> ## 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.
# Panel LLM (Mixture-of-Agents)
> Run multiple advisory models before your acting model for higher-accuracy answers
Multiple reference models consult first — their perspectives fold into context — then a single aggregator model acts with full tool access and produces the final answer.
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Research Assistant",
instructions="Answer with high accuracy.",
llm={
"provider": "panel",
"references": ["gpt-4o", "claude-opus-4-8"],
"aggregator": "claude-sonnet-5",
},
)
agent.start("Which SSD is faster, X or Y, and why?")
```
The user asks a tough question; reference models consult first, then the aggregator answers with tools.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
User["👤 User"] --> Agent["🤖 Agent"]
Agent --> Ref1["🔍 Reference 1\ngpt-4o"]
Agent --> Ref2["🔍 Reference 2\nclaude-opus-4-8"]
Ref1 --> Agg["⚡ Aggregator\nclaude-sonnet-5"]
Ref2 --> Agg
Agg --> Resp["✅ Response"]
classDef user fill:#8B0000,stroke:#7C90A0,color:#fff
classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
classDef ref fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef agg fill:#189AB4,stroke:#7C90A0,color:#fff
classDef resp fill:#10B981,stroke:#7C90A0,color:#fff
class User user
class Agent agent
class Ref1,Ref2 ref
class Agg agg
class Resp resp
```
## Quick Start
Pass a dict with `provider: "panel"` to `Agent(llm=...)`:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Research Assistant",
instructions="Answer with high accuracy.",
llm={
"provider": "panel",
"references": ["gpt-4o", "claude-opus-4-8"],
"aggregator": "claude-sonnet-5",
},
)
agent.start("Compare the performance of NVMe vs SATA SSDs.")
```
Register once, reuse by name anywhere:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents.llm.panel import register_panel_preset
register_panel_preset("deep", {
"references": ["gpt-4o", "claude-opus-4-8"],
"aggregator": "claude-sonnet-5",
})
agent = Agent(
name="Research Assistant",
instructions="Answer with high accuracy.",
llm="panel:deep",
)
agent.start("Compare the performance of NVMe vs SATA SSDs.")
```
***
## How It Works
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Agent
participant Ref as Reference Models
participant Agg as Aggregator
User->>Agent: prompt
Agent->>Ref: advisory call (tool-free, trimmed view)
Ref-->>Agent: perspectives
Agent->>Agg: original prompt + reference guidance (tail-injected)
Agg-->>Agent: final answer (with tools, hooks, memory)
Agent-->>User: response
```
| Phase | What happens |
| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Reference** | Each reference model receives the conversation (tool-free, stripped of system prompt and tool messages) and replies with its perspective |
| **Inject** | Reference guidance is appended to the **tail** of the latest user turn — not the system prompt — so Anthropic prompt caching on the stable prefix is preserved |
| **Aggregate** | The aggregator model acts as the real agent: it has full tool access, hooks, session, and memory |
***
## Selecting a Model
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
Q{"Do you want\nPanel LLM?"} -->|Yes| D{"Have a preset\nalready registered?"}
D -->|No| E["Inline dict descriptor\nAgent(llm={'provider':'panel',...})"]
D -->|Yes| F["Named preset string\nAgent(llm='panel:name')"]
Q -->|No| G["Regular model string\nAgent(llm='gpt-4o')"]
classDef q fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef opt fill:#189AB4,stroke:#7C90A0,color:#fff
classDef normal fill:#10B981,stroke:#7C90A0,color:#fff
class Q,D q
class E,F opt
class G normal
```
***
## Configuration Options
| Option | Type | Default | Description |
| ----------------------- | ----------- | ------- | ------------------------------------------------------------------- |
| `provider` | `str` | — | Must be `"panel"` |
| `references` | `list[str]` | — | Advisory model names (run first, tool-free) |
| `aggregator` | `str` | — | The acting model (gets full agent loop) |
| `enabled` | `bool` | `True` | Set to `False` to disable the panel without changing the descriptor |
| `reference_temperature` | `float` | `0.0` | Temperature for reference calls (stable advice) |
| `base_url` | `str` | `None` | Custom endpoint forwarded to both aggregator and references |
| `api_key` | `str` | `None` | API key forwarded to both aggregator and references |
| `api_version` | `str` | `None` | API version forwarded to both aggregator and references |
Extra options inside the dict descriptor (`base_url`, `api_key`, `temperature`, `api_version`, `auth`) survive both the inline and named-preset paths and reach the aggregator. Explicit `Agent(base_url=..., api_key=...)` arguments take precedence.
***
## Common Patterns
### High-accuracy Q\&A
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Fact Checker",
instructions="Be precise and cite sources.",
llm={
"provider": "panel",
"references": ["gpt-4o", "gemini-1.5-pro"],
"aggregator": "claude-sonnet-5",
},
)
agent.start("What is the capital of Australia?")
```
### Creative writing with multiple voices
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Creative Writer",
instructions="Blend multiple stylistic voices into one coherent piece.",
llm={
"provider": "panel",
"references": ["gpt-4o", "claude-haiku-3-5"],
"aggregator": "claude-opus-4-8",
},
)
agent.start("Write a short story about a robot that discovers music.")
```
### Local + cloud hybrid
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
agent = Agent(
name="Hybrid Assistant",
instructions="Answer using local context and cloud intelligence.",
llm={
"provider": "panel",
"references": ["ollama/mistral"],
"aggregator": "gpt-4o",
"base_url": "http://localhost:11434",
},
)
agent.start("Summarize the attached document.")
```
### Disable panel temporarily
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
agent = Agent(
name="Assistant",
instructions="Answer helpfully.",
llm={
"provider": "panel",
"references": ["gpt-4o"],
"aggregator": "claude-sonnet-5",
"enabled": False,
},
)
```
***
## Best Practices
Reference models run tool-free by design. Only the aggregator has tool access. This is intentional: references provide perspective, not actions. Don't expect tool results from reference calls.
The default `reference_temperature=0.0` gives you stable, deterministic advisory perspectives. Raise it only when you want diverse creative suggestions from references.
References and the aggregator cannot themselves be panel descriptors — the SDK raises `ValueError` on detection. This prevents infinite recursion and makes the call graph explicit.
Reference guidance is injected at the tail of the latest user turn, never in the system prompt. This keeps the stable cached prefix intact so Anthropic prompt caching stays effective.
If a reference model call fails, the SDK logs a warning and replaces the result with `"(unavailable: reference call failed)"`. The aggregator still runs with the available perspectives.
***
## Related
Configure LLM providers and endpoints
Automatic fallback when a model fails
Route requests to different models by criteria
Give agents executable capabilities