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Providers

Providers and setup

Choose a provider, authenticate, discover available models, and configure the first usable setup.

On this page

On first run, term-llm will prompt you to choose a provider (Anthropic, AWS Bedrock, OpenAI, ChatGPT, GitHub Copilot, xAI, Venice, OpenRouter, Gemini, Gemini CLI, Zen, Claude Code (claude-bin), Ollama, or LM Studio).

Option 1: Try it free with Zen

OpenCode Zen provides free access to multiple models. No API key required:

term-llm exec --provider zen "list files"
term-llm ask --provider zen "explain git rebase"
term-llm ask --provider zen:gpt-5-nano "quick question"  # use specific model

Available free models: glm-4.7-free, minimax-m2.1-free, grok-code, big-pickle, gpt-5-nano

Or configure as default:

# In ~/.config/term-llm/config.yaml
default_provider: zen

Option 2: Use API key

Set your API key as an environment variable:

# For Anthropic
export ANTHROPIC_API_KEY=your-key

# For OpenAI
export OPENAI_API_KEY=your-key

# For xAI (Grok)
export XAI_API_KEY=your-key

# For Venice
export VENICE_API_KEY=your-key

# For OpenRouter
export OPENROUTER_API_KEY=your-key

# For Gemini
export GEMINI_API_KEY=your-key

# For Perplexity search (used by search.provider: perplexity)
export PERPLEXITY_API_KEY=your-key

Option 3: Use ChatGPT (Plus/Pro subscription)

If you have a ChatGPT Plus or Pro subscription, you can use the chatgpt provider with native OAuth authentication for both text and image workflows:

term-llm ask --provider chatgpt "explain this code"
term-llm ask --provider chatgpt:gpt-5.2-codex "code question"
term-llm image --provider chatgpt:gpt-5.4 "storybook fox in the snow"

On first use, you’ll be prompted to authenticate via browser. Credentials are stored locally and refreshed automatically.

# In ~/.config/term-llm/config.yaml
default_provider: chatgpt

providers:
  chatgpt:
    model: gpt-5.2-codex
    # Enabled by default for ChatGPT text requests; set false to force HTTP/SSE.
    use_websocket: true

Option 4: Use xAI (Grok)

xAI provides access to Grok models with native web search and X (Twitter) search capabilities.

# In ~/.config/term-llm/config.yaml
default_provider: xai

providers:
  xai:
    model: grok-4-1-fast  # default model

Available models:

Model Context Description
grok-4-1-fast 2M Latest, best for tool calling (default)
grok-4-1-fast-reasoning 2M With chain-of-thought reasoning
grok-4-1-fast-non-reasoning 2M Faster, no reasoning overhead
grok-4 256K Base Grok 4 model
grok-3 / grok-3-fast 131K Previous generation
grok-3-mini / grok-3-mini-fast 131K Smaller, faster
grok-code-fast-1 256K Optimized for coding tasks

Or use the --provider flag:

term-llm ask --provider xai "explain quantum computing"
term-llm ask --provider xai -s "latest xAI news"  # uses native web + X search
term-llm ask --provider xai:grok-4-1-fast-reasoning "solve this step by step"
term-llm ask --provider xai:grok-code-fast-1 "review this code"

Option 5: Use Venice

Venice exposes a wide mix of hosted text models behind an OpenAI-compatible API, including Venice’s own uncensored models plus Claude, Gemini, Grok, Qwen, GLM, Kimi, DeepSeek, and more. term-llm also enables Venice native web search when you use -s / --search.

# In ~/.config/term-llm/config.yaml
default_provider: venice

providers:
  venice:
    model: venice-uncensored  # default model
    fast_model: llama-3.2-3b  # lightweight control-plane model

Or use the --provider flag directly:

term-llm ask --provider venice "explain quantum computing"
term-llm ask --provider venice:grok-4-20-beta -s "latest xAI news"
term-llm ask --provider venice:qwen3-coder-480b-a35b-instruct "review this code"
term-llm models --provider venice

Option 6: Use OpenRouter

OpenRouter provides a unified OpenAI-compatible API across many models. term-llm sends attribution headers by default.

