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Merge branch 'dev' into swiftyos/secrt-1916-optimize-find_block-response-size-90k-chars

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Swifty
2026-02-10 16:57:08 +01:00
parent 65f60596fe 017a00af46
commit a03fe3494f
181 changed files with 12456 additions and 9033 deletions
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4
.github/workflows/platform-fullstack-ci.yml vendored
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@@ -56,7 +56,7 @@ jobs:
run: pnpm install --frozen-lockfile
types:
runs-on: ubuntu-latest
runs-on: big-boi
needs: setup
strategy:
fail-fast: false
@@ -85,7 +85,7 @@ jobs:
- name: Run docker compose
run: |
docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
docker compose -f ../docker-compose.yml --profile local up -d deps_backend
- name: Restore dependencies cache
uses: actions/cache@v5

220
autogpt_platform/autogpt_libs/poetry.lock generated
View File

@@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
[[package]]
name = "annotated-doc"
@@ -67,7 +67,7 @@ description = "Backport of asyncio.Runner, a context manager that controls event
optional = false
python-versions = "<3.11,>=3.8"
groups = ["dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
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]
[package.dependencies]
@@ -523,7 +541,7 @@ description = "Backport of PEP 654 (exception groups)"
optional = false
python-versions = ">=3.7"
groups = ["main", "dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
{file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
{file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
@@ -2162,23 +2180,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
[[package]]
name = "pytest-cov"
version = "6.2.1"
version = "7.0.0"
description = "Pytest plugin for measuring coverage."
optional = false
python-versions = ">=3.9"
groups = ["dev"]
files = [
{file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
{file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
{file = "pytest_cov-7.0.0-py3-none-any.whl", hash = "sha256:3b8e9558b16cc1479da72058bdecf8073661c7f57f7d3c5f22a1c23507f2d861"},
{file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
]
[package.dependencies]
coverage = {version = ">=7.5", extras = ["toml"]}
coverage = {version = ">=7.10.6", extras = ["toml"]}
pluggy = ">=1.2"
pytest = ">=6.2.5"
pytest = ">=7"
[package.extras]
testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
testing = ["process-tests", "pytest-xdist", "virtualenv"]
[[package]]
name = "pytest-mock"
@@ -2545,7 +2563,7 @@ description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
groups = ["dev"]
markers = "python_version == \"3.10\""
markers = "python_version < \"3.11\""
files = [
{file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
{file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -2893,4 +2911,4 @@ type = ["pytest-mypy"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<4.0"
content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"
content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"

2
autogpt_platform/autogpt_libs/pyproject.toml
View File

@@ -26,7 +26,7 @@ pyright = "^1.1.408"
pytest = "^8.4.1"
pytest-asyncio = "^1.3.0"
pytest-mock = "^3.15.1"
pytest-cov = "^6.2.1"
pytest-cov = "^7.0.0"
ruff = "^0.15.0"
[build-system]

6
autogpt_platform/backend/backend/api/features/chat/config.py
View File

@@ -93,6 +93,12 @@ class ChatConfig(BaseSettings):
description="Name of the prompt in Langfuse to fetch",
)
# Extended thinking configuration for Claude models
thinking_enabled: bool = Field(
default=True,
description="Enable adaptive thinking for Claude models via OpenRouter",
)
@field_validator("api_key", mode="before")
@classmethod
def get_api_key(cls, v):

5
autogpt_platform/backend/backend/api/features/chat/db.py
View File

@@ -45,10 +45,7 @@ async def create_chat_session(
successfulAgentRuns=SafeJson({}),
successfulAgentSchedules=SafeJson({}),
)
return await PrismaChatSession.prisma().create(
data=data,
include={"Messages": True},
)
return await PrismaChatSession.prisma().create(data=data)
async def update_chat_session(

47
autogpt_platform/backend/backend/api/features/chat/response_model.py
View File

@@ -18,6 +18,10 @@ class ResponseType(str, Enum):
START = "start"
FINISH = "finish"
# Step lifecycle (one LLM API call within a message)
START_STEP = "start-step"
FINISH_STEP = "finish-step"
# Text streaming
TEXT_START = "text-start"
TEXT_DELTA = "text-delta"
@@ -57,6 +61,16 @@ class StreamStart(StreamBaseResponse):
description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-protocol fields like taskId."""
import json
data: dict[str, Any] = {
"type": self.type.value,
"messageId": self.messageId,
}
return f"data: {json.dumps(data)}\n\n"
class StreamFinish(StreamBaseResponse):
"""End of message/stream."""
@@ -64,6 +78,26 @@ class StreamFinish(StreamBaseResponse):
type: ResponseType = ResponseType.FINISH
class StreamStartStep(StreamBaseResponse):
"""Start of a step (one LLM API call within a message).
The AI SDK uses this to add a step-start boundary to message.parts,
enabling visual separation between multiple LLM calls in a single message.
"""
type: ResponseType = ResponseType.START_STEP
class StreamFinishStep(StreamBaseResponse):
"""End of a step (one LLM API call within a message).
The AI SDK uses this to reset activeTextParts and activeReasoningParts,
so the next LLM call in a tool-call continuation starts with clean state.
"""
type: ResponseType = ResponseType.FINISH_STEP
# ========== Text Streaming ==========
@@ -117,7 +151,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
toolCallId: str = Field(..., description="Tool call ID this responds to")
output: str | dict[str, Any] = Field(..., description="Tool execution output")
# Additional fields for internal use (not part of AI SDK spec but useful)
# Keep these for internal backend use
toolName: str | None = Field(
default=None, description="Name of the tool that was executed"
)
@@ -125,6 +159,17 @@ class StreamToolOutputAvailable(StreamBaseResponse):
default=True, description="Whether the tool execution succeeded"
)
def to_sse(self) -> str:
"""Convert to SSE format, excluding non-spec fields."""
import json
data = {
"type": self.type.value,
"toolCallId": self.toolCallId,
"output": self.output,
}
return f"data: {json.dumps(data)}\n\n"
# ========== Other ==========

348
autogpt_platform/backend/backend/api/features/chat/routes.py
View File

@@ -6,7 +6,7 @@ from collections.abc import AsyncGenerator
from typing import Annotated
from autogpt_libs import auth
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
@@ -17,7 +17,29 @@ from . import stream_registry
from .completion_handler import process_operation_failure, process_operation_success
from .config import ChatConfig
from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
from .response_model import StreamFinish, StreamHeartbeat, StreamStart
from .response_model import StreamFinish, StreamHeartbeat
from .tools.models import (
AgentDetailsResponse,
AgentOutputResponse,
AgentPreviewResponse,
AgentSavedResponse,
AgentsFoundResponse,
BlockListResponse,
BlockOutputResponse,
ClarificationNeededResponse,
DocPageResponse,
DocSearchResultsResponse,
ErrorResponse,
ExecutionStartedResponse,
InputValidationErrorResponse,
NeedLoginResponse,
NoResultsResponse,
OperationInProgressResponse,
OperationPendingResponse,
OperationStartedResponse,
SetupRequirementsResponse,
UnderstandingUpdatedResponse,
)
config = ChatConfig()
@@ -266,12 +288,36 @@ async def stream_chat_post(
"""
import asyncio
import time
stream_start_time = time.perf_counter()
log_meta = {"component": "ChatStream", "session_id": session_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] stream_chat_post STARTED, session={session_id}, "
f"user={user_id}, message_len={len(request.message)}",
extra={"json_fields": log_meta},
)
session = await _validate_and_get_session(session_id, user_id)
logger.info(
f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
extra={
"json_fields": {
**log_meta,
"duration_ms": (time.perf_counter() - stream_start_time) * 1000,
}
},
)
# Create a task in the stream registry for reconnection support
task_id = str(uuid_module.uuid4())
operation_id = str(uuid_module.uuid4())
log_meta["task_id"] = task_id
task_create_start = time.perf_counter()
await stream_registry.create_task(
task_id=task_id,
session_id=session_id,
@@ -280,14 +326,28 @@ async def stream_chat_post(
tool_name="chat",
operation_id=operation_id,
)
logger.info(
f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
extra={
"json_fields": {
**log_meta,
"duration_ms": (time.perf_counter() - task_create_start) * 1000,
}
},
)
# Background task that runs the AI generation independently of SSE connection
async def run_ai_generation():
try:
# Emit a start event with task_id for reconnection
start_chunk = StreamStart(messageId=task_id, taskId=task_id)
await stream_registry.publish_chunk(task_id, start_chunk)
import time as time_module
gen_start_time = time_module.perf_counter()
logger.info(
f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
extra={"json_fields": log_meta},
)
first_chunk_time, ttfc = None, None
chunk_count = 0
try:
async for chunk in chat_service.stream_chat_completion(
session_id,
request.message,
@@ -295,25 +355,79 @@ async def stream_chat_post(
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
context=request.context,
_task_id=task_id, # Pass task_id so service emits start with taskId for reconnection
):
chunk_count += 1
if first_chunk_time is None:
first_chunk_time = time_module.perf_counter()
ttfc = first_chunk_time - gen_start_time
logger.info(
f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"chunk_type": type(chunk).__name__,
"time_to_first_chunk_ms": ttfc * 1000,
}
},
)
# Write to Redis (subscribers will receive via XREAD)
await stream_registry.publish_chunk(task_id, chunk)
# Mark task as completed
gen_end_time = time_module.perf_counter()
total_time = (gen_end_time - gen_start_time) * 1000
logger.info(
f"[TIMING] run_ai_generation FINISHED in {total_time/1000:.1f}s; "
f"task={task_id}, session={session_id}, "
f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"time_to_first_chunk_ms": (
ttfc * 1000 if ttfc is not None else None
),
"n_chunks": chunk_count,
}
},
)
await stream_registry.mark_task_completed(task_id, "completed")
except Exception as e:
elapsed = time_module.perf_counter() - gen_start_time
logger.error(
f"Error in background AI generation for session {session_id}: {e}"
f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed * 1000,
"error": str(e),
}
},
)
await stream_registry.mark_task_completed(task_id, "failed")
# Start the AI generation in a background task
bg_task = asyncio.create_task(run_ai_generation())
await stream_registry.set_task_asyncio_task(task_id, bg_task)
setup_time = (time.perf_counter() - stream_start_time) * 1000
logger.info(
f"[TIMING] Background task started, setup={setup_time:.1f}ms",
extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
)
# SSE endpoint that subscribes to the task's stream
async def event_generator() -> AsyncGenerator[str, None]:
import time as time_module
event_gen_start = time_module.perf_counter()
logger.info(
f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
f"user={user_id}",
extra={"json_fields": log_meta},
)
subscriber_queue = None
first_chunk_yielded = False
chunks_yielded = 0
try:
# Subscribe to the task stream (this replays existing messages + live updates)
subscriber_queue = await stream_registry.subscribe_to_task(
@@ -328,22 +442,70 @@ async def stream_chat_post(
return
# Read from the subscriber queue and yield to SSE
logger.info(
"[TIMING] Starting to read from subscriber_queue",
extra={"json_fields": log_meta},
)
while True:
try:
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
chunks_yielded += 1
if not first_chunk_yielded:
first_chunk_yielded = True
elapsed = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
f"type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"chunk_type": type(chunk).__name__,
"elapsed_ms": elapsed * 1000,
}
},
)
yield chunk.to_sse()
# Check for finish signal
if isinstance(chunk, StreamFinish):
total_time = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] StreamFinish received in {total_time:.2f}s; "
f"n_chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"chunks_yielded": chunks_yielded,
"total_time_ms": total_time * 1000,
}
},
)
break
except asyncio.TimeoutError:
# Send heartbeat to keep connection alive
yield StreamHeartbeat().to_sse()
except GeneratorExit:
logger.info(
f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"chunks_yielded": chunks_yielded,
"reason": "client_disconnect",
}
},
)
pass # Client disconnected - background task continues
except Exception as e:
logger.error(f"Error in SSE stream for task {task_id}: {e}")
elapsed = (time_module.perf_counter() - event_gen_start) * 1000
logger.error(
f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
extra={
"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
},
)
finally:
# Unsubscribe when client disconnects or stream ends to prevent resource leak
if subscriber_queue is not None:
@@ -357,6 +519,18 @@ async def stream_chat_post(
exc_info=True,
)
# AI SDK protocol termination - always yield even if unsubscribe fails
total_time = time_module.perf_counter() - event_gen_start
logger.info(
f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time * 1000,
"chunks_yielded": chunks_yielded,
}
},
)
yield "data: [DONE]\n\n"
return StreamingResponse(
@@ -374,63 +548,90 @@ async def stream_chat_post(
@router.get(
"/sessions/{session_id}/stream",
)
async def stream_chat_get(
async def resume_session_stream(
session_id: str,
message: Annotated[str, Query(min_length=1, max_length=10000)],
user_id: str | None = Depends(auth.get_user_id),
is_user_message: bool = Query(default=True),
):
"""
Stream chat responses for a session (GET - legacy endpoint).
Resume an active stream for a session.
Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
- Text fragments as they are generated
- Tool call UI elements (if invoked)
- Tool execution results
Called by the AI SDK's ``useChat(resume: true)`` on page load.
Checks for an active (in-progress) task on the session and either replays
the full SSE stream or returns 204 No Content if nothing is running.
Args:
session_id: The chat session identifier to associate with the streamed messages.
message: The user's new message to process.
session_id: The chat session identifier.
user_id: Optional authenticated user ID.
is_user_message: Whether the message is a user message.
Returns:
StreamingResponse: SSE-formatted response chunks.
Returns:
StreamingResponse (SSE) when an active stream exists,
or 204 No Content when there is nothing to resume.
"""
session = await _validate_and_get_session(session_id, user_id)
import asyncio
active_task, _last_id = await stream_registry.get_active_task_for_session(
session_id, user_id
)
if not active_task:
return Response(status_code=204)
subscriber_queue = await stream_registry.subscribe_to_task(
task_id=active_task.task_id,
user_id=user_id,
last_message_id="0-0", # Full replay so useChat rebuilds the message
)
if subscriber_queue is None:
return Response(status_code=204)
async def event_generator() -> AsyncGenerator[str, None]:
chunk_count = 0
first_chunk_type: str | None = None
async for chunk in chat_service.stream_chat_completion(
session_id,
message,
is_user_message=is_user_message,
user_id=user_id,
session=session, # Pass pre-fetched session to avoid double-fetch
):
if chunk_count < 3:
logger.info(
"Chat stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
try:
while True:
try:
chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
if chunk_count < 3:
logger.info(
"Resume stream chunk",
extra={
"session_id": session_id,
"chunk_type": str(chunk.type),
},
)
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
if isinstance(chunk, StreamFinish):
break
except asyncio.TimeoutError:
yield StreamHeartbeat().to_sse()
except GeneratorExit:
pass
except Exception as e:
logger.error(f"Error in resume stream for session {session_id}: {e}")
finally:
try:
await stream_registry.unsubscribe_from_task(
active_task.task_id, subscriber_queue
)
if not first_chunk_type:
first_chunk_type = str(chunk.type)
chunk_count += 1
yield chunk.to_sse()
logger.info(
"Chat stream completed",
extra={
"session_id": session_id,
"chunk_count": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
# AI SDK protocol termination
yield "data: [DONE]\n\n"
except Exception as unsub_err:
logger.error(
f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
exc_info=True,
)
logger.info(
"Resume stream completed",
extra={
"session_id": session_id,
"n_chunks": chunk_count,
"first_chunk_type": first_chunk_type,
},
)
yield "data: [DONE]\n\n"
return StreamingResponse(
event_generator(),
@@ -438,8 +639,8 @@ async def stream_chat_get(
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
"X-Accel-Buffering": "no", # Disable nginx buffering
"x-vercel-ai-ui-message-stream": "v1", # AI SDK protocol header
"X-Accel-Buffering": "no",
"x-vercel-ai-ui-message-stream": "v1",
},
)
@@ -751,3 +952,42 @@ async def health_check() -> dict:
"service": "chat",
"version": "0.1.0",
}
# ========== Schema Export (for OpenAPI / Orval codegen) ==========
ToolResponseUnion = (
AgentsFoundResponse
| NoResultsResponse
| AgentDetailsResponse
| SetupRequirementsResponse
| ExecutionStartedResponse
| NeedLoginResponse
| ErrorResponse
| InputValidationErrorResponse
| AgentOutputResponse
| UnderstandingUpdatedResponse
| AgentPreviewResponse
| AgentSavedResponse
| ClarificationNeededResponse
| BlockListResponse
| BlockOutputResponse
| DocSearchResultsResponse
| DocPageResponse
| OperationStartedResponse
| OperationPendingResponse
| OperationInProgressResponse
)
@router.get(
"/schema/tool-responses",
response_model=ToolResponseUnion,
include_in_schema=True,
summary="[Dummy] Tool response type export for codegen",
description="This endpoint is not meant to be called. It exists solely to "
"expose tool response models in the OpenAPI schema for frontend codegen.",
)
async def _tool_response_schema() -> ToolResponseUnion: # type: ignore[return]
"""Never called at runtime. Exists only so Orval generates TS types."""
raise HTTPException(status_code=501, detail="Schema-only endpoint")

195
autogpt_platform/backend/backend/api/features/chat/service.py
View File

@@ -52,8 +52,10 @@ from .response_model import (
StreamBaseResponse,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -351,6 +353,10 @@ async def stream_chat_completion(
retry_count: int = 0,
session: ChatSession | None = None,
context: dict[str, str] | None = None, # {url: str, content: str}
_continuation_message_id: (
str | None
) = None, # Internal: reuse message ID for tool call continuations
_task_id: str | None = None, # Internal: task ID for SSE reconnection support
) -> AsyncGenerator[StreamBaseResponse, None]:
"""Main entry point for streaming chat completions with database handling.
@@ -371,21 +377,45 @@ async def stream_chat_completion(
ValueError: If max_context_messages is exceeded
"""
completion_start = time.monotonic()
# Build log metadata for structured logging
log_meta = {"component": "ChatService", "session_id": session_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
f"message_len={len(message) if message else 0}, is_user={is_user_message}",
extra={
"json_fields": {
**log_meta,
"message_len": len(message) if message else 0,
"is_user_message": is_user_message,
}
},
)
# Only fetch from Redis if session not provided (initial call)
if session is None:
fetch_start = time.monotonic()
session = await get_chat_session(session_id, user_id)
fetch_time = (time.monotonic() - fetch_start) * 1000
logger.info(
f"Fetched session from Redis: {session.session_id if session else 'None'}, "
f"message_count={len(session.messages) if session else 0}"
f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
f"n_messages={len(session.messages) if session else 0}",
extra={
"json_fields": {
**log_meta,
"duration_ms": fetch_time,
"n_messages": len(session.messages) if session else 0,
}
},
)
else:
logger.info(
f"Using provided session object: {session.session_id}, "
f"message_count={len(session.messages)}"
f"[TIMING] Using provided session, messages={len(session.messages)}",
extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
)
if not session:
@@ -406,17 +436,25 @@ async def stream_chat_completion(
# Track user message in PostHog
if is_user_message:
posthog_start = time.monotonic()
track_user_message(
user_id=user_id,
session_id=session_id,
message_length=len(message),
)
posthog_time = (time.monotonic() - posthog_start) * 1000
logger.info(
f"[TIMING] track_user_message took {posthog_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
)
logger.info(
f"Upserting session: {session.session_id} with user id {session.user_id}, "
f"message_count={len(session.messages)}"
)
upsert_start = time.monotonic()
session = await upsert_chat_session(session)
upsert_time = (time.monotonic() - upsert_start) * 1000
logger.info(
f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
)
assert session, "Session not found"
# Generate title for new sessions on first user message (non-blocking)
@@ -454,7 +492,13 @@ async def stream_chat_completion(
asyncio.create_task(_update_title())
# Build system prompt with business understanding
prompt_start = time.monotonic()
system_prompt, understanding = await _build_system_prompt(user_id)
prompt_time = (time.monotonic() - prompt_start) * 1000
logger.info(
f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
)
# Initialize variables for streaming
assistant_response = ChatMessage(
@@ -479,13 +523,27 @@ async def stream_chat_completion(
# Generate unique IDs for AI SDK protocol
import uuid as uuid_module
message_id = str(uuid_module.uuid4())
is_continuation = _continuation_message_id is not None
message_id = _continuation_message_id or str(uuid_module.uuid4())
text_block_id = str(uuid_module.uuid4())
# Yield message start
yield StreamStart(messageId=message_id)
# Only yield message start for the initial call, not for continuations.
setup_time = (time.monotonic() - completion_start) * 1000
logger.info(
f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
)
if not is_continuation:
yield StreamStart(messageId=message_id, taskId=_task_id)
# Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
yield StreamStartStep()
try:
logger.info(
"[TIMING] Calling _stream_chat_chunks",
extra={"json_fields": log_meta},
)
async for chunk in _stream_chat_chunks(
session=session,
tools=tools,
@@ -585,6 +643,10 @@ async def stream_chat_completion(
)
yield chunk
elif isinstance(chunk, StreamFinish):
if has_done_tool_call:
# Tool calls happened — close the step but don't send message-level finish.
# The continuation will open a new step, and finish will come at the end.
yield StreamFinishStep()
if not has_done_tool_call:
# Emit text-end before finish if we received text but haven't closed it
if has_received_text and not text_streaming_ended:
@@ -616,6 +678,8 @@ async def stream_chat_completion(
has_saved_assistant_message = True
has_yielded_end = True
# Emit finish-step before finish (resets AI SDK text/reasoning state)
yield StreamFinishStep()
yield chunk
elif isinstance(chunk, StreamError):
has_yielded_error = True
@@ -665,6 +729,10 @@ async def stream_chat_completion(
logger.info(
f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
)
# Close the current step before retrying so the recursive call's
# StreamStartStep doesn't produce unbalanced step events.
if not has_yielded_end:
yield StreamFinishStep()
should_retry = True
else:
# Non-retryable error or max retries exceeded
@@ -700,6 +768,7 @@ async def stream_chat_completion(
error_response = StreamError(errorText=error_message)
yield error_response
if not has_yielded_end:
yield StreamFinishStep()
yield StreamFinish()
return
@@ -714,6 +783,8 @@ async def stream_chat_completion(
retry_count=retry_count + 1,
session=session,
context=context,
_continuation_message_id=message_id, # Reuse message ID since start was already sent
_task_id=_task_id,
):
yield chunk
return # Exit after retry to avoid double-saving in finally block
@@ -783,6 +854,8 @@ async def stream_chat_completion(
session=session, # Pass session object to avoid Redis refetch
context=context,
tool_call_response=str(tool_response_messages),
_continuation_message_id=message_id, # Reuse message ID to avoid duplicates
_task_id=_task_id,
):
yield chunk
@@ -893,9 +966,21 @@ async def _stream_chat_chunks(
SSE formatted JSON response objects
"""
import time as time_module
stream_chunks_start = time_module.perf_counter()
model = config.model
logger.info("Starting pure chat stream")
# Build log metadata for structured logging
log_meta = {"component": "ChatService", "session_id": session.session_id}
if session.user_id:
log_meta["user_id"] = session.user_id
logger.info(
f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
f"user={session.user_id}, n_messages={len(session.messages)}",
extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
)
messages = session.to_openai_messages()
if system_prompt:
@@ -906,12 +991,18 @@ async def _stream_chat_chunks(
messages = [system_message] + messages
# Apply context window management
context_start = time_module.perf_counter()
context_result = await _manage_context_window(
messages=messages,
model=model,
api_key=config.api_key,
base_url=config.base_url,
)
context_time = (time_module.perf_counter() - context_start) * 1000
logger.info(
f"[TIMING] _manage_context_window took {context_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": context_time}},
)
if context_result.error:
if "System prompt dropped" in context_result.error:
@@ -946,9 +1037,19 @@ async def _stream_chat_chunks(
while retry_count <= MAX_RETRIES:
try:
elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
retry_info = (
f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
)
logger.info(
f"Creating OpenAI chat completion stream..."
f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"retry_count": retry_count,
}
},
)
# Build extra_body for OpenRouter tracing and PostHog analytics
@@ -965,6 +1066,11 @@ async def _stream_chat_chunks(
:128
] # OpenRouter limit
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in model.lower():
extra_body["reasoning"] = {"enabled": True}
api_call_start = time_module.perf_counter()
stream = await client.chat.completions.create(
model=model,
messages=cast(list[ChatCompletionMessageParam], messages),
@@ -974,6 +1080,11 @@ async def _stream_chat_chunks(
stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
extra_body=extra_body,
)
api_init_time = (time_module.perf_counter() - api_call_start) * 1000
logger.info(
f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
)
# Variables to accumulate tool calls
tool_calls: list[dict[str, Any]] = []
@@ -984,10 +1095,13 @@ async def _stream_chat_chunks(
# Track if we've started the text block
text_started = False
first_content_chunk = True
chunk_count = 0
# Process the stream
chunk: ChatCompletionChunk
async for chunk in stream:
chunk_count += 1
if chunk.usage:
yield StreamUsage(
promptTokens=chunk.usage.prompt_tokens,
@@ -1010,6 +1124,23 @@ async def _stream_chat_chunks(
if not text_started and text_block_id:
yield StreamTextStart(id=text_block_id)
text_started = True
# Log timing for first content chunk
if first_content_chunk:
first_content_chunk = False
ttfc = (
time_module.perf_counter() - api_call_start
) * 1000
logger.info(
f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
f"(since API call), n_chunks={chunk_count}",
extra={
"json_fields": {
**log_meta,
"time_to_first_chunk_ms": ttfc,
"n_chunks": chunk_count,
}
},
)
# Stream the text delta
text_response = StreamTextDelta(
id=text_block_id or "",
@@ -1066,7 +1197,21 @@ async def _stream_chat_chunks(
toolName=tool_calls[idx]["function"]["name"],
)
emitted_start_for_idx.add(idx)
logger.info(f"Stream complete. Finish reason: {finish_reason}")
stream_duration = time_module.perf_counter() - api_call_start
logger.info(
f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
f"duration={stream_duration:.2f}s, "
f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
extra={
"json_fields": {
**log_meta,
"stream_duration_ms": stream_duration * 1000,
"finish_reason": finish_reason,
"n_chunks": chunk_count,
"n_tool_calls": len(tool_calls),
}
},
)
# Yield all accumulated tool calls after the stream is complete
# This ensures all tool call arguments have been fully received
@@ -1086,6 +1231,12 @@ async def _stream_chat_chunks(
# Re-raise to trigger retry logic in the parent function
raise
total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
logger.info(
f"[TIMING] _stream_chat_chunks COMPLETED in {total_time/1000:.1f}s; "
f"session={session.session_id}, user={session.user_id}",
extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
)
yield StreamFinish()
return
except Exception as e:
@@ -1565,6 +1716,7 @@ async def _execute_long_running_tool_with_streaming(
task_id,
StreamError(errorText=str(e)),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())
await _update_pending_operation(
@@ -1681,6 +1833,10 @@ async def _generate_llm_continuation(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
retry_count = 0
last_error: Exception | None = None
response = None
@@ -1811,6 +1967,10 @@ async def _generate_llm_continuation_with_streaming(
if session_id:
extra_body["session_id"] = session_id[:128]
# Enable adaptive thinking for Anthropic models via OpenRouter
if config.thinking_enabled and "anthropic" in config.model.lower():
extra_body["reasoning"] = {"enabled": True}
# Make streaming LLM call (no tools - just text response)
from typing import cast
@@ -1822,6 +1982,7 @@ async def _generate_llm_continuation_with_streaming(
# Publish start event
await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
await stream_registry.publish_chunk(task_id, StreamStartStep())
await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
# Stream the response
@@ -1845,6 +2006,7 @@ async def _generate_llm_continuation_with_streaming(
# Publish end events
await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
await stream_registry.publish_chunk(task_id, StreamFinishStep())
if assistant_content:
# Reload session from DB to avoid race condition with user messages
@@ -1886,4 +2048,5 @@ async def _generate_llm_continuation_with_streaming(
task_id,
StreamError(errorText=f"Failed to generate response: {e}"),
)
await stream_registry.publish_chunk(task_id, StreamFinishStep())
await stream_registry.publish_chunk(task_id, StreamFinish())

289
autogpt_platform/backend/backend/api/features/chat/stream_registry.py
View File

