Build a research agent

Build an AgentApp that searches and fetches public web sources over a bounded number of tool-call rounds. It replays earlier conversation turns and can recover from connector failures.

The finished project uses:

• the OpenAI SDK for model requests

• agent.connectors.tools for runtime-provided schemas

• agent.connectors.call for function calls

• agent.events.emit for frontend-visible model events

• agent.events.get_trace for conversation history

It uses only web_search and web_fetch. Neither requires an external account.

Create the project

Start from the AgentApp template on Flower Hub:

$ uvx --from flwr==1.39.0 flwr new @flwrlabs/agent
$ cd agent

You will replace the generated agent/agent_app.py while keeping its project structure:

agent/
├── .gitignore
├── LICENSE
├── README.md
├── pyproject.toml
└── agent/
    ├── __init__.py
    └── agent_app.py

Configure the project

Keep the generated build-system and Hatch sections. Update the AgentApp-related parts of pyproject.toml:

[project]
name = "research-agent"
version = "0.1.0"
description = "A bounded public-web research AgentApp"
license = { file = "LICENSE" }
requires-python = ">=3.11,<4.0"
dependencies = ["flwr>=1.39.0,<2.0", "openai>=2.16.0,<3.0.0"]

[tool.flwr.app]
publisher = "local"
fab-format-version = 1
flwr-version-target = "1.39.0"
fab-include = ["agent/**/*.py", "LICENSE"]

[tool.flwr.app.components]
agentapp = "agent.agent_app:app"

The configuration sets the Flower version target, lists the SDK dependency, and tells Flower where to load the AgentApp object.

Implement the AgentApp

Build agent/agent_app.py one section at a time. Add the following snippets in order.

Define the app and its limits

The main function receives:

• AgentSession for the prompt, connectors, and frontend-visible events

• Context for run configuration and state shared by the run series

The OpenAI client sends model requests through the runtime URL and credential injected into the AgentApp process. Keep the model, connector references, and tool-turn limit near the top of the file.

from __future__ import annotations

import json
import os
from typing import Any

from flwr.agentapp import AgentApp, AgentSession
from flwr.app import Context
from openai import OpenAI

MODEL = "openai/gpt-5.6-sol"
TOOL_REFS = ("web_search", "web_fetch")
MAX_TOOL_TURNS = 3

app = AgentApp()

Rebuild conversation input

Each chat message starts a new run. Flower keeps related runs in a run series, but the model sees only the input passed to client.responses.create. To support follow-up questions, replay the stored user and assistant messages.

Flower stores the user input and AgentApp-published events in the run-series trace. The trace also contains connector and reasoning activity, so load only user-message events and completed assistant output:

def message_text(content: Any) -> str:
    """Normalize a stored Responses message to plain text."""
    if isinstance(content, str):
        return content
    if isinstance(content, list):
        parts = []
        for part in content:
            if not isinstance(part, dict):
                raise TypeError("Message content parts must be objects")
            value = part.get("text", part.get("refusal"))
            if not isinstance(value, str):
                raise TypeError("Message content parts must contain text or refusal")
            parts.append(value)
        return "\n".join(parts)
    raise TypeError("Message content must be text or a list of content parts")


def conversation_messages(agent: AgentSession) -> list[dict[str, Any]]:
    """Rebuild completed user and assistant messages from the event trace."""
    run_order: list[int] = []
    turns_by_run: dict[int, list[dict[str, Any]]] = {}
    assistant_parts_by_run: dict[int, list[str]] = {}

    for entry in agent.events.get_trace():
        run_id = entry.get("run_id")
        event_type = entry.get("event")
        data = entry.get("data")
        if not isinstance(run_id, int) or not isinstance(data, dict):
            continue

        if event_type == "message" and data.get("role") == "user":
            assistant_parts_by_run.pop(run_id, None)
            if run_id not in turns_by_run:
                run_order.append(run_id)
            turns_by_run[run_id] = [
                {
                    "type": "message",
                    "role": "user",
                    "content": message_text(data.get("content")),
                }
            ]
        elif event_type in {
            "response.output_text.delta",
            "response.refusal.delta",
        }:
            delta = data.get("delta")
            if isinstance(delta, str):
                assistant_parts_by_run.setdefault(run_id, []).append(delta)
        elif event_type == "response.completed":
            assistant_parts = assistant_parts_by_run.pop(run_id, [])
            turn = turns_by_run.get(run_id)
            if assistant_parts and turn is not None:
                turn.append(
                    {
                        "type": "message",
                        "role": "assistant",
                        "content": "".join(assistant_parts),
                    }
                )
        elif event_type in {"error", "response.failed", "response.incomplete"}:
            assistant_parts_by_run.pop(run_id, None)

    return [message for run_id in run_order for message in turns_by_run[run_id]]

message_text raises an error for an unexpected shape instead of silently sending incomplete history to the model. The loader groups each user message and completed assistant response by run, then flattens the turns in the order their user events appear. This keeps overlapping runs from mixing their output. Failed or incomplete responses are not replayed as finished answers.

