CopilotKit

Agent Config

Forward typed configuration from your UI into the agent's reasoning loop.

"""MS Agent Framework agent backing the Agent Config Object demo.Reads three forwarded properties -- tone, expertise, responseLength -- from theAG-UI run input's ``forwardedProps`` and composes its system prompt dynamicallyper turn.The CopilotKit provider's ``properties`` prop is wired through the runtime as``forwardedProps`` on each AG-UI run. Because Microsoft Agent Framework agentsstore their system prompt in ``default_options["instructions"]``, we subclass``AgentFrameworkAgent`` and intercept ``run`` to swap in a freshly-builtinstruction string for the duration of each invocation.Invalid or missing values fall back to the corresponding ``DEFAULT_*``constant -- this function never raises so the demo can't deadlock on a badpayload."""from __future__ import annotationsfrom collections.abc import AsyncGeneratorfrom textwrap import dedentfrom typing import Any, Literalfrom ag_ui.core import BaseEventfrom agent_framework import Agent, BaseChatClientfrom agent_framework_ag_ui import AgentFrameworkAgentTone = Literal["professional", "casual", "enthusiastic"]Expertise = Literal["beginner", "intermediate", "expert"]ResponseLength = Literal["concise", "detailed"]DEFAULT_TONE: Tone = "professional"DEFAULT_EXPERTISE: Expertise = "intermediate"DEFAULT_RESPONSE_LENGTH: ResponseLength = "concise"VALID_TONES: set[str] = {"professional", "casual", "enthusiastic"}VALID_EXPERTISE: set[str] = {"beginner", "intermediate", "expert"}VALID_RESPONSE_LENGTHS: set[str] = {"concise", "detailed"}def read_properties(forwarded_props: Any) -> dict[str, str]:    """Read forwarded props with defensive defaults.    Any missing or unrecognized value falls back to the corresponding    ``DEFAULT_*`` constant. Never raises.    """    props = forwarded_props if isinstance(forwarded_props, dict) else {}    tone = props.get("tone", DEFAULT_TONE)    expertise = props.get("expertise", DEFAULT_EXPERTISE)    response_length = props.get("responseLength", DEFAULT_RESPONSE_LENGTH)    if tone not in VALID_TONES:        tone = DEFAULT_TONE    if expertise not in VALID_EXPERTISE:        expertise = DEFAULT_EXPERTISE    if response_length not in VALID_RESPONSE_LENGTHS:        response_length = DEFAULT_RESPONSE_LENGTH    return {        "tone": tone,        "expertise": expertise,        "response_length": response_length,    }def build_system_prompt(tone: str, expertise: str, response_length: str) -> str:    """Compose the system prompt from the three axes."""    tone_rules = {        "professional": ("Use neutral, precise language. No emoji. Short sentences."),        "casual": (            "Use friendly, conversational language. Contractions OK. "            "Light humor welcome."        ),        "enthusiastic": (            "Use upbeat, energetic language. Exclamation points OK. Emoji OK."        ),    }    expertise_rules = {        "beginner": "Assume no prior knowledge. Define jargon. Use analogies.",        "intermediate": (            "Assume common terms are understood; explain specialized terms."        ),        "expert": ("Assume technical fluency. Use precise terminology. Skip basics."),    }    length_rules = {        "concise": "Respond in 1-3 sentences.",        "detailed": ("Respond in multiple paragraphs with examples where relevant."),    }    return (        "You are a helpful assistant.\n\n"        f"Tone: {tone_rules[tone]}\n"        f"Expertise level: {expertise_rules[expertise]}\n"        f"Response length: {length_rules[response_length]}"    )class AgentConfigFrameworkAgent(AgentFrameworkAgent):    """AgentFrameworkAgent that rebuilds its system prompt per request.    Overrides ``run`` to read ``forwardedProps`` from the AG-UI input    and temporarily replace the wrapped agent's ``instructions`` option before    delegating to the standard orchestrator chain.    """    async def run(  # type: ignore[override]        self,        input_data: dict[str, Any],    ) -> AsyncGenerator[BaseEvent, None]:        props = read_properties(input_data.get("forwardedProps"))        system_prompt = build_system_prompt(            props["tone"], props["expertise"], props["response_length"]        )        options = getattr(self.agent, "default_options", None)        if not isinstance(options, dict):            async for event in super().run(input_data):                yield event            return        previous_instructions = options.get("instructions")        options["instructions"] = system_prompt        try:            async for event in super().run(input_data):                yield event        finally:            if previous_instructions is None:                options.pop("instructions", None)            else:                options["instructions"] = previous_instructionsdef create_agent_config_agent(chat_client: BaseChatClient) -> AgentConfigFrameworkAgent:    """Instantiate the Agent Config demo agent.    The base MS Agent Framework ``Agent`` carries only a neutral fallback    instruction. The real behavioural steering happens in the per-request    instruction string applied by ``AgentConfigFrameworkAgent.run``.    """    base_agent = Agent(        client=chat_client,        name="agent_config",        instructions=dedent(            """            You are a helpful assistant. Follow the tone, expertise level, and            response-length directives provided in the system message for each            turn. If no directive is provided, use professional / intermediate            / concise defaults.            """.strip()        ),        tools=[],    )    return AgentConfigFrameworkAgent(        agent=base_agent,        name="AgentConfigObjectDemo",        description=(            "Reads tone / expertise / responseLength from forwardedProps "            "and builds its system prompt per turn."        ),        require_confirmation=False,    )

