feat(rnd): add FastAPI support to existing project outline

rnd/autogpt_server/README.md

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+# Next Gen AutoGPT 
+
+This is a research project into creating the next generation of autogpt, which is an autogpt agent server.
+
+The agent server will enable the creation of composite multi-agent system that utilize AutoGPT Agent as its default agent.
+
+
+## Project Outline
+
+Currently the project mainly consist of these components:
+
+*agent_api*
+A component that will expose API endpoints for the creation & execution of agents.
+This component will make connections to the database to persist and read the agents.
+It will also trigger the agent execution by pushing its execution request to the ExecutionQueue.
+
+*agent_executor*
+A component that will execute the agents.
+This component will be a pool of processes/threads that will consume the ExecutionQueue and execute the agent accordingly. 
+The result and progress of its execution will be persisted in the database.

rnd/autogpt_server/autogpt_server/agent_api/__init__.py

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+from .server import start_server  # noqa

rnd/autogpt_server/autogpt_server/agent_api/server.py

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+import uvicorn
+from fastapi import FastAPI
+
+from autogpt_server.data import ExecutionQueue
+
+app = FastAPI(
+    title="AutoGPT Agent Server",
+    description=(
+        "This server is used to execute agents that are created by the AutoGPT system."
+    ),
+    summary="AutoGPT Agent Server",
+    version="0.1",
+)
+
+execution_queue: ExecutionQueue = None
+
+
+@app.post("/agents/{agent_id}/execute")
+def execute_agent(agent_id: str):
+    execution_id = execution_queue.add(agent_id)
+    return {"execution_id": execution_id, "agent_id": agent_id}
+
+
+def start_server(queue: ExecutionQueue):
+    global execution_queue
+    execution_queue = queue
+    uvicorn.run(app)

rnd/autogpt_server/autogpt_server/agent_executor/__init__.py

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+from .executor import start_executor  # noqa

rnd/autogpt_server/autogpt_server/agent_executor/executor.py

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+import logging
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+from autogpt_server.data import ExecutionQueue
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: Replace this by an actual Agent Execution.
+def __execute(id: str, data: str) -> None:
+    logger.warning(f"Executor processing started, execution_id: {id}, data: {data}")
+    for i in range(5):
+        logger.warning(
+            f"Executor processing step {i}, execution_id: {id}, data: {data}"
+        )
+        time.sleep(1)
+    logger.warning(f"Executor processing completed, execution_id: {id}, data: {data}")
+
+
+def start_executor(pool_size: int, queue: ExecutionQueue) -> None:
+    with ThreadPoolExecutor(max_workers=pool_size) as executor:
+        while True:
+            execution = queue.get()
+            if not execution:
+                time.sleep(1)
+                continue
+            executor.submit(__execute, execution.execution_id, execution.data)

rnd/autogpt_server/autogpt_server/app.py

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+from multiprocessing import Process
+
+from autogpt_server.agent_api import start_server
+from autogpt_server.agent_executor import start_executor
+from autogpt_server.data import ExecutionQueue
+
+if __name__ == "__main__":
+    queue = ExecutionQueue()
+    executor_process = Process(target=start_executor, args=(5, queue))
+    executor_process.start()
+    start_server(queue)

rnd/autogpt_server/autogpt_server/data.py

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+import uuid
+from multiprocessing import Queue
+
+
+class Execution:
+    """Data model for an execution of an Agent"""
+
+    def __init__(self, execution_id: str, data: str):
+        self.execution_id = execution_id
+        self.data = data
+
+
+# TODO: Replace this by a persistent queue.
+# One very likely candidate would be persisted Redis,
+# Tt will also open the possibility of using it for other purposes like
+# caching, execution engine broker (like Celery), etc.
+class ExecutionQueue:
+    """
+    Queue for managing the execution of agents.
+    This will be shared between different processes
+    """
+
+    def __init__(self):
+        self.queue = Queue()
+
+    def add(self, data: str) -> str:
+        execution_id = uuid.uuid4()
+        self.queue.put(Execution(str(execution_id), data))
+        return str(execution_id)
+
+    def get(self) -> Execution | None:
+        return self.queue.get()
+
+    def empty(self) -> bool:
+        return self.queue.empty()

rnd/nextgenautogpt/pyproject.toml → rnd/autogpt_server/pyproject.toml

@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "rnd"
+name = "autogpt_server"
 version = "0.1.0"
 description = ""
 authors = ["SwiftyOS <craigswift13@gmail.com>"]

rnd/autogpt_server/run.sh

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+python3 -m autogpt_server.app