Let the model recover from connector failures

A connector can fail after the model requests it, and the model can return malformed arguments. The next model turn still needs an output for that call ID. Convert the exception into a function_call_output item so the model can explain the limitation or finish with the evidence it already has:

def connector_error_output(
    tool_call: dict[str, Any], exc: Exception
) -> dict[str, Any]:
    """Return an error item the model can handle in its next turn."""
    return {
        "type": "function_call_output",
        "call_id": tool_call["call_id"],
        "output": json.dumps({"error": str(exc)}),
    }

Orchestrate the tool loop

The main function has five phases:

1. Rebuild the conversation messages from the trace

2. Create the OpenAI client and request the connector tool schemas

3. Execute up to MAX_TOOL_TURNS rounds of model-requested function calls

4. Make one final model request without tools and publish its stream

5. Log the completed assistant message

Add the entry point:

@app.main()
def main(agent: AgentSession, context: Context) -> None:
    """Research the chat input with a bounded connector loop."""
    client = OpenAI(
        base_url=os.environ["FLWR_RUNTIME_BASE_URL"],
        api_key=os.environ["FLWR_RUNTIME_API_KEY"],
        max_retries=0,
    )
    input_items = conversation_messages(agent)

    tools = agent.connectors.tools(TOOL_REFS)
    allowed_tool_names = {
        tool["name"] for tool in tools if isinstance(tool.get("name"), str)
    }

    for _ in range(MAX_TOOL_TURNS):
        response = client.responses.create(
            model=MODEL,
            input=input_items,
            instructions=(
                "Research the user's question using public sources when useful. "
                "Request all independent tool calls for a turn together."
            ),
            tools=tools,
            tool_choice="auto",
        )
        response_output = [item.to_dict() for item in response.output]
        tool_calls = [
            item for item in response_output if item.get("type") == "function_call"
        ]
        if not tool_calls:
            break

        function_outputs = []
        for tool_call in tool_calls:
            if tool_call.get("name") not in allowed_tool_names:
                function_outputs.append(
                    connector_error_output(
                        tool_call,
                        RuntimeError(
                            f"Tool {tool_call.get('name')!r} was not exposed"
                        ),
                    )
                )
                continue
            try:
                arguments = tool_call.get("arguments")
                if isinstance(arguments, str):
                    arguments = json.loads(arguments)
                if not isinstance(arguments, dict):
                    raise ValueError("Tool call arguments must be a JSON object")
                function_outputs.append(agent.connectors.call(tool_call))
            except (RuntimeError, ValueError) as exc:
                function_outputs.append(connector_error_output(tool_call, exc))

        input_items.extend(response_output)
        input_items.extend(function_outputs)

    stream = client.responses.create(
        model=MODEL,
        input=input_items,
        instructions=(
            "Answer the user's question from the available evidence. "
            "Mention any failed source access and do not invent results."
        ),
        stream=True,
    )
    output_text = []
    for event in stream:
        agent.events.emit(event.to_dict())
        if event.type in {"error", "response.failed", "response.incomplete"}:
            raise RuntimeError(f"Model response did not complete: {event}")
        if event.type in {
            "response.output_text.delta",
            "response.refusal.delta",
        }:
            output_text.append(event.delta)

    final_text = "".join(output_text)
    print(final_text)

The trace already contains the current prompt’s user-message event when the AgentApp starts. The planning calls remain local to this run because the app publishes only the final streamed response. The complete planning output and connector outputs stay in input_items for subsequent tool turns within this run; the trace loader does not replay them on later runs.

The allowed tool names come from the returned schemas because one connector reference can expose several tools. The final request omits tools so the model cannot request another connector round. The app publishes the stream and collects answer or refusal text for its logs. If the stream is incomplete, the app raises an error and the trace loader discards its partial text on the next run.

Note

Connector calls still record their outputs and activity for run inspection. The trace loader ignores those event types, so connector activity and function outputs are not treated as conversation messages.

Copy the complete file

If you prefer to start from the finished version, expand the block below and copy it into agent/agent_app.py.

Complete agent/agent_app.py

from __future__ import annotations

import json
import os
from typing import Any

from flwr.agentapp import AgentApp, AgentSession
from flwr.app import Context
from openai import OpenAI