@@ -104,6 +104,24 @@ async def create_task(
Returns:
The created ActiveTask instance (metadata only)
"""
import time
start_time = time.perf_counter()
# Build log metadata for structured logging
log_meta = {
"component": "StreamRegistry",
"task_id": task_id,
"session_id": session_id,
}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
extra={"json_fields": log_meta},
)
task = ActiveTask(
task_id=task_id,
session_id=session_id,
@@ -114,10 +132,18 @@ async def create_task(
)
# Store metadata in Redis
redis_start = time.perf_counter()
redis = await get_redis_async()
redis_time = (time.perf_counter() - redis_start) * 1000
logger.info(
f"[TIMING] get_redis_async took {redis_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
)
meta_key = _get_task_meta_key(task_id)
op_key = _get_operation_mapping_key(operation_id)
hset_start = time.perf_counter()
await redis.hset( # type: ignore[misc]
meta_key,
mapping={
@@ -131,12 +157,22 @@ async def create_task(
"created_at": task.created_at.isoformat(),
},
)
hset_time = (time.perf_counter() - hset_start) * 1000
logger.info(
f"[TIMING] redis.hset took {hset_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
)
await redis.expire(meta_key, config.stream_ttl)
# Create operation_id -> task_id mapping for webhook lookups
await redis.set(op_key, task_id, ex=config.stream_ttl)
logger.debug(f"Created task {task_id} for session {session_id}")
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
)
return task
@@ -156,26 +192,60 @@ async def publish_chunk(
Returns:
The Redis Stream message ID
"""
import time
start_time = time.perf_counter()
chunk_type = type(chunk).__name__
chunk_json = chunk.model_dump_json()
message_id = "0-0"
# Build log metadata
log_meta = {
"component": "StreamRegistry",
"task_id": task_id,
"chunk_type": chunk_type,
}
try:
redis = await get_redis_async()
stream_key = _get_task_stream_key(task_id)
# Write to Redis Stream for persistence and real-time delivery
xadd_start = time.perf_counter()
raw_id = await redis.xadd(
stream_key,
{"data": chunk_json},
maxlen=config.stream_max_length,
)
xadd_time = (time.perf_counter() - xadd_start) * 1000
message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
# Set TTL on stream to match task metadata TTL
await redis.expire(stream_key, config.stream_ttl)
total_time = (time.perf_counter() - start_time) * 1000
# Only log timing for significant chunks or slow operations
if (
chunk_type
in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
or total_time > 50
):
logger.info(
f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"xadd_time_ms": xadd_time,
"message_id": message_id,
}
},
)
except Exception as e:
elapsed = (time.perf_counter() - start_time) * 1000
logger.error(
f"Failed to publish chunk for task {task_id}: {e}",
f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
exc_info=True,
)
@@ -200,24 +270,61 @@ async def subscribe_to_task(
An asyncio Queue that will receive stream chunks, or None if task not found
or user doesn't have access
"""
import time
start_time = time.perf_counter()
# Build log metadata
log_meta = {"component": "StreamRegistry", "task_id": task_id}
if user_id:
log_meta["user_id"] = user_id
logger.info(
f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
)
redis_start = time.perf_counter()
redis = await get_redis_async()
meta_key = _get_task_meta_key(task_id)
meta: dict[Any, Any] = await redis.hgetall(meta_key) # type: ignore[misc]
hgetall_time = (time.perf_counter() - redis_start) * 1000
logger.info(
f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
)
if not meta:
logger.debug(f"Task {task_id} not found in Redis")
elapsed = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"reason": "task_not_found",
}
},
)
return None
# Note: Redis client uses decode_responses=True, so keys are strings
task_status = meta.get("status", "")
task_user_id = meta.get("user_id", "") or None
log_meta["session_id"] = meta.get("session_id", "")
# Validate ownership - if task has an owner, requester must match
if task_user_id:
if user_id != task_user_id:
logger.warning(
f"User {user_id} denied access to task {task_id} "
f"owned by {task_user_id}"
f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
extra={
"json_fields": {
**log_meta,
"task_owner": task_user_id,
"reason": "access_denied",
}
},
)
return None
@@ -225,7 +332,19 @@ async def subscribe_to_task(
stream_key = _get_task_stream_key(task_id)
# Step 1: Replay messages from Redis Stream
xread_start = time.perf_counter()
messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
xread_time = (time.perf_counter() - xread_start) * 1000
logger.info(
f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
extra={
"json_fields": {
**log_meta,
"duration_ms": xread_time,
"task_status": task_status,
}
},
)
replayed_count = 0
replay_last_id = last_message_id
@@ -244,19 +363,48 @@ async def subscribe_to_task(
except Exception as e:
logger.warning(f"Failed to replay message: {e}")
logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
logger.info(
f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
extra={
"json_fields": {
**log_meta,
"n_messages_replayed": replayed_count,
"replay_last_id": replay_last_id,
}
},
)
# Step 2: If task is still running, start stream listener for live updates
if task_status == "running":
logger.info(
"[TIMING] Task still running, starting _stream_listener",
extra={"json_fields": {**log_meta, "task_status": task_status}},
)
listener_task = asyncio.create_task(
_stream_listener(task_id, subscriber_queue, replay_last_id)
_stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
)
# Track listener task for cleanup on unsubscribe
_listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
else:
# Task is completed/failed - add finish marker
logger.info(
f"[TIMING] Task already {task_status}, adding StreamFinish",
extra={"json_fields": {**log_meta, "task_status": task_status}},
)
await subscriber_queue.put(StreamFinish())
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
f"n_messages_replayed={replayed_count}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"n_messages_replayed": replayed_count,
}
},
)
return subscriber_queue
@@ -264,6 +412,7 @@ async def _stream_listener(
task_id: str,
subscriber_queue: asyncio.Queue[StreamBaseResponse],
last_replayed_id: str,
log_meta: dict | None = None,
) -> None:
"""Listen to Redis Stream for new messages using blocking XREAD.
@@ -274,10 +423,27 @@ async def _stream_listener(
task_id: Task ID to listen for
subscriber_queue: Queue to deliver messages to
last_replayed_id: Last message ID from replay (continue from here)
log_meta: Structured logging metadata
"""
import time
start_time = time.perf_counter()
# Use provided log_meta or build minimal one
if log_meta is None:
log_meta = {"component": "StreamRegistry", "task_id": task_id}
logger.info(
f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
)
queue_id = id(subscriber_queue)
# Track the last successfully delivered message ID for recovery hints
last_delivered_id = last_replayed_id
messages_delivered = 0
first_message_time = None
xread_count = 0
try:
redis = await get_redis_async()
@@ -287,9 +453,39 @@ async def _stream_listener(
while True:
# Block for up to 30 seconds waiting for new messages
# This allows periodic checking if task is still running
xread_start = time.perf_counter()
xread_count += 1
messages = await redis.xread(
{stream_key: current_id}, block=30000, count=100
)
xread_time = (time.perf_counter() - xread_start) * 1000
if messages:
msg_count = sum(len(msgs) for _, msgs in messages)
logger.info(
f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
extra={
"json_fields": {
**log_meta,
"xread_count": xread_count,
"n_messages": msg_count,
"duration_ms": xread_time,
}
},
)
elif xread_time > 1000:
# Only log timeouts (30s blocking)
logger.info(
f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
extra={
"json_fields": {
**log_meta,
"xread_count": xread_count,
"duration_ms": xread_time,
"reason": "timeout",
}
},
)
if not messages:
# Timeout - check if task is still running
@@ -326,10 +522,30 @@ async def _stream_listener(
)
# Update last delivered ID on successful delivery
last_delivered_id = current_id
messages_delivered += 1
if first_message_time is None:
first_message_time = time.perf_counter()
elapsed = (first_message_time - start_time) * 1000
logger.info(
f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"chunk_type": type(chunk).__name__,
}
},
)
except asyncio.TimeoutError:
logger.warning(
f"Subscriber queue full for task {task_id}, "
f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
extra={
"json_fields": {
**log_meta,
"timeout_s": QUEUE_PUT_TIMEOUT,
"reason": "queue_full",
}
},
)
# Send overflow error with recovery info
try:
@@ -351,15 +567,44 @@ async def _stream_listener(
# Stop listening on finish
if isinstance(chunk, StreamFinish):
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"messages_delivered": messages_delivered,
}
},
)
return
except Exception as e:
logger.warning(f"Error processing stream message: {e}")
logger.warning(
f"Error processing stream message: {e}",
extra={"json_fields": {**log_meta, "error": str(e)}},
)
except asyncio.CancelledError:
logger.debug(f"Stream listener cancelled for task {task_id}")
elapsed = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
extra={
"json_fields": {
**log_meta,
"elapsed_ms": elapsed,
"messages_delivered": messages_delivered,
"reason": "cancelled",
}
},
)
raise # Re-raise to propagate cancellation
except Exception as e:
logger.error(f"Stream listener error for task {task_id}: {e}")
elapsed = (time.perf_counter() - start_time) * 1000
logger.error(
f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
)
# On error, send finish to unblock subscriber
try:
await asyncio.wait_for(
@@ -368,10 +613,24 @@ async def _stream_listener(
)
except (asyncio.TimeoutError, asyncio.QueueFull):
logger.warning(
f"Could not deliver finish event for task {task_id} after error"
"Could not deliver finish event after error",
extra={"json_fields": log_meta},
)
finally:
# Clean up listener task mapping on exit
total_time = (time.perf_counter() - start_time) * 1000
logger.info(
f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
f"delivered={messages_delivered}, xread_count={xread_count}",
extra={
"json_fields": {
**log_meta,
"total_time_ms": total_time,
"messages_delivered": messages_delivered,
"xread_count": xread_count,
}
},
)
_listener_tasks.pop(queue_id, None)
@@ -598,8 +857,10 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
ResponseType,
StreamError,
StreamFinish,
StreamFinishStep,
StreamHeartbeat,
StreamStart,
StreamStartStep,
StreamTextDelta,
StreamTextEnd,
StreamTextStart,
@@ -613,6 +874,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
type_to_class: dict[str, type[StreamBaseResponse]] = {
ResponseType.START.value: StreamStart,
ResponseType.FINISH.value: StreamFinish,
ResponseType.START_STEP.value: StreamStartStep,
ResponseType.FINISH_STEP.value: StreamFinishStep,
ResponseType.TEXT_START.value: StreamTextStart,
ResponseType.TEXT_DELTA.value: StreamTextDelta,
ResponseType.TEXT_END.value: StreamTextEnd,

29
autogpt_platform/backend/backend/api/features/chat/tools/helpers.py Normal file
View File

@@ -0,0 +1,29 @@
"""Shared helpers for chat tools."""
from typing import Any
def get_inputs_from_schema(
input_schema: dict[str, Any],
exclude_fields: set[str] | None = None,
) -> list[dict[str, Any]]:
"""Extract input field info from JSON schema."""
if not isinstance(input_schema, dict):
return []
exclude = exclude_fields or set()
properties = input_schema.get("properties", {})
required = set(input_schema.get("required", []))
return [
{
"name": name,
"title": schema.get("title", name),
"type": schema.get("type", "string"),
"description": schema.get("description", ""),
"required": name in required,
"default": schema.get("default"),
}
for name, schema in properties.items()
if name not in exclude
]

21
autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
View File

@@ -24,6 +24,7 @@ from backend.util.timezone_utils import (
)
from .base import BaseTool
from .helpers import get_inputs_from_schema
from .models import (
AgentDetails,
AgentDetailsResponse,
@@ -261,7 +262,7 @@ class RunAgentTool(BaseTool):
),
requirements={
"credentials": requirements_creds_list,
"inputs": self._get_inputs_list(graph.input_schema),
"inputs": get_inputs_from_schema(graph.input_schema),
"execution_modes": self._get_execution_modes(graph),
},
),
@@ -369,22 +370,6 @@ class RunAgentTool(BaseTool):
session_id=session_id,
)
def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
"""Extract inputs list from schema."""
inputs_list = []
if isinstance(input_schema, dict) and "properties" in input_schema:
for field_name, field_schema in input_schema["properties"].items():
inputs_list.append(
{
"name": field_name,
"title": field_schema.get("title", field_name),
"type": field_schema.get("type", "string"),
"description": field_schema.get("description", ""),
"required": field_name in input_schema.get("required", []),
}
)
return inputs_list
def _get_execution_modes(self, graph: GraphModel) -> list[str]:
"""Get available execution modes for the graph."""
trigger_info = graph.trigger_setup_info
@@ -398,7 +383,7 @@ class RunAgentTool(BaseTool):
suffix: str,
) -> str:
"""Build a message describing available inputs for an agent."""
inputs_list = self._get_inputs_list(graph.input_schema)
inputs_list = get_inputs_from_schema(graph.input_schema)
required_names = [i["name"] for i in inputs_list if i["required"]]
optional_names = [i["name"] for i in inputs_list if not i["required"]]

185
autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
View File

@@ -12,14 +12,15 @@ from backend.api.features.chat.tools.find_block import (
COPILOT_EXCLUDED_BLOCK_IDS,
COPILOT_EXCLUDED_BLOCK_TYPES,
)
from backend.data.block import get_block
from backend.data.block import AnyBlockSchema, get_block
from backend.data.execution import ExecutionContext
from backend.data.model import CredentialsMetaInput
from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
from backend.data.workspace import get_or_create_workspace
from backend.integrations.creds_manager import IntegrationCredentialsManager
from backend.util.exceptions import BlockError
from .base import BaseTool
from .helpers import get_inputs_from_schema
from .models import (
BlockDetails,
BlockDetailsResponse,
@@ -30,7 +31,10 @@ from .models import (
ToolResponseBase,
UserReadiness,
)
from .utils import build_missing_credentials_from_field_info
from .utils import (
build_missing_credentials_from_field_info,
match_credentials_to_requirements,
)
logger = logging.getLogger(__name__)
@@ -80,91 +84,6 @@ class RunBlockTool(BaseTool):
def requires_auth(self) -> bool:
return True
async def _check_block_credentials(
self,
user_id: str,
block: Any,
input_data: dict[str, Any] | None = None,
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Check if user has required credentials for a block.
Args:
user_id: User ID
block: Block to check credentials for
input_data: Input data for the block (used to determine provider via discriminator)
Returns:
tuple[matched_credentials, missing_credentials]
"""
matched_credentials: dict[str, CredentialsMetaInput] = {}
missing_credentials: list[CredentialsMetaInput] = []
input_data = input_data or {}
# Get credential field info from block's input schema
credentials_fields_info = block.input_schema.get_credentials_fields_info()
if not credentials_fields_info:
return matched_credentials, missing_credentials
# Get user's available credentials
creds_manager = IntegrationCredentialsManager()
available_creds = await creds_manager.store.get_all_creds(user_id)
for field_name, field_info in credentials_fields_info.items():
effective_field_info = field_info
if field_info.discriminator and field_info.discriminator_mapping:
# Get discriminator from input, falling back to schema default
discriminator_value = input_data.get(field_info.discriminator)
if discriminator_value is None:
field = block.input_schema.model_fields.get(
field_info.discriminator
)
if field and field.default is not PydanticUndefined:
discriminator_value = field.default
if (
discriminator_value
and discriminator_value in field_info.discriminator_mapping
):
effective_field_info = field_info.discriminate(discriminator_value)
logger.debug(
f"Discriminated provider for {field_name}: "
f"{discriminator_value} -> {effective_field_info.provider}"
)
matching_cred = next(
(
cred
for cred in available_creds
if cred.provider in effective_field_info.provider
and cred.type in effective_field_info.supported_types
),
None,
)
if matching_cred:
matched_credentials[field_name] = CredentialsMetaInput(
id=matching_cred.id,
provider=matching_cred.provider, # type: ignore
type=matching_cred.type,
title=matching_cred.title,
)
else:
# Create a placeholder for the missing credential
provider = next(iter(effective_field_info.provider), "unknown")
cred_type = next(iter(effective_field_info.supported_types), "api_key")
missing_credentials.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=field_name.replace("_", " ").title(),
)
)
return matched_credentials, missing_credentials
async def _execute(
self,
user_id: str | None,
@@ -235,8 +154,8 @@ class RunBlockTool(BaseTool):
logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
creds_manager = IntegrationCredentialsManager()
matched_credentials, missing_credentials = await self._check_block_credentials(
user_id, block, input_data
matched_credentials, missing_credentials = (
await self._resolve_block_credentials(user_id, block, input_data)
)
# Get block schemas for details/validation
@@ -418,29 +337,75 @@ class RunBlockTool(BaseTool):
session_id=session_id,
)
def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
async def _resolve_block_credentials(
self,
user_id: str,
block: AnyBlockSchema,
input_data: dict[str, Any] | None = None,
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Resolve credentials for a block by matching user's available credentials.
Args:
user_id: User ID
block: Block to resolve credentials for
input_data: Input data for the block (used to determine provider via discriminator)
Returns:
tuple of (matched_credentials, missing_credentials) - matched credentials
are used for block execution, missing ones indicate setup requirements.
"""
input_data = input_data or {}
requirements = self._resolve_discriminated_credentials(block, input_data)
if not requirements:
return {}, []
return await match_credentials_to_requirements(user_id, requirements)
def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
"""Extract non-credential inputs from block schema."""
inputs_list = []
schema = block.input_schema.jsonschema()
properties = schema.get("properties", {})
required_fields = set(schema.get("required", []))
# Get credential field names to exclude
credentials_fields = set(block.input_schema.get_credentials_fields().keys())
return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
for field_name, field_schema in properties.items():
# Skip credential fields
if field_name in credentials_fields:
continue
def _resolve_discriminated_credentials(
self,
block: AnyBlockSchema,
input_data: dict[str, Any],
) -> dict[str, CredentialsFieldInfo]:
"""Resolve credential requirements, applying discriminator logic where needed."""
credentials_fields_info = block.input_schema.get_credentials_fields_info()
if not credentials_fields_info:
return {}
inputs_list.append(
{
"name": field_name,
"title": field_schema.get("title", field_name),
"type": field_schema.get("type", "string"),
"description": field_schema.get("description", ""),
"required": field_name in required_fields,
}
)
resolved: dict[str, CredentialsFieldInfo] = {}
return inputs_list
for field_name, field_info in credentials_fields_info.items():
effective_field_info = field_info
if field_info.discriminator and field_info.discriminator_mapping:
discriminator_value = input_data.get(field_info.discriminator)
if discriminator_value is None:
field = block.input_schema.model_fields.get(
field_info.discriminator
)
if field and field.default is not PydanticUndefined:
discriminator_value = field.default
if (
discriminator_value
and discriminator_value in field_info.discriminator_mapping
):
effective_field_info = field_info.discriminate(discriminator_value)
# For host-scoped credentials, add the discriminator value
# (e.g., URL) so _credential_is_for_host can match it
effective_field_info.discriminator_values.add(discriminator_value)
logger.debug(
f"Discriminated provider for {field_name}: "
f"{discriminator_value} -> {effective_field_info.provider}"
)
resolved[field_name] = effective_field_info
return resolved

96
autogpt_platform/backend/backend/api/features/chat/tools/utils.py
View File

@@ -8,6 +8,7 @@ from backend.api.features.library import model as library_model
from backend.api.features.store import db as store_db
from backend.data.graph import GraphModel
from backend.data.model import (
Credentials,
CredentialsFieldInfo,
CredentialsMetaInput,
HostScopedCredentials,
@@ -223,6 +224,99 @@ async def get_or_create_library_agent(
return library_agents[0]
async def match_credentials_to_requirements(
user_id: str,
requirements: dict[str, CredentialsFieldInfo],
) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
"""
Match user's credentials against a dictionary of credential requirements.
This is the core matching logic shared by both graph and block credential matching.
"""
matched: dict[str, CredentialsMetaInput] = {}
missing: list[CredentialsMetaInput] = []
if not requirements:
return matched, missing
available_creds = await get_user_credentials(user_id)
for field_name, field_info in requirements.items():
matching_cred = find_matching_credential(available_creds, field_info)
if matching_cred:
try:
matched[field_name] = create_credential_meta_from_match(matching_cred)
except Exception as e:
logger.error(
f"Failed to create CredentialsMetaInput for field '{field_name}': "
f"provider={matching_cred.provider}, type={matching_cred.type}, "
f"credential_id={matching_cred.id}",
exc_info=True,
)
provider = next(iter(field_info.provider), "unknown")
cred_type = next(iter(field_info.supported_types), "api_key")
missing.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=f"{field_name} (validation failed: {e})",
)
)
else:
provider = next(iter(field_info.provider), "unknown")
cred_type = next(iter(field_info.supported_types), "api_key")
missing.append(
CredentialsMetaInput(
id=field_name,
provider=provider, # type: ignore
type=cred_type, # type: ignore
title=field_name.replace("_", " ").title(),
)
)
return matched, missing
async def get_user_credentials(user_id: str) -> list[Credentials]:
"""Get all available credentials for a user."""
creds_manager = IntegrationCredentialsManager()
return await creds_manager.store.get_all_creds(user_id)
def find_matching_credential(
available_creds: list[Credentials],
field_info: CredentialsFieldInfo,
) -> Credentials | None:
"""Find a credential that matches the required provider, type, scopes, and host."""
for cred in available_creds:
if cred.provider not in field_info.provider:
continue
if cred.type not in field_info.supported_types:
continue
if cred.type == "oauth2" and not _credential_has_required_scopes(
cred, field_info
):
continue
if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
continue
return cred
return None
def create_credential_meta_from_match(
matching_cred: Credentials,
) -> CredentialsMetaInput:
"""Create a CredentialsMetaInput from a matched credential."""
return CredentialsMetaInput(
id=matching_cred.id,
provider=matching_cred.provider, # type: ignore
type=matching_cred.type,
title=matching_cred.title,
)
async def match_user_credentials_to_graph(
user_id: str,
graph: GraphModel,
@@ -331,8 +425,6 @@ def _credential_has_required_scopes(
# If no scopes are required, any credential matches
if not requirements.required_scopes:
return True
# Check that credential scopes are a superset of required scopes
return set(credential.scopes).issuperset(requirements.required_scopes)

94
autogpt_platform/backend/backend/api/features/store/hybrid_search.py
View File

@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
import logging
import re
import time
from dataclasses import dataclass
from typing import Any, Literal
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
results = await query_raw_with_schema(sql_query, *params)
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
total = results[0]["total_count"] if results else 0
# Apply BM25 reranking
@@ -686,7 +691,11 @@ async def hybrid_search(
LIMIT {limit_param} OFFSET {offset_param}
"""
results = await query_raw_with_schema(sql_query, *params)
try:
results = await query_raw_with_schema(sql_query, *params)
except Exception as e:
await _log_vector_error_diagnostics(e)
raise
total = results[0]["total_count"] if results else 0
@@ -718,6 +727,87 @@ async def hybrid_search_simple(
return await hybrid_search(query=query, page=page, page_size=page_size)
# ============================================================================
# Diagnostics
# ============================================================================
# Rate limit: only log vector error diagnostics once per this interval
_VECTOR_DIAG_INTERVAL_SECONDS = 60
_last_vector_diag_time: float = 0
async def _log_vector_error_diagnostics(error: Exception) -> None:
"""Log diagnostic info when 'type vector does not exist' error occurs.
Note: Diagnostic queries use query_raw_with_schema which may run on a different
pooled connection than the one that failed. Session-level search_path can differ,
so these diagnostics show cluster-wide state, not necessarily the failed session.
Includes rate limiting to avoid log spam - only logs once per minute.
Caller should re-raise the error after calling this function.
"""
global _last_vector_diag_time
# Check if this is the vector type error
error_str = str(error).lower()
if not (
"type" in error_str and "vector" in error_str and "does not exist" in error_str
):
return
# Rate limit: only log once per interval
now = time.time()
if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
return
_last_vector_diag_time = now
try:
diagnostics: dict[str, object] = {}
try:
search_path_result = await query_raw_with_schema("SHOW search_path")
diagnostics["search_path"] = search_path_result
except Exception as e:
diagnostics["search_path"] = f"Error: {e}"
try:
schema_result = await query_raw_with_schema("SELECT current_schema()")
diagnostics["current_schema"] = schema_result
except Exception as e:
diagnostics["current_schema"] = f"Error: {e}"
try:
user_result = await query_raw_with_schema(
"SELECT current_user, session_user, current_database()"
)
diagnostics["user_info"] = user_result
except Exception as e:
diagnostics["user_info"] = f"Error: {e}"
try:
# Check pgvector extension installation (cluster-wide, stable info)
ext_result = await query_raw_with_schema(
"SELECT extname, extversion, nspname as schema "
"FROM pg_extension e "
"JOIN pg_namespace n ON e.extnamespace = n.oid "
"WHERE extname = 'vector'"
)
diagnostics["pgvector_extension"] = ext_result
except Exception as e:
diagnostics["pgvector_extension"] = f"Error: {e}"
logger.error(
f"Vector type error diagnostics:\n"
f" Error: {error}\n"
f" search_path: {diagnostics.get('search_path')}\n"
f" current_schema: {diagnostics.get('current_schema')}\n"
f" user_info: {diagnostics.get('user_info')}\n"
f" pgvector_extension: {diagnostics.get('pgvector_extension')}"
)
except Exception as diag_error:
logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
# for existing code that expects the popularity parameter
HybridSearchWeights = StoreAgentSearchWeights

81
autogpt_platform/backend/backend/data/rabbitmq.py
View File

@@ -1,3 +1,4 @@
import asyncio
import logging
from abc import ABC, abstractmethod
from enum import Enum
@@ -225,6 +226,10 @@ class SyncRabbitMQ(RabbitMQBase):
class AsyncRabbitMQ(RabbitMQBase):
"""Asynchronous RabbitMQ client"""
def __init__(self, config: RabbitMQConfig):
super().__init__(config)
self._reconnect_lock: asyncio.Lock | None = None
@property
def is_connected(self) -> bool:
return bool(self._connection and not self._connection.is_closed)
@@ -235,7 +240,17 @@ class AsyncRabbitMQ(RabbitMQBase):
@conn_retry("AsyncRabbitMQ", "Acquiring async connection")
async def connect(self):
if self.is_connected:
if self.is_connected and self._channel and not self._channel.is_closed:
return
if (
self.is_connected
and self._connection
and (self._channel is None or self._channel.is_closed)
):
self._channel = await self._connection.channel()
await self._channel.set_qos(prefetch_count=1)
await self.declare_infrastructure()
return
self._connection = await aio_pika.connect_robust(
@@ -291,24 +306,46 @@ class AsyncRabbitMQ(RabbitMQBase):
exchange, routing_key=queue.routing_key or queue.name
)
@func_retry
async def publish_message(
@property
def _lock(self) -> asyncio.Lock:
if self._reconnect_lock is None:
self._reconnect_lock = asyncio.Lock()
return self._reconnect_lock
async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
"""Get a valid channel, reconnecting if the current one is stale.
Uses a lock to prevent concurrent reconnection attempts from racing.
"""
if self.is_ready:
return self._channel # type: ignore # is_ready guarantees non-None
async with self._lock:
# Double-check after acquiring lock
if self.is_ready:
return self._channel # type: ignore
self._channel = None
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
return self._channel
async def _publish_once(
self,
routing_key: str,
message: str,
exchange: Optional[Exchange] = None,
persistent: bool = True,
) -> None:
if not self.is_ready:
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
channel = await self._ensure_channel()
if exchange:
exchange_obj = await self._channel.get_exchange(exchange.name)
exchange_obj = await channel.get_exchange(exchange.name)
else:
exchange_obj = self._channel.default_exchange
exchange_obj = channel.default_exchange
await exchange_obj.publish(
aio_pika.Message(
@@ -322,9 +359,23 @@ class AsyncRabbitMQ(RabbitMQBase):
routing_key=routing_key,
)
@func_retry
async def publish_message(
self,
routing_key: str,
message: str,
exchange: Optional[Exchange] = None,
persistent: bool = True,
) -> None:
try:
await self._publish_once(routing_key, message, exchange, persistent)
except aio_pika.exceptions.ChannelInvalidStateError:
logger.warning(
"RabbitMQ channel invalid, forcing reconnect and retrying publish"
)
async with self._lock:
self._channel = None
await self._publish_once(routing_key, message, exchange, persistent)
async def get_channel(self) -> aio_pika.abc.AbstractChannel:
if not self.is_ready:
await self.connect()
if self._channel is None:
raise RuntimeError("Channel should be established after connect")
return self._channel
return await self._ensure_channel()