# In ~/.config/term-llm/config.yaml
default_provider: openrouter

providers:
  openrouter:
    model: x-ai/grok-code-fast-1
    app_url: https://github.com/samsaffron/term-llm
    app_title: term-llm

Model Discovery

List available models from any supported provider:

term-llm models --provider anthropic  # List Anthropic models
term-llm models --provider openrouter # List OpenRouter models
term-llm models --provider ollama     # List local Ollama models
term-llm models --provider lmstudio   # List local LM Studio models
term-llm models --json                # Output as JSON

Provider Discovery

List all available LLM providers and their configuration status:

term-llm providers                 # List all providers
term-llm providers --configured    # Only show configured providers
term-llm providers --builtin       # Only show built-in providers
term-llm providers anthropic       # Show details for specific provider
term-llm providers --json          # JSON output

Option 7: Use AWS Bedrock

AWS Bedrock provides access to Anthropic Claude models through your AWS account. This is useful for organizations that route AI usage through AWS billing, need VPC/PrivateLink access, or use application inference profiles for rate/cost management.

term-llm ask --provider bedrock "explain this code"
term-llm ask --provider bedrock:claude-opus-4-6-thinking "complex question"

Authentication uses the standard AWS credential chain. Configure credentials via any method the AWS SDK supports:

# Environment variables
export AWS_ACCESS_KEY_ID=AKIA...
export AWS_SECRET_ACCESS_KEY=...
export AWS_REGION=us-west-2

Or configure fully in ~/.config/term-llm/config.yaml:

default_provider: bedrock

providers:
  bedrock:
    region: us-west-2
    model: claude-sonnet-4-6-thinking

Explicit credentials (with 1Password, vaults, or $() command resolution):

providers:
  bedrock:
    region: us-west-2
    access_key_id: $(op-cache read "op://Private/AWS Bedrock/AWS_ACCESS_KEY_ID")
    secret_access_key: $(op-cache read "op://Private/AWS Bedrock/AWS_SECRET_ACCESS_KEY")
    model: claude-sonnet-4-6-thinking

Application inference profiles: use model_map to alias friendly model names to Bedrock ARNs or model IDs. The -thinking and -1m suffixes work with mapped names:

providers:
  bedrock:
    region: us-west-2
    access_key_id: $(op-cache read "op://Private/AWS Bedrock/AWS_ACCESS_KEY_ID")
    secret_access_key: $(op-cache read "op://Private/AWS Bedrock/AWS_SECRET_ACCESS_KEY")
    model: claude-sonnet-4-6-thinking
    model_map:
      claude-sonnet-4-6: arn:aws:bedrock:us-west-2:123456789:application-inference-profile/abc123
      claude-opus-4-6: arn:aws:bedrock:us-west-2:123456789:application-inference-profile/def456
      claude-haiku-4-5: arn:aws:bedrock:us-west-2:123456789:application-inference-profile/ghi789

With this config, --provider bedrock:claude-opus-4-6-1m-thinking strips the suffixes, resolves claude-opus-4-6 through model_map to the ARN, and enables adaptive thinking + 1M context.

Available models (same as direct Anthropic, translated to Bedrock IDs automatically):

Model Suffixes Description
claude-sonnet-4-6 -thinking, -1m Latest Sonnet
claude-opus-4-6 -thinking, -1m Latest Opus
claude-haiku-4-5 -thinking Fast, lightweight

The geographic prefix (us., eu., ap.) is derived from your configured region. For example, eu-west-1 produces eu.anthropic.* IDs, etc. This ensures data residency matches your region.

You can also pass raw Bedrock model IDs directly (e.g., us.anthropic.claude-sonnet-4-6 or full ARNs). These bypass the translation layer.

Features: full parity with the direct Anthropic provider: streaming, tool use, extended thinking, 1M context, images, prompt caching, web search/fetch all work through Bedrock.

Config field Description
region AWS region. Falls back to AWS_REGION / AWS_DEFAULT_REGION env vars, then us-east-1.
profile AWS profile name from ~/.aws/credentials.
access_key_id Explicit AWS access key. Supports $(), op://, ${ENV} resolution.
secret_access_key Explicit AWS secret key. Same resolution support.
session_token Optional session token for temporary/assumed-role credentials.
model_map Map of friendly names to Bedrock model IDs or ARNs.
model Default model (friendly name, Bedrock ID, or ARN).

Option 8: Use local LLMs (Ollama, LM Studio)

Run models locally with Ollama or LM Studio:

# List available models from your local server
term-llm models --provider ollama
term-llm models --provider lmstudio

# Configure in ~/.config/term-llm/config.yaml

default_provider: ollama

providers:
  ollama:
    type: openai_compatible
    base_url: http://localhost:11434/v1
    model: llama3.2:latest

  lmstudio:
    type: openai_compatible
    base_url: http://localhost:1234/v1
    model: deepseek-coder-v2

For other OpenAI-compatible servers (vLLM, text-generation-inference, etc.):

providers:
  my-server:
    type: openai_compatible
    base_url: http://your-server:8080/v1
    model: mixtral-8x7b
    models:  # optional: list models for shell autocomplete
      - mixtral-8x7b
      - llama-3-70b

The models list enables tab completion for --provider my-server:<TAB>. The configured model is always included in completions.

