENAI	OpenAI-compatible only	Set to 1 to enable the OpenAI-compatible provider path
OPENAI_API_KEYS	One of OPENAI_API_KEYS or OPENAI_API_KEY for non-local OpenAI-compatible cloud routes*	Comma-separated OpenAI-compatible API key pool. Takes precedence over OPENAI_API_KEY and rotates to the next key on auth, quota, or rate-limit failures (* not needed for local models like Ollama, LM Studio, Atomic Chat, or other local OpenAI-compatible proxies).
OPENAI_API_KEY	Required only when OPENAI_API_KEYS is unset or empty for non-local OpenAI-compatible cloud routes*	Your API key (* not needed for local models like Ollama, LM Studio, Atomic Chat, or other local OpenAI-compatible proxies). A comma-separated list also enables key rotation.
OPENAI_MODEL	OpenAI-compatible only	Model name such as gpt-4o, deepseek-v4-flash, or llama3.3:70b
OPENAI_BASE_URL	No	API endpoint, defaulting to https://api.openai.com/v1
OPENAI_API_BASE	No	Compatibility alias for OPENAI_BASE_URL
OPENCLAUDE_OLLAMA_NUM_CTX	Ollama only	Request-level Ollama context window. Defaults to 32768; set a larger value for longer same-session history if your model and hardware can handle it.
CLAUDE_CODE_OPENAI_CONTEXT_WINDOWS	No	JSON map of OpenAI-compatible model names to context windows, such as {"custom-model":1000000}. Use this when a custom provider does not expose context metadata from /v1/models.
CLAUDE_CODE_OPENAI_MAX_OUTPUT_TOKENS	No	JSON map of OpenAI-compatible model names to max output tokens, such as {"custom-model":32768}. Use this when a custom provider does not expose output-limit metadata from /v1/models.
OPENCODE_API_KEY	OpenCode Zen / Go	Shared API key for OpenCode Zen (pay-as-you-go) and OpenCode Go (subscription); get yours from https://opencode.ai
MIMO_API_KEY	Xiaomi MiMo route	Xiaomi MiMo API key for https://api.xiaomimimo.com/v1; mirrored into the OpenAI-compatible auth env when the MiMo route is active
CLAUDE_CODE_USE_GEMINI	Gemini only	Set to 1 to enable the direct Gemini provider path
GEMINI_API_KEY / GOOGLE_API_KEY	Gemini API-key auth	Gemini API key for direct Gemini setup
GEMINI_MODEL	Gemini only	Model name such as gemini-3-flash-preview or gemini-2.5-pro
GEMINI_BASE_URL	No	Override the Gemini base URL
CLAUDE_CODE_USE_MISTRAL	Mistral only	Set to 1 to enable the dedicated Mistral provider path
MISTRAL_API_KEY	Mistral only	Mistral API key
MISTRAL_MODEL	Mistral only	Model name such as devstral-latest
MISTRAL_BASE_URL	No	Override the Mistral base URL
CODEX_API_KEY	Codex only	Codex or ChatGPT access token override
CHATGPT_ACCOUNT_ID / CODEX_ACCOUNT_ID	Codex only	Required for manual Codex env setup when the account id is not coming from auth.json or stored OAuth credentials
CODEX_AUTH_JSON_PATH	Codex only	Path to a Codex CLI auth.json file
CODEX_HOME	Codex only	Alternative Codex home directory
OPENCLAUDE_MAX_RETRIES	No	Maximum retry attempts for retryable API failures, capped at 100 (default: 10). Set to 0 to disable retries after the initial request. If unset, deprecated CLAUDE_CODE_MAX_RETRIES is still honored for compatibility.
OPENCLAUDE_RETRY_DELAY_MS	No	Base retry delay in milliseconds for APIs that do not send Retry-After; exponential backoff starts from this value, capped at 60000 (default: 500)
OPENCLAUDE_QUERY_HARD_MAX_MS	No	Foreground query hard maximum in milliseconds. Defaults to 1800000 (30 minutes). Use a larger positive integer for long autonomous sessions; invalid, zero, negative, fractional, or timer-overflow values are ignored with a warning.
OPENCLAUDE_DISABLE_CO_AUTHORED_BY	No	Suppress the default Co-Authored-By trailer in generated git commits
OPENCLAUDE_LOG_TOKEN_USAGE	No	When truthy (e.g. verbose), emits one JSON line on stderr per API request with input/output/cache tokens and the resolved provider. User-facing debug output — complements the REPL display controlled by /config showCacheStats. Distinct from CLAUDE_CODE_ENABLE_TOKEN_USAGE_ATTACHMENT, which is model-facing (injects context usage info into the prompt itself). Both can run together.