14
autogpt_platform/backend/poetry.lock generated
View File

@@ -1382,14 +1382,14 @@ tzdata = "*"
[[package]]
name = "fastapi"
version = "0.128.5"
version = "0.128.6"
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
optional = false
python-versions = ">=3.9"
groups = ["main"]
files = [
{file = "fastapi-0.128.5-py3-none-any.whl", hash = "sha256:bceec0de8aa6564599c5bcc0593b0d287703562c848271fca8546fd2c87bf4dd"},
{file = "fastapi-0.128.5.tar.gz", hash = "sha256:a7173579fc162d6471e3c6fbd9a4b7610c7a3b367bcacf6c4f90d5d022cab711"},
{file = "fastapi-0.128.6-py3-none-any.whl", hash = "sha256:bb1c1ef87d6086a7132d0ab60869d6f1ee67283b20fbf84ec0003bd335099509"},
{file = "fastapi-0.128.6.tar.gz", hash = "sha256:0cb3946557e792d731b26a42b04912f16367e3c3135ea8290f620e234f2b604f"},
]
[package.dependencies]
@@ -3078,14 +3078,14 @@ type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]
[[package]]
name = "langfuse"
version = "3.13.0"
version = "3.14.1"
description = "A client library for accessing langfuse"
optional = false
python-versions = "<4.0,>=3.10"
groups = ["main"]
files = [
{file = "langfuse-3.13.0-py3-none-any.whl", hash = "sha256:71912ddac1cc831a65df895eae538a556f564c094ae51473e747426e9ded1a9d"},
{file = "langfuse-3.13.0.tar.gz", hash = "sha256:dacea8111ca4442e97dbfec4f8d676cf9709b35357a26e468f8887b95de0012f"},
{file = "langfuse-3.14.1-py3-none-any.whl", hash = "sha256:17bed605dbfc9947cbd1738a715f6d27c1b80b6da9f2946586171958fa5820d0"},
{file = "langfuse-3.14.1.tar.gz", hash = "sha256:404a6104cd29353d7829aa417ec46565b04917e5599afdda96c5b0865f4bc991"},
]
[package.dependencies]
@@ -8440,4 +8440,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<3.14"
content-hash = "14686ee0e2dc446a75d0db145b08dc410dc31c357e25085bb0f9b0174711c4b1"
content-hash = "fc135114e01de39c8adf70f6132045e7d44a19473c1279aee0978de65aad1655"

4
autogpt_platform/backend/pyproject.toml
View File

@@ -21,7 +21,7 @@ cryptography = "^46.0"
discord-py = "^2.5.2"
e2b-code-interpreter = "^1.5.2"
elevenlabs = "^1.50.0"
fastapi = "^0.128.5"
fastapi = "^0.128.6"
feedparser = "^6.0.11"
flake8 = "^7.3.0"
google-api-python-client = "^2.177.0"
@@ -34,7 +34,7 @@ html2text = "^2024.2.26"
jinja2 = "^3.1.6"
jsonref = "^1.1.0"
jsonschema = "^4.25.0"
langfuse = "^3.11.0"
langfuse = "^3.14.1"
launchdarkly-server-sdk = "^9.14.1"
mem0ai = "^0.1.115"
moviepy = "^2.1.2"

6
autogpt_platform/frontend/Dockerfile
View File

@@ -25,8 +25,12 @@ RUN if [ -f .env.production ]; then \
cp .env.default .env; \
fi
RUN pnpm run generate:api
# Disable source-map generation in Docker builds to halve webpack memory usage.
# Source maps are only useful when SENTRY_AUTH_TOKEN is set (Vercel deploys);
# the Docker image never uploads them, so generating them just wastes RAM.
ENV NEXT_PUBLIC_SOURCEMAPS="false"
# In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi
RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=8192" pnpm build; else NODE_OPTIONS="--max-old-space-size=8192" pnpm build; fi
# Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
FROM node:21-alpine AS prod

51
autogpt_platform/frontend/next.config.mjs
View File

@@ -1,8 +1,12 @@
import { withSentryConfig } from "@sentry/nextjs";
// Allow Docker builds to skip source-map generation (halves memory usage).
// Defaults to true so Vercel/local builds are unaffected.
const enableSourceMaps = process.env.NEXT_PUBLIC_SOURCEMAPS !== "false";
/** @type {import('next').NextConfig} */
const nextConfig = {
productionBrowserSourceMaps: true,
productionBrowserSourceMaps: enableSourceMaps,
// Externalize OpenTelemetry packages to fix Turbopack HMR issues
serverExternalPackages: [
"@opentelemetry/instrumentation",
@@ -14,9 +18,37 @@ const nextConfig = {
serverActions: {
bodySizeLimit: "256mb",
},
// Increase body size limit for API routes (file uploads) - 256MB to match backend limit
proxyClientMaxBodySize: "256mb",
middlewareClientMaxBodySize: "256mb",
// Limit parallel webpack workers to reduce peak memory during builds.
cpus: 2,
},
// Work around cssnano "Invalid array length" bug in Next.js's bundled
// cssnano-simple comment parser when processing very large CSS chunks.
// CSS is still bundled correctly; gzip handles most of the size savings anyway.
webpack: (config, { dev }) => {
if (!dev) {
// Next.js adds CssMinimizerPlugin internally (after user config), so we
// can't filter it from config.plugins. Instead, intercept the webpack
// compilation hooks and replace the buggy plugin's tap with a no-op.
config.plugins.push({
apply(compiler) {
compiler.hooks.compilation.tap(
"DisableCssMinimizer",
(compilation) => {
compilation.hooks.processAssets.intercept({
register: (tap) => {
if (tap.name === "CssMinimizerPlugin") {
return { ...tap, fn: async () => {} };
}
return tap;
},
});
},
);
},
});
}
return config;
},
images: {
domains: [
@@ -54,9 +86,16 @@ const nextConfig = {
transpilePackages: ["geist"],
};
const isDevelopmentBuild = process.env.NODE_ENV !== "production";
// Only run the Sentry webpack plugin when we can actually upload source maps
// (i.e. on Vercel with SENTRY_AUTH_TOKEN set). The Sentry *runtime* SDK
// (imported in app code) still captures errors without the plugin.
// Skipping the plugin saves ~1 GB of peak memory during `next build`.
const skipSentryPlugin =
process.env.NODE_ENV !== "production" ||
!enableSourceMaps ||
!process.env.SENTRY_AUTH_TOKEN;
export default isDevelopmentBuild
export default skipSentryPlugin
? nextConfig
: withSentryConfig(nextConfig, {
// For all available options, see:
@@ -96,7 +135,7 @@ export default isDevelopmentBuild
// This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
sourcemaps: {
disable: false,
disable: !enableSourceMaps,
assets: [".next/**/*.js", ".next/**/*.js.map"],
ignore: ["**/node_modules/**"],
deleteSourcemapsAfterUpload: false, // Source is public anyway :)

14
autogpt_platform/frontend/package.json
View File

@@ -7,7 +7,7 @@
},
"scripts": {
"dev": "pnpm run generate:api:force && next dev --turbo",
"build": "next build",
"build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
"start": "next start",
"start:standalone": "cd .next/standalone && node server.js",
"lint": "next lint && prettier --check .",
@@ -30,6 +30,7 @@
"defaults"
],
"dependencies": {
"@ai-sdk/react": "3.0.61",
"@faker-js/faker": "10.0.0",
"@hookform/resolvers": "5.2.2",
"@next/third-parties": "15.4.6",
@@ -60,6 +61,10 @@
"@rjsf/utils": "6.1.2",
"@rjsf/validator-ajv8": "6.1.2",
"@sentry/nextjs": "10.27.0",
"@streamdown/cjk": "1.0.1",
"@streamdown/code": "1.0.1",
"@streamdown/math": "1.0.1",
"@streamdown/mermaid": "1.0.1",
"@supabase/ssr": "0.7.0",
"@supabase/supabase-js": "2.78.0",
"@tanstack/react-query": "5.90.6",
@@ -68,6 +73,7 @@
"@vercel/analytics": "1.5.0",
"@vercel/speed-insights": "1.2.0",
"@xyflow/react": "12.9.2",
"ai": "6.0.59",
"boring-avatars": "1.11.2",
"class-variance-authority": "0.7.1",
"clsx": "2.1.1",
@@ -87,7 +93,6 @@
"launchdarkly-react-client-sdk": "3.9.0",
"lodash": "4.17.21",
"lucide-react": "0.552.0",
"moment": "2.30.1",
"next": "15.4.10",
"next-themes": "0.4.6",
"nuqs": "2.7.2",
@@ -112,9 +117,11 @@
"remark-math": "6.0.0",
"shepherd.js": "14.5.1",
"sonner": "2.0.7",
"streamdown": "2.1.0",
"tailwind-merge": "2.6.0",
"tailwind-scrollbar": "3.1.0",
"tailwindcss-animate": "1.0.7",
"use-stick-to-bottom": "1.1.2",
"uuid": "11.1.0",
"vaul": "1.1.2",
"zod": "3.25.76",
@@ -172,7 +179,8 @@
},
"pnpm": {
"overrides": {
"@opentelemetry/instrumentation": "0.209.0"
"@opentelemetry/instrumentation": "0.209.0",
"lodash-es": "4.17.23"
}
},
"packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"

1180
autogpt_platform/frontend/pnpm-lock.yaml generated
View File

File diff suppressed because it is too large Load Diff

2
autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts
View File

@@ -1,4 +1,4 @@
import { debounce } from "lodash";
import debounce from "lodash/debounce";
import { useCallback, useEffect, useRef, useState } from "react";
import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
import { getQueryClient } from "@/lib/react-query/queryClient";

4
autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx
View File

@@ -70,10 +70,10 @@ export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
{children}
</div>
{canScrollLeft && (
<div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-white via-white/80 to-white/0" />
<div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-background via-background/80 to-background/0" />
)}
{canScrollRight && (
<div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-white via-white/80 to-white/0" />
<div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-background via-background/80 to-background/0" />
)}
{canScrollLeft && (
<button

76
autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx Normal file
View File

@@ -0,0 +1,76 @@
"use client";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { SidebarProvider } from "@/components/ui/sidebar";
import { ChatContainer } from "./components/ChatContainer/ChatContainer";
import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
import { MobileHeader } from "./components/MobileHeader/MobileHeader";
import { useCopilotPage } from "./useCopilotPage";
export function CopilotPage() {
const {
sessionId,
messages,
status,
error,
stop,
createSession,
onSend,
isLoadingSession,
isCreatingSession,
isUserLoading,
isLoggedIn,
// Mobile drawer
isMobile,
isDrawerOpen,
sessions,
isLoadingSessions,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleSelectSession,
handleNewChat,
} = useCopilotPage();
if (isUserLoading || !isLoggedIn) {
return <LoadingSpinner size="large" cover />;
}
return (
<SidebarProvider
defaultOpen={true}
className="h-[calc(100vh-72px)] min-h-0"
>
{!isMobile && <ChatSidebar />}
<div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
{isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
<div className="flex-1 overflow-hidden">
<ChatContainer
messages={messages}
status={status}
error={error}
sessionId={sessionId}
isLoadingSession={isLoadingSession}
isCreatingSession={isCreatingSession}
onCreateSession={createSession}
onSend={onSend}
onStop={stop}
/>
</div>
</div>
{isMobile && (
<MobileDrawer
isOpen={isDrawerOpen}
sessions={sessions}
currentSessionId={sessionId}
isLoading={isLoadingSessions}
onSelectSession={handleSelectSession}
onNewChat={handleNewChat}
onClose={handleCloseDrawer}
onOpenChange={handleDrawerOpenChange}
/>
)}
</SidebarProvider>
);
}

74
autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx Normal file
View File

@@ -0,0 +1,74 @@
"use client";
import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
import { UIDataTypes, UIMessage, UITools } from "ai";
import { LayoutGroup, motion } from "framer-motion";
import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
import { EmptySession } from "../EmptySession/EmptySession";
export interface ChatContainerProps {
messages: UIMessage<unknown, UIDataTypes, UITools>[];
status: string;
error: Error | undefined;
sessionId: string | null;
isLoadingSession: boolean;
isCreatingSession: boolean;
onCreateSession: () => void | Promise<string>;
onSend: (message: string) => void | Promise<void>;
onStop: () => void;
}
export const ChatContainer = ({
messages,
status,
error,
sessionId,
isLoadingSession,
isCreatingSession,
onCreateSession,
onSend,
onStop,
}: ChatContainerProps) => {
const inputLayoutId = "copilot-2-chat-input";
return (
<CopilotChatActionsProvider onSend={onSend}>
<LayoutGroup id="copilot-2-chat-layout">
<div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
{sessionId ? (
<div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
<ChatMessagesContainer
messages={messages}
status={status}
error={error}
isLoading={isLoadingSession}
/>
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.3 }}
className="relative px-3 pb-2 pt-2"
>
<div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
<ChatInput
inputId="chat-input-session"
onSend={onSend}
disabled={status === "streaming"}
isStreaming={status === "streaming"}
onStop={onStop}
placeholder="What else can I help with?"
/>
</motion.div>
</div>
) : (
<EmptySession
inputLayoutId={inputLayoutId}
isCreatingSession={isCreatingSession}
onCreateSession={onCreateSession}
onSend={onSend}
/>
)}
</div>
</LayoutGroup>
</CopilotChatActionsProvider>
);
};

17
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/ChatInput.tsx
View File

@@ -6,17 +6,19 @@ import {
MicrophoneIcon,
StopIcon,
} from "@phosphor-icons/react";
import { ChangeEvent, useCallback } from "react";
import { RecordingIndicator } from "./components/RecordingIndicator";
import { useChatInput } from "./useChatInput";
import { useVoiceRecording } from "./useVoiceRecording";
export interface Props {
onSend: (message: string) => void;
onSend: (message: string) => void | Promise<void>;
disabled?: boolean;
isStreaming?: boolean;
onStop?: () => void;
placeholder?: string;
className?: string;
inputId?: string;
}
export function ChatInput({
@@ -26,14 +28,14 @@ export function ChatInput({
onStop,
placeholder = "Type your message...",
className,
inputId = "chat-input",
}: Props) {
const inputId = "chat-input";
const {
value,
setValue,
handleKeyDown: baseHandleKeyDown,
handleSubmit,
handleChange,
handleChange: baseHandleChange,
hasMultipleLines,
} = useChatInput({
onSend,
@@ -60,6 +62,15 @@ export function ChatInput({
inputId,
});
// Block text changes when recording
const handleChange = useCallback(
(e: ChangeEvent<HTMLTextAreaElement>) => {
if (isRecording) return;
baseHandleChange(e);
},
[isRecording, baseHandleChange],
);
return (
<form onSubmit={handleSubmit} className={cn("relative flex-1", className)}>
<div className="relative">

0
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/components/AudioWaveform.tsx
View File

0
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/RecordingIndicator.tsx → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/components/RecordingIndicator.tsx
View File

0
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/helpers.ts → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/helpers.ts
View File

44
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useChatInput.ts → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/useChatInput.ts
View File

@@ -21,6 +21,7 @@ export function useChatInput({
}: Args) {
const [value, setValue] = useState("");
const [hasMultipleLines, setHasMultipleLines] = useState(false);
const [isSending, setIsSending] = useState(false);
useEffect(
function focusOnMount() {
@@ -100,34 +101,40 @@ export function useChatInput({
}
}, [value, maxRows, inputId]);
const handleSend = () => {
if (disabled || !value.trim()) return;
onSend(value.trim());
setValue("");
setHasMultipleLines(false);
const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
const wrapper = document.getElementById(
`${inputId}-wrapper`,
) as HTMLDivElement;
if (textarea) {
textarea.style.height = "auto";
async function handleSend() {
if (disabled || isSending || !value.trim()) return;
setIsSending(true);
try {
await onSend(value.trim());
setValue("");
setHasMultipleLines(false);
const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
const wrapper = document.getElementById(
`${inputId}-wrapper`,
) as HTMLDivElement;
if (textarea) {
textarea.style.height = "auto";
}
if (wrapper) {
wrapper.style.height = "";
wrapper.style.maxHeight = "";
}
} finally {
setIsSending(false);
}
if (wrapper) {
wrapper.style.height = "";
wrapper.style.maxHeight = "";
}
};
}
function handleKeyDown(event: KeyboardEvent<HTMLTextAreaElement>) {
if (event.key === "Enter" && !event.shiftKey) {
event.preventDefault();
handleSend();
void handleSend();
}
}
function handleSubmit(e: FormEvent<HTMLFormElement>) {
e.preventDefault();
handleSend();
void handleSend();
}
function handleChange(e: ChangeEvent<HTMLTextAreaElement>) {
@@ -142,5 +149,6 @@ export function useChatInput({
handleSubmit,
handleChange,
hasMultipleLines,
isSending,
};
}

32
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts → autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/useVoiceRecording.ts
View File

@@ -38,9 +38,13 @@ export function useVoiceRecording({
const streamRef = useRef<MediaStream | null>(null);
const isRecordingRef = useRef(false);
const isSupported =
typeof window !== "undefined" &&
!!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia);
const [isSupported, setIsSupported] = useState(false);
useEffect(() => {
setIsSupported(
!!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia),
);
}, []);
const clearTimer = useCallback(() => {
if (timerRef.current) {
@@ -214,17 +218,33 @@ export function useVoiceRecording({
const handleKeyDown = useCallback(
(event: KeyboardEvent<HTMLTextAreaElement>) => {
if (event.key === " " && !value.trim() && !isTranscribing) {
// Allow space to toggle recording (start when empty, stop when recording)
if (event.key === " " && !isTranscribing) {
if (isRecordingRef.current) {
// Stop recording on space
event.preventDefault();
stopRecording();
return;
} else if (!value.trim()) {
// Start recording on space when input is empty
event.preventDefault();
void startRecording();
return;
}
}
// Block all key events when recording (except space handled above)
if (isRecordingRef.current) {
event.preventDefault();
toggleRecording();
return;
}
baseHandleKeyDown(event);
},
[value, isTranscribing, toggleRecording, baseHandleKeyDown],
[value, isTranscribing, stopRecording, startRecording, baseHandleKeyDown],
);
const showMicButton = isSupported;
// Don't include isRecording in disabled state - we need key events to work
// Text input is blocked via handleKeyDown instead
const isInputDisabled = disabled || isStreaming || isTranscribing;
// Cleanup on unmount

274
autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx Normal file
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import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
import {
Conversation,
ConversationContent,
ConversationScrollButton,
} from "@/components/ai-elements/conversation";
import {
Message,
MessageContent,
MessageResponse,
} from "@/components/ai-elements/message";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
import { useEffect, useState } from "react";
import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
// ---------------------------------------------------------------------------
// Workspace media support
// ---------------------------------------------------------------------------
/**
* Resolve workspace:// URLs in markdown text to proxy download URLs.
* Detects MIME type from the hash fragment (e.g. workspace://id#video/mp4)
* and prefixes the alt text with "video:" so the custom img component can
* render a <video> element instead.
*/
function resolveWorkspaceUrls(text: string): string {
return text.replace(
/!\[([^\]]*)\]\(workspace:\/\/([^)#\s]+)(?:#([^)\s]*))?\)/g,
(_match, alt: string, fileId: string, mimeHint?: string) => {
const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
const url = `/api/proxy${apiPath}`;
if (mimeHint?.startsWith("video/")) {
return `![video:${alt || "Video"}](${url})`;
}
return `![${alt || "Image"}](${url})`;
},
);
}
/**
* Custom img component for Streamdown that renders <video> elements
* for workspace video files (detected via "video:" alt-text prefix).
* Falls back to <video> when an <img> fails to load for workspace files.
*/
function WorkspaceMediaImage(props: React.JSX.IntrinsicElements["img"]) {
const { src, alt, ...rest } = props;
const [imgFailed, setImgFailed] = useState(false);
const isWorkspace = src?.includes("/workspace/files/") ?? false;
if (!src) return null;
if (alt?.startsWith("video:") || (imgFailed && isWorkspace)) {
return (
<span className="my-2 inline-block">
<video
controls
className="h-auto max-w-full rounded-md border border-zinc-200"
preload="metadata"
>
<source src={src} />
Your browser does not support the video tag.
</video>
</span>
);
}
return (
// eslint-disable-next-line @next/next/no-img-element
<img
src={src}
alt={alt || "Image"}
className="h-auto max-w-full rounded-md border border-zinc-200"
loading="lazy"
onError={() => {
if (isWorkspace) setImgFailed(true);
}}
{...rest}
/>
);
}
/** Stable components override for Streamdown (avoids re-creating on every render). */
const STREAMDOWN_COMPONENTS = { img: WorkspaceMediaImage };
const THINKING_PHRASES = [
"Thinking...",
"Considering this...",
"Working through this...",
"Analyzing your request...",
"Reasoning...",
"Looking into it...",
"Processing your request...",
"Mulling this over...",
"Piecing it together...",
"On it...",
];
function getRandomPhrase() {
return THINKING_PHRASES[Math.floor(Math.random() * THINKING_PHRASES.length)];
}
interface ChatMessagesContainerProps {
messages: UIMessage<unknown, UIDataTypes, UITools>[];
status: string;
error: Error | undefined;
isLoading: boolean;
}
export const ChatMessagesContainer = ({
messages,
status,
error,
isLoading,
}: ChatMessagesContainerProps) => {
const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
useEffect(() => {
if (status === "submitted") {
setThinkingPhrase(getRandomPhrase());
}
}, [status]);
const lastMessage = messages[messages.length - 1];
const lastAssistantHasVisibleContent =
lastMessage?.role === "assistant" &&
lastMessage.parts.some(
(p) =>
(p.type === "text" && p.text.trim().length > 0) ||
p.type.startsWith("tool-"),
);
const showThinking =
status === "submitted" ||
(status === "streaming" && !lastAssistantHasVisibleContent);
return (
<Conversation className="min-h-0 flex-1">
<ConversationContent className="gap-6 px-3 py-6">
{isLoading && messages.length === 0 && (
<div className="flex flex-1 items-center justify-center">
<LoadingSpinner size="large" className="text-neutral-400" />
</div>
)}
{messages.map((message, messageIndex) => {
const isLastAssistant =
messageIndex === messages.length - 1 &&
message.role === "assistant";
const messageHasVisibleContent = message.parts.some(
(p) =>
(p.type === "text" && p.text.trim().length > 0) ||
p.type.startsWith("tool-"),
);
return (
<Message from={message.role} key={message.id}>
<MessageContent
className={
"text-[1rem] leading-relaxed " +
"group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
"group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
}
>
{message.parts.map((part, i) => {
switch (part.type) {
case "text":
return (
<MessageResponse
key={`${message.id}-${i}`}
components={STREAMDOWN_COMPONENTS}
>
{resolveWorkspaceUrls(part.text)}
</MessageResponse>
);
case "tool-find_block":
return (
<FindBlocksTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-find_agent":
case "tool-find_library_agent":
return (
<FindAgentsTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-search_docs":
case "tool-get_doc_page":
return (
<SearchDocsTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-run_block":
return (
<RunBlockTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-run_agent":
case "tool-schedule_agent":
return (
<RunAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-create_agent":
return (
<CreateAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-edit_agent":
return (
<EditAgentTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
case "tool-view_agent_output":
return (
<ViewAgentOutputTool
key={`${message.id}-${i}`}
part={part as ToolUIPart}
/>
);
default:
return null;
}
})}
{isLastAssistant &&
!messageHasVisibleContent &&
showThinking && (
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
{thinkingPhrase}
</span>
)}
</MessageContent>
</Message>
);
})}
{showThinking && lastMessage?.role !== "assistant" && (
<Message from="assistant">
<MessageContent className="text-[1rem] leading-relaxed">
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
{thinkingPhrase}
</span>
</MessageContent>
</Message>
)}
{error && (
<div className="rounded-lg bg-red-50 p-3 text-red-600">
Error: {error.message}
</div>
)}
</ConversationContent>
<ConversationScrollButton />
</Conversation>
);
};

188
autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx Normal file
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"use client";
import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
import { Button } from "@/components/atoms/Button/Button";
import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
import { Text } from "@/components/atoms/Text/Text";
import {
Sidebar,
SidebarContent,
SidebarFooter,
SidebarHeader,
SidebarTrigger,
useSidebar,
} from "@/components/ui/sidebar";
import { cn } from "@/lib/utils";
import { PlusCircleIcon, PlusIcon } from "@phosphor-icons/react";
import { motion } from "framer-motion";
import { parseAsString, useQueryState } from "nuqs";
export function ChatSidebar() {
const { state } = useSidebar();
const isCollapsed = state === "collapsed";
const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
const { data: sessionsResponse, isLoading: isLoadingSessions } =
useGetV2ListSessions({ limit: 50 });
const sessions =
sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
function handleNewChat() {
setSessionId(null);
}
function handleSelectSession(id: string) {
setSessionId(id);
}
function formatDate(dateString: string) {
const date = new Date(dateString);
const now = new Date();
const diffMs = now.getTime() - date.getTime();
const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
if (diffDays === 0) return "Today";
if (diffDays === 1) return "Yesterday";
if (diffDays < 7) return `${diffDays} days ago`;
const day = date.getDate();
const ordinal =
day % 10 === 1 && day !== 11
? "st"
: day % 10 === 2 && day !== 12
? "nd"
: day % 10 === 3 && day !== 13
? "rd"
: "th";
const month = date.toLocaleDateString("en-US", { month: "short" });
const year = date.getFullYear();
return `${day}${ordinal} ${month} ${year}`;
}
return (
<Sidebar
variant="inset"
collapsible="icon"
className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
>
{isCollapsed && (
<SidebarHeader
className={cn(
"flex",
isCollapsed
? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
: "flex-row items-center justify-between",
)}
>
<motion.div
key={isCollapsed ? "header-collapsed" : "header-expanded"}
className="flex flex-col items-center gap-3 pt-4"
initial={{ opacity: 0, filter: "blur(3px)" }}
animate={{ opacity: 1, filter: "blur(0px)" }}
transition={{ type: "spring", bounce: 0.2 }}
>
<div className="flex flex-col items-center gap-2">
<SidebarTrigger />
<Button
variant="ghost"
onClick={handleNewChat}
style={{ minWidth: "auto", width: "auto" }}
>
<PlusCircleIcon className="!size-5" />
<span className="sr-only">New Chat</span>
</Button>
</div>
</motion.div>
</SidebarHeader>
)}
<SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
{!isCollapsed && (
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.1 }}
className="flex items-center justify-between px-3"
>
<Text variant="h3" size="body-medium">
Your chats
</Text>
<div className="relative left-6">
<SidebarTrigger />
</div>
</motion.div>
)}
{!isCollapsed && (
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.15 }}
className="mt-4 flex flex-col gap-1"
>
{isLoadingSessions ? (
<div className="flex items-center justify-center py-4">
<LoadingSpinner size="small" className="text-neutral-400" />
</div>
) : sessions.length === 0 ? (
<p className="py-4 text-center text-sm text-neutral-500">
No conversations yet
</p>
) : (
sessions.map((session) => (
<button
key={session.id}
onClick={() => handleSelectSession(session.id)}
className={cn(
"w-full rounded-lg px-3 py-2.5 text-left transition-colors",
session.id === sessionId
? "bg-zinc-100"
: "hover:bg-zinc-50",
)}
>
<div className="flex min-w-0 max-w-full flex-col overflow-hidden">
<div className="min-w-0 max-w-full">
<Text
variant="body"
className={cn(
"truncate font-normal",
session.id === sessionId
? "text-zinc-600"
: "text-zinc-800",
)}
>
{session.title || `Untitled chat`}
</Text>
</div>
<Text variant="small" className="text-neutral-400">
{formatDate(session.updated_at)}
</Text>
</div>
</button>
))
)}
</motion.div>
)}
</SidebarContent>
{!isCollapsed && sessionId && (
<SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<motion.div
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.2, delay: 0.2 }}
>
<Button
variant="primary"
size="small"
onClick={handleNewChat}
className="w-full"
leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
>
New Chat
</Button>
</motion.div>
</SidebarFooter>
)}
</Sidebar>
);
}

16
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx Normal file
View File

@@ -0,0 +1,16 @@
"use client";
import { CopilotChatActionsContext } from "./useCopilotChatActions";
interface Props {
onSend: (message: string) => void | Promise<void>;
children: React.ReactNode;
}
export function CopilotChatActionsProvider({ onSend, children }: Props) {
return (
<CopilotChatActionsContext.Provider value={{ onSend }}>
{children}
</CopilotChatActionsContext.Provider>
);
}

23
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts Normal file
View File

@@ -0,0 +1,23 @@
"use client";
import { createContext, useContext } from "react";
interface CopilotChatActions {
onSend: (message: string) => void | Promise<void>;
}
const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
null,
);
export function useCopilotChatActions(): CopilotChatActions {
const ctx = useContext(CopilotChatActionsContext);
if (!ctx) {
throw new Error(
"useCopilotChatActions must be used within CopilotChatActionsProvider",
);
}
return ctx;
}
export { CopilotChatActionsContext };

