> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Multi-Model Panel > Combine perspectives from multiple LLMs into a single selectable model Run multiple models as advisors and one as the actor — all selectable as a single LLM. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent( name="Assistant", instructions="Answer thoughtfully.", llm={ "provider": "panel", "references": ["gpt-4o", "anthropic/claude-3-5-sonnet"], "aggregator": "anthropic/claude-3-5-sonnet", }, ) agent.start("What are the trade-offs of this design?") ``` The user asks once; reference models advise and the aggregator produces the final answer. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Multi-Model Panel" U["💬 User Prompt"] --> R1["🔍 Reference: gpt-4o"] U --> R2["🔍 Reference: Claude"] R1 --> AGG["🧠 Aggregator"] R2 --> AGG U --> AGG AGG --> T["🔧 Tool Loop"] T --> O["✅ Final Answer"] end classDef input fill:#6366F1,stroke:#7C90A0,color:#fff classDef ref fill:#F59E0B,stroke:#7C90A0,color:#fff classDef agg fill:#189AB4,stroke:#7C90A0,color:#fff classDef out fill:#10B981,stroke:#7C90A0,color:#fff class U input class R1,R2 ref class AGG,T agg class O out ``` ## Quick Start Describe the panel inline — no registration needed. The references advise, the aggregator writes the final answer. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent( name="Assistant", instructions="Answer thoughtfully.", llm={ "provider": "panel", "references": ["gpt-4o", "anthropic/claude-3-5-sonnet"], "aggregator": "anthropic/claude-3-5-sonnet", }, ) agent.start("Design a migration plan for moving from REST to GraphQL.") ``` Register a preset once and reference it by name — works from Python, YAML, CLI, and bots. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.llm import register_panel_preset register_panel_preset("deep", { "references": ["gpt-4o", "anthropic/claude-3-5-sonnet"], "aggregator": "anthropic/claude-3-5-sonnet", }) agent = Agent(name="Researcher", llm="panel:deep") agent.start("What are the trade-offs of event sourcing?") ``` ## How It Works ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} sequenceDiagram participant User participant Agent participant Refs as References (tool-free) participant Agg as Aggregator (acting model) participant Tools User->>Agent: "Design X" Agent->>Refs: trimmed view (parallel) Refs-->>Agent: advisory text (cached per turn) Agent->>Agg: prompt + reference guidance (tail-appended) Agg->>Tools: tool calls (normal loop) Tools-->>Agg: results Agg-->>User: final answer ``` On every user turn the references run **once** (tool-free, trimmed view) and their advisory text is **appended to the tail of the latest user turn** — never to the system prompt, so Anthropic prompt caching on the system prefix stays intact. The aggregator then runs the normal tool loop with full tools, hooks, and sessions. ## When To Use This vs. Other Model Features ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Q["Need multi-model perspective?"] -->|No| Single["Use single model"] Q -->|Yes, one model per task| Router["Use model-router"] Q -->|Yes, backup on failure| Fallback["Use model-fallback"] Q -->|Yes, score outputs| Judge["Use llm-as-judge"] Q -->|Yes, one synthesized answer per turn| Panel["Use multi-model-panel ✅"] classDef pick fill:#10B981,stroke:#7C90A0,color:#fff classDef alt fill:#6366F1,stroke:#7C90A0,color:#fff class Panel pick class Single,Router,Fallback,Judge alt ``` \| Feature | What it does | What it does NOT do | \|---Run multiple models as advisors and one as the actor — all selectable as a single LLM. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent agent = Agent( name="Assistant", instructions="Answer thoughtfully.", llm={"provider": "panel", "references": ["gpt-4o", "anthropic/claude-3-5-sonnet"], "aggregator": "anthropic/claude-3-5-sonnet"}, ) agent.start("Design a migration plan for moving from REST to GraphQL.") ``` The user prompts once; reference models advise and the aggregator produces the final answer. \|---|---| \| `model-router` | Picks ONE model per task | Does not ensemble | \| `model-fallback` | Backup on failure | Not parallel perspectives | \| `llm-as-judge` | Scores outputs after-the-fact | Does not act as a model | \| **`multi-model-panel`** | Multiple references advise + one aggregator acts as a single LLM | — | ## Configuration Options `Agent(llm=...)