mirror of
https://github.com/openswarm-ai/openswarm.git
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1580 lines
88 KiB
Python
1580 lines
88 KiB
Python
import asyncio
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import json
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import logging
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import os
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import re
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import sys
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import time
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from datetime import datetime
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from uuid import uuid4
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from typing import Dict, Optional
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from backend.apps.agents.core.models import (
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AgentConfig, AgentSession, Message, MessageBranch, ApprovalRequest, ToolGroupMeta,
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)
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from backend.apps.agents.core.ws_manager import ws_manager
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from backend.apps.settings.settings import load_settings
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from backend.apps.tools_lib.tools_lib import (
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_load_all as load_all_tools,
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_save as save_tool,
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_sanitize_server_name as sanitize_server_name,
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derive_mcp_config,
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load_builtin_permissions,
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load_trusted_sensitive_paths,
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refresh_airtable_token,
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refresh_google_token,
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refresh_hubspot_token,
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resolve_policy_slot,
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save_builtin_permissions,
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save_trusted_sensitive_paths,
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)
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from backend.config.paths import SESSIONS_DIR
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from backend.apps.agents.core.error_classify import (
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_NON_TRANSIENT_PATTERNS as NON_TRANSIENT_PATTERNS,
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_TRANSIENT_CAPACITY_PATTERNS as TRANSIENT_CAPACITY_PATTERNS,
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CAPACITY_BACKOFFS,
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capacity_retry_wait,
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_is_auth_error as is_auth_error,
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_is_free_trial_exhausted as is_free_trial_exhausted,
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_is_long_context_error as is_long_context_error,
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_is_transient_capacity_error as is_transient_capacity_error,
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_is_unknown_model_error as is_unknown_model_error,
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parse_retry_after,
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redact_for_telemetry,
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)
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from backend.apps.agents.manager.session.session_store import (
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_load_session_data as load_session_data,
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_save_session as save_session,
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)
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from backend.apps.agents.manager import metadata
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from backend.apps.agents.manager.session.apply_context_window import apply_context_window
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from backend.apps.agents.manager.permissions import path_gate
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from backend.apps.agents.manager import context_budget
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from backend.apps.agents.manager.streaming.state import ThinkingState, TurnState
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from backend.apps.agents.manager.streaming.hook_context import HookContext
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from backend.apps.agents.manager.streaming import thinking as thinking_mod
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from backend.apps.agents.manager.streaming import tool_result_hook
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from backend.apps.agents.manager.streaming import stop_hook as stop_hook_mod
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from backend.apps.agents.manager.streaming import stream_event
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from backend.apps.agents.manager.streaming import assistant_message
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from backend.apps.agents.manager.streaming import result_message
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from backend.apps.agents.manager.streaming.LivePartial import LivePartial
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from backend.apps.agents.manager.streaming.upsert_message import upsert_message
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from backend.apps.agents.manager.prompt.system_prompt import compose_turn_system_prompt
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from backend.apps.agents.tools.web import should_register_web_mcp
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from backend.apps.agents.manager.session.SessionLifecycleMixin import SessionLifecycleMixin
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from backend.apps.agents.manager.MessagingMixin import MessagingMixin
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from backend.apps.agents.manager.AgentLaunchMixin import AgentLaunchMixin
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from backend.apps.agents.manager.RunSupportMixin import RunSupportMixin
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from backend.apps.agents.manager.permissions import gate_hooks
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from backend.apps.agents.manager.session.workspace_git import _detect_git_identity as detect_git_identity, _ensure_cwd_git_repo
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from backend.apps.agents.manager.prompt.tool_catalog import (
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FULL_TOOLS,
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get_all_known_tool_names,
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get_denied_tool_names,
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is_fully_denied,
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gated_mcp_server_names,
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get_all_tool_names,
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)
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from backend.apps.agents.core.aux_llm import _safe_resp_text as safe_resp_text, clean_short_label, aux_max_tokens_for
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from backend.apps.agents.manager.session.history_compaction import (
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_build_history_prefix as build_history_prefix,
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_estimate_post_compact_input as estimate_post_compact_input,
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_get_branch_messages as get_branch_messages,
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)
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from backend.apps.agents.manager.prompt.prompt_context import resolve_mode
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logger = logging.getLogger(__name__)
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os.environ.setdefault("CLAUDE_CODE_STREAM_CLOSE_TIMEOUT", "3600000")
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class AgentManager(SessionLifecycleMixin, MessagingMixin, AgentLaunchMixin, RunSupportMixin):
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def __init__(self):
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self.sessions: dict[str, AgentSession] = {}
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self.tasks: dict[str, asyncio.Task] = {}
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# Live mirror of the in-flight streamed assistant text per session, so a
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# stop can persist the partial reply instantly instead of waiting out the
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# multi-second SDK teardown the cancel handler sits behind.
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self._live_partial: Dict[str, LivePartial] = {}
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# ------------------------------------------------------------------
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# Compaction & token guard (Phase 2)
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#
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# Triggered by *live* context-usage ratio, not turn count. The signal
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# is the same `ctx_used_pct` we already broadcast to the UI on every
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# turn: input_tokens / context_window. Three escalating thresholds:
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# - compact_threshold_pct (default 0.65): summarize stale tool_results
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# and old user/assistant pairs before the next query() call
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# - context_soft_cap_pct (default 0.90): pre-send hard guard. After
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# compaction, if still over, LRU-trim active_mcps
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# - >= 1.0 hits the proxy/Anthropic 200K ceiling, friendly card
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# surfaces from the catch-all
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# ------------------------------------------------------------------
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async def _run_agent_loop(self, session_id: str, prompt: str, images: list | None = None, context_paths: list | None = None, forced_tools: list[str] | None = None, attached_skills: list | None = None, fork_session: bool = False, selected_browser_ids: list[str] | None = None, selected_app_output_ids: list[str] | None = None, selected_setting_ids: list[str] | None = None):
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"""Run the Claude Agent SDK query loop for a session."""
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session = self.sessions.get(session_id)
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if not session:
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return
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from backend.apps.agents.providers.registry import get_api_type as p_get_api_type
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p_api = p_get_api_type(session.model)
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prompt_content = self.p_build_prompt_content(
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prompt, images, context_paths, forced_tools, attached_skills,
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api_type=p_api, model=session.model,
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)
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try:
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from claude_agent_sdk import (
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query, ClaudeAgentOptions, AssistantMessage, ResultMessage,
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)
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from claude_agent_sdk.types import (
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HookMatcher,
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TextBlock, ToolUseBlock, ThinkingBlock, StreamEvent,
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SystemMessage,
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)
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except ImportError:
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logger.warning("claude_agent_sdk not installed, running in mock mode")
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await self.p_run_mock_agent(session_id, prompt)
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return
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session.status = "running"
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# Resolve the model id now so every closure (approval hook, tool
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# executed handler, etc.) has both the short name and the
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# 9Router-prefixed id available without re-resolving. The short
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# name is what the user sees; the router id is what 9Router
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# reports its per-model counters under.
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from backend.apps.agents.providers.registry import (
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resolve_model_id_for_sdk as p_resolve_model_id_early,
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get_api_type as p_get_api_type_early,
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)
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p_router_model_id = p_resolve_model_id_early(session.model, load_settings())
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p_api_type_for_session = p_get_api_type_early(session.model)
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builtin_perms = load_builtin_permissions()
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# Per-tool DEFAULT policy (overridden by anything the user has set
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# explicitly in builtin_permissions.json). Bash defaults to
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# always_allow like every other builtin, for a frictionless run.
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# Three guards in path_gate STILL force a prompt even on always_allow:
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# the catastrophic-pattern match (rm -rf and friends), OS-scheduling
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# (cron/launchd persistence), and the sensitive-path gate. So the
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# poisoned-email -> destructive-command case is still caught; what
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# this trades away is the prompt on ordinary shell commands. Users
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# who want a prompt on every command can flip Bash to "ask" in the UI.
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hook_ctx = HookContext(
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session=session,
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session_id=session_id,
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prompt=prompt,
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builtin_perms=builtin_perms,
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policy_defaults={},
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sessions=self.sessions,
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)
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async def can_use_tool(tool_name, input_data, context):
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return await gate_hooks.can_use_tool(hook_ctx, tool_name, input_data, context)
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async def pre_tool_hook(input_data, tool_use_id, context):
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return await gate_hooks.pre_tool_hook(hook_ctx, input_data, tool_use_id, context)
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async def post_tool_hook(input_data, tool_use_id, context):
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return await tool_result_hook.post_tool_hook(hook_ctx, input_data, tool_use_id, context)
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try:
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_, mode_sys_prompt, _ = resolve_mode(session.mode, get_all_tool_names)
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# Reconcile active_mcps against currently-enabled tools (Phase 3).