Model env vars are provider-scoped: first-party Anthropic sessions read ANTHROPIC_MODEL, OpenAI-compatible sessions read OPENAI_MODEL, Gemini reads GEMINI_MODEL, and Mistral reads MISTRAL_MODEL. For manual Bedrock, Vertex, or Foundry launches, select the model with --model.

Per-model limit overrides (settings.json)

When a custom OpenAI-compatible provider does not expose context metadata from /v1/models, you can pin a model's context window and max output tokens. In addition to the CLAUDE_CODE_OPENAI_CONTEXT_WINDOWS / CLAUDE_CODE_OPENAI_MAX_OUTPUT_TOKENS env vars above, you can set a modelLimits map in your settings.json (the same file /config writes, e.g. ~/.openclaude/settings.json):

{
  "modelLimits": {
    "my-custom-deployment": { "contextWindow": 262144, "maxOutputTokens": 32768 },
    "api.private-llm.test:my-custom-deployment": { "contextWindow": 1000000 }
  }
}

• Key matching — keys match the model api-name exactly, or by prefix (e.g. my-custom matches my-custom-deployment-v2). An exact key always wins over a prefix key. A host-qualified key (<host>:<model>) only wins over a bare key within the same match kind — a host-qualified exact key beats a bare exact key, and a host-qualified prefix beats a bare prefix, but a bare exact key still beats a host-qualified prefix. So to give the same model different limits per endpoint, use host-qualified exact keys for each endpoint. <host> is the OPENAI_BASE_URL host including the port when the URL has one (new URL(baseUrl).host): for http://localhost:4000/v1 the key is localhost:4000:my-model, not localhost:my-model. Either field may be omitted to override only one limit.
• Precedence — from highest to lowest: an exact env-var override → the built-in catalog value → the discovery-cache value → a prefix env-var override → modelLimits → the descriptor default. (The built-in catalog is checked before the discovery cache.) So env-var overrides always win over modelLimits, and modelLimits mainly fills in models that have no built-in metadata (a known catalog model keeps its catalog limit unless you set an exact env override for it).

Safety strictness

OpenClaude runs several "safety" checks: a model-level refusal directive, bash command-injection validation, and sensitive-file / auto-edit guards. These are conservative by design, but a few of them can surface as refusals or approval prompts for entirely benign, routine coding tasks (e.g. editing .gitmodules, running a build script that contains $(date), or writing a CTF port scanner). See issue #1616.

Set OPENCLAUDE_SAFETY_LEVEL to dial strictness without changing behavior for everyone:

Value	Behavior
strict	Current/default-equivalent non-permissive behavior.
balanced	Default. Same behavior as strict.
permissive	Opt-in mode for users who prefer fewer false-positive stops. It bypasses the legacy bash command-injection validation path entirely, keeps ordinary interpreter allow-rules (Bash(python:*), Bash(npm run:*), …) when entering auto mode, and skips prompts for routine edits to filenames on the broad sensitive-file list. Dangerous directory, Windows-path, symlink-resolved path, and UNC guards remain active. The model-level prompt is not weakened by this flag.

