Files
AutoGPT/autogpt_platform/backend/backend/api/external/v1/tools.py
Reinier van der Leer d23248f065 feat(backend/copilot): Copilot Executor Microservice (#12057)
Uncouple Copilot task execution from the REST API server. This should
help performance and scalability, and allows task execution to continue
regardless of the state of the user's connection.

- Resolves #12023

### Changes 🏗️

- Add `backend.copilot.executor`->`CoPilotExecutor` (setup similar to
`backend.executor`->`ExecutionManager`).

This executor service uses RabbitMQ-based task distribution, and sticks
with the existing Redis Streams setup for task output. It uses a cluster
lock mechanism to ensure a task is only executed by one pod, and the
`DatabaseManager` for pooled DB access.

- Add `backend.data.db_accessors` for automatic choice of direct/proxied
DB access

Chat requests now flow: API → RabbitMQ → CoPilot Executor → Redis
Streams → SSE Client. This enables horizontal scaling of chat processing
and isolates long-running LLM operations from the API service.

- Move non-API Copilot stuff into `backend.copilot` (from
`backend.api.features.chat`)
  - Updated import paths for all usages

- Move `backend.executor.database` to `backend.data.db_manager` and add
methods for copilot executor
  - Updated import paths for all usages
- Make `backend.copilot.db` RPC-compatible (-> DB ops return ~~Prisma~~
Pydantic models)
  - Make `backend.data.workspace` RPC-compatible
  - Make `backend.data.graphs.get_store_listed_graphs` RPC-compatible

DX:
- Add `copilot_executor` service to Docker setup

Config:
- Add `Config.num_copilot_workers` (default 5) and
`Config.copilot_executor_port` (default 8008)
- Remove unused `Config.agent_server_port`

> [!WARNING]
> **This change adds a new microservice to the system, with entrypoint
`backend.copilot.executor`.**
> The `docker compose` setup has been updated, but if you run the
Platform on something else, you'll have to update your deployment config
to include this new service.
>
> When running locally, the `CoPilotExecutor` uses port 8008 by default.

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Copilot works
    - [x] Processes messages when triggered
    - [x] Can use its tools

#### For configuration changes:

- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)

---------

Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
2026-02-17 16:15:28 +00:00

153 lines
5.0 KiB
Python

"""External API routes for chat tools - stateless HTTP endpoints.
Note: These endpoints use ephemeral sessions that are not persisted to Redis.
As a result, session-based rate limiting (max_agent_runs, max_agent_schedules)
is not enforced for external API calls. Each request creates a fresh session
with zeroed counters. Rate limiting for external API consumers should be
handled separately (e.g., via API key quotas).
"""
import logging
from typing import Any
from fastapi import APIRouter, Security
from prisma.enums import APIKeyPermission
from pydantic import BaseModel, Field
from backend.api.external.middleware import require_permission
from backend.copilot.model import ChatSession
from backend.copilot.tools import find_agent_tool, run_agent_tool
from backend.copilot.tools.models import ToolResponseBase
from backend.data.auth.base import APIAuthorizationInfo
logger = logging.getLogger(__name__)
tools_router = APIRouter(prefix="/tools", tags=["tools"])
# Note: We use Security() as a function parameter dependency (auth: APIAuthorizationInfo = Security(...))
# rather than in the decorator's dependencies= list. This avoids duplicate permission checks
# while still enforcing auth AND giving us access to auth for extracting user_id.
# Request models
class FindAgentRequest(BaseModel):
query: str = Field(..., description="Search query for finding agents")
class RunAgentRequest(BaseModel):
"""Request to run or schedule an agent.
The tool automatically handles the setup flow:
- First call returns available inputs so user can decide what values to use
- Returns missing credentials if user needs to configure them
- Executes when inputs are provided OR use_defaults=true
- Schedules execution if schedule_name and cron are provided
"""
username_agent_slug: str = Field(
...,
description="The marketplace agent slug (e.g., 'username/agent-name')",
)
inputs: dict[str, Any] = Field(
default_factory=dict,
description="Dictionary of input values for the agent",
)
use_defaults: bool = Field(
default=False,
description="Set to true to run with default values (user must confirm)",
)
schedule_name: str | None = Field(
None,
description="Name for scheduled execution (triggers scheduling mode)",
)
cron: str | None = Field(
None,
description="Cron expression (5 fields: minute hour day month weekday)",
)
timezone: str = Field(
default="UTC",
description="IANA timezone (e.g., 'America/New_York', 'UTC')",
)
def _create_ephemeral_session(user_id: str) -> ChatSession:
"""Create an ephemeral session for stateless API requests."""
return ChatSession.new(user_id)
@tools_router.post(
path="/find-agent",
)
async def find_agent(
request: FindAgentRequest,
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.USE_TOOLS)
),
) -> dict[str, Any]:
"""
Search for agents in the marketplace based on capabilities and user needs.
Args:
request: Search query for finding agents
Returns:
List of matching agents or no results response
"""
session = _create_ephemeral_session(auth.user_id)
result = await find_agent_tool._execute(
user_id=auth.user_id,
session=session,
query=request.query,
)
return _response_to_dict(result)
@tools_router.post(
path="/run-agent",
)
async def run_agent(
request: RunAgentRequest,
auth: APIAuthorizationInfo = Security(
require_permission(APIKeyPermission.USE_TOOLS)
),
) -> dict[str, Any]:
"""
Run or schedule an agent from the marketplace.
The endpoint automatically handles the setup flow:
- Returns missing inputs if required fields are not provided
- Returns missing credentials if user needs to configure them
- Executes immediately if all requirements are met
- Schedules execution if schedule_name and cron are provided
For scheduled execution:
- Cron format: "minute hour day month weekday"
- Examples: "0 9 * * 1-5" (9am weekdays), "0 0 * * *" (daily at midnight)
- Timezone: Use IANA timezone names like "America/New_York"
Args:
request: Agent slug, inputs, and optional schedule config
Returns:
- setup_requirements: If inputs or credentials are missing
- execution_started: If agent was run or scheduled successfully
- error: If something went wrong
"""
session = _create_ephemeral_session(auth.user_id)
result = await run_agent_tool._execute(
user_id=auth.user_id,
session=session,
username_agent_slug=request.username_agent_slug,
inputs=request.inputs,
use_defaults=request.use_defaults,
schedule_name=request.schedule_name or "",
cron=request.cron or "",
timezone=request.timezone,
)
return _response_to_dict(result)
def _response_to_dict(result: ToolResponseBase) -> dict[str, Any]:
"""Convert a tool response to a dictionary for JSON serialization."""
return result.model_dump()