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# If the user toggled a server off in the Tools page mid-session,
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# drop it from active_mcps automatically so the model isn't told
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# "X is active" while p_build_mcp_servers silently filters it out.
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# Emit a context_status event so the model and UI both know.
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try:
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p_enabled = {
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sanitize_server_name(t.name)
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for t in load_all_tools()
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if t.mcp_config and t.enabled and t.auth_status in ("configured", "connected")
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}
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p_stale = [s for s in session.active_mcps if s not in p_enabled]
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if p_stale:
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session.active_mcps = [s for s in session.active_mcps if s in p_enabled]
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session.needs_fork = True
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await ws_manager.send_to_session(session_id, "agent:context_status", {
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"session_id": session_id,
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"reason": "mcp_disabled_externally",
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"deactivated": p_stale,
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})
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logger.info(f"Reconciled stale active_mcps for session {session_id}: dropped {p_stale}")
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except Exception:
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logger.exception("active_mcps reconciliation failed; proceeding")
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global_settings = load_settings()
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composed_prompt = compose_turn_system_prompt(
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session,
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mode_sys_prompt,
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global_settings.default_system_prompt,
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selected_browser_ids,
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selected_app_output_ids,
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selected_setting_ids,
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)
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# Per-turn estimate of framework overhead (subtracted from displayed
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# input). Conservative on purpose so honest over-shows beat lies.
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# 16K Claude Code preset, 12K base+deferred tools, ~3K/MCP (real
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# MCP tool definitions range 1-10K depending on server; 3K is a
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# rough median that keeps the meter honest without over-trimming),
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# char/4 of composed prompt.
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p_PRESET_OVERHEAD = 16_000
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p_TOOL_DEFS_OVERHEAD = 12_000
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p_PER_MCP_OVERHEAD = 3_000
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p_composed_tokens = len(composed_prompt or "") // 4
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p_mcp_tokens = len(session.active_mcps) * p_PER_MCP_OVERHEAD
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session.framework_overhead_tokens = (
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p_PRESET_OVERHEAD + p_TOOL_DEFS_OVERHEAD + p_composed_tokens + p_mcp_tokens
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)
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# Pass session.active_mcps as the activation filter. Empty list ⇒
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# no MCP tools shipped to the SDK; the model must MCPSearch and
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# MCPActivate first. The product invariant lives here at the
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# dispatch layer (see p_build_mcp_servers docstring).
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mcp_servers = await self.p_build_mcp_servers(session.allowed_tools, session.active_mcps)
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p_browser_delegation_tools = ["CreateBrowserAgent", "BrowserAgent", "BrowserAgents"]
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p_browser_all_denied = all(
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builtin_perms.get(t, "always_allow") == "deny"
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for t in p_browser_delegation_tools
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)
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if not p_browser_all_denied:
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browser_agent_server_path = os.path.join(
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os.path.dirname(__file__), "browser_agent_mcp_server.py"
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)
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backend_port = os.environ.get("OPENSWARM_PORT", "8324")
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# Only the card the user actually picked in select-mode gets claimed for the
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# task, so the sub drives that one instead of opening its own duplicate. Passing
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# EVERY dashboard card here (the old behavior) made the sub force-grab a random,
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# usually-parked card and never navigate it, which broke the bulk of browser tasks.
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pre_selected_bids = [b for b in (selected_browser_ids or []) if b]
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from backend.auth import get_auth_token as p_get_auth_token
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p_auth_tok = p_get_auth_token()
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mcp_servers["openswarm-browser-agent"] = {
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"command": sys.executable,
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"args": [browser_agent_server_path],
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"env": {
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"OPENSWARM_PORT": backend_port,
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"OPENSWARM_AUTH_TOKEN": p_auth_tok,
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"OPENSWARM_AGENT_MODEL": session.model,
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"OPENSWARM_DASHBOARD_ID": session.dashboard_id or "",
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"OPENSWARM_PRE_SELECTED_BROWSER_IDS": ",".join(pre_selected_bids),
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"OPENSWARM_PARENT_SESSION_ID": session.id,
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},
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"type": "stdio",
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}
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p_invoke_agent_tools = ["InvokeAgent"]
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p_invoke_all_denied = all(
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builtin_perms.get(t, "always_allow") == "deny"
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for t in p_invoke_agent_tools
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)
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if not p_invoke_all_denied:
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invoke_agent_server_path = os.path.join(
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os.path.dirname(__file__), "invoke_agent_mcp_server.py"
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)
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backend_port = os.environ.get("OPENSWARM_PORT", "8324")
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from backend.auth import get_auth_token as p_get_auth_token2
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mcp_servers["openswarm-invoke-agent"] = {
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"command": sys.executable,
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"args": [invoke_agent_server_path],
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"env": {
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"OPENSWARM_PORT": backend_port,
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"OPENSWARM_AUTH_TOKEN": p_get_auth_token2(),
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"OPENSWARM_PARENT_SESSION_ID": session.id,
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"OPENSWARM_DASHBOARD_ID": session.dashboard_id or "",
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},
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"type": "stdio",
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}
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# Always-on meta-MCP server. Exposes MCPList / MCPSearch /
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# MCPActivate so the model can discover and activate user MCPs at
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# runtime. The activation gate (active_mcps filter in
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# p_build_mcp_servers above) ensures the model cannot reach any
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# other MCP server's tools without going through this layer first.
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mcp_meta_server_path = os.path.join(
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os.path.dirname(__file__), "mcp_meta_server.py"
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)
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from backend.auth import get_auth_token as p_get_auth_token3
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mcp_servers["openswarm-mcp-meta"] = {
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"command": sys.executable,
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"args": [mcp_meta_server_path],
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"env": {
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"OPENSWARM_PORT": os.environ.get("OPENSWARM_PORT", "8324"),
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"OPENSWARM_AUTH_TOKEN": p_get_auth_token3(),
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"OPENSWARM_PARENT_SESSION_ID": session.id,
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},
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"type": "stdio",
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}
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# Always-on settings-meta server: SettingsRead / SettingsWrite let the
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# agent read and edit its own OpenSwarm Settings autonomously. The
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# backend (/api/settings-meta) enforces the only two guardrails: it
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# can't disconnect the credential powering this run, and reads come
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# back with secrets redacted. No activation gate, Settings is the
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# agent's own house, not a third-party MCP.
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settings_meta_server_path = os.path.join(
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os.path.dirname(__file__), "settings_meta_server.py"
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)
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from backend.auth import get_auth_token as p_get_auth_token4
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mcp_servers["openswarm-settings-meta"] = {
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"command": sys.executable,
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"args": [settings_meta_server_path],
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"env": {
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"OPENSWARM_PORT": os.environ.get("OPENSWARM_PORT", "8324"),
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"OPENSWARM_AUTH_TOKEN": p_get_auth_token4(),
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"OPENSWARM_PARENT_SESSION_ID": session.id,
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},
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"type": "stdio",
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}
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# Register the DDG-backed openswarm-web MCP only when the primary has no reliable
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# native Anthropic web path (decided in tools/web.py); p_m feeds the registration log
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# + provider branch just below, so it stays a loop local.
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p_m = p_router_model_id if isinstance(p_router_model_id, str) else ""
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need_web_mcp = should_register_web_mcp(
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model=session.model,
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router_model_id=p_router_model_id,
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api_type=p_api_type_for_session,
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anthropic_api_key=getattr(global_settings, "anthropic_api_key", None),
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connection_mode=getattr(global_settings, "connection_mode", "own_key"),
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)
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if need_web_mcp:
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web_mcp_server_path = os.path.join(
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os.path.dirname(__file__), "web_mcp_server.py"
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)
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# Tell the MCP which primary the session is using so it
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# can route to that provider's native search tool.