99
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx
View File

@@ -1,99 +0,0 @@
"use client";
import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
import { Text } from "@/components/atoms/Text/Text";
import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
import type { ReactNode } from "react";
import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
import { MobileHeader } from "./components/MobileHeader/MobileHeader";
import { useCopilotShell } from "./useCopilotShell";
interface Props {
children: ReactNode;
}
export function CopilotShell({ children }: Props) {
const {
isMobile,
isDrawerOpen,
isLoading,
isCreatingSession,
isLoggedIn,
hasActiveSession,
sessions,
currentSessionId,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleNewChatClick,
handleSessionClick,
hasNextPage,
isFetchingNextPage,
fetchNextPage,
} = useCopilotShell();
if (!isLoggedIn) {
return (
<div className="flex h-full items-center justify-center">
<ChatLoader />
</div>
);
}
return (
<div
className="flex overflow-hidden bg-[#EFEFF0]"
style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
>
{!isMobile && (
<DesktopSidebar
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={handleSessionClick}
onFetchNextPage={fetchNextPage}
onNewChat={handleNewChatClick}
hasActiveSession={Boolean(hasActiveSession)}
/>
)}
<div className="relative flex min-h-0 flex-1 flex-col">
{isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
<div className="flex min-h-0 flex-1 flex-col">
{isCreatingSession ? (
<div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
<div className="flex flex-col items-center gap-4">
<ChatLoader />
<Text variant="body" className="text-zinc-500">
Creating your chat...
</Text>
</div>
</div>
) : (
children
)}
</div>
</div>
{isMobile && (
<MobileDrawer
isOpen={isDrawerOpen}
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={handleSessionClick}
onFetchNextPage={fetchNextPage}
onNewChat={handleNewChatClick}
onClose={handleCloseDrawer}
onOpenChange={handleDrawerOpenChange}
hasActiveSession={Boolean(hasActiveSession)}
/>
)}
</div>
);
}

70
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx
View File

@@ -1,70 +0,0 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { scrollbarStyles } from "@/components/styles/scrollbars";
import { cn } from "@/lib/utils";
import { Plus } from "@phosphor-icons/react";
import { SessionsList } from "../SessionsList/SessionsList";
interface Props {
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
onNewChat: () => void;
hasActiveSession: boolean;
}
export function DesktopSidebar({
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
onNewChat,
hasActiveSession,
}: Props) {
return (
<aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
<div className="shrink-0 px-6 py-4">
<Text variant="h3" size="body-medium">
Your chats
</Text>
</div>
<div
className={cn(
"flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
scrollbarStyles,
)}
>
<SessionsList
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={onSelectSession}
onFetchNextPage={onFetchNextPage}
/>
</div>
{hasActiveSession && (
<div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<Button
variant="primary"
size="small"
onClick={onNewChat}
className="w-full"
leftIcon={<Plus width="1rem" height="1rem" />}
>
New Chat
</Button>
</div>
)}
</aside>
);
}

91
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx
View File

@@ -1,91 +0,0 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Button } from "@/components/atoms/Button/Button";
import { scrollbarStyles } from "@/components/styles/scrollbars";
import { cn } from "@/lib/utils";
import { PlusIcon, X } from "@phosphor-icons/react";
import { Drawer } from "vaul";
import { SessionsList } from "../SessionsList/SessionsList";
interface Props {
isOpen: boolean;
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
onNewChat: () => void;
onClose: () => void;
onOpenChange: (open: boolean) => void;
hasActiveSession: boolean;
}
export function MobileDrawer({
isOpen,
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
onNewChat,
onClose,
onOpenChange,
hasActiveSession,
}: Props) {
return (
<Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
<Drawer.Portal>
<Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
<Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
<div className="shrink-0 border-b border-zinc-200 p-4">
<div className="flex items-center justify-between">
<Drawer.Title className="text-lg font-semibold text-zinc-800">
Your chats
</Drawer.Title>
<Button
variant="icon"
size="icon"
aria-label="Close sessions"
onClick={onClose}
>
<X width="1.25rem" height="1.25rem" />
</Button>
</div>
</div>
<div
className={cn(
"flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
scrollbarStyles,
)}
>
<SessionsList
sessions={sessions}
currentSessionId={currentSessionId}
isLoading={isLoading}
hasNextPage={hasNextPage}
isFetchingNextPage={isFetchingNextPage}
onSelectSession={onSelectSession}
onFetchNextPage={onFetchNextPage}
/>
</div>
{hasActiveSession && (
<div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<Button
variant="primary"
size="small"
onClick={onNewChat}
className="w-full"
leftIcon={<PlusIcon width="1rem" height="1rem" />}
>
New Chat
</Button>
</div>
)}
</Drawer.Content>
</Drawer.Portal>
</Drawer.Root>
);
}

24
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts
View File

@@ -1,24 +0,0 @@
import { useState } from "react";
export function useMobileDrawer() {
const [isDrawerOpen, setIsDrawerOpen] = useState(false);
const handleOpenDrawer = () => {
setIsDrawerOpen(true);
};
const handleCloseDrawer = () => {
setIsDrawerOpen(false);
};
const handleDrawerOpenChange = (open: boolean) => {
setIsDrawerOpen(open);
};
return {
isDrawerOpen,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
};
}

80
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx
View File

@@ -1,80 +0,0 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Skeleton } from "@/components/__legacy__/ui/skeleton";
import { Text } from "@/components/atoms/Text/Text";
import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
import { cn } from "@/lib/utils";
import { getSessionTitle } from "../../helpers";
interface Props {
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
hasNextPage: boolean;
isFetchingNextPage: boolean;
onSelectSession: (sessionId: string) => void;
onFetchNextPage: () => void;
}
export function SessionsList({
sessions,
currentSessionId,
isLoading,
hasNextPage,
isFetchingNextPage,
onSelectSession,
onFetchNextPage,
}: Props) {
if (isLoading) {
return (
<div className="space-y-1">
{Array.from({ length: 5 }).map((_, i) => (
<div key={i} className="rounded-lg px-3 py-2.5">
<Skeleton className="h-5 w-full" />
</div>
))}
</div>
);
}
if (sessions.length === 0) {
return (
<div className="flex h-full items-center justify-center">
<Text variant="body" className="text-zinc-500">
You don&apos;t have previous chats
</Text>
</div>
);
}
return (
<InfiniteList
items={sessions}
hasMore={hasNextPage}
isFetchingMore={isFetchingNextPage}
onEndReached={onFetchNextPage}
className="space-y-1"
renderItem={(session) => {
const isActive = session.id === currentSessionId;
return (
<button
onClick={() => onSelectSession(session.id)}
className={cn(
"w-full rounded-lg px-3 py-2.5 text-left transition-colors",
isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
)}
>
<Text
variant="body"
className={cn(
"font-normal",
isActive ? "text-zinc-600" : "text-zinc-800",
)}
>
{getSessionTitle(session)}
</Text>
</button>
);
}}
/>
);
}

91
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts
View File

@@ -1,91 +0,0 @@
import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { okData } from "@/app/api/helpers";
import { useEffect, useState } from "react";
const PAGE_SIZE = 50;
export interface UseSessionsPaginationArgs {
enabled: boolean;
}
export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
const [offset, setOffset] = useState(0);
const [accumulatedSessions, setAccumulatedSessions] = useState<
SessionSummaryResponse[]
>([]);
const [totalCount, setTotalCount] = useState<number | null>(null);
const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
{ limit: PAGE_SIZE, offset },
{
query: {
enabled: enabled && offset >= 0,
},
},
);
useEffect(() => {
const responseData = okData(data);
if (responseData) {
const newSessions = responseData.sessions;
const total = responseData.total;
setTotalCount(total);
if (offset === 0) {
setAccumulatedSessions(newSessions);
} else {
setAccumulatedSessions((prev) => [...prev, ...newSessions]);
}
} else if (!enabled) {
setAccumulatedSessions([]);
setTotalCount(null);
}
}, [data, offset, enabled]);
const hasNextPage =
totalCount !== null && accumulatedSessions.length < totalCount;
const areAllSessionsLoaded =
totalCount !== null &&
accumulatedSessions.length >= totalCount &&
!isFetching &&
!isLoading;
useEffect(() => {
if (
hasNextPage &&
!isFetching &&
!isLoading &&
!isError &&
totalCount !== null
) {
setOffset((prev) => prev + PAGE_SIZE);
}
}, [hasNextPage, isFetching, isLoading, isError, totalCount]);
const fetchNextPage = () => {
if (hasNextPage && !isFetching) {
setOffset((prev) => prev + PAGE_SIZE);
}
};
const reset = () => {
// Only reset the offset - keep existing sessions visible during refetch
// The effect will replace sessions when new data arrives at offset 0
setOffset(0);
};
return {
sessions: accumulatedSessions,
isLoading,
isFetching,
hasNextPage,
areAllSessionsLoaded,
totalCount,
fetchNextPage,
reset,
};
}

106
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts
View File

@@ -1,106 +0,0 @@
import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { format, formatDistanceToNow, isToday } from "date-fns";
export function convertSessionDetailToSummary(session: SessionDetailResponse) {
return {
id: session.id,
created_at: session.created_at,
updated_at: session.updated_at,
title: undefined,
};
}
export function filterVisibleSessions(sessions: SessionSummaryResponse[]) {
const fiveMinutesAgo = Date.now() - 5 * 60 * 1000;
return sessions.filter((session) => {
const hasBeenUpdated = session.updated_at !== session.created_at;
if (hasBeenUpdated) return true;
const isRecentlyCreated =
new Date(session.created_at).getTime() > fiveMinutesAgo;
return isRecentlyCreated;
});
}
export function getSessionTitle(session: SessionSummaryResponse) {
if (session.title) return session.title;
const isNewSession = session.updated_at === session.created_at;
if (isNewSession) {
const createdDate = new Date(session.created_at);
if (isToday(createdDate)) {
return "Today";
}
return format(createdDate, "MMM d, yyyy");
}
return "Untitled Chat";
}
export function getSessionUpdatedLabel(session: SessionSummaryResponse) {
if (!session.updated_at) return "";
return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
}
export function mergeCurrentSessionIntoList(
accumulatedSessions: SessionSummaryResponse[],
currentSessionId: string | null,
currentSessionData: SessionDetailResponse | null | undefined,
recentlyCreatedSessions?: Map<string, SessionSummaryResponse>,
) {
const filteredSessions: SessionSummaryResponse[] = [];
const addedIds = new Set<string>();
if (accumulatedSessions.length > 0) {
const visibleSessions = filterVisibleSessions(accumulatedSessions);
if (currentSessionId) {
const currentInAll = accumulatedSessions.find(
(s) => s.id === currentSessionId,
);
if (currentInAll) {
const isInVisible = visibleSessions.some(
(s) => s.id === currentSessionId,
);
if (!isInVisible) {
filteredSessions.push(currentInAll);
addedIds.add(currentInAll.id);
}
}
}
for (const session of visibleSessions) {
if (!addedIds.has(session.id)) {
filteredSessions.push(session);
addedIds.add(session.id);
}
}
}
if (currentSessionId && currentSessionData) {
if (!addedIds.has(currentSessionId)) {
const summarySession = convertSessionDetailToSummary(currentSessionData);
filteredSessions.unshift(summarySession);
addedIds.add(currentSessionId);
}
}
if (recentlyCreatedSessions) {
for (const [sessionId, sessionData] of recentlyCreatedSessions) {
if (!addedIds.has(sessionId)) {
filteredSessions.unshift(sessionData);
addedIds.add(sessionId);
}
}
}
return filteredSessions;
}
export function getCurrentSessionId(searchParams: URLSearchParams) {
return searchParams.get("sessionId");
}

124
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
View File

@@ -1,124 +0,0 @@
"use client";
import {
getGetV2GetSessionQueryKey,
getGetV2ListSessionsQueryKey,
useGetV2GetSession,
} from "@/app/api/__generated__/endpoints/chat/chat";
import { okData } from "@/app/api/helpers";
import { useChatStore } from "@/components/contextual/Chat/chat-store";
import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
import { useQueryClient } from "@tanstack/react-query";
import { usePathname, useSearchParams } from "next/navigation";
import { useCopilotStore } from "../../copilot-page-store";
import { useCopilotSessionId } from "../../useCopilotSessionId";
import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
import { getCurrentSessionId } from "./helpers";
import { useShellSessionList } from "./useShellSessionList";
export function useCopilotShell() {
const pathname = usePathname();
const searchParams = useSearchParams();
const queryClient = useQueryClient();
const breakpoint = useBreakpoint();
const { isLoggedIn } = useSupabase();
const isMobile =
breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
const isOnHomepage = pathname === "/copilot";
const paramSessionId = searchParams.get("sessionId");
const {
isDrawerOpen,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
} = useMobileDrawer();
const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
const currentSessionId = getCurrentSessionId(searchParams);
const { data: currentSessionData } = useGetV2GetSession(
currentSessionId || "",
{
query: {
enabled: !!currentSessionId,
select: okData,
},
},
);
const {
sessions,
isLoading,
isSessionsFetching,
hasNextPage,
fetchNextPage,
resetPagination,
recentlyCreatedSessionsRef,
} = useShellSessionList({
paginationEnabled,
currentSessionId,
currentSessionData,
isOnHomepage,
paramSessionId,
});
const stopStream = useChatStore((s) => s.stopStream);
const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
function handleSessionClick(sessionId: string) {
if (sessionId === currentSessionId) return;
// Stop current stream - SSE reconnection allows resuming later
if (currentSessionId) {
stopStream(currentSessionId);
}
if (recentlyCreatedSessionsRef.current.has(sessionId)) {
queryClient.invalidateQueries({
queryKey: getGetV2GetSessionQueryKey(sessionId),
});
}
setUrlSessionId(sessionId, { shallow: false });
if (isMobile) handleCloseDrawer();
}
function handleNewChatClick() {
// Stop current stream - SSE reconnection allows resuming later
if (currentSessionId) {
stopStream(currentSessionId);
}
resetPagination();
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
setUrlSessionId(null, { shallow: false });
if (isMobile) handleCloseDrawer();
}
return {
isMobile,
isDrawerOpen,
isLoggedIn,
hasActiveSession:
Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
isLoading: isLoading || isCreatingSession,
isCreatingSession,
sessions,
currentSessionId: urlSessionId,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleNewChatClick,
handleSessionClick,
hasNextPage,
isFetchingNextPage: isSessionsFetching,
fetchNextPage,
};
}

113
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts
View File

@@ -1,113 +0,0 @@
import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { useChatStore } from "@/components/contextual/Chat/chat-store";
import { useQueryClient } from "@tanstack/react-query";
import { useEffect, useMemo, useRef } from "react";
import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
import {
convertSessionDetailToSummary,
filterVisibleSessions,
mergeCurrentSessionIntoList,
} from "./helpers";
interface UseShellSessionListArgs {
paginationEnabled: boolean;
currentSessionId: string | null;
currentSessionData: SessionDetailResponse | null | undefined;
isOnHomepage: boolean;
paramSessionId: string | null;
}
export function useShellSessionList({
paginationEnabled,
currentSessionId,
currentSessionData,
isOnHomepage,
paramSessionId,
}: UseShellSessionListArgs) {
const queryClient = useQueryClient();
const onStreamComplete = useChatStore((s) => s.onStreamComplete);
const {
sessions: accumulatedSessions,
isLoading: isSessionsLoading,
isFetching: isSessionsFetching,
hasNextPage,
fetchNextPage,
reset: resetPagination,
} = useSessionsPagination({
enabled: paginationEnabled,
});
const recentlyCreatedSessionsRef = useRef<
Map<string, SessionSummaryResponse>
>(new Map());
useEffect(() => {
if (isOnHomepage && !paramSessionId) {
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
}
}, [isOnHomepage, paramSessionId, queryClient]);
useEffect(() => {
if (currentSessionId && currentSessionData) {
const isNewSession =
currentSessionData.updated_at === currentSessionData.created_at;
const isNotInAccumulated = !accumulatedSessions.some(
(s) => s.id === currentSessionId,
);
if (isNewSession || isNotInAccumulated) {
const summary = convertSessionDetailToSummary(currentSessionData);
recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
}
}
}, [currentSessionId, currentSessionData, accumulatedSessions]);
useEffect(() => {
for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
if (accumulatedSessions.some((s) => s.id === sessionId)) {
recentlyCreatedSessionsRef.current.delete(sessionId);
}
}
}, [accumulatedSessions]);
useEffect(() => {
const unsubscribe = onStreamComplete(() => {
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
});
return unsubscribe;
}, [onStreamComplete, queryClient]);
const sessions = useMemo(
() =>
mergeCurrentSessionIntoList(
accumulatedSessions,
currentSessionId,
currentSessionData,
recentlyCreatedSessionsRef.current,
),
[accumulatedSessions, currentSessionId, currentSessionData],
);
const visibleSessions = useMemo(
() => filterVisibleSessions(sessions),
[sessions],
);
const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
return {
sessions: visibleSessions,
isLoading,
isSessionsFetching,
hasNextPage,
fetchNextPage,
resetPagination,
recentlyCreatedSessionsRef,
};
}

111
autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx Normal file
View File

@@ -0,0 +1,111 @@
"use client";
import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
import { SpinnerGapIcon } from "@phosphor-icons/react";
import { motion } from "framer-motion";
import { useEffect, useState } from "react";
import {
getGreetingName,
getInputPlaceholder,
getQuickActions,
} from "./helpers";
interface Props {
inputLayoutId: string;
isCreatingSession: boolean;
onCreateSession: () => void | Promise<string>;
onSend: (message: string) => void | Promise<void>;
}
export function EmptySession({
inputLayoutId,
isCreatingSession,
onSend,
}: Props) {
const { user } = useSupabase();
const greetingName = getGreetingName(user);
const quickActions = getQuickActions();
const [loadingAction, setLoadingAction] = useState<string | null>(null);
const [inputPlaceholder, setInputPlaceholder] = useState(
getInputPlaceholder(),
);
useEffect(() => {
setInputPlaceholder(getInputPlaceholder(window.innerWidth));
}, [window.innerWidth]);
async function handleQuickActionClick(action: string) {
if (isCreatingSession || loadingAction) return;
setLoadingAction(action);
try {
await onSend(action);
} finally {
setLoadingAction(null);
}
}
return (
<div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-0 py-5 md:px-6 md:py-10">
<motion.div
className="w-full max-w-3xl text-center"
initial={{ opacity: 0 }}
animate={{ opacity: 1 }}
transition={{ duration: 0.3 }}
>
<div className="mx-auto max-w-3xl">
<Text variant="h3" className="mb-1 !text-[1.375rem] text-zinc-700">
Hey, <span className="text-violet-600">{greetingName}</span>
</Text>
<Text variant="h3" className="mb-8 !font-normal">
Tell me about your work — I&apos;ll find what to automate.
</Text>
<div className="mb-6">
<motion.div
layoutId={inputLayoutId}
transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
className="w-full px-2"
>
<ChatInput
inputId="chat-input-empty"
onSend={onSend}
disabled={isCreatingSession}
placeholder={inputPlaceholder}
className="w-full"
/>
</motion.div>
</div>
</div>
<div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
{quickActions.map((action) => (
<Button
key={action}
type="button"
variant="outline"
size="small"
onClick={() => void handleQuickActionClick(action)}
disabled={isCreatingSession || loadingAction !== null}
aria-busy={loadingAction === action}
leftIcon={
loadingAction === action ? (
<SpinnerGapIcon
className="h-4 w-4 animate-spin"
weight="bold"
/>
) : null
}
className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
>
{action}
</Button>
))}
</div>
</motion.div>
</div>
);
}

51
autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts → autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/helpers.ts
View File

@@ -1,6 +1,26 @@
import type { User } from "@supabase/supabase-js";
import { User } from "@supabase/supabase-js";
export function getGreetingName(user?: User | null): string {
export function getInputPlaceholder(width?: number) {
if (!width) return "What's your role and what eats up most of your day?";
if (width < 500) {
return "I'm a chef and I hate...";
}
if (width <= 1080) {
return "What's your role and what eats up most of your day?";
}
return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
}
export function getQuickActions() {
return [
"I don't know where to start, just ask me stuff",
"I do the same thing every week and it's killing me",
"Help me find where I'm wasting my time",
];
}
export function getGreetingName(user?: User | null) {
if (!user) return "there";
const metadata = user.user_metadata as Record<string, unknown> | undefined;
const fullName = metadata?.full_name;
@@ -16,30 +36,3 @@ export function getGreetingName(user?: User | null): string {
}
return "there";
}
export function buildCopilotChatUrl(prompt: string): string {
const trimmed = prompt.trim();
if (!trimmed) return "/copilot/chat";
const encoded = encodeURIComponent(trimmed);
return `/copilot/chat?prompt=${encoded}`;
}
export function getQuickActions(): string[] {
return [
"I don't know where to start, just ask me stuff",
"I do the same thing every week and it's killing me",
"Help me find where I'm wasting my time",
];
}
export function getInputPlaceholder(width?: number) {
if (!width) return "What's your role and what eats up most of your day?";
if (width < 500) {
return "I'm a chef and I hate...";
}
if (width <= 1080) {
return "What's your role and what eats up most of your day?";
}
return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
}

140
autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx Normal file
View File

@@ -0,0 +1,140 @@
import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { scrollbarStyles } from "@/components/styles/scrollbars";
import { cn } from "@/lib/utils";
import { PlusIcon, SpinnerGapIcon, X } from "@phosphor-icons/react";
import { Drawer } from "vaul";
interface Props {
isOpen: boolean;
sessions: SessionSummaryResponse[];
currentSessionId: string | null;
isLoading: boolean;
onSelectSession: (sessionId: string) => void;
onNewChat: () => void;
onClose: () => void;
onOpenChange: (open: boolean) => void;
}
function formatDate(dateString: string) {
const date = new Date(dateString);
const now = new Date();
const diffMs = now.getTime() - date.getTime();
const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
if (diffDays === 0) return "Today";
if (diffDays === 1) return "Yesterday";
if (diffDays < 7) return `${diffDays} days ago`;
const day = date.getDate();
const ordinal =
day % 10 === 1 && day !== 11
? "st"
: day % 10 === 2 && day !== 12
? "nd"
: day % 10 === 3 && day !== 13
? "rd"
: "th";
const month = date.toLocaleDateString("en-US", { month: "short" });
const year = date.getFullYear();
return `${day}${ordinal} ${month} ${year}`;
}
export function MobileDrawer({
isOpen,
sessions,
currentSessionId,
isLoading,
onSelectSession,
onNewChat,
onClose,
onOpenChange,
}: Props) {
return (
<Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
<Drawer.Portal>
<Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
<Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
<div className="shrink-0 border-b border-zinc-200 px-4 py-2">
<div className="flex items-center justify-between">
<Drawer.Title className="text-lg font-semibold text-zinc-800">
Your chats
</Drawer.Title>
<Button
variant="icon"
size="icon"
aria-label="Close sessions"
onClick={onClose}
>
<X width="1rem" height="1rem" />
</Button>
</div>
</div>
<div
className={cn(
"flex min-h-0 flex-1 flex-col gap-1 overflow-y-auto px-3 py-3",
scrollbarStyles,
)}
>
{isLoading ? (
<div className="flex items-center justify-center py-4">
<SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
</div>
) : sessions.length === 0 ? (
<p className="py-4 text-center text-sm text-neutral-500">
No conversations yet
</p>
) : (
sessions.map((session) => (
<button
key={session.id}
onClick={() => onSelectSession(session.id)}
className={cn(
"w-full rounded-lg px-3 py-2.5 text-left transition-colors",
session.id === currentSessionId
? "bg-zinc-100"
: "hover:bg-zinc-50",
)}
>
<div className="flex min-w-0 max-w-full flex-col overflow-hidden">
<div className="min-w-0 max-w-full">
<Text
variant="body"
className={cn(
"truncate font-normal",
session.id === currentSessionId
? "text-zinc-600"
: "text-zinc-800",
)}
>
{session.title || "Untitled chat"}
</Text>
</div>
<Text variant="small" className="text-neutral-400">
{formatDate(session.updated_at)}
</Text>
</div>
</button>
))
)}
</div>
{currentSessionId && (
<div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
<Button
variant="primary"
size="small"
onClick={onNewChat}
className="w-full"
leftIcon={<PlusIcon width="1rem" height="1rem" />}
>
New Chat
</Button>
</div>
)}
</Drawer.Content>
</Drawer.Portal>
</Drawer.Root>
);
}

0
autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx → autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileHeader/MobileHeader.tsx
View File

54
autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx Normal file
View File

@@ -0,0 +1,54 @@
import { cn } from "@/lib/utils";
import { AnimatePresence, motion } from "framer-motion";
interface Props {
text: string;
className?: string;
}
export function MorphingTextAnimation({ text, className }: Props) {
const letters = text.split("");
return (
<div className={cn(className)}>
<AnimatePresence mode="popLayout" initial={false}>
<motion.div key={text} className="whitespace-nowrap">
<motion.span className="inline-flex overflow-hidden">
{letters.map((char, index) => (
<motion.span
key={`${text}-${index}`}
initial={{
opacity: 0,
y: 8,
rotateX: "80deg",
filter: "blur(6px)",
}}
animate={{
opacity: 1,
y: 0,
rotateX: "0deg",
filter: "blur(0px)",
}}
exit={{
opacity: 0,
y: -8,
rotateX: "-80deg",
filter: "blur(6px)",
}}
style={{ willChange: "transform" }}
transition={{
delay: 0.015 * index,
type: "spring",
bounce: 0.5,
}}
className="inline-block"
>
{char === " " ? "\u00A0" : char}
</motion.span>
))}
</motion.span>
</motion.div>
</AnimatePresence>
</div>
);
}

69
autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css Normal file
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@@ -0,0 +1,69 @@
.loader {
position: relative;
animation: rotate 1s infinite;
}
.loader::before,
.loader::after {
border-radius: 50%;
content: "";
display: block;
/* 40% of container size */
height: 40%;
width: 40%;
}
.loader::before {
animation: ball1 1s infinite;
background-color: #a1a1aa; /* zinc-400 */
box-shadow: calc(var(--spacing)) 0 0 #18181b; /* zinc-900 */
margin-bottom: calc(var(--gap));
}
.loader::after {
animation: ball2 1s infinite;
background-color: #18181b; /* zinc-900 */
box-shadow: calc(var(--spacing)) 0 0 #a1a1aa; /* zinc-400 */
}
@keyframes rotate {
0% {
transform: rotate(0deg) scale(0.8);
}
50% {
transform: rotate(360deg) scale(1.2);
}
100% {
transform: rotate(720deg) scale(0.8);
}
}
@keyframes ball1 {
0% {
box-shadow: calc(var(--spacing)) 0 0 #18181b;
}
50% {
box-shadow: 0 0 0 #18181b;
margin-bottom: 0;
transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
}
100% {
box-shadow: calc(var(--spacing)) 0 0 #18181b;
margin-bottom: calc(var(--gap));
}
}
@keyframes ball2 {
0% {
box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
}
50% {
box-shadow: 0 0 0 #a1a1aa;
margin-top: calc(var(--ball-size) * -1);
transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
}
100% {
box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
margin-top: 0;
}
}

28
autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx Normal file
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@@ -0,0 +1,28 @@
import { cn } from "@/lib/utils";
import styles from "./OrbitLoader.module.css";
interface Props {
size?: number;
className?: string;
}
export function OrbitLoader({ size = 24, className }: Props) {
const ballSize = Math.round(size * 0.4);
const spacing = Math.round(size * 0.6);
const gap = Math.round(size * 0.2);
return (
<div
className={cn(styles.loader, className)}
style={
{
width: size,
height: size,
"--ball-size": `${ballSize}px`,
"--spacing": `${spacing}px`,
"--gap": `${gap}px`,
} as React.CSSProperties
}
/>
);
}

26
autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx Normal file
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@@ -0,0 +1,26 @@
import { cn } from "@/lib/utils";
interface Props {
value: number;
label?: string;
className?: string;
}
export function ProgressBar({ value, label, className }: Props) {
const clamped = Math.min(100, Math.max(0, value));
return (
<div className={cn("flex flex-col gap-1.5", className)}>
<div className="flex items-center justify-between text-xs text-neutral-500">
<span>{label ?? "Working on it..."}</span>
<span>{Math.round(clamped)}%</span>
</div>
<div className="h-2 w-full overflow-hidden rounded-full bg-neutral-200">
<div
className="h-full rounded-full bg-neutral-900 transition-[width] duration-300 ease-out"
style={{ width: `${clamped}%` }}
/>
</div>
</div>
);
}

34
autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css Normal file
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@@ -0,0 +1,34 @@
.loader {
position: relative;
display: inline-block;
flex-shrink: 0;
}
.loader::before,
.loader::after {
content: "";
box-sizing: border-box;
width: 100%;
height: 100%;
border-radius: 50%;
background: currentColor;
position: absolute;
left: 0;
top: 0;
animation: ripple 2s linear infinite;
}
.loader::after {
animation-delay: 1s;
}
@keyframes ripple {
0% {
transform: scale(0);
opacity: 1;
}
100% {
transform: scale(1);
opacity: 0;
}
}

