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Conversation with async

A ready-to-run example is available here.

Concurrent agents​

Run multiple agent tasks in parallel using asyncio.gather():

async def main():
    loop = asyncio.get_running_loop()
    callback = AsyncCallbackWrapper(callback_coro, loop)

    # Create multiple conversation tasks running in parallel
    tasks = [
        loop.run_in_executor(None, run_conversation, callback),
        loop.run_in_executor(None, run_conversation, callback),
        loop.run_in_executor(None, run_conversation, callback)
    ]
    results = await asyncio.gather(*tasks)

Ready-to-run example​

This example demonstrates usage of a Conversation in an async context (e.g.: From a fastapi server). The conversation is run in a background thread and a callback with results is executed in the main runloop

"""
This example demonstrates usage of a Conversation in an async context
(e.g.: From a fastapi server). The conversation is run in a background
thread and a callback with results is executed in the main runloop
"""

import asyncio
import os

from pydantic import SecretStr

from faheemcode.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from faheemcode.sdk.conversation.types import ConversationCallbackType
from faheemcode.sdk.tool import Tool
from faheemcode.sdk.utils.async_utils import AsyncCallbackWrapper
from faheemcode.tools.file_editor import FileEditorTool
from faheemcode.tools.task_tracker import TaskTrackerTool
from faheemcode.tools.terminal import TerminalTool

logger = get_logger(__name__)

# Configure LLM
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Tools
cwd = os.getcwd()
tools = [
    Tool(
        name=TerminalTool.name,
    ),
    Tool(name=FileEditorTool.name),
    Tool(name=TaskTrackerTool.name),
]

# Agent
agent = Agent(llm=llm, tools=tools)

llm_messages = []  # collect raw LLM messages

# Callback coroutine
async def callback_coro(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())

# Synchronous run conversation
def run_conversation(callback: ConversationCallbackType):
    conversation = Conversation(agent=agent, callbacks=[callback])

    conversation.send_message(
        "Hello! Can you create a new Python file named hello.py that prints "
        "'Hello, World!'? Use task tracker to plan your steps."
    )
    conversation.run()

    conversation.send_message("Great! Now delete that file.")
    conversation.run()

async def main():
    loop = asyncio.get_running_loop()

    # Create the callback
    callback = AsyncCallbackWrapper(callback_coro, loop)

    # Run the conversation in a background thread and wait for it to finish...
    await loop.run_in_executor(None, run_conversation, callback)

    print("=" * 100)
    print("Conversation finished. Got the following LLM messages:")
    for i, message in enumerate(llm_messages):
        print(f"Message {i}: {str(message)[:200]}")

    # Report cost
    cost = llm.metrics.accumulated_cost
    print(f"EXAMPLE_COST: {cost}")

if __name__ == "__main__":
    asyncio.run(main())

You can run the example code as-is.

Bring your own provider key
export LLM_API_KEY="your-api-key"
export LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.
cd software-agent-sdk
uv run python examples/01_standalone_sdk/11_async.py
Faheem Code Cloud key
# https://app.faheemcode.ai/settings/api-keys
export LLM_API_KEY="example-user-api-key"
export LLM_MODEL="faheemcode/claude-sonnet-4-5-20250929"
cd software-agent-sdk
uv run python examples/01_standalone_sdk/11_async.py

Next steps​