export OPENCLAUDE_SAFETY_LEVEL=permissive   # relax benign-task false positives

Runtime Hardening

Use these commands to validate your setup and catch mistakes early:

# quick startup sanity check
bun run smoke

# validate provider env + reachability
bun run doctor:runtime

# print machine-readable runtime diagnostics
bun run doctor:runtime:json

# persist a diagnostics report to reports/doctor-runtime.json
bun run doctor:report

# print a redacted public issue report
openclaude doctor report --markdown

# write a redacted JSON issue report for attachment
openclaude doctor report --json --out openclaude-report.json

# write a deterministic task report from a session transcript
openclaude report --json --transcript ~/.openclaude/projects/-path-to-project/session-id.jsonl --out task-report.json

# print a human-readable task report from the latest session in the current project
openclaude report --markdown

# full local hardening check (smoke + runtime doctor)
bun run hardening:check

# strict hardening (includes project-wide typecheck)
bun run hardening:strict

Notes:

• doctor:runtime fails fast if CLAUDE_CODE_USE_OPENAI=1 with a placeholder key or a missing key for non-local providers.
• doctor:runtime also validates the dedicated Gemini and Mistral env paths when CLAUDE_CODE_USE_GEMINI=1 or CLAUDE_CODE_USE_MISTRAL=1.
• Local providers such as http://localhost:11434/v1, http://10.0.0.1:11434/v1, and http://127.0.0.1:1337/v1 can run without OPENAI_API_KEY.
• Codex profiles validate CODEX_API_KEY or the Codex CLI auth file and probe POST /responses instead of GET /models.
• openclaude doctor report is redacted by default and is intended for GitHub issues. It summarizes provider/runtime/build/settings state without prompts, transcripts, raw settings files, API keys, MCP command details, or full home-directory paths.
• openclaude report --json and openclaude report --markdown summarize observed session facts such as tool uses, Bash commands, validation commands, changed files, branch metadata, warnings, and linked issue/PR references. Use --transcript <file> for an explicit transcript, --session <id> for a stored session, or omit both to report the latest session for the current project. Large previews are truncated and credential-shaped strings are redacted. When no validation command is observed, the report keeps validations empty and includes a warning instead of claiming checks passed.

Provider Launch Profiles

Use profile launchers to avoid repeated environment setup:

# one-time profile bootstrap (prefer viable local Ollama, otherwise OpenAI)
bun run profile:init

# preview the best provider/model for your goal
bun run profile:recommend -- --goal coding --benchmark

# auto-apply the best available local/openai provider/model for your goal
bun run profile:auto -- --goal latency

# codex bootstrap (defaults to codexplan and ~/.codex/auth.json)
bun run profile:codex

# openai bootstrap with explicit key
bun run profile:init -- --provider openai --api-key sk-...

# gemini bootstrap with explicit key
bun run profile:init -- --provider gemini --api-key ...

# ollama bootstrap with custom model
bun run profile:init -- --provider ollama --model llama3.1:8b

# ollama bootstrap with intelligent model auto-selection
bun run profile:init -- --provider ollama --goal coding

# atomic-chat bootstrap (auto-detects running model)
bun run profile:init -- --provider atomic-chat

# codex bootstrap with a fast model alias
bun run profile:init -- --provider codex --model codexspark

# launch using persisted user-level provider profile
bun run dev:profile

# codex profile (uses CODEX_API_KEY or ~/.codex/auth.json)
bun run dev:codex

# OpenAI profile (uses the saved OpenAI profile, or OPENAI_API_KEYS / OPENAI_API_KEY from your shell)
bun run dev:openai

# Gemini profile (uses the saved Gemini profile, or GEMINI_API_KEY / GOOGLE_API_KEY from your shell)
bun run dev:gemini

# Ollama profile (defaults: localhost:11434, llama3.1:8b)
bun run dev:ollama

# Atomic Chat profile (Apple Silicon local LLMs at 127.0.0.1:1337)
bun run dev:atomic-chat

profile:recommend ranks installed Ollama models for latency, balanced, or coding, and profile:auto can persist the recommendation directly.