Built-in openai and chatgpt text providers use Responses WebSockets by default for faster tool-heavy conversations. openai_compatible providers do not: local/self-hosted compatible APIs stay on HTTP/SSE by default.

If your server rejects stream_options (causing errors on connect), disable it:

providers:
  my-server:
    type: openai_compatible
    base_url: http://your-server:8080/v1
    model: my-model
    no_stream_options: true

For custom models not in the built-in token limit tables, set context_window and max_output_tokens explicitly:

providers:
  my-server:
    type: openai_compatible
    base_url: http://your-server:8080/v1
    model: my-model
    context_window: 32768
    max_output_tokens: 8192

See Providers and models for the full list of OpenAI-compatible provider options.

Option 9: Use Claude Code (claude-bin)

If you have Claude Code installed and logged in, you can use the claude-bin provider to run completions via the Claude Agent SDK. This requires no API key - it uses Claude Code’s existing authentication.

# Use directly via --provider flag (no config needed)
term-llm ask --provider claude-bin "explain this code"
term-llm ask --provider claude-bin:haiku "quick question"  # use haiku model
term-llm exec --provider claude-bin "list files"           # command suggestions
term-llm ask --provider claude-bin -s "latest news"        # with web search

# Or configure as default

# In ~/.config/term-llm/config.yaml
default_provider: claude-bin

providers:
  claude-bin:
    model: sonnet  # opus, sonnet, or haiku
    env:
      IS_SANDBOX: "1"  # useful in trusted/sandboxed containers
      # Generate a long-lived token with: claude setup-token
      # Useful in CI or headless environments where interactive login isn't possible
      CLAUDE_CODE_OAUTH_TOKEN: "your-oauth-token-here"
    # Optional: Claude Code hooks are disabled by default; set to true to opt back in
    # enable_hooks: true

Features:

  • No API key required - uses Claude Code’s existing authentication
  • Full tool support via MCP (exec, search, edit all work)
  • Model selection: opus, sonnet (default), haiku
  • Claude Code hooks are disabled by default to keep user hook automation out of term-llm inference sessions
  • Optional providers.claude-bin.enable_hooks: true to opt back into Claude Code hooks
  • Optional providers.claude-bin.env passthrough for Claude subprocess settings (for example IS_SANDBOX=1 in trusted root-run containers)
  • providers.<name>.env values support the same deferred resolution as other config values, including file://...#json.path, op://..., and $()
  • Works immediately if Claude Code is installed and logged in

Option 10: Use existing CLI credentials (gemini-cli)

If you have gemini-cli installed and logged in, term-llm can use those credentials directly:

# Use gemini-cli credentials (no config needed)
term-llm ask --provider gemini-cli "explain this code"

Or configure as default:

# In ~/.config/term-llm/config.yaml
default_provider: gemini-cli  # uses ~/.gemini/oauth_creds.json

OpenAI-compatible providers support two URL options:

  • base_url: Base URL (e.g., https://api.cerebras.ai/v1) - /chat/completions is appended automatically
  • url: Full URL (e.g., https://api.cerebras.ai/v1/chat/completions) - used as-is without appending

Use url when your endpoint doesn’t follow the standard /chat/completions path, or to paste URLs directly from API documentation.

Option 11: Use GitHub Copilot

If you have GitHub Copilot (free, Individual, or Business), you can use the copilot provider with OAuth device flow authentication:

term-llm ask --provider copilot "explain this code"
term-llm ask --provider copilot:claude-opus-4.5 "complex question"

On first use, you’ll be prompted to authenticate via GitHub device flow. Credentials are stored locally and refreshed automatically.

# In ~/.config/term-llm/config.yaml
default_provider: copilot

providers:
  copilot:
    model: gpt-4.1  # free tier, or gpt-5.2-codex for paid

Available models:

Model Description
gpt-4.1 Default, works on free tier
gpt-5.2-codex Advanced coding model (paid)
gpt-5.1 GPT-5.1 (paid)
claude-opus-4.5 Claude Opus 4.5 via Copilot (paid)
gemini-3-pro Gemini 3 Pro via Copilot (paid)
grok-code-fast-1 Grok coding model via Copilot (paid)

Features:

  • Free tier with GPT-4.1 (no Copilot subscription required, just GitHub account)
  • OAuth device flow authentication (no API key needed)
  • Full tool support via MCP