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if p_m.startswith(("gc/", "gemini/", "ag/")):
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p_primary_hint = "gemini"
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elif p_m.startswith("cx/"):
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p_primary_hint = "openai"
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else:
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p_primary_hint = ""
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from backend.auth import get_auth_token as p_get_auth_token3
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mcp_servers["openswarm-web"] = {
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"command": sys.executable,
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"args": [web_mcp_server_path],
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"env": {
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"OPENSWARM_PORT": backend_port,
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"OPENSWARM_AUTH_TOKEN": p_get_auth_token3(),
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"OPENSWARM_PRIMARY_API": p_primary_hint,
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},
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"type": "stdio",
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}
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logger.info(
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f"[MCP-DEBUG] Primary {p_m} has no reliable native web search, "
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f"registering openswarm-web (DDG search + trafilatura fetch, free)"
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)
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effective_allowed = [
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t for t in session.allowed_tools
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if t in FULL_TOOLS and builtin_perms.get(t, "always_allow") == "always_allow"
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]
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effective_disallowed = [
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t for t in FULL_TOOLS
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if builtin_perms.get(t, "always_allow") == "deny"
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]
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if mcp_servers:
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all_tools_list = load_all_tools()
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for name in mcp_servers:
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if name == "openswarm-browser-agent":
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for bt in p_browser_delegation_tools:
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policy = builtin_perms.get(bt, "always_allow")
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if policy == "always_allow":
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effective_allowed.append(f"mcp__openswarm-browser-agent__{bt}")
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elif policy == "deny":
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effective_disallowed.append(f"mcp__openswarm-browser-agent__{bt}")
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continue
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if name == "openswarm-invoke-agent":
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for it in p_invoke_agent_tools:
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policy = builtin_perms.get(it, "always_allow")
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if policy == "always_allow":
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effective_allowed.append(f"mcp__openswarm-invoke-agent__{it}")
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elif policy == "deny":
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effective_disallowed.append(f"mcp__openswarm-invoke-agent__{it}")
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continue
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|
|
if name == "openswarm-web":
|
|
# Expose our DDG-backed web tools under an MCP prefix.
|
|
# Honor existing WebSearch/WebFetch permission policy
|
|
#, if the user disabled them in Settings, don't offer
|
|
# the MCP variants either.
|
|
for wt in ("WebSearch", "WebFetch"):
|
|
policy = builtin_perms.get(wt, "always_allow")
|
|
if policy == "always_allow":
|
|
effective_allowed.append(f"mcp__openswarm-web__{wt}")
|
|
elif policy == "deny":
|
|
effective_disallowed.append(f"mcp__openswarm-web__{wt}")
|
|
continue
|
|
|
|
tool_def = next(
|
|
(t for t in all_tools_list
|
|
if t.mcp_config and t.enabled and sanitize_server_name(t.name) == name),
|
|
None,
|
|
)
|
|
if tool_def:
|
|
denied = get_denied_tool_names(tool_def)
|
|
known = get_all_known_tool_names(tool_def)
|
|
for tn in known - denied:
|
|
policy = tool_def.tool_permissions.get(tn, "ask")
|
|
if policy == "always_allow":
|
|
effective_allowed.append(f"mcp__{name}__{tn}")
|
|
for tn in denied:
|
|
effective_disallowed.append(f"mcp__{name}__{tn}")
|
|
else:
|
|
effective_allowed.append(f"mcp__{name}__*")
|
|
|
|
# If the openswarm-web MCP was registered, the CLI's built-in
|
|
# WebSearch/WebFetch are guaranteed to fail (no Anthropic
|
|
# backend). Suppress them so the model picks our MCP variants
|
|
# and doesn't waste a turn on a broken tool.
|
|
if need_web_mcp:
|
|
effective_allowed = [t for t in effective_allowed if t not in ("WebSearch", "WebFetch")]
|
|
for p_bt in ("WebSearch", "WebFetch"):
|
|
if p_bt not in effective_disallowed:
|
|
effective_disallowed.append(p_bt)
|
|
|
|
# Tell the model directly which web tools work for this session.
|
|
# The Claude Code CLI's deferred-tool registry still advertises bare
|
|
# `WebSearch` and `WebFetch` even when we've stripped them above;
|
|
# frontier models (Claude/GPT-5/Gemini Pro) intuit the namespaced
|
|
# MCP variant from context, but smaller open-source models (gpt-oss
|
|
# via Ollama, smaller Llama/Qwen, etc.) thrash on the deferred-tool
|
|
# handshake (saw 2+ minutes of repeated `ToolSearch(select:WebSearch)`
|
|
# → empty matches → retry). Naming the working tool here cuts that
|
|
# to a single direct call. Only injected when (a) we registered the
|
|
# web MCP, AND (b) the user hasn't disabled the policy, matches
|
|
# the same gate the MCP allowlist uses, so disabling WebSearch in
|
|
# Settings still wins.
|
|
p_web_tools_available = need_web_mcp and (
|
|
"mcp__openswarm-web__WebSearch" in effective_allowed
|
|
or "mcp__openswarm-web__WebFetch" in effective_allowed
|
|
)
|
|
if p_web_tools_available:
|
|
p_hint_lines = ["<web_tools>"]
|
|
p_hint_lines.append(
|
|
"This session does NOT have the built-in `WebSearch` / "
|
|
"`WebFetch` tools (they delegate to Anthropic Haiku, which "
|
|
"isn't reachable on this primary). Use the MCP-backed "
|
|
"equivalents instead, call them DIRECTLY, no ToolSearch "
|
|
"step needed:"
|
|
)
|
|
if "mcp__openswarm-web__WebSearch" in effective_allowed:
|
|
p_hint_lines.append(
|
|
"- `mcp__openswarm-web__WebSearch(query: str, "
|
|
"num_results?: int)`, DuckDuckGo search."
|
|
)
|
|
if "mcp__openswarm-web__WebFetch" in effective_allowed:
|
|
p_hint_lines.append(
|
|
"- `mcp__openswarm-web__WebFetch(url: str, prompt?: "
|
|
"str)`, fetch a URL and return readable text."
|
|
)
|
|
p_hint_lines.append(
|
|
"Do not call `ToolSearch(select:WebSearch)`, bare "
|
|
"`WebSearch` is unavailable on this session and that path "
|
|
"will return empty matches."
|
|
)
|
|
p_hint_lines.append("</web_tools>")
|
|
p_web_hint = "\n".join(p_hint_lines)
|
|
composed_prompt = (
|
|
f"{composed_prompt}\n\n{p_web_hint}" if composed_prompt else p_web_hint
|
|
)
|
|
|
|
# Log effective tool lists
|
|
google_allowed = [t for t in effective_allowed if "google-workspace" in t]
|
|
reddit_allowed = [t for t in effective_allowed if "reddit" in t]
|
|
builtin_allowed = [t for t in effective_allowed if not t.startswith("mcp__")]
|
|
logger.info(f"[MCP-DEBUG] effective_allowed: {len(effective_allowed)} total "
|
|
f"(builtins={len(builtin_allowed)}, google={len(google_allowed)}, reddit={len(reddit_allowed)})")
|
|
if effective_disallowed:
|
|
logger.info(f"[MCP-DEBUG] effective_disallowed: {effective_disallowed}")
|
|
|
|
# `p_router_model_id` and `p_api_type_for_session` were resolved
|
|
# at the top of _run_agent_loop (before any closures were
|
|
# defined) so analytics closures could tag events with them.
|
|
# Reuse those values here and keep session.provider in sync.
|
|
resolved_model = p_router_model_id
|
|
api_type = p_api_type_for_session
|
|
session.provider = api_type
|
|
|
|
# Capture the Claude CLI's stderr into a buffer so the retry
|
|
# classifier can see the real cause of a process crash (e.g.
|
|
# "No pool capacity available" from the OpenSwarm proxy, or the
|
|
# Anthropic SDK's 429/overloaded error body). Without this the
|
|
# SDK's ProcessError only stringifies to "Command failed with
|
|
# exit code 1 / Check stderr output for details", which masks
|
|
# transient capacity issues.
|
|
p_stderr_buffer: list[str] = []
|
|
|
|
def _stderr_cb(line: str) -> None:
|
|
p_stderr_buffer.append(line)
|
|
# Cap the buffer so a runaway subprocess can't balloon RAM.
|
|
if len(p_stderr_buffer) > 500:
|
|
del p_stderr_buffer[:250]
|
|
|
|
async def stop_hook(input_data, tool_use_id, context):
|
|
return await stop_hook_mod.stop_hook(hook_ctx, input_data, tool_use_id, context)