16
autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx Normal file
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@@ -0,0 +1,16 @@
import { cn } from "@/lib/utils";
import styles from "./PulseLoader.module.css";
interface Props {
size?: number;
className?: string;
}
export function PulseLoader({ size = 24, className }: Props) {
return (
<div
className={cn(styles.loader, className)}
style={{ width: size, height: size }}
/>
);
}

57
autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css Normal file
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@@ -0,0 +1,57 @@
.loader {
position: relative;
display: inline-block;
flex-shrink: 0;
transform: rotateZ(45deg);
perspective: 1000px;
border-radius: 50%;
color: currentColor;
}
.loader::before,
.loader::after {
content: "";
display: block;
position: absolute;
top: 0;
left: 0;
width: inherit;
height: inherit;
border-radius: 50%;
transform: rotateX(70deg);
animation: spin 1s linear infinite;
}
.loader::after {
color: var(--spinner-accent, #a855f7);
transform: rotateY(70deg);
animation-delay: 0.4s;
}
@keyframes spin {
0%,
100% {
box-shadow: 0.2em 0 0 0 currentColor;
}
12% {
box-shadow: 0.2em 0.2em 0 0 currentColor;
}
25% {
box-shadow: 0 0.2em 0 0 currentColor;
}
37% {
box-shadow: -0.2em 0.2em 0 0 currentColor;
}
50% {
box-shadow: -0.2em 0 0 0 currentColor;
}
62% {
box-shadow: -0.2em -0.2em 0 0 currentColor;
}
75% {
box-shadow: 0 -0.2em 0 0 currentColor;
}
87% {
box-shadow: 0.2em -0.2em 0 0 currentColor;
}
}

16
autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx Normal file
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@@ -0,0 +1,16 @@
import { cn } from "@/lib/utils";
import styles from "./SpinnerLoader.module.css";
interface Props {
size?: number;
className?: string;
}
export function SpinnerLoader({ size = 24, className }: Props) {
return (
<div
className={cn(styles.loader, className)}
style={{ width: size, height: size }}
/>
);
}

235
autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx Normal file
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@@ -0,0 +1,235 @@
import { Link } from "@/components/atoms/Link/Link";
import { Text } from "@/components/atoms/Text/Text";
import { cn } from "@/lib/utils";
/* ------------------------------------------------------------------ */
/* Layout */
/* ------------------------------------------------------------------ */
export function ContentGrid({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return <div className={cn("grid gap-2", className)}>{children}</div>;
}
/* ------------------------------------------------------------------ */
/* Card */
/* ------------------------------------------------------------------ */
export function ContentCard({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<div
className={cn(
"rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
className,
)}
>
<div className="rounded-lg bg-neutral-100 p-3">{children}</div>
</div>
);
}
/** Flex row with a left content area (`children`) and an optional right‑side `action`. */
export function ContentCardHeader({
children,
action,
className,
}: {
children: React.ReactNode;
action?: React.ReactNode;
className?: string;
}) {
return (
<div className={cn("flex items-start justify-between gap-2", className)}>
<div className="min-w-0">{children}</div>
{action}
</div>
);
}
export function ContentCardTitle({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text
variant="body-medium"
className={cn("truncate text-zinc-800", className)}
>
{children}
</Text>
);
}
export function ContentCardSubtitle({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text
variant="small"
className={cn("mt-0.5 truncate font-mono text-zinc-800", className)}
>
{children}
</Text>
);
}
export function ContentCardDescription({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text variant="body" className={cn("mt-2 text-zinc-800", className)}>
{children}
</Text>
);
}
/* ------------------------------------------------------------------ */
/* Text */
/* ------------------------------------------------------------------ */
export function ContentMessage({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text variant="body" className={cn("text-zinc-800", className)}>
{children}
</Text>
);
}
export function ContentHint({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text variant="small" className={cn("text-neutral-500", className)}>
{children}
</Text>
);
}
/* ------------------------------------------------------------------ */
/* Code / data */
/* ------------------------------------------------------------------ */
export function ContentCodeBlock({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<pre
className={cn(
"whitespace-pre-wrap rounded-lg border bg-black p-3 text-xs text-neutral-200",
className,
)}
>
{children}
</pre>
);
}
/* ------------------------------------------------------------------ */
/* Inline elements */
/* ------------------------------------------------------------------ */
export function ContentBadge({
children,
className,
}: {
children: React.ReactNode;
className?: string;
}) {
return (
<Text
variant="small"
as="span"
className={cn(
"shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-zinc-800",
className,
)}
>
{children}
</Text>
);
}
export function ContentLink({
href,
children,
className,
...rest
}: Omit<React.ComponentProps<typeof Link>, "className"> & {
className?: string;
}) {
return (
<Link
variant="primary"
isExternal
href={href}
className={cn("shrink-0 text-xs text-purple-500", className)}
{...rest}
>
{children}
</Link>
);
}
/* ------------------------------------------------------------------ */
/* Lists */
/* ------------------------------------------------------------------ */
export function ContentSuggestionsList({
items,
max = 5,
className,
}: {
items: string[];
max?: number;
className?: string;
}) {
if (items.length === 0) return null;
return (
<ul
className={cn(
"mt-2 list-disc space-y-1 pl-5 font-sans text-[0.75rem] leading-[1.125rem] text-zinc-800",
className,
)}
>
{items.slice(0, max).map((s) => (
<li key={s}>{s}</li>
))}
</ul>
);
}

102
autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx Normal file
View File

@@ -0,0 +1,102 @@
"use client";
import { cn } from "@/lib/utils";
import { CaretDownIcon } from "@phosphor-icons/react";
import { AnimatePresence, motion, useReducedMotion } from "framer-motion";
import { useId } from "react";
import { useToolAccordion } from "./useToolAccordion";
interface Props {
icon: React.ReactNode;
title: React.ReactNode;
titleClassName?: string;
description?: React.ReactNode;
children: React.ReactNode;
className?: string;
defaultExpanded?: boolean;
expanded?: boolean;
onExpandedChange?: (expanded: boolean) => void;
}
export function ToolAccordion({
icon,
title,
titleClassName,
description,
children,
className,
defaultExpanded,
expanded,
onExpandedChange,
}: Props) {
const shouldReduceMotion = useReducedMotion();
const contentId = useId();
const { isExpanded, toggle } = useToolAccordion({
expanded,
defaultExpanded,
onExpandedChange,
});
return (
<div
className={cn(
"mt-2 w-full rounded-lg border border-slate-200 bg-slate-100 px-3 py-2",
className,
)}
>
<button
type="button"
aria-expanded={isExpanded}
aria-controls={contentId}
onClick={toggle}
className="flex w-full items-center justify-between gap-3 py-1 text-left"
>
<div className="flex min-w-0 items-center gap-3">
<span className="flex shrink-0 items-center text-gray-800">
{icon}
</span>
<div className="min-w-0">
<p
className={cn(
"truncate text-sm font-medium text-gray-800",
titleClassName,
)}
>
{title}
</p>
{description && (
<p className="truncate text-xs text-slate-800">{description}</p>
)}
</div>
</div>
<CaretDownIcon
className={cn(
"h-4 w-4 shrink-0 text-slate-500 transition-transform",
isExpanded && "rotate-180",
)}
weight="bold"
/>
</button>
<AnimatePresence initial={false}>
{isExpanded && (
<motion.div
id={contentId}
initial={{ height: 0, opacity: 0, filter: "blur(10px)" }}
animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
exit={{ height: 0, opacity: 0, filter: "blur(10px)" }}
transition={
shouldReduceMotion
? { duration: 0 }
: { type: "spring", bounce: 0.35, duration: 0.55 }
}
className="overflow-hidden"
style={{ willChange: "height, opacity, filter" }}
>
<div className="pb-2 pt-3">{children}</div>
</motion.div>
)}
</AnimatePresence>
</div>
);
}

32
autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts Normal file
View File

@@ -0,0 +1,32 @@
import { useState } from "react";
interface UseToolAccordionOptions {
expanded?: boolean;
defaultExpanded?: boolean;
onExpandedChange?: (expanded: boolean) => void;
}
interface UseToolAccordionResult {
isExpanded: boolean;
toggle: () => void;
}
export function useToolAccordion({
expanded,
defaultExpanded = false,
onExpandedChange,
}: UseToolAccordionOptions): UseToolAccordionResult {
const [uncontrolledExpanded, setUncontrolledExpanded] =
useState(defaultExpanded);
const isControlled = typeof expanded === "boolean";
const isExpanded = isControlled ? expanded : uncontrolledExpanded;
function toggle() {
const next = !isExpanded;
if (!isControlled) setUncontrolledExpanded(next);
onExpandedChange?.(next);
}
return { isExpanded, toggle };
}

56
autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts
View File

@@ -1,56 +0,0 @@
"use client";
import { create } from "zustand";
interface CopilotStoreState {
isStreaming: boolean;
isSwitchingSession: boolean;
isCreatingSession: boolean;
isInterruptModalOpen: boolean;
pendingAction: (() => void) | null;
}
interface CopilotStoreActions {
setIsStreaming: (isStreaming: boolean) => void;
setIsSwitchingSession: (isSwitchingSession: boolean) => void;
setIsCreatingSession: (isCreating: boolean) => void;
openInterruptModal: (onConfirm: () => void) => void;
confirmInterrupt: () => void;
cancelInterrupt: () => void;
}
type CopilotStore = CopilotStoreState & CopilotStoreActions;
export const useCopilotStore = create<CopilotStore>((set, get) => ({
isStreaming: false,
isSwitchingSession: false,
isCreatingSession: false,
isInterruptModalOpen: false,
pendingAction: null,
setIsStreaming(isStreaming) {
set({ isStreaming });
},
setIsSwitchingSession(isSwitchingSession) {
set({ isSwitchingSession });
},
setIsCreatingSession(isCreatingSession) {
set({ isCreatingSession });
},
openInterruptModal(onConfirm) {
set({ isInterruptModalOpen: true, pendingAction: onConfirm });
},
confirmInterrupt() {
const { pendingAction } = get();
set({ isInterruptModalOpen: false, pendingAction: null });
if (pendingAction) pendingAction();
},
cancelInterrupt() {
set({ isInterruptModalOpen: false, pendingAction: null });
},
}));

128
autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts Normal file
View File

@@ -0,0 +1,128 @@
import type { UIMessage, UIDataTypes, UITools } from "ai";
interface SessionChatMessage {
role: string;
content: string | null;
tool_call_id: string | null;
tool_calls: unknown[] | null;
}
function coerceSessionChatMessages(
rawMessages: unknown[],
): SessionChatMessage[] {
return rawMessages
.map((m) => {
if (!m || typeof m !== "object") return null;
const msg = m as Record<string, unknown>;
const role = typeof msg.role === "string" ? msg.role : null;
if (!role) return null;
return {
role,
content:
typeof msg.content === "string"
? msg.content
: msg.content == null
? null
: String(msg.content),
tool_call_id:
typeof msg.tool_call_id === "string"
? msg.tool_call_id
: msg.tool_call_id == null
? null
: String(msg.tool_call_id),
tool_calls: Array.isArray(msg.tool_calls) ? msg.tool_calls : null,
};
})
.filter((m): m is SessionChatMessage => m !== null);
}
function safeJsonParse(value: string): unknown {
try {
return JSON.parse(value) as unknown;
} catch {
return value;
}
}
function toToolInput(rawArguments: unknown): unknown {
if (typeof rawArguments === "string") {
const trimmed = rawArguments.trim();
return trimmed ? safeJsonParse(trimmed) : {};
}
if (rawArguments && typeof rawArguments === "object") return rawArguments;
return {};
}
export function convertChatSessionMessagesToUiMessages(
sessionId: string,
rawMessages: unknown[],
): UIMessage<unknown, UIDataTypes, UITools>[] {
const messages = coerceSessionChatMessages(rawMessages);
const toolOutputsByCallId = new Map<string, unknown>();
for (const msg of messages) {
if (msg.role !== "tool") continue;
if (!msg.tool_call_id) continue;
if (msg.content == null) continue;
toolOutputsByCallId.set(msg.tool_call_id, msg.content);
}
const uiMessages: UIMessage<unknown, UIDataTypes, UITools>[] = [];
messages.forEach((msg, index) => {
if (msg.role === "tool") return;
if (msg.role !== "user" && msg.role !== "assistant") return;
const parts: UIMessage<unknown, UIDataTypes, UITools>["parts"] = [];
if (typeof msg.content === "string" && msg.content.trim()) {
parts.push({ type: "text", text: msg.content, state: "done" });
}
if (msg.role === "assistant" && Array.isArray(msg.tool_calls)) {
for (const rawToolCall of msg.tool_calls) {
if (!rawToolCall || typeof rawToolCall !== "object") continue;
const toolCall = rawToolCall as {
id?: unknown;
function?: { name?: unknown; arguments?: unknown };
};
const toolCallId = String(toolCall.id ?? "").trim();
const toolName = String(toolCall.function?.name ?? "").trim();
if (!toolCallId || !toolName) continue;
const input = toToolInput(toolCall.function?.arguments);
const output = toolOutputsByCallId.get(toolCallId);
if (output !== undefined) {
parts.push({
type: `tool-${toolName}`,
toolCallId,
state: "output-available",
input,
output: typeof output === "string" ? safeJsonParse(output) : output,
});
} else {
parts.push({
type: `tool-${toolName}`,
toolCallId,
state: "input-available",
input,
});
}
}
}
if (parts.length === 0) return;
uiMessages.push({
id: `${sessionId}-${index}`,
role: msg.role,
parts,
});
});
return uiMessages;
}

2
autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotSessionId.ts → autogpt_platform/frontend/src/app/(platform)/copilot/hooks/Untitled
View File

@@ -7,4 +7,4 @@ export function useCopilotSessionId() {
);
return { urlSessionId, setUrlSessionId };
}
}

7
autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts → autogpt_platform/frontend/src/app/(platform)/copilot/hooks/useAsymptoticProgress.ts
View File

@@ -5,17 +5,16 @@ import { useEffect, useRef, useState } from "react";
* asymptotically approaching but never reaching the max value.
*
* Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
* This creates the "game loading bar" effect where:
* This creates a "loading bar" effect where:
* - 50% is reached at halfLifeSeconds
* - 75% is reached at 2 * halfLifeSeconds
* - 87.5% is reached at 3 * halfLifeSeconds
* - and so on...
*
* @param isActive - Whether the progress should be animating
* @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
* @param maxProgress - Maximum progress value to approach (default: 100)
* @param intervalMs - Update interval in milliseconds (default: 100)
* @returns Current progress value (0-maxProgress)
* @returns Current progress value (0–maxProgress)
*/
export function useAsymptoticProgress(
isActive: boolean,
@@ -35,8 +34,6 @@ export function useAsymptoticProgress(
const interval = setInterval(() => {
elapsedTimeRef.current += intervalMs / 1000;
// Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
// At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
const newProgress =
maxProgress *
(1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));

13
autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
View File

@@ -1,13 +0,0 @@
"use client";
import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
import { Flag } from "@/services/feature-flags/use-get-flag";
import { type ReactNode } from "react";
import { CopilotShell } from "./components/CopilotShell/CopilotShell";
export default function CopilotLayout({ children }: { children: ReactNode }) {
return (
<FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
<CopilotShell>{children}</CopilotShell>
</FeatureFlagPage>
);
}

150
autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
View File

@@ -1,149 +1,13 @@
"use client";
import { Button } from "@/components/atoms/Button/Button";
import { Skeleton } from "@/components/atoms/Skeleton/Skeleton";
import { Text } from "@/components/atoms/Text/Text";
import { Chat } from "@/components/contextual/Chat/Chat";
import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
import { Dialog } from "@/components/molecules/Dialog/Dialog";
import { useEffect, useState } from "react";
import { useCopilotStore } from "./copilot-page-store";
import { getInputPlaceholder } from "./helpers";
import { useCopilotPage } from "./useCopilotPage";
export default function CopilotPage() {
const { state, handlers } = useCopilotPage();
const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
const [inputPlaceholder, setInputPlaceholder] = useState(
getInputPlaceholder(),
);
useEffect(() => {
const handleResize = () => {
setInputPlaceholder(getInputPlaceholder(window.innerWidth));
};
handleResize();
window.addEventListener("resize", handleResize);
return () => window.removeEventListener("resize", handleResize);
}, []);
const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
state;
const {
handleQuickAction,
startChatWithPrompt,
handleSessionNotFound,
handleStreamingChange,
} = handlers;
if (hasSession) {
return (
<div className="flex h-full flex-col">
<Chat
className="flex-1"
initialPrompt={initialPrompt}
onSessionNotFound={handleSessionNotFound}
onStreamingChange={handleStreamingChange}
/>
<Dialog
title="Interrupt current chat?"
styling={{ maxWidth: 300, width: "100%" }}
controlled={{
isOpen: isInterruptModalOpen,
set: (open) => {
if (!open) cancelInterrupt();
},
}}
onClose={cancelInterrupt}
>
<Dialog.Content>
<div className="flex flex-col gap-4">
<Text variant="body">
The current chat response will be interrupted. Are you sure you
want to continue?
</Text>
<Dialog.Footer>
<Button
type="button"
variant="outline"
onClick={cancelInterrupt}
>
Cancel
</Button>
<Button
type="button"
variant="primary"
onClick={confirmInterrupt}
>
Continue
</Button>
</Dialog.Footer>
</div>
</Dialog.Content>
</Dialog>
</div>
);
}
import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
import { Flag } from "@/services/feature-flags/use-get-flag";
import { CopilotPage } from "./CopilotPage";
export default function Page() {
return (
<div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
<div className="w-full text-center">
{isLoading ? (
<div className="mx-auto max-w-2xl">
<Skeleton className="mx-auto mb-3 h-8 w-64" />
<Skeleton className="mx-auto mb-8 h-6 w-80" />
<div className="mb-8">
<Skeleton className="mx-auto h-14 w-full rounded-lg" />
</div>
<div className="flex flex-wrap items-center justify-center gap-3">
{Array.from({ length: 4 }).map((_, i) => (
<Skeleton key={i} className="h-9 w-48 rounded-md" />
))}
</div>
</div>
) : (
<>
<div className="mx-auto max-w-3xl">
<Text
variant="h3"
className="mb-1 !text-[1.375rem] text-zinc-700"
>
Hey, <span className="text-violet-600">{greetingName}</span>
</Text>
<Text variant="h3" className="mb-8 !font-normal">
Tell me about your work — I&apos;ll find what to automate.
</Text>
<div className="mb-6">
<ChatInput
onSend={startChatWithPrompt}
placeholder={inputPlaceholder}
/>
</div>
</div>
<div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
{quickActions.map((action) => (
<Button
key={action}
type="button"
variant="outline"
size="small"
onClick={() => handleQuickAction(action)}
className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
>
{action}
</Button>
))}
</div>
</>
)}
</div>
</div>
<FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
<CopilotPage />
</FeatureFlagPage>
);
}

1533
autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx Normal file
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237
autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx Normal file
View File

@@ -0,0 +1,237 @@
"use client";
import { WarningDiamondIcon } from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
import {
ContentCardDescription,
ContentCodeBlock,
ContentGrid,
ContentHint,
ContentLink,
ContentMessage,
} from "../../components/ToolAccordion/AccordionContent";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
import {
ClarificationQuestionsCard,
ClarifyingQuestion,
} from "./components/ClarificationQuestionsCard";
import {
AccordionIcon,
formatMaybeJson,
getAnimationText,
getCreateAgentToolOutput,
isAgentPreviewOutput,
isAgentSavedOutput,
isClarificationNeededOutput,
isErrorOutput,
isOperationInProgressOutput,
isOperationPendingOutput,
isOperationStartedOutput,
ToolIcon,
truncateText,
type CreateAgentToolOutput,
} from "./helpers";
export interface CreateAgentToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: CreateAgentToolPart;
}
function getAccordionMeta(output: CreateAgentToolOutput): {
icon: React.ReactNode;
title: React.ReactNode;
titleClassName?: string;
description?: string;
} {
const icon = <AccordionIcon />;
if (isAgentSavedOutput(output)) {
return { icon, title: output.agent_name };
}
if (isAgentPreviewOutput(output)) {
return {
icon,
title: output.agent_name,
description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
};
}
if (isClarificationNeededOutput(output)) {
const questions = output.questions ?? [];
return {
icon,
title: "Needs clarification",
description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
};
}
if (
isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output)
) {
return {
icon: <OrbitLoader size={32} />,
title: "Creating agent, this may take a few minutes. Sit back and relax.",
};
}
return {
icon: (
<WarningDiamondIcon size={32} weight="light" className="text-red-500" />
),
title: "Error",
titleClassName: "text-red-500",
};
}
export function CreateAgentTool({ part }: Props) {
const text = getAnimationText(part);
const { onSend } = useCopilotChatActions();
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const output = getCreateAgentToolOutput(part);
const isError =
part.state === "output-error" || (!!output && isErrorOutput(output));
const isOperating =
!!output &&
(isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output));
const progress = useAsymptoticProgress(isOperating);
const hasExpandableContent =
part.state === "output-available" &&
!!output &&
(isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output) ||
isAgentPreviewOutput(output) ||
isAgentSavedOutput(output) ||
isClarificationNeededOutput(output) ||
isErrorOutput(output));
function handleClarificationAnswers(answers: Record<string, string>) {
const questions =
output && isClarificationNeededOutput(output)
? (output.questions ?? [])
: [];
const contextMessage = questions
.map((q) => {
const answer = answers[q.keyword] || "";
return `> ${q.question}\n\n${answer}`;
})
.join("\n\n");
onSend(
`**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
);
}
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && output && (
<ToolAccordion
{...getAccordionMeta(output)}
defaultExpanded={isOperating || isClarificationNeededOutput(output)}
>
{isOperating && (
<ContentGrid>
<ProgressBar value={progress} className="max-w-[280px]" />
<ContentHint>
This could take a few minutes, grab a coffee ☕
</ContentHint>
</ContentGrid>
)}
{isAgentSavedOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
<div className="flex flex-wrap gap-2">
<ContentLink href={output.library_agent_link}>
Open in library
</ContentLink>
<ContentLink href={output.agent_page_link}>
Open in builder
</ContentLink>
</div>
<ContentCodeBlock>
{truncateText(
formatMaybeJson({ agent_id: output.agent_id }),
800,
)}
</ContentCodeBlock>
</ContentGrid>
)}
{isAgentPreviewOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.description?.trim() && (
<ContentCardDescription>
{output.description}
</ContentCardDescription>
)}
<ContentCodeBlock>
{truncateText(formatMaybeJson(output.agent_json), 1600)}
</ContentCodeBlock>
</ContentGrid>
)}
{isClarificationNeededOutput(output) && (
<ClarificationQuestionsCard
questions={(output.questions ?? []).map((q) => {
const item: ClarifyingQuestion = {
question: q.question,
keyword: q.keyword,
};
const example =
typeof q.example === "string" && q.example.trim()
? q.example.trim()
: null;
if (example) item.example = example;
return item;
})}
message={output.message}
onSubmitAnswers={handleClarificationAnswers}
/>
)}
{isErrorOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.error && (
<ContentCodeBlock>
{formatMaybeJson(output.error)}
</ContentCodeBlock>
)}
{output.details && (
<ContentCodeBlock>
{formatMaybeJson(output.details)}
</ContentCodeBlock>
)}
</ContentGrid>
)}
</ToolAccordion>
)}
</div>
);
}

14
autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx → autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/components/ClarificationQuestionsCard.tsx
View File

@@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input";
import { Text } from "@/components/atoms/Text/Text";
import { cn } from "@/lib/utils";
import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react";
import { useState, useEffect, useRef } from "react";
import { useEffect, useRef, useState } from "react";
export interface ClarifyingQuestion {
question: string;
@@ -24,12 +24,7 @@ interface Props {
className?: string;
}
function getStorageKey(sessionId?: string): string | null {
if (!sessionId) return null;
return `clarification_answers_${sessionId}`;
}
export function ClarificationQuestionsWidget({
export function ClarificationQuestionsCard({
questions,
message,
sessionId,
@@ -241,3 +236,8 @@ export function ClarificationQuestionsWidget({
</div>
);
}
function getStorageKey(sessionId?: string): string | null {
if (!sessionId) return null;
return `clarification_answers_${sessionId}`;
}

186
autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx Normal file
View File

@@ -0,0 +1,186 @@
import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import {
PlusCircleIcon,
PlusIcon,
WarningDiamondIcon,
} from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
export type CreateAgentToolOutput =
| OperationStartedResponse
| OperationPendingResponse
| OperationInProgressResponse
| AgentPreviewResponse
| AgentSavedResponse
| ClarificationNeededResponse
| ErrorResponse;
function parseOutput(output: unknown): CreateAgentToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (
type === ResponseType.operation_started ||
type === ResponseType.operation_pending ||
type === ResponseType.operation_in_progress ||
type === ResponseType.agent_preview ||
type === ResponseType.agent_saved ||
type === ResponseType.clarification_needed ||
type === ResponseType.error
) {
return output as CreateAgentToolOutput;
}
if ("operation_id" in output && "tool_name" in output)
return output as OperationStartedResponse | OperationPendingResponse;
if ("tool_call_id" in output) return output as OperationInProgressResponse;
if ("agent_json" in output && "agent_name" in output)
return output as AgentPreviewResponse;
if ("agent_id" in output && "library_agent_id" in output)
return output as AgentSavedResponse;
if ("questions" in output) return output as ClarificationNeededResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function getCreateAgentToolOutput(
part: unknown,
): CreateAgentToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
export function isOperationStartedOutput(
output: CreateAgentToolOutput,
): output is OperationStartedResponse {
return (
output.type === ResponseType.operation_started ||
("operation_id" in output && "tool_name" in output)
);
}
export function isOperationPendingOutput(
output: CreateAgentToolOutput,
): output is OperationPendingResponse {
return output.type === ResponseType.operation_pending;
}
export function isOperationInProgressOutput(
output: CreateAgentToolOutput,
): output is OperationInProgressResponse {
return (
output.type === ResponseType.operation_in_progress ||
"tool_call_id" in output
);
}
export function isAgentPreviewOutput(
output: CreateAgentToolOutput,
): output is AgentPreviewResponse {
return output.type === ResponseType.agent_preview || "agent_json" in output;
}
export function isAgentSavedOutput(
output: CreateAgentToolOutput,
): output is AgentSavedResponse {
return (
output.type === ResponseType.agent_saved || "agent_page_link" in output
);
}
export function isClarificationNeededOutput(
output: CreateAgentToolOutput,
): output is ClarificationNeededResponse {
return (
output.type === ResponseType.clarification_needed || "questions" in output
);
}
export function isErrorOutput(
output: CreateAgentToolOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
export function getAnimationText(part: {
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
switch (part.state) {
case "input-streaming":
case "input-available":
return "Creating a new agent";
case "output-available": {
const output = parseOutput(part.output);
if (!output) return "Creating a new agent";
if (isOperationStartedOutput(output)) return "Agent creation started";
if (isOperationPendingOutput(output)) return "Agent creation in progress";
if (isOperationInProgressOutput(output))
return "Agent creation already in progress";
if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
if (isClarificationNeededOutput(output)) return "Needs clarification";
return "Error creating agent";
}
case "output-error":
return "Error creating agent";
default:
return "Creating a new agent";
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
if (isError) {
return (
<WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
);
}
if (isStreaming) {
return <OrbitLoader size={24} />;
}
return <PlusIcon size={14} weight="regular" className="text-neutral-400" />;
}
export function AccordionIcon() {
return <PlusCircleIcon size={32} weight="light" />;
}
export function formatMaybeJson(value: unknown): string {
if (typeof value === "string") return value;
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}
export function truncateText(text: string, maxChars: number): string {
const trimmed = text.trim();
if (trimmed.length <= maxChars) return trimmed;
return `${trimmed.slice(0, maxChars).trimEnd()}…`;
}

234
autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx Normal file
View File

