Alishahryar1/free-claude-code

• OpenAI uses your ChatGPT subscription rather than an API key. Connect from Providers → Connected accounts in the Admin UI. Use device code on headless systems. Restart an already-running agent after connecting.
• Azure OpenAI uses the deployment names from your resource. Set AZURE_OPENAI_BASE_URL to its complete v1 endpoint, such as https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/, and select a deployment that supports Chat Completions. Enter the deployment name as a custom model slug if it does not appear in the model dropdown.
• Mistral Codestral uses a separate key from Mistral La Plateforme.
• Kimi Code subscription keys use kimi_code/; Kimi API credit keys use kimi/. Kimi Code plans are for personal interactive coding-agent use under Kimi's community guidelines.
• QwenCloud Coding Plan keys use qwencloud_coding/; QwenCloud Token Plan keys use qwencloud/. The keys and endpoints are not interchangeable. Coding Plan is for local, personal, interactive coding-agent use under the Coding Plan terms.
• OpenCode Zen and OpenCode Go share OPENCODE_API_KEY but use the explicit opencode_zen/ and opencode_go/ model prefixes.
• For Amazon Bedrock, set BEDROCK_BASE_URL to the URL for the same region as the API key and select one of the listed models.
• Vertex AI uses Google Application Default Credentials instead of an API key. Locally, run gcloud auth application-default login once; service-account files and attached service accounts also work. Set VERTEX_PROJECT_ID, and optionally change VERTEX_LOCATION from its global default.
• Cloudflare requires both its API token and account ID.
• For Ollama Cloud, use the exact model IDs shown in the model picker. Local Ollama uses the separate ollama/ prefix.
• Prefer tool-capable models for coding agents. Local models also need enough context for the agent's system prompt and tool definitions.

LM Studio

Start LM Studio's local server, load a tool-capable model, and use the model identifier shown by LM Studio with the lmstudio/ prefix. The default URL is http://localhost:1234/v1.

llama.cpp

Start llama-server with its OpenAI-compatible Chat Completions API and enough context for the model. Use the local model ID with the llamacpp/ prefix. LLAMACPP_BASE_URL defaults to http://localhost:8080/v1; FCC accepts either the server root or an explicit /v1 suffix.

Ollama

ollama pull llama3.1
ollama serve

Use the tag shown by ollama list with the ollama/ prefix. OLLAMA_BASE_URL defaults to http://localhost:11434; FCC accepts either the root URL or an explicit /v1 suffix.

MODEL is the fallback for every request. Select a model for MODEL_FABLE, MODEL_OPUS, MODEL_SONNET, or MODEL_HAIKU to override an individual Claude Code tier; select None to use MODEL.

For example, route Opus to nvidia_nim/nvidia/nemotron-3-super-120b-a12b, Sonnet to open_router/openrouter/free, Haiku to lmstudio/qwen3.5-coder, and keep MODEL on zai/glm-5.2.

Open Admin UI → Model Config → Reasoning and select the behavior you want.

Providers that do not support a selected control retain their own behavior.

Install the Claude Code extension. Open VS Code's user settings as JSON and add:

"claudeCode.disableLoginPrompt": true,
"claudeCode.environmentVariables": [
  { "name": "ANTHROPIC_BASE_URL", "value": "http://localhost:8082" },
  { "name": "ANTHROPIC_AUTH_TOKEN", "value": "freecc" },
  { "name": "CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY", "value": "1" },
  { "name": "CLAUDE_CODE_AUTO_COMPACT_WINDOW", "value": "190000" },
  { "name": "DISABLE_AUTOUPDATER", "value": "1" },
  { "name": "DISABLE_FEEDBACK_COMMAND", "value": "1" },
  { "name": "DISABLE_ERROR_REPORTING", "value": "1" }
]

Match the port and authentication token to the Admin UI, then reload the extension.

Start FCC, then edit your Codex configuration:

• Windows: %USERPROFILE%\.codex\config.toml
• macOS: ~/.codex/config.toml

Add the matching model-catalog path and replace YOUR_USERNAME.