|
|
|
|
options_kwargs = {
|
|
"model": resolved_model,
|
|
# 64 MB ceiling on the SDK <-> CLI JSON-RPC channel. The
|
|
# default 5 MB blocked any base64'd PDF over ~3.5 MB; we
|
|
# now route PDFs/images as native content blocks, which
|
|
# base64-expand by ~33%. 64 MB clears the largest single
|
|
# Anthropic PDF (32 MB raw) with headroom for prompt +
|
|
# tool results sharing the same frame.
|
|
"max_buffer_size": 64 * 1024 * 1024,
|
|
"permission_mode": "default",
|
|
"can_use_tool": can_use_tool,
|
|
"stderr": _stderr_cb,
|
|
"hooks": {
|
|
"PreToolUse": [HookMatcher(matcher=None, hooks=[pre_tool_hook])],
|
|
"PostToolUse": [HookMatcher(matcher=None, hooks=[post_tool_hook])],
|
|
"Stop": [HookMatcher(matcher=None, hooks=[stop_hook])],
|
|
},
|
|
"allowed_tools": effective_allowed,
|
|
"disallowed_tools": effective_disallowed,
|
|
"include_partial_messages": True,
|
|
}
|
|
# cc/cx/gc/ag/gemini/openrouter prefixes force 9Router; route="api"
|
|
# bypasses to the provider's host directly; otherwise Pro proxy or key.
|
|
from backend.apps.nine_router import is_running as p_9r_running
|
|
from backend.apps.agents.providers.registry import _NINEROUTER_MODEL_PREFIXES as p_NINEROUTER_MODEL_PREFIXES
|
|
resolved_is_9router = isinstance(resolved_model, str) and resolved_model.startswith(p_NINEROUTER_MODEL_PREFIXES)
|
|
|
|
from backend.apps.agents.providers.registry import _find_builtin_model as p_find_builtin_model
|
|
p_model_entry = p_find_builtin_model(session.model)
|
|
p_is_pinned_api_route = (
|
|
p_model_entry is not None
|
|
and p_model_entry.get("route") == "api"
|
|
)
|
|
p_api_route_provider = (p_model_entry or {}).get("api") if p_is_pinned_api_route else None
|
|
|
|
if p_is_pinned_api_route and p_api_route_provider == "anthropic" and getattr(global_settings, "anthropic_api_key", None):
|
|
options_kwargs["env"] = {
|
|
"ANTHROPIC_API_KEY": global_settings.anthropic_api_key,
|
|
"ANTHROPIC_BASE_URL": "https://api.anthropic.com",
|
|
# Pin subagent envs so they don't drift back to the proxy.
|
|
"CLAUDE_CODE_SUBAGENT_MODEL": "claude-sonnet-4-6",
|
|
"ANTHROPIC_SMALL_FAST_MODEL": "claude-haiku-4-5",
|
|
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "claude-haiku-4-5",
|
|
}
|
|
logger.info(f"[MCP-DEBUG] Using direct Anthropic API key (route=api) for {session.model}")
|
|
elif p_is_pinned_api_route and p_api_route_provider == "openai" and getattr(global_settings, "openai_api_key", None):
|
|
# Goes through 9Router's Anthropic→OpenAI translator like
|
|
# other own-key routes, but we point OPENAI_BASE_URL at a
|
|
# tiny local pass-through (/api/openai-passthrough/v1) that
|
|
# renames max_tokens → max_completion_tokens before relaying
|
|
# to api.openai.com. OpenAI's GPT-5 family rejects max_tokens
|
|
# with HTTP 400, and 9Router 0.3.60 doesn't know about
|
|
# max_completion_tokens yet (its CLI<->OpenAI translator
|
|
# emits the legacy field). The pin on 0.3.60 is intentional
|
|
# (newer 9Router versions regress WebSearch, see
|
|
# nine_router.py comment) so we patch the boundary instead
|
|
# of bumping. Pre-fix: every gpt-5.* / gpt-5.* own-key
|
|
# session 400'd silently.
|
|
from backend.auth import get_auth_token as p_get_auth_token_o
|
|
p_passthrough_url = f"http://127.0.0.1:{os.environ.get('OPENSWARM_PORT', '8324')}/api/openai-passthrough/v1"
|
|
options_kwargs["env"] = {
|
|
"OPENAI_API_KEY": global_settings.openai_api_key,
|
|
"OPENAI_BASE_URL": p_passthrough_url,
|
|
"ANTHROPIC_API_KEY": p_get_auth_token_o() or "9router",
|
|
"ANTHROPIC_BASE_URL": "http://localhost:20128",
|
|
}
|
|
logger.info(f"[MCP-DEBUG] Using direct OpenAI API key (route=api) for {session.model} via openai-passthrough")
|
|
elif p_is_pinned_api_route and p_api_route_provider == "custom":
|
|
# User-configured OpenAI-compatible endpoint (Ollama Cloud,
|
|
# Together, local Ollama, etc.). Routes through 9Router's
|
|
# openai-compatible provider node we synced from settings.
|
|
from backend.apps.nine_router import ensure_running as p_9r_ensure_c
|
|
if not p_9r_running():
|
|
logger.info(f"[MCP-DEBUG] custom provider selected but 9Router not running; waiting for startup")
|
|
await p_9r_ensure_c()
|
|
if not p_9r_running():
|
|
raise ValueError(
|
|
"9Router could not start. Custom OpenAI-compatible "
|
|
"providers need 9Router to translate the Anthropic "
|
|
"protocol, install Node.js and restart the app."
|
|
)
|
|
from backend.apps.agents.providers.registry import _find_custom_provider_for_value as p_find_custom_provider_for_value
|
|
cp = p_find_custom_provider_for_value(global_settings, session.model)
|
|
env = {
|
|
"ANTHROPIC_API_KEY": "9router",
|
|
"ANTHROPIC_BASE_URL": "http://localhost:20128",
|
|
"ENABLE_TOOL_SEARCH": "auto",
|
|
}
|
|
if cp:
|
|
# Local OpenAI-compatible servers (LM Studio, Ollama, ...)
|
|
# often run with auth disabled, the user leaves api_key
|
|
# blank in Settings. The OpenAI-style SDK insists on a
|
|
# non-empty key; substitute a harmless placeholder so the
|
|
# CLI can issue requests. Servers that DO check auth always
|
|
# have a real key configured.
|
|
env["OPENAI_API_KEY"] = (cp.api_key or "").strip() or "no-auth-required"
|
|
from backend.apps.nine_router import normalize_openai_compat_base_url as p_norm_cp_url
|
|
env["OPENAI_BASE_URL"] = p_norm_cp_url(cp.base_url or "")
|
|
# Pin subagent ids, without these, CLI's default Haiku 4.5
|
|
# gets sent to the custom provider and 404s.
|
|
if global_settings.anthropic_api_key:
|
|
env["CLAUDE_CODE_SUBAGENT_MODEL"] = "claude-sonnet-4-6"
|
|
env["ANTHROPIC_SMALL_FAST_MODEL"] = "claude-haiku-4-5-20251001"
|
|
env["ANTHROPIC_DEFAULT_HAIKU_MODEL"] = "claude-haiku-4-5-20251001"
|
|
else:
|
|
# Pin to the same custom-provider model so subagents stay
|
|
# within the user's configured endpoint instead of hitting
|
|
# an unconfigured Anthropic lane.
|
|
env["CLAUDE_CODE_SUBAGENT_MODEL"] = resolved_model
|
|
env["ANTHROPIC_SMALL_FAST_MODEL"] = resolved_model
|
|
env["ANTHROPIC_DEFAULT_HAIKU_MODEL"] = resolved_model
|
|
options_kwargs["env"] = env
|
|
logger.info(f"[MCP-DEBUG] Using custom provider for {session.model} → {resolved_model}")
|
|
elif p_is_pinned_api_route and p_api_route_provider == "gemini" and getattr(global_settings, "google_api_key", None):
|
|
# Routed through the local anthropic-proxy so it can scrub the
|
|
# JSON-Schema fields Gemini's API rejects ($schema, additionalProperties,
|
|
# propertyNames, exclusiveMinimum, nested const) that 9Router 0.3.60 misses.
|
|
from backend.auth import get_auth_token as p_get_auth_token_g
|
|
p_proxy_url = f"http://127.0.0.1:{os.environ.get('OPENSWARM_PORT', '8324')}/api/anthropic-proxy"
|
|
options_kwargs["env"] = {
|
|
"GEMINI_API_KEY": global_settings.google_api_key,
|
|
"GOOGLE_API_KEY": global_settings.google_api_key,
|
|
"ANTHROPIC_API_KEY": p_get_auth_token_g() or "9router",
|
|
"ANTHROPIC_BASE_URL": p_proxy_url,
|
|
}
|
|
logger.info(f"[MCP-DEBUG] Using direct Google API key (route=api) for {session.model} via local proxy")
|
|
elif api_type == "openrouter" and getattr(global_settings, "openrouter_api_key", None):
|
|
# OpenRouter primary. The route="openrouter" entry's
|
|
# router_model_id is `openrouter/<vendor>/<model>` so
|
|
# 9Router routes via the apikey connection synced from
|
|
# CLI's WebSearch delegation needs an Anthropic-shaped lane;
|
|
# if the user has no Anthropic key/sub/Pro, fall back to OR's
|
|
# resold Claude so subagents stay on the same OR billing.
|
|
if not p_9r_running():
|
|
from backend.apps.nine_router import ensure_running as p_9r_ensure
|
|
logger.info(f"[MCP-DEBUG] OpenRouter selected but 9Router not running; waiting for startup")
|
|
await p_9r_ensure()
|
|
if not p_9r_running():
|
|
raise ValueError(
|
|
"9Router could not start. OpenRouter routing requires "
|
|
"Node.js, install it and restart the app, or pick a "
|
|
"model that uses a direct API key (Anthropic, OpenAI, "
|
|
"or Google AI Studio)."
