> ## Documentation Index > Fetch the complete documentation index at: https://docs.agents-squads.com/llms.txt > Use this file to discover all available pages before exploring further. # Multi-LLM Usage > Mix different LLM providers and models for optimal results ## CLI Delegation Agents Squads enables multi-LLM support by delegating to native provider CLIs. No SDK integrations needed—just shell out to `claude`, `gemini`, `codex`, `grok`, etc. ### Check Available Providers ```bash theme={null} squads providers ``` ``` Provider CLI Status ──────────────────────────────────────── Anthropic claude ● ready DeepSeek (via aider) aider ● ready Google gemini ● ready OpenAI codex ● ready Mistral vibe ● ready xAI grok ● ready Ollama ollama ● ready ``` ### Run with Specific Provider ```bash theme={null} # Override provider for this run squads run research/analyst --execute --provider=google # Uses Gemini CLI instead of Claude ``` ### Provider Resolution The CLI resolves providers in this order: 1. **Agent frontmatter** - `provider:` field in the agent's .md file 2. **CLI flag** - `--provider=deepseek` 3. **Squad default** - `providers.default` in SQUAD.md 4. **Fallback** - `anthropic` ## File-Based Executors Some providers run as *file-based executors*. DeepSeek (added in v0.8.0) delegates to `aider`: the executor reads and edits files and the result is committed, but it runs no shell commands and has no web access. File-based executors fit validators, formatters, and summarizers — agents whose job is read files → write files. Executor output is harvested from an isolated git worktree, so completed work is never lost even if the run ends abnormally. Every provider run is recorded in observability (`squads exec list`) with real token and cost figures parsed from the executor's output. *** ## Configuration ### Squad-Level Providers Configure default providers in SQUAD.md frontmatter: ```yaml theme={null} --- name: intelligence mission: Market research and competitive analysis providers: default: anthropic # CLI: claude vision: openai # CLI: codex (for image analysis) realtime: xai # CLI: grok (for real-time data) cheap: google # CLI: gemini (for high-volume) --- ``` ### Agent-Level Override Override the provider for specific agents: ```markdown theme={null} --- provider: xai --- # Social Monitor ## Purpose Real-time X/Twitter trend detection using Grok. ``` ```markdown theme={null} # Social Monitor ## Provider xai ## Purpose Real-time X/Twitter trend detection. ``` *** ## Supported CLIs | Provider | CLI | Install | Non-Interactive Flag | | --------- | -------- | ------------------------------------------------------- | ------------------------- | | Anthropic | `claude` | `npm i -g @anthropic-ai/claude-code` | `--print` | | Google | `gemini` | `npm i -g @google/gemini-cli` | `--prompt` | | OpenAI | `codex` | `npm i -g @openai/codex` | `exec` subcommand | | Mistral | `vibe` | `curl -LsSf https://mistral.ai/vibe/install.sh \| bash` | `--prompt --auto-approve` | | xAI | `grok` | `bun add -g @vibe-kit/grok-cli` | `--prompt` | | Multi | `aider` | `pip install aider-install && aider-install` | `--message --yes` | | Local | `ollama` | `brew install ollama` | `run ""` | ### Environment Variables Each CLI reads its own API keys: ```bash theme={null} # .env (or shell profile) ANTHROPIC_API_KEY=sk-ant-... # claude GOOGLE_API_KEY=AIza... # gemini (or OAuth) OPENAI_API_KEY=sk-... # codex MISTRAL_API_KEY=... # vibe XAI_API_KEY=... # grok ``` *** ## Why Use Multiple LLMs? Different LLMs excel at different tasks. A well-designed agent system can leverage: * **Claude** - Complex reasoning, nuanced analysis, long context * **GPT-4** - General purpose, wide knowledge, tool use * **Gemini** - Multimodal, Google ecosystem integration * **Grok** - Real-time data, X/Twitter integration * **Llama/Open models** - Privacy, self-hosting, cost control ## Provider Comparison | Provider | Strengths | Best For | | ------------------ | ------------------------------- | -------------------------------- | | Claude (Anthropic) | Reasoning, safety, long context | Complex analysis, code review | | GPT-4 (OpenAI) | Versatility, ecosystem | General tasks, plugins | | Gemini (Google) | Multimodal, speed | Vision tasks, Google integration | | Grok (xAI) | Real-time, humor | Social media, current events | | Llama (Meta) | Open source, self-host | Privacy-sensitive, offline | | Mistral | European, efficient | EU compliance, edge deployment | ## Model Tiers (Within Providers) Each provider offers different capability tiers: | Model | Use Case | Cost | | ------------- | ------------------ | ------ | | Claude Opus | Complex reasoning | \$\$\$ | | Claude Sonnet | Balanced default | \$\$ | | Claude Haiku | Fast, simple tasks | \$ | | Model | Use Case | Cost | | ----------- | -------------------- | -------- | | GPT-4o | Multimodal, flagship | \$\$\$ | | GPT-4o-mini | Fast, efficient | \$ | | o1/o3 | Deep reasoning | \$\$\$\$ | | Model | Use Case | Cost | | ------------ | --------------- | ------ | | Gemini Ultra | Most capable | \$\$\$ | | Gemini Pro | Balanced | \$\$ | | Gemini Flash | Speed optimized | \$ | | Model | Use Case | Cost | | ----------- | ---------------- | ------ | | Grok-2 | Full capability | \$\$\$ | | Grok-2-mini | Faster responses | \$\$ | ## Squad Configuration ### Agent-Level Provider Selection Assign different providers to different agents: ```markdown theme={null} # SQUAD.md - Intelligence Squad ## Agents ### market-researcher **Provider**: Claude Sonnet **Purpose**: Deep market analysis requiring nuanced reasoning ### social-monitor **Provider**: Grok **Purpose**: Real-time X/Twitter monitoring and trend detection ### data-analyst **Provider**: GPT-4o **Purpose**: Spreadsheet analysis with vision capabilities ### summarizer **Provider**: Gemini Flash **Purpose**: Fast summarization of research findings ``` ### Environment Configuration Set up API keys for each provider: ```bash theme={null} # .env ANTHROPIC_API_KEY=sk-ant-... OPENAI_API_KEY=sk-... GOOGLE_API_KEY=AIza... XAI_API_KEY=xai-... ``` ### Provider Selection Per Agent No SDK code to write — set `provider:`/`model:` in the agent's frontmatter and `squads run` shells out to that CLI instead: ```markdown theme={null} --- provider: anthropic model: claude-sonnet-4-20250514 --- # Agent: Market Researcher ## Instructions Analyze market trends using deep reasoning... ``` ```markdown theme={null} --- provider: openai model: gpt-4o --- # Agent: Chart Generator ``` ```markdown theme={null} --- provider: google model: gemini-1.5-flash --- # Agent: Fast Summarizer ``` ```markdown theme={null} --- provider: xai model: grok-2 --- # Agent: Trend Scanner ``` ## Routing Patterns ### Task-Based Routing Route tasks to the best provider: | Task Requirement | Best Provider | | ------------------ | ---------------------- | | Real-time data | Grok | | Image analysis | GPT-4o / Gemini | | Deep reasoning | Claude Opus | | Google integration | Gemini | | General tasks | Claude Sonnet / GPT-4o | ### Cascade Pattern Start cheap, escalate when needed: Gemini Flash (fastest, cheapest) Escalate to Claude Sonnet Escalate to Claude Opus ### Consensus Pattern Use multiple providers for critical decisions: **Critical Decision** → Run in parallel across Claude, GPT-4o, and Gemini → **Voting/Synthesis** → Final Answer ## Cost Optimization ### Price Comparison (approximate per 1M tokens) | Provider | Input | Output | | ------------- | ------- | ------- | | Claude Haiku | \$0.25 | \$1.25 | | GPT-4o-mini | \$0.15 | \$0.60 | | Gemini Flash | \$0.075 | \$0.30 | | Claude Sonnet | \$3.00 | \$15.00 | | GPT-4o | \$2.50 | \$10.00 | | Gemini Pro | \$1.25 | \$5.00 | | Claude Opus | \$15.00 | \$75.00 | Prices change frequently. Check provider pricing pages for current rates. ### Cost Strategy ```markdown theme={null} ## Budget Allocation Example - 60% → Gemini Flash / GPT-4o-mini (high-volume tasks) - 30% → Claude Sonnet / GPT-4o (standard tasks) - 10% → Claude Opus / o1 (complex reasoning) ``` ## Implementation Examples ### Multi-Provider Squad The squad-level `providers:` block sets the default; each agent's own frontmatter overrides it for that agent specifically: ```markdown theme={null} --- # .agents/squads/intelligence/SQUAD.md name: intelligence mission: Multi-provider research and analysis providers: default: anthropic --- ## Agents | Agent | Role | Provider | Model | |-------|------|----------|-------| | `trend-scanner` | Real-time social trend detection | grok | grok-2-mini | | `deep-researcher` | In-depth analysis and synthesis | anthropic | claude-sonnet-4-20250514 | | `data-visualizer` | Chart and image generation | openai | gpt-4o | | `fast-summarizer` | Quick summaries and translations | google | gemini-1.5-flash | ``` Each agent's own `.md` file carries the `provider:`/`model:` frontmatter shown in [Provider Selection Per Agent](#provider-selection-per-agent) above — the table here is just the roster view. ### The CLI Is the Abstraction There's no adapter layer to build — `squads` itself is the unified interface. Set `providers.default` (and per-purpose overrides) once in `SQUAD.md`, as shown in [Squad-Level Providers](#squad-level-providers) above, and every agent in the squad resolves its provider through the same precedence chain (frontmatter → `--provider` flag → squad default → `anthropic` fallback) with no code on your side. ## Best Practices * Match provider strengths to task requirements * Use cheaper models for high-volume, simple tasks * Reserve expensive models for complex reasoning * Monitor costs per provider (`squads exec list`, `squads usage`) * Set `providers.default` at the squad level so switching is a one-line edit **Avoid:** * Using one provider for everything (miss optimizations) * Ignoring rate limits (each provider has different limits) * Routing file-based executors (DeepSeek/aider) to roles that need shell or web access — they have neither * Forgetting about latency differences ## Related Optimize costs across providers Run multi-provider agents concurrently