` accepts the panel in two equivalent forms: ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # 1. Named preset (must register first) agent = Agent(name="assistant", llm="panel:deep") # 2. Inline dict descriptor (no registration needed) agent = Agent( name="assistant", llm={ "provider": "panel", "references": ["gpt-4o", "anthropic/claude-3-5-sonnet"], "aggregator": "anthropic/claude-3-5-sonnet", "enabled": True, }, ) ``` ### Panel descriptor keys | Key | Type | Default | Description | | ------------------------- | ------------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------- | | `aggregator` *(required)* | `str` | — | Model string that acts in the loop (e.g. `"gpt-4o"`, `"anthropic/claude-3-5-sonnet"`). Inherits the full tool loop, hooks, and session. | | `references` | `list[str] \| None` | `None` | Advisory model strings called first, tool-free, with a trimmed view. Empty list = aggregator-alone. | | `enabled` | `bool` | `True` | When `False`, references are skipped entirely — behaves identically to selecting the aggregator directly (no added cost). | | `reference_temperature` | `float` | `0.0` | Temperature passed to the advisory reference calls (kept deterministic by default for caching). | | `base_url` | `str \| None` | `None` | Base URL forwarded to the underlying `LLM` and propagated to reference LLMs so they hit the same backend. | | `api_key` | `str \| None` | `None` | API key forwarded to the underlying `LLM` and propagated to reference LLMs. | | `api_version` | `str \| None` | `None` | API version forwarded to the underlying `LLM` and propagated to reference LLMs. | | `auth` | `object \| None` | `None` | Auth object forwarded to the underlying `LLM` and propagated to reference LLMs. | ### Module helpers Helper APIs such as `register_panel_preset`, `resolve_panel_config`, `is_panel_descriptor`, `create_panel_llm`, and the `PANEL_PRESETS` registry are internal module symbols — see the auto-generated SDK reference for signatures and full details. This page stays focused on using the panel feature rather than documenting module internals. ## Common Patterns ### With tools (aggregator gets full tool loop) ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonaiagents import Agent from praisonaiagents.tools import duckduckgo agent = Agent( name="Research Assistant", llm="panel:deep", tools=[duckduckgo], ) agent.start("Research the latest Anthropic releases and summarize.") ``` ### YAML ```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} agent: name: assistant llm: panel:deep tools: - duckduckgo ``` ### Local models (Ollama, with `base_url`) ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} agent = Agent( name="Local Panel", llm={ "provider": "panel", "references": ["ollama/llama3", "ollama/mistral"], "aggregator": "ollama/llama3", "base_url": "http://localhost:11434", }, ) ``` ### Disabled panel (escape hatch) ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} agent = Agent( name="Assistant", llm={ "provider": "panel", "references": ["gpt-4o"], "aggregator": "anthropic/claude-3-5-sonnet", "enabled": False, # skip references; aggregator acts alone }, ) ``` ## Behavioral Guarantees * **Selectable like any model** — flows through CLI `--model panel:deep`, YAML `llm: panel:deep`, bot `/model panel:deep`, and Python `Agent(llm=...)`. * **Prompt-cache safe** — reference outputs are injected at the tail of the latest user turn, never on the system prompt or history. * **Per-turn reference caching** — references run **once per user turn**, cached across tool-loop iterations by a deterministic signature of the trimmed view. Bounded FIFO cache (max 128 entries). * **Partial-failure tolerance** — if one reference fails (network, credentials), it becomes a labeled `(unavailable: reference call failed)` note and the turn continues. The whole turn never fails just because a reference did. * **Strict-provider safety** — references get a trimmed view: system prompt dropped, `tool`-role messages dropped, `tool_calls` payloads dropped. Strict providers won't 400 on orphan tool messages. * **Recursion guard** — a panel preset cannot reference another panel preset (rejected at config-validation time with `ValueError`). * **`enabled=False` collapses cleanly** — same cost as selecting the aggregator directly. * **Tool-free references** — references never see the tool schema and cannot emit tool calls. Only the aggregator acts. ## Best Practices Deterministic reference outputs maximize cache hits and keep the aggregator's view stable across tool-loop iterations. Each reference is an extra API call per turn. Marginal value drops fast beyond two or three references, while cost grows linearly. The aggregator writes the final answer and runs the tool loop. It should be the most capable model in your panel, not necessarily the cheapest. A panel preset can't reference another panel preset — the config validator rejects it with `ValueError`. Keep panels flat. ## Related Pick one model per task. Retry on alternate models when the primary fails.