You have a working agent and want the user to be able to tune how it behaves: tone, expertise level, response length, language, persona. By the end of this guide, your UI will own a typed config object that the agent reads on every run and rebuilds its system prompt from.

When to use this#

Reach for agent config whenever the agent's behaviour depends on user-controllable settings that don't fit naturally as chat input:

  • Tone, voice, persona: "playful", "formal", "casual"
  • Expertise level: "beginner", "intermediate", "expert"
  • Response shape: short / medium / long, structured / prose, language
  • Domain switches: which knowledge base to consult, which tool subset to enable

If the values are a channel the user occasionally tunes (a settings panel, a toolbar of selects), agent config is the right shape. If the values are content the agent should write back to (notes, a document, a plan), use Shared State instead.

How agent config flows from the UI into the agent's reasoning loop depends on your runtime architecture. Agents living behind a runtime read it from agent state on every run, while in-process agents receive the same object as forwarded properties on the provider — same UX, slightly different wiring on each side.

How it works#

Agent config is a typed object the frontend owns and publishes to the agent as runtime context. There are two pieces: the UI side, which owns the React state and publishes every change with useAgentContext, and the backend node, which reads that context entry and turns it into a system prompt.

The UI side stays simple. Hold the typed config in React state, then mirror every change into the agent through useAgentContext:

frontend/src/app/page.tsx — UI publishes the typed config
function ConfigContextRelay({ config }: { config: AgentConfig }) {
  useAgentContext({
    description: "Agent response preferences",
    value: {
      tone: config.tone,
      expertise: config.expertise,
      responseLength: config.responseLength,
    },
  });
  return null;
}

The backend half is also a single node. Read the latest config context at the top of every run and use it to build the system prompt for that turn:

backend/agent.py — agent reads config and rebuilds the system prompt
import json

CONFIG_KEYS = ("tone", "expertise", "responseLength")

def read_config_value(entry):
    value = entry.get("value")
    if isinstance(value, str):
        try:
            value = json.loads(value)
        except json.JSONDecodeError:
            return None
    if not isinstance(value, dict):
        return None
    if any(key in value for key in CONFIG_KEYS):
        return value
    return None

async def my_agent_node(state: AgentState, config: RunnableConfig):
    context_entries = state.get("copilotkit", {}).get("context", [])
    cfg = next(
        (
            value
            for entry in reversed(context_entries)
            if (value := read_config_value(entry)) is not None
        ),
        {},
    )
    tone = cfg.get("tone", "professional")
    expertise = cfg.get("expertise", "intermediate")
    response_length = cfg.get("responseLength", "concise")
    system_prompt = build_system_prompt(tone, expertise, response_length)
    # ...

The agent reads the latest typed config at the start of every turn, rebuilds the system prompt, runs the turn. This is the same shape as the shared-state write-side pattern; agent config is just a specific use of that pattern with a UI-owned typed object on top.

On this page

When to use thisHow it works