MODEL = "openai/gpt-5.6-sol"
TOOL_REFS = ("web_search", "web_fetch")
MAX_TOOL_TURNS = 3

app = AgentApp()


def message_text(content: Any) -> str:
    """Normalize a stored Responses message to plain text."""
    if isinstance(content, str):
        return content
    if isinstance(content, list):
        parts = []
        for part in content:
            if not isinstance(part, dict):
                raise TypeError("Message content parts must be objects")
            value = part.get("text", part.get("refusal"))
            if not isinstance(value, str):
                raise TypeError("Message content parts must contain text or refusal")
            parts.append(value)
        return "\n".join(parts)
    raise TypeError("Message content must be text or a list of content parts")


def conversation_messages(agent: AgentSession) -> list[dict[str, Any]]:
    """Rebuild completed user and assistant messages from the event trace."""
    run_order: list[int] = []
    turns_by_run: dict[int, list[dict[str, Any]]] = {}
    assistant_parts_by_run: dict[int, list[str]] = {}

    for entry in agent.events.get_trace():
        run_id = entry.get("run_id")
        event_type = entry.get("event")
        data = entry.get("data")
        if not isinstance(run_id, int) or not isinstance(data, dict):
            continue

        if event_type == "message" and data.get("role") == "user":
            assistant_parts_by_run.pop(run_id, None)
            if run_id not in turns_by_run:
                run_order.append(run_id)
            turns_by_run[run_id] = [
                {
                    "type": "message",
                    "role": "user",
                    "content": message_text(data.get("content")),
                }
            ]
        elif event_type in {
            "response.output_text.delta",
            "response.refusal.delta",
        }:
            delta = data.get("delta")
            if isinstance(delta, str):
                assistant_parts_by_run.setdefault(run_id, []).append(delta)
        elif event_type == "response.completed":
            assistant_parts = assistant_parts_by_run.pop(run_id, [])
            turn = turns_by_run.get(run_id)
            if assistant_parts and turn is not None:
                turn.append(
                    {
                        "type": "message",
                        "role": "assistant",
                        "content": "".join(assistant_parts),
                    }
                )
        elif event_type in {"error", "response.failed", "response.incomplete"}:
            assistant_parts_by_run.pop(run_id, None)

    return [message for run_id in run_order for message in turns_by_run[run_id]]


def connector_error_output(
    tool_call: dict[str, Any], exc: Exception
) -> dict[str, Any]:
    """Return an error item the model can handle in its next turn."""
    return {
        "type": "function_call_output",
        "call_id": tool_call["call_id"],
        "output": json.dumps({"error": str(exc)}),
    }


@app.main()
def main(agent: AgentSession, context: Context) -> None:
    """Research the chat input with a bounded connector loop."""
    client = OpenAI(
        base_url=os.environ["FLWR_RUNTIME_BASE_URL"],
        api_key=os.environ["FLWR_RUNTIME_API_KEY"],
        max_retries=0,
    )
    input_items = conversation_messages(agent)

    tools = agent.connectors.tools(TOOL_REFS)
    allowed_tool_names = {
        tool["name"] for tool in tools if isinstance(tool.get("name"), str)
    }

    for _ in range(MAX_TOOL_TURNS):
        response = client.responses.create(
            model=MODEL,
            input=input_items,
            instructions=(
                "Research the user's question using public sources when useful. "
                "Request all independent tool calls for a turn together."
            ),
            tools=tools,
            tool_choice="auto",
        )
        response_output = [item.to_dict() for item in response.output]
        tool_calls = [
            item for item in response_output if item.get("type") == "function_call"
        ]
        if not tool_calls:
            break

        function_outputs = []
        for tool_call in tool_calls:
            if tool_call.get("name") not in allowed_tool_names:
                function_outputs.append(
                    connector_error_output(
                        tool_call,
                        RuntimeError(
                            f"Tool {tool_call.get('name')!r} was not exposed"
                        ),
                    )
                )
                continue
            try:
                arguments = tool_call.get("arguments")
                if isinstance(arguments, str):
                    arguments = json.loads(arguments)
                if not isinstance(arguments, dict):
                    raise ValueError("Tool call arguments must be a JSON object")
                function_outputs.append(agent.connectors.call(tool_call))
            except (RuntimeError, ValueError) as exc:
                function_outputs.append(connector_error_output(tool_call, exc))

        input_items.extend(response_output)
        input_items.extend(function_outputs)

    stream = client.responses.create(
        model=MODEL,
        input=input_items,
        instructions=(
            "Answer the user's question from the available evidence. "
            "Mention any failed source access and do not invent results."
        ),
        stream=True,
    )
    output_text = []
    for event in stream:
        agent.events.emit(event.to_dict())
        if event.type in {"error", "response.failed", "response.incomplete"}:
            raise RuntimeError(f"Model response did not complete: {event}")
        if event.type in {
            "response.output_text.delta",
            "response.refusal.delta",
        }:
            output_text.append(event.delta)

    final_text = "".join(output_text)
    print(final_text)

Build and run

$ uv sync
$ uv run flwr build
$ uv run flwr login supergrid
$ uv run flwr chat

At the chat prompt:

/load .
Find two public sources that explain federated AI and compare them.

Success checkpoint

The answer streams into the chat transcript. SuperGrid run activity shows any search or fetch calls and connector failures.

Adapt it safely

• Keep TOOL_REFS limited to the capabilities the task needs

• Keep a finite tool-turn limit even when you change models

• Validate every required run-config value before making a model call

• Never put credentials in prompts or connector arguments

• Use Connect accounts before adding an account connector, and remember that those runs are personal-workspace-only

• Follow Create automations before exposing start_automation for explicit future or recurring requests