|
|
)
|
|
env = {
|
|
"ANTHROPIC_API_KEY": "9router",
|
|
"ANTHROPIC_BASE_URL": "http://localhost:20128",
|
|
}
|
|
if global_settings.anthropic_api_key:
|
|
env["CLAUDE_CODE_SUBAGENT_MODEL"] = "claude-sonnet-4-6"
|
|
env["ANTHROPIC_SMALL_FAST_MODEL"] = "claude-haiku-4-5-20251001"
|
|
env["ANTHROPIC_DEFAULT_HAIKU_MODEL"] = "claude-haiku-4-5-20251001"
|
|
else:
|
|
env["CLAUDE_CODE_SUBAGENT_MODEL"] = "openrouter/anthropic/claude-sonnet-4.5"
|
|
env["ANTHROPIC_SMALL_FAST_MODEL"] = "openrouter/anthropic/claude-haiku-4.5"
|
|
env["ANTHROPIC_DEFAULT_HAIKU_MODEL"] = "openrouter/anthropic/claude-haiku-4.5"
|
|
env["ENABLE_TOOL_SEARCH"] = "auto"
|
|
options_kwargs["env"] = env
|
|
logger.info(f"[MCP-DEBUG] Using OpenRouter for {session.model}")
|
|
elif api_type == "anthropic" and not resolved_is_9router and getattr(global_settings, "connection_mode", "own_key") in ("openswarm-pro", "free-trial"):
|
|
from backend.apps.settings.credentials import proxy_auth
|
|
bearer, proxy_url = proxy_auth(global_settings)
|
|
bearer = bearer or ""
|
|
options_kwargs["env"] = {
|
|
"ANTHROPIC_AUTH_TOKEN": bearer,
|
|
"ANTHROPIC_BASE_URL": proxy_url,
|
|
# Pin subagent ids; CLI default 'claude-haiku-4-5-20251001'
|
|
# gets rejected by Pro's surface as "No credentials for provider: anthropic".
|
|
# (Free-trial clamps to its allowed Claude set + weights credits server-side.)
|
|
"CLAUDE_CODE_SUBAGENT_MODEL": "claude-sonnet-4-6",
|
|
"ANTHROPIC_SMALL_FAST_MODEL": "claude-haiku-4-5-20251001",
|
|
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "claude-haiku-4-5-20251001",
|
|
# auto, never the bare default: tengu_defer_all_bn4 marks every tool
|
|
# defer_loading=true, which collides with our cache_control and 400s the
|
|
# first tool-laden request (the other anthropic branches all set this).
|
|
"ENABLE_TOOL_SEARCH": "auto",
|
|
}
|
|
# Free lane meters one run per agent task: tag every call of this task (and its
|
|
# subagents, which inherit the env) AND its aux calls (title-gen, see generate_title)
|
|
# with the session id, so a query plus its title generation is ONE run, not two.
|
|
# The base goes straight to the cloud (no 9Router), so the header rides through.
|
|
if getattr(global_settings, "connection_mode", "own_key") == "free-trial":
|
|
options_kwargs["env"]["ANTHROPIC_CUSTOM_HEADERS"] = f"X-Openswarm-Task-Id: {session.id}"
|
|
# The cloud serves every free run as Haiku, so keep the subagent on Haiku too:
|
|
# a sonnet subagent makes the CLI attach `effort`, which Haiku 400s on.
|
|
options_kwargs["env"]["CLAUDE_CODE_SUBAGENT_MODEL"] = "claude-haiku-4-5-20251001"
|
|
logger.info(f"[MCP-DEBUG] Using OpenSwarm cloud proxy at {proxy_url}")
|
|
elif api_type == "anthropic" and not resolved_is_9router and global_settings.anthropic_api_key:
|
|
options_kwargs["env"] = {"ANTHROPIC_API_KEY": global_settings.anthropic_api_key}
|
|
logger.info("[MCP-DEBUG] Using direct Anthropic API key")
|
|
elif p_9r_running():
|
|
# Gemini-bound ids go through the local proxy for schema scrubbing;
|
|
# everything else hits 9Router directly.
|
|
p_is_gemini_bound = (
|
|
isinstance(resolved_model, str)
|
|
and resolved_model.startswith(("gemini/", "gc/", "ag/"))
|
|
)
|
|
if p_is_gemini_bound:
|
|
from backend.auth import get_auth_token as p_get_auth_token_g2
|
|
p_base_url = f"http://127.0.0.1:{os.environ.get('OPENSWARM_PORT', '8324')}/api/anthropic-proxy"
|
|
env = {
|
|
"ANTHROPIC_API_KEY": p_get_auth_token_g2() or "9router",
|
|
"ANTHROPIC_BASE_URL": p_base_url,
|
|
}
|
|
else:
|
|
env = {
|
|
"ANTHROPIC_API_KEY": "9router",
|
|
"ANTHROPIC_BASE_URL": "http://localhost:20128",
|
|
}
|
|
# Pin subagent ids to whichever lane the user has, else CLI's
|
|
# default Haiku 4.5 hits 9Router with no Claude route and 401s.
|
|
try:
|
|
p_sub_conns = _conns # reuse list fetched above
|
|
except NameError:
|
|
p_sub_conns = []
|
|
p_active = {c.get("provider") for c in p_sub_conns
|
|
if isinstance(c, dict) and c.get("isActive")}
|
|
p_sub_model = None
|
|
p_small_model = None
|
|
if global_settings.anthropic_api_key:
|
|
p_sub_model = "claude-sonnet-4-6"
|
|
p_small_model = "claude-haiku-4-5-20251001"
|
|
elif "claude" in p_active or "anthropic" in p_active:
|
|
p_sub_model = "cc/claude-sonnet-4-6"
|
|
p_small_model = "cc/claude-haiku-4-5-20251001"
|
|
elif "antigravity" in p_active:
|
|
p_sub_model = "ag/gemini-3-flash"
|
|
p_small_model = "ag/gemini-3-flash"
|
|
elif "gemini-cli" in p_active:
|
|
p_sub_model = "gc/gemini-2.5-flash"
|
|
p_small_model = "gc/gemini-2.5-flash"
|
|
elif "codex" in p_active:
|
|
p_sub_model = "cx/gpt-5.4-mini"
|
|
p_small_model = "cx/gpt-5.4-mini"
|
|
if p_sub_model:
|
|
env["CLAUDE_CODE_SUBAGENT_MODEL"] = p_sub_model
|
|
if p_small_model:
|
|
env["ANTHROPIC_SMALL_FAST_MODEL"] = p_small_model
|
|
env["ANTHROPIC_DEFAULT_HAIKU_MODEL"] = p_small_model
|
|
logger.info(
|
|
f"[MCP-DEBUG] 9Router direct, subagent_model={p_sub_model}, small_fast={p_small_model}"
|
|
)
|
|
# ENABLE_TOOL_SEARCH=auto: without it, CLI's tengu_defer_all_bn4
|
|
# Statsig flag defers 16 tools with no way to load them on non-
|
|
# Anthropic networks. "auto" eagerly loads tools when schema
|
|
# budget fits in ~10% of context. Don't pass --bare, sets
|
|
# CLAUDE_CODE_SIMPLE=1 which strips the system prompt scaffolding.
|
|
env["ENABLE_TOOL_SEARCH"] = "auto"
|
|
options_kwargs["env"] = env
|
|
logger.info(f"[MCP-DEBUG] Using 9Router (api_type={api_type})")
|
|
else:
|
|
if api_type != "anthropic":
|
|
from backend.apps.nine_router import ensure_running as p_9r_ensure
|
|
logger.info(f"[MCP-DEBUG] 9Router not running for non-Anthropic model {session.model}; waiting for startup")
|
|
await p_9r_ensure()
|
|
if p_9r_running():
|
|
options_kwargs["env"] = {
|
|
"ANTHROPIC_API_KEY": "9router",
|
|
"ANTHROPIC_BASE_URL": "http://localhost:20128",
|
|
}
|
|
logger.info(f"[MCP-DEBUG] 9Router started; routing {session.model} via 9Router")
|
|
else:
|
|
raise ValueError(
|
|
f"9Router is not running; cannot use {session.model}. "
|
|
"Install Node.js and restart the app, or switch to a model "
|
|
"with a direct API key."