@@ -0,0 +1,234 @@
"use client";
import { WarningDiamondIcon } from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
import {
ContentCardDescription,
ContentCodeBlock,
ContentGrid,
ContentHint,
ContentLink,
ContentMessage,
} from "../../components/ToolAccordion/AccordionContent";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
import {
ClarificationQuestionsCard,
ClarifyingQuestion,
} from "../CreateAgent/components/ClarificationQuestionsCard";
import {
AccordionIcon,
formatMaybeJson,
getAnimationText,
getEditAgentToolOutput,
isAgentPreviewOutput,
isAgentSavedOutput,
isClarificationNeededOutput,
isErrorOutput,
isOperationInProgressOutput,
isOperationPendingOutput,
isOperationStartedOutput,
ToolIcon,
truncateText,
type EditAgentToolOutput,
} from "./helpers";
export interface EditAgentToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: EditAgentToolPart;
}
function getAccordionMeta(output: EditAgentToolOutput): {
icon: React.ReactNode;
title: string;
titleClassName?: string;
description?: string;
} {
const icon = <AccordionIcon />;
if (isAgentSavedOutput(output)) {
return { icon, title: output.agent_name };
}
if (isAgentPreviewOutput(output)) {
return {
icon,
title: output.agent_name,
description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
};
}
if (isClarificationNeededOutput(output)) {
const questions = output.questions ?? [];
return {
icon,
title: "Needs clarification",
description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
};
}
if (
isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output)
) {
return { icon: <OrbitLoader size={32} />, title: "Editing agent" };
}
return {
icon: (
<WarningDiamondIcon size={32} weight="light" className="text-red-500" />
),
title: "Error",
titleClassName: "text-red-500",
};
}
export function EditAgentTool({ part }: Props) {
const text = getAnimationText(part);
const { onSend } = useCopilotChatActions();
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const output = getEditAgentToolOutput(part);
const isError =
part.state === "output-error" || (!!output && isErrorOutput(output));
const isOperating =
!!output &&
(isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output));
const progress = useAsymptoticProgress(isOperating);
const hasExpandableContent =
part.state === "output-available" &&
!!output &&
(isOperationStartedOutput(output) ||
isOperationPendingOutput(output) ||
isOperationInProgressOutput(output) ||
isAgentPreviewOutput(output) ||
isAgentSavedOutput(output) ||
isClarificationNeededOutput(output) ||
isErrorOutput(output));
function handleClarificationAnswers(answers: Record<string, string>) {
const questions =
output && isClarificationNeededOutput(output)
? (output.questions ?? [])
: [];
const contextMessage = questions
.map((q) => {
const answer = answers[q.keyword] || "";
return `> ${q.question}\n\n${answer}`;
})
.join("\n\n");
onSend(
`**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with editing the agent.`,
);
}
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && output && (
<ToolAccordion
{...getAccordionMeta(output)}
defaultExpanded={isOperating || isClarificationNeededOutput(output)}
>
{isOperating && (
<ContentGrid>
<ProgressBar value={progress} className="max-w-[280px]" />
<ContentHint>
This could take a few minutes, grab a coffee ☕
</ContentHint>
</ContentGrid>
)}
{isAgentSavedOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
<div className="flex flex-wrap gap-2">
<ContentLink href={output.library_agent_link}>
Open in library
</ContentLink>
<ContentLink href={output.agent_page_link}>
Open in builder
</ContentLink>
</div>
<ContentCodeBlock>
{truncateText(
formatMaybeJson({ agent_id: output.agent_id }),
800,
)}
</ContentCodeBlock>
</ContentGrid>
)}
{isAgentPreviewOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.description?.trim() && (
<ContentCardDescription>
{output.description}
</ContentCardDescription>
)}
<ContentCodeBlock>
{truncateText(formatMaybeJson(output.agent_json), 1600)}
</ContentCodeBlock>
</ContentGrid>
)}
{isClarificationNeededOutput(output) && (
<ClarificationQuestionsCard
questions={(output.questions ?? []).map((q) => {
const item: ClarifyingQuestion = {
question: q.question,
keyword: q.keyword,
};
const example =
typeof q.example === "string" && q.example.trim()
? q.example.trim()
: null;
if (example) item.example = example;
return item;
})}
message={output.message}
onSubmitAnswers={handleClarificationAnswers}
/>
)}
{isErrorOutput(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.error && (
<ContentCodeBlock>
{formatMaybeJson(output.error)}
</ContentCodeBlock>
)}
{output.details && (
<ContentCodeBlock>
{formatMaybeJson(output.details)}
</ContentCodeBlock>
)}
</ContentGrid>
)}
</ToolAccordion>
)}
</div>
);
}

188
autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx Normal file
View File

@@ -0,0 +1,188 @@
import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import {
NotePencilIcon,
PencilLineIcon,
WarningDiamondIcon,
} from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
export type EditAgentToolOutput =
| OperationStartedResponse
| OperationPendingResponse
| OperationInProgressResponse
| AgentPreviewResponse
| AgentSavedResponse
| ClarificationNeededResponse
| ErrorResponse;
function parseOutput(output: unknown): EditAgentToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (
type === ResponseType.operation_started ||
type === ResponseType.operation_pending ||
type === ResponseType.operation_in_progress ||
type === ResponseType.agent_preview ||
type === ResponseType.agent_saved ||
type === ResponseType.clarification_needed ||
type === ResponseType.error
) {
return output as EditAgentToolOutput;
}
if ("operation_id" in output && "tool_name" in output)
return output as OperationStartedResponse | OperationPendingResponse;
if ("tool_call_id" in output) return output as OperationInProgressResponse;
if ("agent_json" in output && "agent_name" in output)
return output as AgentPreviewResponse;
if ("agent_id" in output && "library_agent_id" in output)
return output as AgentSavedResponse;
if ("questions" in output) return output as ClarificationNeededResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function getEditAgentToolOutput(
part: unknown,
): EditAgentToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
export function isOperationStartedOutput(
output: EditAgentToolOutput,
): output is OperationStartedResponse {
return (
output.type === ResponseType.operation_started ||
("operation_id" in output && "tool_name" in output)
);
}
export function isOperationPendingOutput(
output: EditAgentToolOutput,
): output is OperationPendingResponse {
return output.type === ResponseType.operation_pending;
}
export function isOperationInProgressOutput(
output: EditAgentToolOutput,
): output is OperationInProgressResponse {
return (
output.type === ResponseType.operation_in_progress ||
"tool_call_id" in output
);
}
export function isAgentPreviewOutput(
output: EditAgentToolOutput,
): output is AgentPreviewResponse {
return output.type === ResponseType.agent_preview || "agent_json" in output;
}
export function isAgentSavedOutput(
output: EditAgentToolOutput,
): output is AgentSavedResponse {
return (
output.type === ResponseType.agent_saved || "agent_page_link" in output
);
}
export function isClarificationNeededOutput(
output: EditAgentToolOutput,
): output is ClarificationNeededResponse {
return (
output.type === ResponseType.clarification_needed || "questions" in output
);
}
export function isErrorOutput(
output: EditAgentToolOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
export function getAnimationText(part: {
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
switch (part.state) {
case "input-streaming":
case "input-available":
return "Editing the agent";
case "output-available": {
const output = parseOutput(part.output);
if (!output) return "Editing the agent";
if (isOperationStartedOutput(output)) return "Agent update started";
if (isOperationPendingOutput(output)) return "Agent update in progress";
if (isOperationInProgressOutput(output))
return "Agent update already in progress";
if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
if (isClarificationNeededOutput(output)) return "Needs clarification";
return "Error editing agent";
}
case "output-error":
return "Error editing agent";
default:
return "Editing the agent";
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
if (isError) {
return (
<WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
);
}
if (isStreaming) {
return <OrbitLoader size={24} />;
}
return (
<PencilLineIcon size={14} weight="regular" className="text-neutral-400" />
);
}
export function AccordionIcon() {
return <NotePencilIcon size={32} weight="light" />;
}
export function formatMaybeJson(value: unknown): string {
if (typeof value === "string") return value;
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}
export function truncateText(text: string, maxChars: number): string {
const trimmed = text.trim();
if (trimmed.length <= maxChars) return trimmed;
return `${trimmed.slice(0, maxChars).trimEnd()}…`;
}

127
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"use client";
import { ToolUIPart } from "ai";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import {
ContentBadge,
ContentCard,
ContentCardDescription,
ContentCardHeader,
ContentCardTitle,
ContentGrid,
ContentLink,
} from "../../components/ToolAccordion/AccordionContent";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import {
AccordionIcon,
getAgentHref,
getAnimationText,
getFindAgentsOutput,
getSourceLabelFromToolType,
isAgentsFoundOutput,
isErrorOutput,
ToolIcon,
} from "./helpers";
export interface FindAgentsToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: FindAgentsToolPart;
}
export function FindAgentsTool({ part }: Props) {
const text = getAnimationText(part);
const output = getFindAgentsOutput(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const isError =
part.state === "output-error" || (!!output && isErrorOutput(output));
const query =
typeof part.input === "object" && part.input !== null
? String((part.input as { query?: unknown }).query ?? "").trim()
: "";
const agentsFoundOutput =
part.state === "output-available" && output && isAgentsFoundOutput(output)
? output
: null;
const hasAgents =
!!agentsFoundOutput &&
agentsFoundOutput.agents.length > 0 &&
(typeof agentsFoundOutput.count !== "number" ||
agentsFoundOutput.count > 0);
const totalCount = agentsFoundOutput ? agentsFoundOutput.count : 0;
const { source } = getSourceLabelFromToolType(part.type);
const scopeText =
source === "library"
? "in your library"
: source === "marketplace"
? "in marketplace"
: "";
const accordionDescription = `Found ${totalCount}${scopeText ? ` ${scopeText}` : ""}${
query ? ` for "${query}"` : ""
}`;
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon
toolType={part.type}
isStreaming={isStreaming}
isError={isError}
/>
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasAgents && agentsFoundOutput && (
<ToolAccordion
icon={<AccordionIcon toolType={part.type} />}
title="Agent results"
description={accordionDescription}
>
<ContentGrid className="sm:grid-cols-2">
{agentsFoundOutput.agents.map((agent) => {
const href = getAgentHref(agent);
const agentSource =
agent.source === "library"
? "Library"
: agent.source === "marketplace"
? "Marketplace"
: null;
return (
<ContentCard key={agent.id}>
<ContentCardHeader
action={
href ? <ContentLink href={href}>Open</ContentLink> : null
}
>
<div className="flex items-center gap-2">
<ContentCardTitle>{agent.name}</ContentCardTitle>
{agentSource && (
<ContentBadge>{agentSource}</ContentBadge>
)}
</div>
<ContentCardDescription className="mt-1 line-clamp-2">
{agent.description}
</ContentCardDescription>
</ContentCardHeader>
</ContentCard>
);
})}
</ContentGrid>
</ToolAccordion>
)}
</div>
);
}

187
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import type { AgentInfo } from "@/app/api/__generated__/models/agentInfo";
import type { AgentsFoundResponse } from "@/app/api/__generated__/models/agentsFoundResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import {
FolderOpenIcon,
MagnifyingGlassIcon,
SquaresFourIcon,
StorefrontIcon,
} from "@phosphor-icons/react";
import { ToolUIPart } from "ai";
export interface FindAgentInput {
query: string;
}
export type FindAgentsOutput =
| AgentsFoundResponse
| NoResultsResponse
| ErrorResponse;
export type FindAgentsToolType =
| "tool-find_agent"
| "tool-find_library_agent"
| (string & {});
function parseOutput(output: unknown): FindAgentsOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (
type === ResponseType.agents_found ||
type === ResponseType.no_results ||
type === ResponseType.error
) {
return output as FindAgentsOutput;
}
if ("agents" in output && "count" in output)
return output as AgentsFoundResponse;
if ("suggestions" in output && !("error" in output))
return output as NoResultsResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function getFindAgentsOutput(part: unknown): FindAgentsOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
export function isAgentsFoundOutput(
output: FindAgentsOutput,
): output is AgentsFoundResponse {
return output.type === ResponseType.agents_found || "agents" in output;
}
export function isNoResultsOutput(
output: FindAgentsOutput,
): output is NoResultsResponse {
return (
output.type === ResponseType.no_results ||
("suggestions" in output && !("error" in output))
);
}
export function isErrorOutput(
output: FindAgentsOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
export function getSourceLabelFromToolType(toolType?: FindAgentsToolType): {
source: "marketplace" | "library" | "unknown";
label: string;
} {
if (toolType === "tool-find_library_agent") {
return { source: "library", label: "Library" };
}
if (toolType === "tool-find_agent") {
return { source: "marketplace", label: "Marketplace" };
}
return { source: "unknown", label: "Agents" };
}
export function getAnimationText(part: {
type?: FindAgentsToolType;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
const { source } = getSourceLabelFromToolType(part.type);
const query = (part.input as FindAgentInput | undefined)?.query?.trim();
// Action phrase matching legacy ToolCallMessage
const actionPhrase =
source === "library"
? "Looking for library agents"
: "Looking for agents in the marketplace";
const queryText = query ? ` matching "${query}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `${actionPhrase}${queryText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) {
return `${actionPhrase}${queryText}`;
}
if (isNoResultsOutput(output)) {
return `No agents found${queryText}`;
}
if (isAgentsFoundOutput(output)) {
const count = output.count ?? output.agents?.length ?? 0;
return `Found ${count} agent${count === 1 ? "" : "s"}${queryText}`;
}
if (isErrorOutput(output)) {
return `Error finding agents${queryText}`;
}
return `${actionPhrase}${queryText}`;
}
case "output-error":
return `Error finding agents${queryText}`;
default:
return actionPhrase;
}
}
export function getAgentHref(agent: AgentInfo): string | null {
if (agent.source === "library") {
return `/library/agents/${encodeURIComponent(agent.id)}`;
}
const [creator, slug, ...rest] = agent.id.split("/");
if (!creator || !slug || rest.length > 0) return null;
return `/marketplace/agent/${encodeURIComponent(creator)}/${encodeURIComponent(slug)}`;
}
export function ToolIcon({
toolType,
isStreaming,
isError,
}: {
toolType?: FindAgentsToolType;
isStreaming?: boolean;
isError?: boolean;
}) {
const { source } = getSourceLabelFromToolType(toolType);
const IconComponent =
source === "library" ? MagnifyingGlassIcon : SquaresFourIcon;
return (
<IconComponent
size={14}
weight="regular"
className={
isError
? "text-red-500"
: isStreaming
? "text-neutral-500"
: "text-neutral-400"
}
/>
);
}
export function AccordionIcon({ toolType }: { toolType?: FindAgentsToolType }) {
const { source } = getSourceLabelFromToolType(toolType);
const IconComponent = source === "library" ? FolderOpenIcon : StorefrontIcon;
return <IconComponent size={32} weight="light" />;
}

92
autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/FindBlocks.tsx Normal file
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"use client";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import {
ContentCard,
ContentCardDescription,
ContentCardTitle,
} from "../../components/ToolAccordion/AccordionContent";
import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
import type { BlockInfoSummary } from "@/app/api/__generated__/models/blockInfoSummary";
import { ToolUIPart } from "ai";
import { HorizontalScroll } from "@/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll";
import {
AccordionIcon,
getAnimationText,
parseOutput,
ToolIcon,
} from "./helpers";
export interface FindBlockInput {
query: string;
}
export type FindBlockOutput = BlockListResponse;
export interface FindBlockToolPart {
type: string;
toolName?: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: FindBlockInput | unknown;
output?: string | FindBlockOutput | unknown;
title?: string;
}
interface Props {
part: FindBlockToolPart;
}
function BlockCard({ block }: { block: BlockInfoSummary }) {
return (
<ContentCard className="w-48 shrink-0">
<ContentCardTitle>{block.name}</ContentCardTitle>
<ContentCardDescription className="mt-1 line-clamp-2">
{block.description}
</ContentCardDescription>
</ContentCard>
);
}
export function FindBlocksTool({ part }: Props) {
const text = getAnimationText(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const isError = part.state === "output-error";
const parsed =
part.state === "output-available" ? parseOutput(part.output) : null;
const hasBlocks = !!parsed && parsed.blocks.length > 0;
const query = (part.input as FindBlockInput | undefined)?.query?.trim();
const accordionDescription = parsed
? `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${query ? ` for "${query}"` : ""}`
: undefined;
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasBlocks && parsed && (
<ToolAccordion
icon={<AccordionIcon />}
title="Block results"
description={accordionDescription}
>
<HorizontalScroll dependencyList={[parsed.blocks.length]}>
{parsed.blocks.map((block) => (
<BlockCard key={block.id} block={block} />
))}
</HorizontalScroll>
</ToolAccordion>
)}
</div>
);
}

75
autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/helpers.tsx Normal file
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import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import { CubeIcon, PackageIcon } from "@phosphor-icons/react";
import { FindBlockInput, FindBlockToolPart } from "./FindBlocks";
export function parseOutput(output: unknown): BlockListResponse | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (type === ResponseType.block_list || "blocks" in output) {
return output as BlockListResponse;
}
}
return null;
}
export function getAnimationText(part: FindBlockToolPart): string {
const query = (part.input as FindBlockInput | undefined)?.query?.trim();
const queryText = query ? ` matching "${query}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `Searching for blocks${queryText}`;
case "output-available": {
const parsed = parseOutput(part.output);
if (parsed) {
return `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${queryText}`;
}
return `Searching for blocks${queryText}`;
}
case "output-error":
return `Error finding blocks${queryText}`;
default:
return "Searching for blocks";
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
return (
<PackageIcon
size={14}
weight="regular"
className={
isError
? "text-red-500"
: isStreaming
? "text-neutral-500"
: "text-neutral-400"
}
/>
);
}
export function AccordionIcon() {
return <CubeIcon size={32} weight="light" />;
}

93
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx Normal file
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"use client";
import type { ToolUIPart } from "ai";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import { ContentMessage } from "../../components/ToolAccordion/AccordionContent";
import {
getAccordionMeta,
getAnimationText,
getRunAgentToolOutput,
isRunAgentAgentDetailsOutput,
isRunAgentErrorOutput,
isRunAgentExecutionStartedOutput,
isRunAgentNeedLoginOutput,
isRunAgentSetupRequirementsOutput,
ToolIcon,
} from "./helpers";
import { ExecutionStartedCard } from "./components/ExecutionStartedCard/ExecutionStartedCard";
import { AgentDetailsCard } from "./components/AgentDetailsCard/AgentDetailsCard";
import { SetupRequirementsCard } from "./components/SetupRequirementsCard/SetupRequirementsCard";
import { ErrorCard } from "./components/ErrorCard/ErrorCard";
export interface RunAgentToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: RunAgentToolPart;
}
export function RunAgentTool({ part }: Props) {
const text = getAnimationText(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const output = getRunAgentToolOutput(part);
const isError =
part.state === "output-error" ||
(!!output && isRunAgentErrorOutput(output));
const hasExpandableContent =
part.state === "output-available" &&
!!output &&
(isRunAgentExecutionStartedOutput(output) ||
isRunAgentAgentDetailsOutput(output) ||
isRunAgentSetupRequirementsOutput(output) ||
isRunAgentNeedLoginOutput(output) ||
isRunAgentErrorOutput(output));
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && output && (
<ToolAccordion
{...getAccordionMeta(output)}
defaultExpanded={
isRunAgentExecutionStartedOutput(output) ||
isRunAgentSetupRequirementsOutput(output) ||
isRunAgentAgentDetailsOutput(output)
}
>
{isRunAgentExecutionStartedOutput(output) && (
<ExecutionStartedCard output={output} />
)}
{isRunAgentAgentDetailsOutput(output) && (
<AgentDetailsCard output={output} />
)}
{isRunAgentSetupRequirementsOutput(output) && (
<SetupRequirementsCard output={output} />
)}
{isRunAgentNeedLoginOutput(output) && (
<ContentMessage>{output.message}</ContentMessage>
)}
{isRunAgentErrorOutput(output) && <ErrorCard output={output} />}
</ToolAccordion>
)}
</div>
);
}

116
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/AgentDetailsCard.tsx Normal file
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"use client";
import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
import { AnimatePresence, motion } from "framer-motion";
import { useState } from "react";
import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
import { ContentMessage } from "../../../../components/ToolAccordion/AccordionContent";
import { buildInputSchema } from "./helpers";
interface Props {
output: AgentDetailsResponse;
}
export function AgentDetailsCard({ output }: Props) {
const { onSend } = useCopilotChatActions();
const [showInputForm, setShowInputForm] = useState(false);
const [inputValues, setInputValues] = useState<Record<string, unknown>>({});
function handleRunWithExamples() {
onSend(
`Run the agent "${output.agent.name}" with placeholder/example values so I can test it.`,
);
}
function handleRunWithInputs() {
const nonEmpty = Object.fromEntries(
Object.entries(inputValues).filter(
([, v]) => v !== undefined && v !== null && v !== "",
),
);
onSend(
`Run the agent "${output.agent.name}" with these inputs: ${JSON.stringify(nonEmpty, null, 2)}`,
);
setShowInputForm(false);
setInputValues({});
}
return (
<div className="grid gap-2">
<ContentMessage>
Run this agent with example values or your own inputs.
</ContentMessage>
<div className="flex gap-2 pt-4">
<Button size="small" className="w-fit" onClick={handleRunWithExamples}>
Run with example values
</Button>
<Button
variant="outline"
size="small"
className="w-fit"
onClick={() => setShowInputForm((prev) => !prev)}
>
Run with my inputs
</Button>
</div>
<AnimatePresence initial={false}>
{showInputForm && buildInputSchema(output.agent.inputs) && (
<motion.div
initial={{ height: 0, opacity: 0, filter: "blur(6px)" }}
animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
exit={{ height: 0, opacity: 0, filter: "blur(6px)" }}
transition={{
height: { type: "spring", bounce: 0.15, duration: 0.5 },
opacity: { duration: 0.25 },
filter: { duration: 0.2 },
}}
className="overflow-hidden"
style={{ willChange: "height, opacity, filter" }}
>
<div className="mt-4 rounded-2xl border bg-background p-3 pt-4">
<Text variant="body-medium">Enter your inputs</Text>
<FormRenderer
jsonSchema={buildInputSchema(output.agent.inputs)!}
handleChange={(v) => setInputValues(v.formData ?? {})}
uiSchema={{
"ui:submitButtonOptions": { norender: true },
}}
initialValues={inputValues}
formContext={{
showHandles: false,
size: "small",
}}
/>
<div className="-mt-8 flex gap-2">
<Button
variant="primary"
size="small"
className="w-fit"
onClick={handleRunWithInputs}
>
Run
</Button>
<Button
variant="secondary"
size="small"
className="w-fit"
onClick={() => {
setShowInputForm(false);
setInputValues({});
}}
>
Cancel
</Button>
</div>
</div>
</motion.div>
)}
</AnimatePresence>
</div>
);
}

8
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/helpers.ts Normal file
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import type { RJSFSchema } from "@rjsf/utils";
export function buildInputSchema(inputs: unknown): RJSFSchema | null {
if (!inputs || typeof inputs !== "object") return null;
const properties = inputs as RJSFSchema["properties"];
if (!properties || Object.keys(properties).length === 0) return null;
return inputs as RJSFSchema;
}

27
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ErrorCard/ErrorCard.tsx Normal file
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"use client";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import {
ContentCodeBlock,
ContentGrid,
ContentMessage,
} from "../../../../components/ToolAccordion/AccordionContent";
import { formatMaybeJson } from "../../helpers";
interface Props {
output: ErrorResponse;
}
export function ErrorCard({ output }: Props) {
return (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.error && (
<ContentCodeBlock>{formatMaybeJson(output.error)}</ContentCodeBlock>
)}
{output.details && (
<ContentCodeBlock>{formatMaybeJson(output.details)}</ContentCodeBlock>
)}
</ContentGrid>
);
}

39
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ExecutionStartedCard/ExecutionStartedCard.tsx Normal file
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"use client";
import type { ExecutionStartedResponse } from "@/app/api/__generated__/models/executionStartedResponse";
import { Button } from "@/components/atoms/Button/Button";
import { useRouter } from "next/navigation";
import {
ContentCard,
ContentCardDescription,
ContentCardSubtitle,
ContentCardTitle,
ContentGrid,
} from "../../../../components/ToolAccordion/AccordionContent";
interface Props {
output: ExecutionStartedResponse;
}
export function ExecutionStartedCard({ output }: Props) {
const router = useRouter();
return (
<ContentGrid>
<ContentCard>
<ContentCardTitle>Execution started</ContentCardTitle>
<ContentCardSubtitle>{output.execution_id}</ContentCardSubtitle>
<ContentCardDescription>{output.message}</ContentCardDescription>
{output.library_agent_link && (
<Button
size="small"
className="mt-3"
onClick={() => router.push(output.library_agent_link!)}
>
View Execution
</Button>
)}
</ContentCard>
</ContentGrid>
);
}

105
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/SetupRequirementsCard/SetupRequirementsCard.tsx Normal file
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"use client";
import { useState } from "react";
import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
import { Button } from "@/components/atoms/Button/Button";
import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
import {
ContentBadge,
ContentCardDescription,
ContentCardTitle,
ContentMessage,
} from "../../../../components/ToolAccordion/AccordionContent";
import { coerceCredentialFields, coerceExpectedInputs } from "./helpers";
interface Props {
output: SetupRequirementsResponse;
}
export function SetupRequirementsCard({ output }: Props) {
const { onSend } = useCopilotChatActions();
const [inputCredentials, setInputCredentials] = useState<
Record<string, CredentialsMetaInput | undefined>
>({});
const [hasSent, setHasSent] = useState(false);
const { credentialFields, requiredCredentials } = coerceCredentialFields(
output.setup_info.user_readiness?.missing_credentials,
);
const expectedInputs = coerceExpectedInputs(
(output.setup_info.requirements as Record<string, unknown>)?.inputs,
);
function handleCredentialChange(key: string, value?: CredentialsMetaInput) {
setInputCredentials((prev) => ({ ...prev, [key]: value }));
}
const isAllComplete =
credentialFields.length > 0 &&
[...requiredCredentials].every((key) => !!inputCredentials[key]);
function handleProceed() {
setHasSent(true);
onSend(
"I've configured the required credentials. Please check if everything is ready and proceed with running the agent.",
);
}
return (
<div className="grid gap-2">
<ContentMessage>{output.message}</ContentMessage>
{credentialFields.length > 0 && (
<div className="rounded-2xl border bg-background p-3">
<CredentialsGroupedView
credentialFields={credentialFields}
requiredCredentials={requiredCredentials}
inputCredentials={inputCredentials}
inputValues={{}}
onCredentialChange={handleCredentialChange}
/>
{isAllComplete && !hasSent && (
<Button
variant="primary"
size="small"
className="mt-3 w-full"
onClick={handleProceed}
>
Proceed
</Button>
)}
</div>
)}
{expectedInputs.length > 0 && (
<div className="rounded-2xl border bg-background p-3">
<ContentCardTitle className="text-xs">
Expected inputs
</ContentCardTitle>
<div className="mt-2 grid gap-2">
{expectedInputs.map((input) => (
<div key={input.name} className="rounded-xl border p-2">
<div className="flex items-center justify-between gap-2">
<ContentCardTitle className="text-xs">
{input.title}
</ContentCardTitle>
<ContentBadge>
{input.required ? "Required" : "Optional"}
</ContentBadge>
</div>
<ContentCardDescription className="mt-1">
{input.name} &bull; {input.type}
{input.description ? ` \u2022 ${input.description}` : ""}
</ContentCardDescription>
</div>
))}
</div>
</div>
)}
</div>
);
}

116
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import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
const VALID_CREDENTIAL_TYPES = new Set([
"api_key",
"oauth2",
"user_password",
"host_scoped",
]);
/**
* Transforms raw missing_credentials from SetupRequirementsResponse
* into CredentialField[] tuples compatible with CredentialsGroupedView.
*
* Each CredentialField is [key, schema] where schema matches
* BlockIOCredentialsSubSchema shape.
*/
export function coerceCredentialFields(rawMissingCredentials: unknown): {
credentialFields: CredentialField[];
requiredCredentials: Set<string>;
} {
const missing =
rawMissingCredentials && typeof rawMissingCredentials === "object"
? (rawMissingCredentials as Record<string, unknown>)
: {};
const credentialFields: CredentialField[] = [];
const requiredCredentials = new Set<string>();
Object.entries(missing).forEach(([key, value]) => {
if (!value || typeof value !== "object") return;
const cred = value as Record<string, unknown>;
const provider =
typeof cred.provider === "string" ? cred.provider.trim() : "";
if (!provider) return;
const types =
Array.isArray(cred.types) && cred.types.length > 0
? cred.types
: typeof cred.type === "string"
? [cred.type]
: [];
const credentialTypes = types
.map((t) => (typeof t === "string" ? t.trim() : ""))
.filter((t) => VALID_CREDENTIAL_TYPES.has(t));
if (credentialTypes.length === 0) return;
const scopes = Array.isArray(cred.scopes)
? cred.scopes.filter((s): s is string => typeof s === "string")
: undefined;
const schema = {
type: "object" as const,
properties: {},
credentials_provider: [provider],
credentials_types: credentialTypes,
credentials_scopes: scopes,
};
credentialFields.push([key, schema]);
requiredCredentials.add(key);
});
return { credentialFields, requiredCredentials };
}
export function coerceExpectedInputs(rawInputs: unknown): Array<{
name: string;
title: string;
type: string;
description?: string;
required: boolean;
}> {
if (!Array.isArray(rawInputs)) return [];
const results: Array<{
name: string;
title: string;
type: string;
description?: string;
required: boolean;
}> = [];
rawInputs.forEach((value, index) => {
if (!value || typeof value !== "object") return;
const input = value as Record<string, unknown>;
const name =
typeof input.name === "string" && input.name.trim()
? input.name.trim()
: `input-${index}`;
const title =
typeof input.title === "string" && input.title.trim()
? input.title.trim()
: name;
const type = typeof input.type === "string" ? input.type : "unknown";
const description =
typeof input.description === "string" && input.description.trim()
? input.description.trim()
: undefined;
const required = Boolean(input.required);
const item: {
name: string;
title: string;
type: string;
description?: string;
required: boolean;
} = { name, title, type, required };
if (description) item.description = description;
results.push(item);
});
return results;
}