If no profile exists yet, dev:profile uses the same goal-aware defaults when picking the initial model.

Provider Profile Model Picker Mode

When a saved provider profile is active, /model can either show the provider's catalog/discovered models or only the models explicitly listed in the profile. Configure this in ~/.openclaude.json:

{
  "providerProfileModelPickerMode": "auto"
}

Supported values:

• auto (default): single-model profiles show the provider catalog; multi-model profiles show the explicit profile list; native vendor routes keep their full provider catalog.
• provider: show the provider catalog/discovery list first and append profile-only custom model IDs.
• profile: show only explicitly configured profile models.

When the provider-profile env workflow is active (i.e. a profile has been applied and CLAUDE_CODE_PROVIDER_PROFILE_ENV_APPLIED=1 is set — as it is after launching with a saved profile) and you have more than one saved provider profile, /model also lists models from your inactive profiles, grouped under their profile name. Selecting one activates that provider profile and switches to the chosen model in a single step, reconciling fast mode if the target provider cannot run it. These cross-profile entries appear only in the interactive /model picker — they are never returned to SDK/automation callers and are hidden from inline pickers (such as the prompt hotkey or Settings), which cannot switch the active profile. Simply having multiple profiles configured without the env workflow active does not surface them.

Use --provider ollama when you want a local-only path. Auto mode falls back to OpenAI when no viable local chat model is installed.

Use --provider atomic-chat when you want Atomic Chat as the local Apple Silicon provider.

Use profile:codex or --provider codex when you want the ChatGPT Codex backend.

dev:openai, dev:gemini, dev:ollama, dev:atomic-chat, and dev:codex run doctor:runtime first and only launch the app if checks pass.

For dev:ollama, make sure Ollama is running locally before launch.

For dev:atomic-chat, make sure Atomic Chat is running with a model loaded before launch.

Message-Count Compaction Threshold

By default, OpenClaude compacts conversations based on token usage and also applies a safety hard cap of 1000 active messages. The hard cap catches long sessions that accumulate many small messages with negligible token cost.

This hard cap is a safety net: it can still trigger compaction even when DISABLE_COMPACT, DISABLE_AUTO_COMPACT, or a disabled auto-compact setting would otherwise prevent it. Set OPENCLAUDE_MAX_ACTIVE_MESSAGES_HARD_CAP=0 only when you need to suppress that safety cap for diagnostics.

If you frequently resume long sessions that accumulate hundreds of small tool-result messages with negligible token cost, you can opt in to message-count compaction via the in-app /config command:

/config

Select Message-count compaction and choose a threshold (100, 200, 500, or 1000). Setting it to off (default) leaves only the built-in hard cap.

This setting is intended for power users debugging specific edge cases. Most users should leave it at off.

The legacy OPENCLAUDE_MAX_ACTIVE_MESSAGES environment variable is still honored when the setting is off. OPENCLAUDE_MAX_ACTIVE_MESSAGES_HARD_CAP can override the safety cap; set it to 0 only for diagnostics.

Long-session memory guard validation

For changes that touch auto-compact, provider request conversion, transcript retention, or in-process teammates, run the focused long-session guard checks:

bun test --feature=UNATTENDED_RETRY src/query/autoCompactCooldown.test.ts src/utils/maxActiveMessages.test.ts src/services/api/openaiShim.test.ts

These tests cover repeated over-cap turns, auto-compact cooldown blocking, teammate active-message compaction, malformed hard-cap overrides, and pruned-history tool-call/tool-result pairing. They are not a substitute for a multi-hour manual soak, but they pin the bounded-history and conversion invariants that previously let long sessions grow until Node/V8 OOM.