|
|
)
|
|
else:
|
|
raise ValueError("No AI provider configured. Set an API key or connect a subscription.")
|
|
if mcp_servers:
|
|
options_kwargs["mcp_servers"] = mcp_servers
|
|
mcp_json_len = len(json.dumps({"mcpServers": mcp_servers}))
|
|
logger.info(f"[MCP-DEBUG] mcp_servers passed to SDK: {list(mcp_servers.keys())}, JSON length={mcp_json_len}")
|
|
# claude_code preset for BOTH system_prompt and tools so the CLI's
|
|
# deferred-tools scaffolding survives. Raw string would replace it.
|
|
options_kwargs["tools"] = {
|
|
"type": "preset",
|
|
"preset": "claude_code",
|
|
}
|
|
# exclude_dynamic_sections=True moves cwd/git/OS grounding out of
|
|
# the cached prefix and into the first user message, unlocks
|
|
# Anthropic prompt cache (~80% input-token cut, 13-31% faster TTFT).
|
|
# Trade-off: grounding freezes at turn 1.
|
|
if composed_prompt:
|
|
options_kwargs["system_prompt"] = {
|
|
"type": "preset",
|
|
"preset": "claude_code",
|
|
"append": composed_prompt,
|
|
"exclude_dynamic_sections": True,
|
|
}
|
|
else:
|
|
options_kwargs["system_prompt"] = {
|
|
"type": "preset",
|
|
"preset": "claude_code",
|
|
"exclude_dynamic_sections": True,
|
|
}
|
|
if session.max_turns:
|
|
options_kwargs["max_turns"] = session.max_turns
|
|
|
|
# The claude_code preset auto-attaches the user's claude.ai-
|
|
# connected partner MCPs (`mcp__claude_ai_*`). Those bypass our
|
|
# MCPActivate gate, don't share OAuth state with the OpenSwarm
|
|
# Gmail/Calendar/Drive connectors the user actually configured
|
|
# here, and confuse the model into picking the partner shim
|
|
# instead of our vetted server. Hard-block them at the SDK
|
|
# layer so the model can't even attempt the call.
|
|
options_kwargs["disallowed_tools"] = [
|
|
"mcp__claude_ai_*",
|
|
]
|
|
|
|
if session.cwd:
|
|
# Pre-existing sessions may have workspaces that predate
|
|
# the git-init block in launch_agent, leaving them
|
|
# without a valid HEAD. Ensure it here so subagent
|
|
# worktree-add always works.
|
|
ensure_cwd_git_repo(session.cwd)
|
|
options_kwargs["cwd"] = session.cwd
|
|
|
|
try:
|
|
level = getattr(session, "thinking_level", "auto") or "auto"
|
|
# Trivially short prompts ("hi", "thanks") don't benefit from
|
|
# 5-30s of hidden reasoning. Override per-turn only, session
|
|
# setting is untouched so the UI pill keeps reflecting the
|
|
# user's choice.
|
|
p_prompt_len = len((prompt or "").strip())
|
|
if 0 < p_prompt_len < 50 and level != "off":
|
|
level = "off"
|
|
# gc/gemini-3* without Antigravity 400s every multi-step turn
|
|
# on thoughtSignature continuity. Force-disable thinking.
|
|
if (
|
|
isinstance(resolved_model, str)
|
|
and resolved_model.startswith("gc/gemini-3")
|
|
and level != "off"
|
|
):
|
|
logger.info(
|
|
"Forcing thinking_level=off for %s (gc/ thoughtSignature isn't roundtrippable; connect Antigravity for reasoning).",
|
|
resolved_model,
|
|
)
|
|
level = "off"
|
|
if api_type == "anthropic":
|
|
if level == "off":
|
|
# Fable 5 400s on an explicit thinking:disabled; you turn it
|
|
# off there by omitting the param (off is Fable's default).
|
|
if not (isinstance(resolved_model, str) and "fable" in resolved_model):
|
|
options_kwargs["thinking"] = {"type": "disabled"}
|
|
elif level in ("low", "medium", "high"):
|
|
options_kwargs["effort"] = level
|
|
elif api_type in ("openai", "codex"):
|
|
# GPT-5 family + Codex take reasoning_effort; 9Router carries
|
|
# the Anthropic-shaped `effort` across to it, so the slider
|
|
# works for OpenAI too, not just Claude. Every OpenAI/Codex
|
|
# model we expose is reasoning-capable (registry has no
|
|
# non-reasoning ones), so no per-model gate. No "disabled"
|
|
# form on these, so "off" just omits the param.
|
|
if level in ("low", "medium", "high"):
|
|
options_kwargs["effort"] = level
|
|
except Exception as e:
|
|
logger.debug(f"thinking_level param injection skipped: {e}")
|
|
|
|
# Fresh-restart path: some session changes must not reuse the
|
|
# CLI's resume transcript. MCPActivate needs a new transport so
|
|
# tool schemas are reread; branch edits/switches need the model
|
|
# to see only get_branch_messages(session), not facts from the
|
|
# old branch's SDK transcript. Soft restart: drop resume +
|
|
# sdk_session_id, replay local history via the prompt, let the
|
|
# SDK build a clean session from the current app state.
|
|
if session.needs_fresh_session:
|
|
if session.sdk_session_id:
|
|
logger.info(
|
|
f"Fresh-session restart for {session_id}: dropping "
|
|
f"sdk_session_id={session.sdk_session_id}; active_mcps={session.active_mcps}"
|
|
)
|
|
session.sdk_session_id = None
|
|
session.needs_fresh_session = False
|
|
session.needs_fork = False # superseded by the fresh restart
|
|
|
|
if session.sdk_session_id:
|
|
options_kwargs["resume"] = session.sdk_session_id
|
|
if fork_session or session.needs_fork:
|
|
options_kwargs["fork_session"] = True
|
|
if session.needs_fork:
|
|
session.needs_fork = False
|
|
elif len(session.messages) > 1:
|
|
history = build_history_prefix(
|
|
get_branch_messages(session),
|
|
cutoff_msg_id=session.compacted_through_msg_id,
|
|
)
|
|
if history:
|
|
if isinstance(prompt_content, str):
|
|
prompt_content = history + "\n\n" + prompt_content
|
|
elif isinstance(prompt_content, list):
|
|
prompt_content.insert(0, {"type": "text", "text": history})
|
|
|
|
# Compaction trigger (Phase 2). Driven by live ctx_used ratio
|
|
# rather than turn count, fires when input_tokens/context_window
|
|
# crosses session.compact_threshold_pct (default 0.65). Cheap,
|
|
# programmatic summarization (no aux LLM call) so this adds
|
|
# zero latency on the user's turn.
|
|
try:
|
|
if self.p_maybe_compact(session):
|
|
new_input = estimate_post_compact_input(session)
|
|
await ws_manager.send_to_session(session_id, "agent:context_status", {
|
|
"session_id": session_id,
|
|
"reason": "compacted",
|
|
"compacted_through_msg_id": session.compacted_through_msg_id,
|
|
})
|
|
await self.p_emit_context_update(
|
|
session_id,
|
|
session,
|
|
input_tokens=new_input,
|
|
output_tokens=session.tokens.get("output", 0),
|
|
)
|
|
except Exception:
|
|
logger.exception("compaction failed; proceeding without it")