248
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import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { ExecutionStartedResponse } from "@/app/api/__generated__/models/executionStartedResponse";
import type { NeedLoginResponse } from "@/app/api/__generated__/models/needLoginResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
import {
PlayIcon,
RocketLaunchIcon,
WarningDiamondIcon,
} from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { SpinnerLoader } from "../../components/SpinnerLoader/SpinnerLoader";
export interface RunAgentInput {
username_agent_slug?: string;
library_agent_id?: string;
inputs?: Record<string, unknown>;
use_defaults?: boolean;
schedule_name?: string;
cron?: string;
timezone?: string;
}
export type RunAgentToolOutput =
| SetupRequirementsResponse
| ExecutionStartedResponse
| AgentDetailsResponse
| NeedLoginResponse
| ErrorResponse;
const RUN_AGENT_OUTPUT_TYPES = new Set<string>([
ResponseType.setup_requirements,
ResponseType.execution_started,
ResponseType.agent_details,
ResponseType.need_login,
ResponseType.error,
]);
export function isRunAgentSetupRequirementsOutput(
output: RunAgentToolOutput,
): output is SetupRequirementsResponse {
return (
output.type === ResponseType.setup_requirements ||
("setup_info" in output && typeof output.setup_info === "object")
);
}
export function isRunAgentExecutionStartedOutput(
output: RunAgentToolOutput,
): output is ExecutionStartedResponse {
return (
output.type === ResponseType.execution_started || "execution_id" in output
);
}
export function isRunAgentAgentDetailsOutput(
output: RunAgentToolOutput,
): output is AgentDetailsResponse {
return output.type === ResponseType.agent_details || "agent" in output;
}
export function isRunAgentNeedLoginOutput(
output: RunAgentToolOutput,
): output is NeedLoginResponse {
return output.type === ResponseType.need_login;
}
export function isRunAgentErrorOutput(
output: RunAgentToolOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
function parseOutput(output: unknown): RunAgentToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (typeof type === "string" && RUN_AGENT_OUTPUT_TYPES.has(type)) {
return output as RunAgentToolOutput;
}
if ("execution_id" in output) return output as ExecutionStartedResponse;
if ("setup_info" in output) return output as SetupRequirementsResponse;
if ("agent" in output) return output as AgentDetailsResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
if (type === ResponseType.need_login) return output as NeedLoginResponse;
}
return null;
}
export function getRunAgentToolOutput(
part: unknown,
): RunAgentToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
function getAgentIdentifierText(
input: RunAgentInput | undefined,
): string | null {
if (!input) return null;
const slug = input.username_agent_slug?.trim();
if (slug) return slug;
const libraryId = input.library_agent_id?.trim();
if (libraryId) return `Library agent ${libraryId}`;
return null;
}
export function getAnimationText(part: {
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
const input = part.input as RunAgentInput | undefined;
const agentIdentifier = getAgentIdentifierText(input);
const isSchedule = Boolean(
input?.schedule_name?.trim() || input?.cron?.trim(),
);
const actionPhrase = isSchedule
? "Scheduling the agent to run"
: "Running the agent";
const identifierText = agentIdentifier ? ` "${agentIdentifier}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `${actionPhrase}${identifierText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) return `${actionPhrase}${identifierText}`;
if (isRunAgentExecutionStartedOutput(output)) {
return `Started "${output.graph_name}"`;
}
if (isRunAgentAgentDetailsOutput(output)) {
return `Agent inputs needed for "${output.agent.name}"`;
}
if (isRunAgentSetupRequirementsOutput(output)) {
return `Setup needed for "${output.setup_info.agent_name}"`;
}
if (isRunAgentNeedLoginOutput(output))
return "Sign in required to run agent";
return "Error running agent";
}
case "output-error":
return "Error running agent";
default:
return actionPhrase;
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
if (isError) {
return (
<WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
);
}
if (isStreaming) {
return <SpinnerLoader size={40} className="text-neutral-700" />;
}
return <PlayIcon size={14} weight="regular" className="text-neutral-400" />;
}
export function AccordionIcon() {
return <RocketLaunchIcon size={28} weight="light" />;
}
export function formatMaybeJson(value: unknown): string {
if (typeof value === "string") return value;
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}
export function getAccordionMeta(output: RunAgentToolOutput): {
icon: React.ReactNode;
title: string;
titleClassName?: string;
description?: string;
} {
const icon = <AccordionIcon />;
if (isRunAgentExecutionStartedOutput(output)) {
const statusText =
typeof output.status === "string" && output.status.trim()
? output.status.trim()
: "started";
return {
icon: <SpinnerLoader size={28} className="text-neutral-700" />,
title: output.graph_name,
description: `Status: ${statusText}`,
};
}
if (isRunAgentAgentDetailsOutput(output)) {
return {
icon,
title: output.agent.name,
description: "Inputs required",
};
}
if (isRunAgentSetupRequirementsOutput(output)) {
const missingCredsCount = Object.keys(
(output.setup_info.user_readiness?.missing_credentials ?? {}) as Record<
string,
unknown
>,
).length;
return {
icon,
title: output.setup_info.agent_name,
description:
missingCredsCount > 0
? `Missing ${missingCredsCount} credential${missingCredsCount === 1 ? "" : "s"}`
: output.message,
};
}
if (isRunAgentNeedLoginOutput(output)) {
return { icon, title: "Sign in required" };
}
return {
icon: (
<WarningDiamondIcon size={28} weight="light" className="text-red-500" />
),
title: "Error",
titleClassName: "text-red-500",
};
}

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"use client";
import type { ToolUIPart } from "ai";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import { BlockOutputCard } from "./components/BlockOutputCard/BlockOutputCard";
import { ErrorCard } from "./components/ErrorCard/ErrorCard";
import { SetupRequirementsCard } from "./components/SetupRequirementsCard/SetupRequirementsCard";
import {
getAccordionMeta,
getAnimationText,
getRunBlockToolOutput,
isRunBlockBlockOutput,
isRunBlockErrorOutput,
isRunBlockSetupRequirementsOutput,
ToolIcon,
} from "./helpers";
export interface RunBlockToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: RunBlockToolPart;
}
export function RunBlockTool({ part }: Props) {
const text = getAnimationText(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const output = getRunBlockToolOutput(part);
const isError =
part.state === "output-error" ||
(!!output && isRunBlockErrorOutput(output));
const hasExpandableContent =
part.state === "output-available" &&
!!output &&
(isRunBlockBlockOutput(output) ||
isRunBlockSetupRequirementsOutput(output) ||
isRunBlockErrorOutput(output));
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && output && (
<ToolAccordion
{...getAccordionMeta(output)}
defaultExpanded={
isRunBlockBlockOutput(output) ||
isRunBlockSetupRequirementsOutput(output)
}
>
{isRunBlockBlockOutput(output) && <BlockOutputCard output={output} />}
{isRunBlockSetupRequirementsOutput(output) && (
<SetupRequirementsCard output={output} />
)}
{isRunBlockErrorOutput(output) && <ErrorCard output={output} />}
</ToolAccordion>
)}
</div>
);
}

133
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/components/BlockOutputCard/BlockOutputCard.tsx Normal file
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"use client";
import React, { useState } from "react";
import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
import { Button } from "@/components/atoms/Button/Button";
import type { BlockOutputResponse } from "@/app/api/__generated__/models/blockOutputResponse";
import {
globalRegistry,
OutputItem,
} from "@/components/contextual/OutputRenderers";
import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
import {
ContentBadge,
ContentCard,
ContentCardTitle,
ContentGrid,
ContentMessage,
} from "../../../../components/ToolAccordion/AccordionContent";
interface Props {
output: BlockOutputResponse;
}
const COLLAPSED_LIMIT = 3;
function isWorkspaceRef(value: unknown): value is string {
return typeof value === "string" && value.startsWith("workspace://");
}
function resolveForRenderer(value: unknown): {
value: unknown;
metadata?: OutputMetadata;
} {
if (!isWorkspaceRef(value)) return { value };
const withoutPrefix = value.replace("workspace://", "");
const fileId = withoutPrefix.split("#")[0];
const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
const url = `/api/proxy${apiPath}`;
const hashIndex = value.indexOf("#");
const mimeHint =
hashIndex !== -1 ? value.slice(hashIndex + 1) || undefined : undefined;
const metadata: OutputMetadata = {};
if (mimeHint) {
metadata.mimeType = mimeHint;
if (mimeHint.startsWith("image/")) metadata.type = "image";
else if (mimeHint.startsWith("video/")) metadata.type = "video";
}
return { value: url, metadata };
}
function RenderOutputValue({ value }: { value: unknown }) {
const resolved = resolveForRenderer(value);
const renderer = globalRegistry.getRenderer(
resolved.value,
resolved.metadata,
);
if (renderer) {
return (
<OutputItem
value={resolved.value}
metadata={resolved.metadata}
renderer={renderer}
/>
);
}
// Fallback for audio workspace refs
if (
isWorkspaceRef(value) &&
resolved.metadata?.mimeType?.startsWith("audio/")
) {
return (
<audio controls src={String(resolved.value)} className="mt-2 w-full" />
);
}
return null;
}
function OutputKeySection({
outputKey,
items,
}: {
outputKey: string;
items: unknown[];
}) {
const [expanded, setExpanded] = useState(false);
const hasMoreItems = items.length > COLLAPSED_LIMIT;
const visibleItems = expanded ? items : items.slice(0, COLLAPSED_LIMIT);
return (
<ContentCard>
<div className="flex items-center justify-between gap-2">
<ContentCardTitle className="text-xs">{outputKey}</ContentCardTitle>
<ContentBadge>
{items.length} item{items.length === 1 ? "" : "s"}
</ContentBadge>
</div>
<div className="mt-2">
{visibleItems.map((item, i) => (
<RenderOutputValue key={i} value={item} />
))}
</div>
{hasMoreItems && (
<Button
variant="ghost"
size="small"
className="mt-1 h-auto px-0 py-0.5 text-[11px] text-muted-foreground"
onClick={() => setExpanded((prev) => !prev)}
>
{expanded ? "Show less" : `Show all ${items.length} items`}
</Button>
)}
</ContentCard>
);
}
export function BlockOutputCard({ output }: Props) {
return (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{Object.entries(output.outputs ?? {}).map(([key, items]) => (
<OutputKeySection key={key} outputKey={key} items={items} />
))}
</ContentGrid>
);
}

27
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/components/ErrorCard/ErrorCard.tsx Normal file
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"use client";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import {
ContentCodeBlock,
ContentGrid,
ContentMessage,
} from "../../../../components/ToolAccordion/AccordionContent";
import { formatMaybeJson } from "../../helpers";
interface Props {
output: ErrorResponse;
}
export function ErrorCard({ output }: Props) {
return (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.error && (
<ContentCodeBlock>{formatMaybeJson(output.error)}</ContentCodeBlock>
)}
{output.details && (
<ContentCodeBlock>{formatMaybeJson(output.details)}</ContentCodeBlock>
)}
</ContentGrid>
);
}

197
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/components/SetupRequirementsCard/SetupRequirementsCard.tsx Normal file
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"use client";
import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
import { AnimatePresence, motion } from "framer-motion";
import { useState } from "react";
import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
import {
ContentBadge,
ContentCardDescription,
ContentCardTitle,
ContentMessage,
} from "../../../../components/ToolAccordion/AccordionContent";
import {
buildExpectedInputsSchema,
coerceCredentialFields,
coerceExpectedInputs,
} from "./helpers";
interface Props {
output: SetupRequirementsResponse;
}
export function SetupRequirementsCard({ output }: Props) {
const { onSend } = useCopilotChatActions();
const [inputCredentials, setInputCredentials] = useState<
Record<string, CredentialsMetaInput | undefined>
>({});
const [hasSentCredentials, setHasSentCredentials] = useState(false);
const [showInputForm, setShowInputForm] = useState(false);
const [inputValues, setInputValues] = useState<Record<string, unknown>>({});
const { credentialFields, requiredCredentials } = coerceCredentialFields(
output.setup_info.user_readiness?.missing_credentials,
);
const expectedInputs = coerceExpectedInputs(
(output.setup_info.requirements as Record<string, unknown>)?.inputs,
);
const inputSchema = buildExpectedInputsSchema(expectedInputs);
function handleCredentialChange(key: string, value?: CredentialsMetaInput) {
setInputCredentials((prev) => ({ ...prev, [key]: value }));
}
const isAllCredentialsComplete =
credentialFields.length > 0 &&
[...requiredCredentials].every((key) => !!inputCredentials[key]);
function handleProceedCredentials() {
setHasSentCredentials(true);
onSend(
"I've configured the required credentials. Please re-run the block now.",
);
}
function handleRunWithInputs() {
const nonEmpty = Object.fromEntries(
Object.entries(inputValues).filter(
([, v]) => v !== undefined && v !== null && v !== "",
),
);
onSend(
`Run the block with these inputs: ${JSON.stringify(nonEmpty, null, 2)}`,
);
setShowInputForm(false);
setInputValues({});
}
return (
<div className="grid gap-2">
<ContentMessage>{output.message}</ContentMessage>
{credentialFields.length > 0 && (
<div className="rounded-2xl border bg-background p-3">
<CredentialsGroupedView
credentialFields={credentialFields}
requiredCredentials={requiredCredentials}
inputCredentials={inputCredentials}
inputValues={{}}
onCredentialChange={handleCredentialChange}
/>
{isAllCredentialsComplete && !hasSentCredentials && (
<Button
variant="primary"
size="small"
className="mt-3 w-full"
onClick={handleProceedCredentials}
>
Proceed
</Button>
)}
</div>
)}
{inputSchema && (
<div className="flex gap-2 pt-2">
<Button
variant="outline"
size="small"
className="w-fit"
onClick={() => setShowInputForm((prev) => !prev)}
>
{showInputForm ? "Hide inputs" : "Fill in inputs"}
</Button>
</div>
)}
<AnimatePresence initial={false}>
{showInputForm && inputSchema && (
<motion.div
initial={{ height: 0, opacity: 0, filter: "blur(6px)" }}
animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
exit={{ height: 0, opacity: 0, filter: "blur(6px)" }}
transition={{
height: { type: "spring", bounce: 0.15, duration: 0.5 },
opacity: { duration: 0.25 },
filter: { duration: 0.2 },
}}
className="overflow-hidden"
style={{ willChange: "height, opacity, filter" }}
>
<div className="rounded-2xl border bg-background p-3 pt-4">
<Text variant="body-medium">Block inputs</Text>
<FormRenderer
jsonSchema={inputSchema}
handleChange={(v) => setInputValues(v.formData ?? {})}
uiSchema={{
"ui:submitButtonOptions": { norender: true },
}}
initialValues={inputValues}
formContext={{
showHandles: false,
size: "small",
}}
/>
<div className="-mt-8 flex gap-2">
<Button
variant="primary"
size="small"
className="w-fit"
onClick={handleRunWithInputs}
>
Run
</Button>
<Button
variant="secondary"
size="small"
className="w-fit"
onClick={() => {
setShowInputForm(false);
setInputValues({});
}}
>
Cancel
</Button>
</div>
</div>
</motion.div>
)}
</AnimatePresence>
{expectedInputs.length > 0 && !inputSchema && (
<div className="rounded-2xl border bg-background p-3">
<ContentCardTitle className="text-xs">
Expected inputs
</ContentCardTitle>
<div className="mt-2 grid gap-2">
{expectedInputs.map((input) => (
<div key={input.name} className="rounded-xl border p-2">
<div className="flex items-center justify-between gap-2">
<ContentCardTitle className="text-xs">
{input.title}
</ContentCardTitle>
<ContentBadge>
{input.required ? "Required" : "Optional"}
</ContentBadge>
</div>
<ContentCardDescription className="mt-1">
{input.name} &bull; {input.type}
{input.description ? ` \u2022 ${input.description}` : ""}
</ContentCardDescription>
</div>
))}
</div>
</div>
)}
</div>
);
}

156
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/components/SetupRequirementsCard/helpers.ts Normal file
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import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
import type { RJSFSchema } from "@rjsf/utils";
const VALID_CREDENTIAL_TYPES = new Set([
"api_key",
"oauth2",
"user_password",
"host_scoped",
]);
export function coerceCredentialFields(rawMissingCredentials: unknown): {
credentialFields: CredentialField[];
requiredCredentials: Set<string>;
} {
const missing =
rawMissingCredentials && typeof rawMissingCredentials === "object"
? (rawMissingCredentials as Record<string, unknown>)
: {};
const credentialFields: CredentialField[] = [];
const requiredCredentials = new Set<string>();
Object.entries(missing).forEach(([key, value]) => {
if (!value || typeof value !== "object") return;
const cred = value as Record<string, unknown>;
const provider =
typeof cred.provider === "string" ? cred.provider.trim() : "";
if (!provider) return;
const types =
Array.isArray(cred.types) && cred.types.length > 0
? cred.types
: typeof cred.type === "string"
? [cred.type]
: [];
const credentialTypes = types
.map((t) => (typeof t === "string" ? t.trim() : ""))
.filter((t) => VALID_CREDENTIAL_TYPES.has(t));
if (credentialTypes.length === 0) return;
const scopes = Array.isArray(cred.scopes)
? cred.scopes.filter((s): s is string => typeof s === "string")
: undefined;
const schema = {
type: "object" as const,
properties: {},
credentials_provider: [provider],
credentials_types: credentialTypes,
credentials_scopes: scopes,
};
credentialFields.push([key, schema]);
requiredCredentials.add(key);
});
return { credentialFields, requiredCredentials };
}
export function coerceExpectedInputs(rawInputs: unknown): Array<{
name: string;
title: string;
type: string;
description?: string;
required: boolean;
}> {
if (!Array.isArray(rawInputs)) return [];
const results: Array<{
name: string;
title: string;
type: string;
description?: string;
required: boolean;
}> = [];
rawInputs.forEach((value, index) => {
if (!value || typeof value !== "object") return;
const input = value as Record<string, unknown>;
const name =
typeof input.name === "string" && input.name.trim()
? input.name.trim()
: `input-${index}`;
const title =
typeof input.title === "string" && input.title.trim()
? input.title.trim()
: name;
const type = typeof input.type === "string" ? input.type : "unknown";
const description =
typeof input.description === "string" && input.description.trim()
? input.description.trim()
: undefined;
const required = Boolean(input.required);
const item: {
name: string;
title: string;
type: string;
description?: string;
required: boolean;
} = { name, title, type, required };
if (description) item.description = description;
results.push(item);
});
return results;
}
/**
* Build an RJSF schema from expected inputs so they can be rendered
* as a dynamic form via FormRenderer.
*/
export function buildExpectedInputsSchema(
expectedInputs: Array<{
name: string;
title: string;
type: string;
description?: string;
required: boolean;
}>,
): RJSFSchema | null {
if (expectedInputs.length === 0) return null;
const TYPE_MAP: Record<string, string> = {
string: "string",
str: "string",
text: "string",
number: "number",
int: "integer",
integer: "integer",
float: "number",
boolean: "boolean",
bool: "boolean",
};
const properties: Record<string, Record<string, unknown>> = {};
const required: string[] = [];
for (const input of expectedInputs) {
properties[input.name] = {
type: TYPE_MAP[input.type.toLowerCase()] ?? "string",
title: input.title,
...(input.description ? { description: input.description } : {}),
};
if (input.required) required.push(input.name);
}
return {
type: "object",
properties,
...(required.length > 0 ? { required } : {}),
};
}

185
autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/helpers.tsx Normal file
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import type { BlockOutputResponse } from "@/app/api/__generated__/models/blockOutputResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
import {
PlayCircleIcon,
PlayIcon,
WarningDiamondIcon,
} from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
import { SpinnerLoader } from "../../components/SpinnerLoader/SpinnerLoader";
export interface RunBlockInput {
block_id?: string;
input_data?: Record<string, unknown>;
}
export type RunBlockToolOutput =
| SetupRequirementsResponse
| BlockOutputResponse
| ErrorResponse;
const RUN_BLOCK_OUTPUT_TYPES = new Set<string>([
ResponseType.setup_requirements,
ResponseType.block_output,
ResponseType.error,
]);
export function isRunBlockSetupRequirementsOutput(
output: RunBlockToolOutput,
): output is SetupRequirementsResponse {
return (
output.type === ResponseType.setup_requirements ||
("setup_info" in output && typeof output.setup_info === "object")
);
}
export function isRunBlockBlockOutput(
output: RunBlockToolOutput,
): output is BlockOutputResponse {
return output.type === ResponseType.block_output || "block_id" in output;
}
export function isRunBlockErrorOutput(
output: RunBlockToolOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
function parseOutput(output: unknown): RunBlockToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (typeof type === "string" && RUN_BLOCK_OUTPUT_TYPES.has(type)) {
return output as RunBlockToolOutput;
}
if ("block_id" in output) return output as BlockOutputResponse;
if ("setup_info" in output) return output as SetupRequirementsResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function getRunBlockToolOutput(
part: unknown,
): RunBlockToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
export function getAnimationText(part: {
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
const input = part.input as RunBlockInput | undefined;
const blockId = input?.block_id?.trim();
const blockText = blockId ? ` "${blockId}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `Running the block${blockText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) return `Running the block${blockText}`;
if (isRunBlockBlockOutput(output)) return `Ran "${output.block_name}"`;
if (isRunBlockSetupRequirementsOutput(output)) {
return `Setup needed for "${output.setup_info.agent_name}"`;
}
return "Error running block";
}
case "output-error":
return "Error running block";
default:
return "Running the block";
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
if (isError) {
return (
<WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
);
}
if (isStreaming) {
return <SpinnerLoader size={40} className="text-neutral-700" />;
}
return <PlayIcon size={14} weight="regular" className="text-neutral-400" />;
}
export function AccordionIcon() {
return <PlayCircleIcon size={32} weight="light" />;
}
export function formatMaybeJson(value: unknown): string {
if (typeof value === "string") return value;
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}
export function getAccordionMeta(output: RunBlockToolOutput): {
icon: React.ReactNode;
title: string;
titleClassName?: string;
description?: string;
} {
const icon = <AccordionIcon />;
if (isRunBlockBlockOutput(output)) {
const keys = Object.keys(output.outputs ?? {});
return {
icon: <SpinnerLoader size={32} className="text-neutral-700" />,
title: output.block_name,
description:
keys.length > 0
? `${keys.length} output key${keys.length === 1 ? "" : "s"}`
: output.message,
};
}
if (isRunBlockSetupRequirementsOutput(output)) {
const missingCredsCount = Object.keys(
(output.setup_info.user_readiness?.missing_credentials ?? {}) as Record<
string,
unknown
>,
).length;
return {
icon,
title: output.setup_info.agent_name,
description:
missingCredsCount > 0
? `Missing ${missingCredsCount} credential${missingCredsCount === 1 ? "" : "s"}`
: output.message,
};
}
return {
icon: (
<WarningDiamondIcon size={32} weight="light" className="text-red-500" />
),
title: "Error",
titleClassName: "text-red-500",
};
}

186
autogpt_platform/frontend/src/app/(platform)/copilot/tools/SearchDocs/SearchDocs.tsx Normal file
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"use client";
import type { ToolUIPart } from "ai";
import { useMemo } from "react";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import {
ContentCard,
ContentCardDescription,
ContentCardHeader,
ContentCardSubtitle,
ContentCardTitle,
ContentGrid,
ContentLink,
ContentMessage,
ContentSuggestionsList,
} from "../../components/ToolAccordion/AccordionContent";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import {
AccordionIcon,
getAnimationText,
getDocsToolOutput,
getDocsToolTitle,
getToolLabel,
isDocPageOutput,
isDocSearchResultsOutput,
isErrorOutput,
isNoResultsOutput,
toDocsUrl,
ToolIcon,
type DocsToolType,
} from "./helpers";
export interface DocsToolPart {
type: DocsToolType;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: DocsToolPart;
}
function truncate(text: string, maxChars: number): string {
const trimmed = text.trim();
if (trimmed.length <= maxChars) return trimmed;
return `${trimmed.slice(0, maxChars).trimEnd()}…`;
}
export function SearchDocsTool({ part }: Props) {
const output = getDocsToolOutput(part);
const text = getAnimationText(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const isError =
part.state === "output-error" || (!!output && isErrorOutput(output));
const normalized = useMemo(() => {
if (!output) return null;
const title = getDocsToolTitle(part.type, output);
const label = getToolLabel(part.type);
return { title, label };
}, [output, part.type]);
const isOutputAvailable = part.state === "output-available" && !!output;
const docSearchOutput =
isOutputAvailable && output && isDocSearchResultsOutput(output)
? output
: null;
const docPageOutput =
isOutputAvailable && output && isDocPageOutput(output) ? output : null;
const noResultsOutput =
isOutputAvailable && output && isNoResultsOutput(output) ? output : null;
const errorOutput =
isOutputAvailable && output && isErrorOutput(output) ? output : null;
const hasExpandableContent =
isOutputAvailable &&
((!!docSearchOutput && docSearchOutput.count > 0) ||
!!docPageOutput ||
!!noResultsOutput ||
!!errorOutput);
const accordionDescription =
hasExpandableContent && docSearchOutput
? `Found ${docSearchOutput.count} result${docSearchOutput.count === 1 ? "" : "s"} for "${docSearchOutput.query}"`
: hasExpandableContent && docPageOutput
? docPageOutput.path
: hasExpandableContent && (noResultsOutput || errorOutput)
? ((noResultsOutput ?? errorOutput)?.message ?? null)
: null;
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon
toolType={part.type}
isStreaming={isStreaming}
isError={isError}
/>
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && normalized && (
<ToolAccordion
icon={<AccordionIcon toolType={part.type} />}
title={normalized.title}
description={accordionDescription}
>
{docSearchOutput && (
<ContentGrid>
{docSearchOutput.results.map((r) => {
const href = r.doc_url ?? toDocsUrl(r.path);
return (
<ContentCard key={r.path}>
<ContentCardHeader
action={<ContentLink href={href}>Open</ContentLink>}
>
<ContentCardTitle>{r.title}</ContentCardTitle>
<ContentCardSubtitle>
{r.path}
{r.section ? ` • ${r.section}` : ""}
</ContentCardSubtitle>
<ContentCardDescription>
{truncate(r.snippet, 240)}
</ContentCardDescription>
</ContentCardHeader>
</ContentCard>
);
})}
</ContentGrid>
)}
{docPageOutput && (
<div>
<ContentCardHeader
action={
<ContentLink
href={
docPageOutput.doc_url ?? toDocsUrl(docPageOutput.path)
}
>
Open
</ContentLink>
}
>
<ContentCardTitle>{docPageOutput.title}</ContentCardTitle>
<ContentCardSubtitle>{docPageOutput.path}</ContentCardSubtitle>
</ContentCardHeader>
<ContentCardDescription className="whitespace-pre-wrap">
{truncate(docPageOutput.content, 800)}
</ContentCardDescription>
</div>
)}
{noResultsOutput && (
<div>
<ContentMessage>{noResultsOutput.message}</ContentMessage>
{noResultsOutput.suggestions &&
noResultsOutput.suggestions.length > 0 && (
<ContentSuggestionsList items={noResultsOutput.suggestions} />
)}
</div>
)}
{errorOutput && (
<div>
<ContentMessage>{errorOutput.message}</ContentMessage>
{errorOutput.error && (
<ContentCardDescription>
{errorOutput.error}
</ContentCardDescription>
)}
</div>
)}
</ToolAccordion>
)}
</div>
);
}