Windows:

model_catalog_json = "C:/Users/YOUR_USERNAME/.fcc/codex-model-catalog.json"

macOS:

model_catalog_json = "/Users/YOUR_USERNAME/.fcc/codex-model-catalog.json"

Then add the shared FCC settings:

model_provider = "fcc"
model = "nvidia_nim/nvidia/nemotron-3-super-120b-a12b"

[model_providers.fcc]
name = "Free Claude Code"
base_url = "http://127.0.0.1:8082/v1"
wire_api = "responses"

[model_providers.fcc.auth]
command = "fcc-codex"
args = ["--print-proxy-auth-token"]

Match the model and port to the Admin UI. The auth command reads FCC's current proxy token automatically. Restart the Codex App after setup or model changes, then select an FCC model from its model picker.

Install the Codex extension. Create or edit ~/.codex/config.toml (%USERPROFILE%\.codex\config.toml on Windows):

model_provider = "fcc"
model = "nvidia_nim/nvidia/nemotron-3-super-120b-a12b"

[model_providers.fcc]
name = "Free Claude Code"
base_url = "http://127.0.0.1:8082/v1"
wire_api = "responses"

[model_providers.fcc.auth]
command = "fcc-codex"
args = ["--print-proxy-auth-token"]

Match model and the port to the Admin UI. The auth command reads FCC's current proxy token automatically. Restart VS Code after setup or model changes. For WSL-backed Codex, edit the file inside WSL.

Edit the installed Claude ACP configuration:

• Windows: C:\Users\%USERNAME%\AppData\Roaming\JetBrains\acp-agents\installed.json
• Linux/macOS: ~/.jetbrains/acp.json

Set the environment for acp.registry.claude-acp:

"env": {
  "ANTHROPIC_BASE_URL": "http://localhost:8082",
  "ANTHROPIC_AUTH_TOKEN": "freecc",
  "CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY": "1",
  "CLAUDE_CODE_AUTO_COMPACT_WINDOW": "190000",
  "DISABLE_AUTOUPDATER": "1",
  "DISABLE_FEEDBACK_COMMAND": "1",
  "DISABLE_ERROR_REPORTING": "1"
}

Match the port and token to the Admin UI, then restart the IDE.

If Claude Code asks you to log in after you configure the FCC URL and token, open its state file:

• Windows: %USERPROFILE%\.claude.json
• macOS/Linux/WSL: ~/.claude.json

Merge this property into the existing JSON without removing its other fields:

"hasCompletedOnboarding": true

If the file does not exist, create it with a complete JSON object:

{
  "hasCompletedOnboarding": true
}

Restart Claude Code or the IDE after saving the file.

1. Create a bot in the Discord Developer Portal.
2. Enable Message Content Intent and invite it with read, send, message-history, and Manage Messages permissions so /clear can remove user prompts.
3. Set Messaging Platform to discord.
4. Enter Discord Bot Token, Allowed Discord Channels, and an absolute Allowed Directory.
5. Apply the settings and restart the server if requested.

1. Create a bot with @BotFather.
2. Get your numeric user ID from @userinfobot. In groups, grant the bot permission to delete messages.
3. Set Messaging Platform to telegram.
4. Enter Telegram Bot Token, Allowed Telegram User ID, and an absolute Allowed Directory.
5. Apply the settings and restart the server if requested.

Choose the voice backend you want, then re-run the installer with its option.

The examples below install NVIDIA NIM transcription. To use another backend, replace the final option with the matching one from the table.

macOS/Linux:

curl -fsSL "https://raw.githubusercontent.com/Alishahryar1/free-claude-code/main/scripts/install.sh" | sh -s -- --voice-nim

Windows PowerShell:

& ([scriptblock]::Create((irm "https://raw.githubusercontent.com/Alishahryar1/free-claude-code/main/scripts/install.ps1"))) -VoiceNim

Restart fcc-server. In Admin UI → Messaging → Voice, enable voice notes, select cpu, cuda, or nvidia_nim, and choose the Whisper model. Local gated models need HUGGINGFACE_API_KEY; NVIDIA NIM transcription needs NVIDIA_NIM_API_KEY.