215
autogpt_platform/frontend/src/app/(platform)/copilot/tools/SearchDocs/helpers.tsx Normal file
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import type { DocPageResponse } from "@/app/api/__generated__/models/docPageResponse";
import type { DocSearchResultsResponse } from "@/app/api/__generated__/models/docSearchResultsResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import {
ArticleIcon,
FileMagnifyingGlassIcon,
FileTextIcon,
} from "@phosphor-icons/react";
import { ToolUIPart } from "ai";
export interface SearchDocsInput {
query: string;
}
export interface GetDocPageInput {
path: string;
}
export type DocsToolOutput =
| DocSearchResultsResponse
| DocPageResponse
| NoResultsResponse
| ErrorResponse;
export type DocsToolType = "tool-search_docs" | "tool-get_doc_page" | string;
export function getToolLabel(toolType: DocsToolType): string {
switch (toolType) {
case "tool-search_docs":
return "Docs";
case "tool-get_doc_page":
return "Docs page";
default:
return "Docs";
}
}
function parseOutput(output: unknown): DocsToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (
type === ResponseType.doc_search_results ||
type === ResponseType.doc_page ||
type === ResponseType.no_results ||
type === ResponseType.error
) {
return output as DocsToolOutput;
}
if ("results" in output && "query" in output)
return output as DocSearchResultsResponse;
if ("content" in output && "path" in output)
return output as DocPageResponse;
if ("suggestions" in output && !("error" in output))
return output as NoResultsResponse;
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function getDocsToolOutput(part: unknown): DocsToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
export function isDocSearchResultsOutput(
output: DocsToolOutput,
): output is DocSearchResultsResponse {
return output.type === ResponseType.doc_search_results || "results" in output;
}
export function isDocPageOutput(
output: DocsToolOutput,
): output is DocPageResponse {
return output.type === ResponseType.doc_page || "content" in output;
}
export function isNoResultsOutput(
output: DocsToolOutput,
): output is NoResultsResponse {
return (
output.type === ResponseType.no_results ||
("suggestions" in output && !("error" in output))
);
}
export function isErrorOutput(output: DocsToolOutput): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
export function getDocsToolTitle(
toolType: DocsToolType,
output: DocsToolOutput,
): string {
if (toolType === "tool-search_docs") {
if (isDocSearchResultsOutput(output)) return "Documentation results";
if (isNoResultsOutput(output)) return "No documentation found";
return "Documentation search error";
}
if (isDocPageOutput(output)) return "Documentation page";
if (isNoResultsOutput(output)) return "No documentation found";
return "Documentation page error";
}
export function getAnimationText(part: {
type: DocsToolType;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
switch (part.type) {
case "tool-search_docs": {
const query = (part.input as SearchDocsInput | undefined)?.query?.trim();
const queryText = query ? ` for "${query}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `Searching documentation${queryText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) return `Searching documentation${queryText}`;
if (isDocSearchResultsOutput(output)) {
const count = output.count ?? output.results.length;
return `Found ${count} result${count === 1 ? "" : "s"}${queryText}`;
}
if (isNoResultsOutput(output)) {
return `No results found${queryText}`;
}
return `Error searching documentation${queryText}`;
}
case "output-error":
return `Error searching documentation${queryText}`;
default:
return "Searching documentation";
}
}
case "tool-get_doc_page": {
const path = (part.input as GetDocPageInput | undefined)?.path?.trim();
const pathText = path ? ` "${path}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `Loading documentation page${pathText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) return `Loading documentation page${pathText}`;
if (isDocPageOutput(output)) return `Loaded "${output.title}"`;
if (isNoResultsOutput(output)) return "Documentation page not found";
return "Error loading documentation page";
}
case "output-error":
return "Error loading documentation page";
default:
return "Loading documentation page";
}
}
}
return "Processing";
}
export function ToolIcon({
toolType,
isStreaming,
isError,
}: {
toolType: DocsToolType;
isStreaming?: boolean;
isError?: boolean;
}) {
const IconComponent =
toolType === "tool-get_doc_page" ? FileTextIcon : FileMagnifyingGlassIcon;
return (
<IconComponent
size={14}
weight="regular"
className={
isError
? "text-red-500"
: isStreaming
? "text-neutral-500"
: "text-neutral-400"
}
/>
);
}
export function AccordionIcon({ toolType }: { toolType: DocsToolType }) {
const IconComponent =
toolType === "tool-get_doc_page" ? ArticleIcon : FileTextIcon;
return <IconComponent size={32} weight="light" />;
}
export function toDocsUrl(path: string): string {
const urlPath = path.includes(".")
? path.slice(0, path.lastIndexOf("."))
: path;
return `https://docs.agpt.co/${urlPath}`;
}

261
autogpt_platform/frontend/src/app/(platform)/copilot/tools/ViewAgentOutput/ViewAgentOutput.tsx Normal file
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"use client";
import type { ToolUIPart } from "ai";
import React from "react";
import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
import {
globalRegistry,
OutputItem,
} from "@/components/contextual/OutputRenderers";
import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
import {
ContentBadge,
ContentCard,
ContentCardHeader,
ContentCardSubtitle,
ContentCardTitle,
ContentCodeBlock,
ContentGrid,
ContentLink,
ContentMessage,
ContentSuggestionsList,
} from "../../components/ToolAccordion/AccordionContent";
import {
formatMaybeJson,
getAnimationText,
getViewAgentOutputToolOutput,
isAgentOutputResponse,
isErrorResponse,
isNoResultsResponse,
AccordionIcon,
ToolIcon,
type ViewAgentOutputToolOutput,
} from "./helpers";
export interface ViewAgentOutputToolPart {
type: string;
toolCallId: string;
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}
interface Props {
part: ViewAgentOutputToolPart;
}
function isWorkspaceRef(value: unknown): value is string {
return typeof value === "string" && value.startsWith("workspace://");
}
function resolveForRenderer(value: unknown): {
value: unknown;
metadata?: OutputMetadata;
} {
if (!isWorkspaceRef(value)) return { value };
const withoutPrefix = value.replace("workspace://", "");
const fileId = withoutPrefix.split("#")[0];
const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
const url = `/api/proxy${apiPath}`;
const hashIndex = value.indexOf("#");
const mimeHint =
hashIndex !== -1 ? value.slice(hashIndex + 1) || undefined : undefined;
const metadata: OutputMetadata = {};
if (mimeHint) {
metadata.mimeType = mimeHint;
if (mimeHint.startsWith("image/")) metadata.type = "image";
else if (mimeHint.startsWith("video/")) metadata.type = "video";
}
return { value: url, metadata };
}
function RenderOutputValue({ value }: { value: unknown }) {
const resolved = resolveForRenderer(value);
const renderer = globalRegistry.getRenderer(
resolved.value,
resolved.metadata,
);
if (renderer) {
return (
<OutputItem
value={resolved.value}
metadata={resolved.metadata}
renderer={renderer}
/>
);
}
// Fallback for audio workspace refs
if (
isWorkspaceRef(value) &&
resolved.metadata?.mimeType?.startsWith("audio/")
) {
return (
<audio controls src={String(resolved.value)} className="mt-2 w-full" />
);
}
return null;
}
function getAccordionMeta(output: ViewAgentOutputToolOutput): {
icon: React.ReactNode;
title: string;
description?: string;
} {
const icon = <AccordionIcon />;
if (isAgentOutputResponse(output)) {
const status = output.execution?.status;
return {
icon,
title: output.agent_name,
description: status ? `Status: ${status}` : output.message,
};
}
if (isNoResultsResponse(output)) {
return { icon, title: "No results" };
}
return { icon, title: "Error" };
}
export function ViewAgentOutputTool({ part }: Props) {
const text = getAnimationText(part);
const isStreaming =
part.state === "input-streaming" || part.state === "input-available";
const output = getViewAgentOutputToolOutput(part);
const isError =
part.state === "output-error" || (!!output && isErrorResponse(output));
const hasExpandableContent =
part.state === "output-available" &&
!!output &&
(isAgentOutputResponse(output) ||
isNoResultsResponse(output) ||
isErrorResponse(output));
return (
<div className="py-2">
<div className="flex items-center gap-2 text-sm text-muted-foreground">
<ToolIcon isStreaming={isStreaming} isError={isError} />
<MorphingTextAnimation
text={text}
className={isError ? "text-red-500" : undefined}
/>
</div>
{hasExpandableContent && output && (
<ToolAccordion {...getAccordionMeta(output)}>
{isAgentOutputResponse(output) && (
<ContentGrid>
<ContentCardHeader
className="gap-3"
action={
output.library_agent_link ? (
<ContentLink href={output.library_agent_link}>
Open
</ContentLink>
) : null
}
>
<ContentMessage>{output.message}</ContentMessage>
</ContentCardHeader>
{output.execution ? (
<ContentGrid>
<ContentCard>
<ContentCardTitle className="text-xs">
Execution
</ContentCardTitle>
<ContentCardSubtitle className="mt-1">
{output.execution.execution_id}
</ContentCardSubtitle>
<ContentCardSubtitle className="mt-1">
Status: {output.execution.status}
</ContentCardSubtitle>
</ContentCard>
{output.execution.inputs_summary && (
<ContentCard>
<ContentCardTitle className="text-xs">
Inputs summary
</ContentCardTitle>
<div className="mt-2">
<RenderOutputValue
value={output.execution.inputs_summary}
/>
</div>
</ContentCard>
)}
{Object.entries(output.execution.outputs ?? {}).map(
([key, items]) => (
<ContentCard key={key}>
<div className="flex items-center justify-between gap-2">
<ContentCardTitle className="text-xs">
{key}
</ContentCardTitle>
<ContentBadge>
{items.length} item
{items.length === 1 ? "" : "s"}
</ContentBadge>
</div>
<div className="mt-2">
{items.slice(0, 3).map((item, i) => (
<RenderOutputValue key={i} value={item} />
))}
</div>
</ContentCard>
),
)}
</ContentGrid>
) : (
<ContentCard>
<ContentMessage>No execution selected.</ContentMessage>
<ContentCardSubtitle className="mt-1">
Try asking for a specific run or execution_id.
</ContentCardSubtitle>
</ContentCard>
)}
</ContentGrid>
)}
{isNoResultsResponse(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.suggestions && output.suggestions.length > 0 && (
<ContentSuggestionsList
items={output.suggestions}
className="mt-1"
/>
)}
</ContentGrid>
)}
{isErrorResponse(output) && (
<ContentGrid>
<ContentMessage>{output.message}</ContentMessage>
{output.error && (
<ContentCodeBlock>
{formatMaybeJson(output.error)}
</ContentCodeBlock>
)}
{output.details && (
<ContentCodeBlock>
{formatMaybeJson(output.details)}
</ContentCodeBlock>
)}
</ContentGrid>
)}
</ToolAccordion>
)}
</div>
);
}

158
autogpt_platform/frontend/src/app/(platform)/copilot/tools/ViewAgentOutput/helpers.tsx Normal file
View File

@@ -0,0 +1,158 @@
import type { AgentOutputResponse } from "@/app/api/__generated__/models/agentOutputResponse";
import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
import { ResponseType } from "@/app/api/__generated__/models/responseType";
import { EyeIcon, MonitorIcon } from "@phosphor-icons/react";
import type { ToolUIPart } from "ai";
export interface ViewAgentOutputInput {
agent_name?: string;
library_agent_id?: string;
store_slug?: string;
execution_id?: string;
run_time?: string;
}
export type ViewAgentOutputToolOutput =
| AgentOutputResponse
| NoResultsResponse
| ErrorResponse;
function parseOutput(output: unknown): ViewAgentOutputToolOutput | null {
if (!output) return null;
if (typeof output === "string") {
const trimmed = output.trim();
if (!trimmed) return null;
try {
return parseOutput(JSON.parse(trimmed) as unknown);
} catch {
return null;
}
}
if (typeof output === "object") {
const type = (output as { type?: unknown }).type;
if (
type === ResponseType.agent_output ||
type === ResponseType.no_results ||
type === ResponseType.error
) {
return output as ViewAgentOutputToolOutput;
}
if ("agent_id" in output && "agent_name" in output) {
return output as AgentOutputResponse;
}
if ("suggestions" in output && !("error" in output)) {
return output as NoResultsResponse;
}
if ("error" in output || "details" in output)
return output as ErrorResponse;
}
return null;
}
export function isAgentOutputResponse(
output: ViewAgentOutputToolOutput,
): output is AgentOutputResponse {
return output.type === ResponseType.agent_output || "agent_id" in output;
}
export function isNoResultsResponse(
output: ViewAgentOutputToolOutput,
): output is NoResultsResponse {
return (
output.type === ResponseType.no_results ||
("suggestions" in output && !("error" in output))
);
}
export function isErrorResponse(
output: ViewAgentOutputToolOutput,
): output is ErrorResponse {
return output.type === ResponseType.error || "error" in output;
}
export function getViewAgentOutputToolOutput(
part: unknown,
): ViewAgentOutputToolOutput | null {
if (!part || typeof part !== "object") return null;
return parseOutput((part as { output?: unknown }).output);
}
function getAgentIdentifierText(
input: ViewAgentOutputInput | undefined,
): string | null {
if (!input) return null;
const libraryId = input.library_agent_id?.trim();
if (libraryId) return `Library agent ${libraryId}`;
const slug = input.store_slug?.trim();
if (slug) return slug;
const name = input.agent_name?.trim();
if (name) return name;
return null;
}
export function getAnimationText(part: {
state: ToolUIPart["state"];
input?: unknown;
output?: unknown;
}): string {
const input = part.input as ViewAgentOutputInput | undefined;
const agent = getAgentIdentifierText(input);
const agentText = agent ? ` "${agent}"` : "";
switch (part.state) {
case "input-streaming":
case "input-available":
return `Retrieving agent output${agentText}`;
case "output-available": {
const output = parseOutput(part.output);
if (!output) return `Retrieving agent output${agentText}`;
if (isAgentOutputResponse(output)) {
if (output.execution)
return `Retrieved output (${output.execution.status})`;
return "Retrieved agent output";
}
if (isNoResultsResponse(output)) return "No outputs found";
return "Error loading agent output";
}
case "output-error":
return "Error loading agent output";
default:
return "Retrieving agent output";
}
}
export function ToolIcon({
isStreaming,
isError,
}: {
isStreaming?: boolean;
isError?: boolean;
}) {
return (
<EyeIcon
size={14}
weight="regular"
className={
isError
? "text-red-500"
: isStreaming
? "text-neutral-500"
: "text-neutral-400"
}
/>
);
}
export function AccordionIcon() {
return <MonitorIcon size={32} weight="light" />;
}
export function formatMaybeJson(value: unknown): string {
if (typeof value === "string") return value;
try {
return JSON.stringify(value, null, 2);
} catch {
return String(value);
}
}

109
autogpt_platform/frontend/src/app/(platform)/copilot/useChatSession.ts Normal file
View File

@@ -0,0 +1,109 @@
import {
getGetV2GetSessionQueryKey,
getGetV2ListSessionsQueryKey,
useGetV2GetSession,
usePostV2CreateSession,
} from "@/app/api/__generated__/endpoints/chat/chat";
import { toast } from "@/components/molecules/Toast/use-toast";
import * as Sentry from "@sentry/nextjs";
import { useQueryClient } from "@tanstack/react-query";
import { parseAsString, useQueryState } from "nuqs";
import { useEffect, useMemo, useRef } from "react";
import { convertChatSessionMessagesToUiMessages } from "./helpers/convertChatSessionToUiMessages";
export function useChatSession() {
const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
const queryClient = useQueryClient();
const sessionQuery = useGetV2GetSession(sessionId ?? "", {
query: {
enabled: !!sessionId,
staleTime: Infinity,
refetchOnWindowFocus: false,
refetchOnReconnect: false,
},
});
// When the user navigates away from a session, invalidate its query cache.
// useChat destroys its Chat instance on id change, so messages are lost.
// Invalidating ensures the next visit fetches fresh data from the API
// instead of hydrating from stale cache that's missing recent messages.
const prevSessionIdRef = useRef(sessionId);
useEffect(() => {
const prev = prevSessionIdRef.current;
prevSessionIdRef.current = sessionId;
if (prev && prev !== sessionId) {
queryClient.invalidateQueries({
queryKey: getGetV2GetSessionQueryKey(prev),
});
}
}, [sessionId, queryClient]);
// Memoize so the effect in useCopilotPage doesn't infinite-loop on a new
// array reference every render. Re-derives only when query data changes.
const hydratedMessages = useMemo(() => {
if (sessionQuery.data?.status !== 200 || !sessionId) return undefined;
return convertChatSessionMessagesToUiMessages(
sessionId,
sessionQuery.data.data.messages ?? [],
);
}, [sessionQuery.data, sessionId]);
const { mutateAsync: createSessionMutation, isPending: isCreatingSession } =
usePostV2CreateSession({
mutation: {
onSuccess: (response) => {
if (response.status === 200 && response.data?.id) {
setSessionId(response.data.id);
queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
}
},
},
});
async function createSession() {
if (sessionId) return sessionId;
try {
const response = await createSessionMutation();
if (response.status !== 200 || !response.data?.id) {
const error = new Error("Failed to create session");
Sentry.captureException(error, {
extra: { status: response.status },
});
toast({
variant: "destructive",
title: "Could not start a new chat session",
description: "Please try again.",
});
throw error;
}
return response.data.id;
} catch (error) {
if (
error instanceof Error &&
error.message === "Failed to create session"
) {
throw error; // already handled above
}
Sentry.captureException(error);
toast({
variant: "destructive",
title: "Could not start a new chat session",
description: "Please try again.",
});
throw error;
}
}
return {
sessionId,
setSessionId,
hydratedMessages,
isLoadingSession: sessionQuery.isLoading,
createSession,
isCreatingSession,
};
}

221
autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts
View File

@@ -1,127 +1,142 @@
import {
getGetV2ListSessionsQueryKey,
postV2CreateSession,
} from "@/app/api/__generated__/endpoints/chat/chat";
import { useToast } from "@/components/molecules/Toast/use-toast";
import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
import * as Sentry from "@sentry/nextjs";
import { useQueryClient } from "@tanstack/react-query";
import { useRouter } from "next/navigation";
import { useEffect } from "react";
import { useCopilotStore } from "./copilot-page-store";
import { getGreetingName, getQuickActions } from "./helpers";
import { useCopilotSessionId } from "./useCopilotSessionId";
import { useChat } from "@ai-sdk/react";
import { DefaultChatTransport } from "ai";
import { useEffect, useMemo, useState } from "react";
import { useChatSession } from "./useChatSession";
export function useCopilotPage() {
const router = useRouter();
const queryClient = useQueryClient();
const { user, isLoggedIn, isUserLoading } = useSupabase();
const { toast } = useToast();
const { completeStep } = useOnboarding();
const { isUserLoading, isLoggedIn } = useSupabase();
const [isDrawerOpen, setIsDrawerOpen] = useState(false);
const [pendingMessage, setPendingMessage] = useState<string | null>(null);
const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
const setIsStreaming = useCopilotStore((s) => s.setIsStreaming);
const isCreating = useCopilotStore((s) => s.isCreatingSession);
const setIsCreating = useCopilotStore((s) => s.setIsCreatingSession);
const {
sessionId,
setSessionId,
hydratedMessages,
isLoadingSession,
createSession,
isCreatingSession,
} = useChatSession();
const greetingName = getGreetingName(user);
const quickActions = getQuickActions();
const breakpoint = useBreakpoint();
const isMobile =
breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
const hasSession = Boolean(urlSessionId);
const initialPrompt = urlSessionId
? getInitialPrompt(urlSessionId)
: undefined;
const transport = useMemo(
() =>
sessionId
? new DefaultChatTransport({
api: `/api/chat/sessions/${sessionId}/stream`,
prepareSendMessagesRequest: ({ messages }) => {
const last = messages[messages.length - 1];
return {
body: {
message: (
last.parts?.map((p) => (p.type === "text" ? p.text : "")) ??
[]
).join(""),
is_user_message: last.role === "user",
context: null,
},
};
},
})
: null,
[sessionId],
);
const { messages, sendMessage, stop, status, error, setMessages } = useChat({
id: sessionId ?? undefined,
transport: transport ?? undefined,
});
useEffect(() => {
if (isLoggedIn) completeStep("VISIT_COPILOT");
}, [completeStep, isLoggedIn]);
if (!hydratedMessages || hydratedMessages.length === 0) return;
setMessages((prev) => {
if (prev.length >= hydratedMessages.length) return prev;
return hydratedMessages;
});
}, [hydratedMessages, setMessages]);
async function startChatWithPrompt(prompt: string) {
if (!prompt?.trim()) return;
if (isCreating) return;
// Clear messages when session is null
useEffect(() => {
if (!sessionId) setMessages([]);
}, [sessionId, setMessages]);
const trimmedPrompt = prompt.trim();
setIsCreating(true);
useEffect(() => {
if (!sessionId || !pendingMessage) return;
const msg = pendingMessage;
setPendingMessage(null);
sendMessage({ text: msg });
}, [sessionId, pendingMessage, sendMessage]);
try {
const sessionResponse = await postV2CreateSession({
body: JSON.stringify({}),
});
async function onSend(message: string) {
const trimmed = message.trim();
if (!trimmed) return;
if (sessionResponse.status !== 200 || !sessionResponse.data?.id) {
throw new Error("Failed to create session");
}
const sessionId = sessionResponse.data.id;
setInitialPrompt(sessionId, trimmedPrompt);
await queryClient.invalidateQueries({
queryKey: getGetV2ListSessionsQueryKey(),
});
await setUrlSessionId(sessionId, { shallow: true });
} catch (error) {
console.error("[CopilotPage] Failed to start chat:", error);
toast({ title: "Failed to start chat", variant: "destructive" });
Sentry.captureException(error);
} finally {
setIsCreating(false);
if (sessionId) {
sendMessage({ text: trimmed });
return;
}
setPendingMessage(trimmed);
await createSession();
}
function handleQuickAction(action: string) {
startChatWithPrompt(action);
const { data: sessionsResponse, isLoading: isLoadingSessions } =
useGetV2ListSessions(
{ limit: 50 },
{ query: { enabled: !isUserLoading && isLoggedIn } },
);
const sessions =
sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
function handleOpenDrawer() {
setIsDrawerOpen(true);
}
function handleSessionNotFound() {
router.replace("/copilot");
function handleCloseDrawer() {
setIsDrawerOpen(false);
}
function handleStreamingChange(isStreamingValue: boolean) {
setIsStreaming(isStreamingValue);
function handleDrawerOpenChange(open: boolean) {
setIsDrawerOpen(open);
}
function handleSelectSession(id: string) {
setSessionId(id);
if (isMobile) setIsDrawerOpen(false);
}
function handleNewChat() {
setSessionId(null);
if (isMobile) setIsDrawerOpen(false);
}
return {
state: {
greetingName,
quickActions,
isLoading: isUserLoading,
hasSession,
initialPrompt,
},
handlers: {
handleQuickAction,
startChatWithPrompt,
handleSessionNotFound,
handleStreamingChange,
},
sessionId,
messages,
status,
error,
stop,
isLoadingSession,
isCreatingSession,
isUserLoading,
isLoggedIn,
createSession,
onSend,
// Mobile drawer
isMobile,
isDrawerOpen,
sessions,
isLoadingSessions,
handleOpenDrawer,
handleCloseDrawer,
handleDrawerOpenChange,
handleSelectSession,
handleNewChat,
};
}
function getInitialPrompt(sessionId: string): string | undefined {
try {
const prompts = JSON.parse(
sessionStorage.get(SessionKey.CHAT_INITIAL_PROMPTS) || "{}",
);
return prompts[sessionId];
} catch {
return undefined;
}
}
function setInitialPrompt(sessionId: string, prompt: string): void {
try {
const prompts = JSON.parse(
sessionStorage.get(SessionKey.CHAT_INITIAL_PROMPTS) || "{}",
);
prompts[sessionId] = prompt;
sessionStorage.set(
SessionKey.CHAT_INITIAL_PROMPTS,
JSON.stringify(prompts),
);
} catch {
// Ignore storage errors
}
}

9
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/RunDetailHeader/RunDetailHeader.tsx
View File

@@ -2,7 +2,7 @@ import { GraphExecution } from "@/app/api/__generated__/models/graphExecution";
import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { Text } from "@/components/atoms/Text/Text";
import { ClockClockwiseIcon } from "@phosphor-icons/react";
import moment from "moment";
import { formatDistanceToNow, formatDistanceStrict } from "date-fns";
import { AGENT_LIBRARY_SECTION_PADDING_X } from "../../../helpers";
import { RunStatusBadge } from "../SelectedRunView/components/RunStatusBadge";
@@ -43,7 +43,10 @@ export function RunDetailHeader({ agent, run, scheduleRecurrence }: Props) {
{run ? (
<div className="mt-1 flex flex-wrap items-center gap-2 gap-y-1 text-zinc-400">
<Text variant="small" className="text-zinc-500">
Started {moment(run.started_at).fromNow()}
Started{" "}
{run.started_at
? formatDistanceToNow(run.started_at, { addSuffix: true })
: "—"}
</Text>
<span className="mx-1 inline-block text-zinc-200">|</span>
<Text variant="small" className="text-zinc-500">
@@ -62,7 +65,7 @@ export function RunDetailHeader({ agent, run, scheduleRecurrence }: Props) {
<span className="mx-1 inline-block text-zinc-200">|</span>
<Text variant="small" className="text-zinc-500">
Duration:{" "}
{moment.duration(run.stats.duration, "seconds").humanize()}
{formatDistanceStrict(0, run.stats.duration * 1000)}
</Text>
</>
)}

6
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/sidebar/SidebarRunsList/components/ScheduleListItem.tsx
View File

@@ -3,7 +3,7 @@
import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { ClockClockwiseIcon } from "@phosphor-icons/react";
import moment from "moment";
import { formatDistanceToNow } from "date-fns";
import { IconWrapper } from "./IconWrapper";
import { ScheduleActionsDropdown } from "./ScheduleActionsDropdown";
import { SidebarItemCard } from "./SidebarItemCard";
@@ -26,7 +26,9 @@ export function ScheduleListItem({
return (
<SidebarItemCard
title={schedule.name}
description={moment(schedule.next_run_time).fromNow()}
description={formatDistanceToNow(schedule.next_run_time, {
addSuffix: true,
})}
onClick={onClick}
selected={selected}
icon={

8
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/sidebar/SidebarRunsList/components/TaskListItem.tsx
View File

@@ -11,7 +11,7 @@ import {
WarningCircleIcon,
XCircleIcon,
} from "@phosphor-icons/react";
import moment from "moment";
import { formatDistanceToNow } from "date-fns";
import React from "react";
import { IconWrapper } from "./IconWrapper";
import { SidebarItemCard } from "./SidebarItemCard";
@@ -76,7 +76,11 @@ export function TaskListItem({
<SidebarItemCard
icon={statusIconMap[run.status]}
title={title}
description={moment(run.started_at).fromNow()}
description={
run.started_at
? formatDistanceToNow(run.started_at, { addSuffix: true })
: "—"
}
onClick={onClick}
selected={selected}
actions={

6
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/sidebar/SidebarRunsList/components/TemplateListItem.tsx
View File

@@ -3,7 +3,7 @@
import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
import { FileTextIcon } from "@phosphor-icons/react";
import moment from "moment";
import { formatDistanceToNow } from "date-fns";
import { IconWrapper } from "./IconWrapper";
import { SidebarItemCard } from "./SidebarItemCard";
import { TemplateActionsDropdown } from "./TemplateActionsDropdown";
@@ -31,7 +31,9 @@ export function TemplateListItem({
</IconWrapper>
}
title={template.name}
description={moment(template.updated_at).fromNow()}
description={formatDistanceToNow(template.updated_at, {
addSuffix: true,
})}
onClick={onClick}
selected={selected}
actions={

4
autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/sidebar/SidebarRunsList/components/TriggerListItem.tsx
View File

@@ -3,7 +3,7 @@
import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
import { LightningIcon } from "@phosphor-icons/react";
import moment from "moment";
import { formatDistanceToNow } from "date-fns";
import { IconWrapper } from "./IconWrapper";
import { SidebarItemCard } from "./SidebarItemCard";
import { TriggerActionsDropdown } from "./TriggerActionsDropdown";
@@ -31,7 +31,7 @@ export function TriggerListItem({
</IconWrapper>
}
title={trigger.name}
description={moment(trigger.updated_at).fromNow()}
description={formatDistanceToNow(trigger.updated_at, { addSuffix: true })}
onClick={onClick}
selected={selected}
actions={

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