|
|
|
|
# Pre-send hard guard (Phase 2). After compaction, if the
|
|
# session is still over context_soft_cap_pct of the window,
|
|
# LRU-trim oldest active_mcps. Stops the 429 from ever
|
|
# firing on predictable overflow paths.
|
|
try:
|
|
# Use the most recent measurement (the prior turn's
|
|
# input_tokens) as the estimate. Conservative because the
|
|
# current turn's user prompt + any new history adds on top
|
|
#, but the first turn of a fresh session has tokens=0 so
|
|
# we only act once we've seen real numbers.
|
|
p_est_tokens = session.tokens.get("input", 0)
|
|
p_hard_cap = int(session.context_window * session.context_soft_cap_pct)
|
|
if p_est_tokens >= p_hard_cap:
|
|
trimmed: list[str] = []
|
|
while p_est_tokens >= p_hard_cap and len(session.active_mcps) > 1:
|
|
# Keep at least one MCP active so the model can
|
|
# finish whatever it was doing; trim from oldest
|
|
# which is FIFO order in the list.
|
|
trimmed.append(f"mcp:{session.active_mcps.pop(0)}")
|
|
p_est_tokens -= 8_000 # rough per-MCP schema cost
|
|
if trimmed:
|
|
await ws_manager.send_to_session(session_id, "agent:context_status", {
|
|
"session_id": session_id,
|
|
"reason": "trimmed",
|
|
"trimmed": trimmed,
|
|
"estimate_after": p_est_tokens,
|
|
})
|
|
# Surface a visible system breadcrumb in the chat so
|
|
# the user (and the model on the next turn) know
|
|
# which MCPs got dropped. Without this, the model
|
|
# may keep trying to call a now-missing tool and
|
|
# the user has no idea why.
|
|
try:
|
|
p_names = ", ".join(t.replace("mcp:", "") for t in trimmed)
|
|
p_trim_msg = Message(
|
|
role="system",
|
|
content=(
|
|
f"Trimmed {len(trimmed)} app{'s' if len(trimmed) != 1 else ''} from this session to fit "
|
|
f"the model's context: {p_names}. Re-activate via MCPSearch + MCPActivate "
|
|
"if you still need them."
|
|
),
|
|
branch_id=session.active_branch_id,
|
|
)
|
|
session.messages.append(p_trim_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": p_trim_msg.model_dump(mode="json"),
|
|
})
|
|
except Exception:
|
|
logger.exception("failed to emit MCP-trimmed breadcrumb")
|
|
# Trimming changes mcp_servers / outputs context →
|
|
# rebuild options. The cheapest correct path is
|
|
# to flag for fork on next turn via needs_fork
|
|
# and let the existing fork path handle it.
|
|
session.needs_fork = True
|
|
except Exception:
|
|
logger.exception("pre-send token guard failed; proceeding")
|
|
|
|
logger.info(f"[MCP-DEBUG] Creating ClaudeAgentOptions short={session.model} resolved={resolved_model} api_type={api_type}")
|
|
options = ClaudeAgentOptions(**options_kwargs)
|
|
logger.info(f"[MCP-DEBUG] ClaudeAgentOptions created. Starting query...")
|
|
|
|
async def prompt_stream():
|
|
yield {
|
|
"type": "user",
|
|
"message": {"role": "user", "content": prompt_content},
|
|
}
|
|
|
|
turn = TurnState()
|
|
# Mirror of the streamed assistant text. The SDK envelope that
|
|
# normally commits a reply never lands when a turn is stopped
|
|
# mid-stream, so without this the text the user just watched
|
|
# appear would evaporate. Cleared the instant a block commits.
|
|
# Per-turn aggregate trackers for the consolidated thinking
|
|
# message. We accumulate across every AssistantMessage in the
|
|
# turn (think → tool → think → tool → answer) and stream
|
|
# incremental updates to the SAME persisted Message id so the
|
|
# ThinkingBubble pill ticks live: "Thought for 18s · 412
|
|
# tokens · 3 tools used". Reset only at turn boundaries.
|
|
thinking = ThinkingState()
|
|
# Persistent id for the turn's single thinking message. We
|
|
# reuse it across multi-step turns so the frontend's
|
|
# addMessage dedupe replaces the bubble in place rather
|
|
# than stacking N pills above the answer. Reset at the
|
|
# next user turn (next prompt_stream iteration).
|
|
# Wall-clock turn duration (ms), covers thinking + tool
|
|
# execution + assistant text. Updated continuously as the
|
|
# turn unfolds. Used for the "Thought for Ns" segment so
|
|
# the duration reflects the entire user-visible wait, not
|
|
# just thinking-only time.
|
|
# Total output tokens across every AssistantMessage in the
|
|
# turn (thinking + visible text + tool-call JSON args). The
|
|
# consolidated thinking pill's `tokens` segment uses this
|
|
# rather than thinking-text-only chars/3.6, answers the
|
|
# question "how much work did the model produce on this
|
|
# turn" honestly. Populated from each AssistantMessage's
|
|
# usage.output_tokens; fallback heuristic kicks in only
|
|
# when usage is absent.
|
|
# Running char counts for the streaming portions of the
|
|
# turn, used to grow the token estimate while assistant
|
|
# text and tool-call JSON args are still streaming, BEFORE
|
|
# the SDK has emitted a final usage.output_tokens count
|
|
# for those blocks. Once the AssistantMessage lands with
|
|
# real usage data, turn.output_tokens supersedes these.
|
|
# Latest Gemini thoughtSignature captured from this turn's
|
|
# ThinkingBlocks. We persist it on the consolidated thinking
|
|
# Message so subsequent turns can re-attach it to the
|
|
# assistant turn we feed back to Gemini, satisfying
|
|
# Google's reasoning-continuity check (the source of the
|
|
# "Thought signature is not valid" 400). None for providers
|
|
# that don't use signatures.
|
|
# session.tokens accumulates SDK running totals across turns,
|
|
# so subtract the turn-start baseline to get this turn's delta.
|
|
# Background ticker handle. Re-emits the consolidated
|
|
# thinking message every 1s so the elapsed counter keeps
|
|
# ticking through gaps where no SDK events fire (tool
|
|
# execution, slow text generation). Started at first
|
|
# AssistantMessage of the turn, cancelled at ResultMessage.
|
|
# True between the first non-ResultMessage of a turn and the
|
|
# following ResultMessage; False at turn boundaries. The retry
|
|
# layer below only retries at boundaries, resuming mid-turn via
|
|
# sdk_session_id would risk duplicating user-visible output.
|
|
|
|
# Silently absorb transient upstream capacity errors (429/500/503/
|
|
# 529/overloaded/network blips) by waiting with exponential
|
|
# backoff and restarting the query with resume=sdk_session_id.
|
|
# The session keeps its conversation state across retries so the
|
|
# user just sees a pause, not a red error card. Hard errors
|
|
# (auth, plan limit, invalid args) fall through to the existing
|
|
# error handler unchanged.
|
|
|
|
async def _run_streaming_turn():
|
|
# Per-turn thinking aggregation trackers (added for the
|
|
# "Thought for Ns · M tokens" persisted label). Without
|
|
# nonlocal, the int reassignments at AssistantMessage emission
|
|
# below shadow them as locals and the dict access at
|
|
# content_block_start crashes with UnboundLocalError.
|
|
async for message in query(
|
|
prompt=prompt_stream(),
|
|
options=options,
|
|
):
|
|
if isinstance(message, ResultMessage):
|
|
turn.current_turn_emitted = False
|
|
else:
|
|
turn.current_turn_emitted = True
|
|
# Stamp the turn's wall-clock start at the FIRST
|
|
# non-Result message we see, this is when the
|
|
# user actually started waiting. We use the same
|
|
# timestamp as the basis for "Thought for Ns"
|
|
# so the duration covers thinking + tool exec
|
|
# + assistant text generation.
|
|
if turn.started_ts is None:
|
|
turn.started_ts = time.time()
|
|
# Snapshot cumulative tokens at turn start;
|
|
# subtracted at emit time for per-turn deltas.
|
|
try:
|
|
# Baselines track the SAME fresh lane the pill reads,
|
|
# so the per-turn delta is fresh-minus-fresh.
|
|
if isinstance(session.tokens, dict):
|
|
turn.baseline_session_in = int(session.tokens.get("input_fresh", 0) or 0)
|
|
turn.baseline_session_out = int(session.tokens.get("output", 0) or 0)
|
|
p_ch_in = 0
|
|
p_ch_out = 0
|
|
for p_child in self.sessions.values():
|
|
if getattr(p_child, "parent_session_id", None) != session.id:
|
|
continue
|
|
p_ct = getattr(p_child, "tokens", None)
|
|
if not isinstance(p_ct, dict):
|
|
continue
|
|
p_ch_in += int(p_ct.get("input_fresh", 0) or 0)
|
|
p_ch_out += int(p_ct.get("output", 0) or 0)
|
|
turn.baseline_children_in = p_ch_in
|
|
turn.baseline_children_out = p_ch_out
|
|
turn.baseline_captured = True
|
|
except Exception:
|
|
pass
|
|
# Pre-emit thinking pill for routes whose
|
|
# translator strips reasoning content (cx/, gc/,
|
|
# ag/, gemini/). Without this, the pill emits
|
|
# at turn end and lands BELOW the assistant
|
|
# text in session.messages, visually wrong.
|
|
# Pre-emitting here gives the pill the same
|
|
# ordering as Anthropic's natural streaming
|
|
# path. Updates in place at turn end via the
|
|
# stable thinking.msg_id dedupe.
|
|
try:
|
|
p_route_strips_reasoning_pre = (
|
|
isinstance(resolved_model, str)
|
|
and resolved_model.startswith(("cx/", "gc/", "ag/", "gemini/"))
|
|
)
|
|
if p_route_strips_reasoning_pre:
|
|
await thinking_mod.emit_consolidated_thinking(thinking, turn, session, session_id, self.sessions, force_provider_unavailable=True)
|
|
except Exception:
|
|
logger.exception("pre-emit thinking pill failed; continuing")
|
|
|
|
if turn.first_event:
|
|
logger.info(f"[MCP-DEBUG] First event received: {type(message).__name__}")
|
|
turn.first_event = False
|
|
|
|
# Log system messages (MCP server status, errors, etc.)
|
|
if isinstance(message, SystemMessage):
|
|
raw = message.__dict__ if hasattr(message, '__dict__') else str(message)
|
|
logger.info(f"[MCP-DEBUG] SystemMessage: {raw}")
|
|
|
|
if isinstance(message, StreamEvent):
|
|
await stream_event.handle_stream_event(
|
|
message, session, session_id, turn, thinking, self._live_partial
|
|
)
|
|
|
|
elif isinstance(message, AssistantMessage):
|
|
await assistant_message.handle_assistant_message(
|
|
message, session, session_id, turn, thinking, self._live_partial, self.sessions
|
|
)
|
|
elif isinstance(message, ResultMessage):
|
|
await result_message.handle_result_message(
|
|
message, session, session_id, turn, thinking, self.sessions,
|
|
resolved_model, api_type, global_settings,
|
|
)
|
|
|
|
capacity_retry_attempt = 0
|
|
while True:
|
|
try:
|
|
await _run_streaming_turn()
|
|
break
|
|
except Exception as e:
|
|
# Make sure the consolidated-thinking ticker doesn't
|
|
# outlive the turn on error/retry. Without this, an
|
|
# exception mid-stream leaves a dangling task that
|
|
# keeps re-emitting against a stale msg id.
|
|
if thinking.ticker_task is not None and not thinking.ticker_task.done():
|
|
thinking.ticker_task.cancel()
|
|
try:
|
|
await thinking.ticker_task
|
|
except (asyncio.CancelledError, Exception):
|
|
pass
|
|
thinking.ticker_task = None
|
|
stderr_snapshot = "\n".join(p_stderr_buffer[-50:])
|
|
wait = capacity_retry_wait(e, capacity_retry_attempt, extra_text=stderr_snapshot)
|
|
if wait is not None:
|
|
capacity_retry_attempt += 1
|
|
mid_stream = turn.current_turn_emitted
|
|
logger.warning(
|
|
f"Transient upstream error on session {session_id} "
|
|
f"(attempt {capacity_retry_attempt}/{len(CAPACITY_BACKOFFS)}, "
|
|
f"mid_stream={mid_stream}); sleeping {wait}s before retry. "
|
|
f"exc={e!r} stderr_tail={stderr_snapshot[-400:]!r}"
|
|
)
|
|
# Finalize any in-flight stream messages so the UI
|
|
# doesn't leave them pinned as "still streaming" while
|
|
# we wait and restart. On resume the CLI re-runs the
|
|
# last turn from scratch (Anthropic doesn't persist
|
|
# in-progress responses), so the partial assistant
|
|
# text / tool call we emitted is now orphaned, cap
|
|
# it with stream_end and start the fresh turn under a
|
|
# new message id.
|
|
if turn.stream_text_msg_id:
|
|
await ws_manager.send_to_session(session_id, "agent:stream_end", {
|
|
"session_id": session_id,
|
|
"message_id": turn.stream_text_msg_id,
|
|
})
|
|
turn.stream_text_msg_id = None
|
|
turn.stream_text_accum = ""
|
|
self._live_partial.pop(session_id, None)
|
|
for p_tool_msg_id in turn.stream_tool_msg_ids_ordered:
|
|
await ws_manager.send_to_session(session_id, "agent:stream_end", {
|
|
"session_id": session_id,
|
|
"message_id": p_tool_msg_id,
|
|
})
|
|
turn.stream_tool_msg_ids_ordered = []
|
|
turn.stream_block_index_map = {}
|
|
turn.current_turn_emitted = False
|
|
await asyncio.sleep(wait)
|
|
p_stderr_buffer.clear()
|
|
if session.sdk_session_id:
|
|
options_kwargs["resume"] = session.sdk_session_id
|
|
options = ClaudeAgentOptions(**options_kwargs)
|
|
continue
|
|
raise
|
|
|
|
session.status = "completed"
|
|
|
|
# Auto-continuation hook (Phase 3). If MCPActivate (or any
|
|
# analogous flow) flagged pending_continuation during this
|
|
# turn, kick off a follow-up turn immediately with the
|
|
# captured prompt. We dispatch as a fire-and-forget task so
|
|
# the current _run_agent_loop frame can unwind cleanly
|
|
# before the next turn's options + history rebuild kicks in.
|
|
# The follow-up is `hidden=True` so it doesn't add a user
|
|
# bubble to the visible chat; the model sees it as a
|
|
# synthetic prompt to keep working.
|
|
try:
|
|
if getattr(session, "pending_continuation", False):
|
|
p_continuation_prompt = session.pending_continuation_prompt or "Continue."
|
|
session.pending_continuation = False
|
|
session.pending_continuation_prompt = None
|
|
asyncio.create_task(self.send_message(
|
|
session_id,
|
|
p_continuation_prompt,
|
|
hidden=True,
|
|
))
|
|
logger.info(f"Auto-continuing session {session_id} with hidden prompt")
|
|
except Exception:
|
|
logger.exception("auto-continuation dispatch failed")
|
|
except asyncio.CancelledError:
|
|
# Only act if we're still the session's live task. A user stop pops
|
|
# this task (stop_agent already finalized status + partial), and a
|
|
# follow-up message may have started a newer turn; either way this
|
|
# dying task must NOT clobber the live status or pop the new turn's
|
|
# in-flight partial mirror.
|
|
if self.tasks.get(session_id) is asyncio.current_task():
|
|
session.status = "stopped"
|
|
# A cancelled turn desyncs the CLI's resume transcript from
|
|
# session.messages (the SDK never recorded the interrupted
|
|
# turn), so force the next turn to rebuild history from
|
|
# session.messages, else resume/follow-ups replay a transcript
|
|
# with no trace of the stopped reply ("nothing to continue").
|
|
session.needs_fresh_session = True
|
|
# Persist whatever streamed before the cancel (edit / branch
|
|
# switch paths; the user-stop path already did this in stop_agent).
|
|
await self.p_commit_partial_now(session)
|
|
turn.stream_text_msg_id = None
|
|
turn.stream_text_accum = ""
|
|
except Exception as e:
|
|
logger.exception(f"Agent {session_id} error: {e}")
|
|
session.status = "error"
|
|
|
|
# Long-context-required 429 fork: surface a friendly overflow event
|
|
# so the frontend can render an actionable card ("Switch to Chat
|
|
# mode" / "Start a fresh chat") instead of a raw error blob. The
|
|
# user can't recover by waiting, this is a tier-gate, not a rate
|
|
# limit, so the UX matters.
|
|
try:
|
|
p_stderr_tail = "\n".join(p_stderr_buffer[-50:])
|
|
except Exception:
|
|
p_stderr_tail = ""
|
|
# If we already streamed a substantive assistant response this
|
|
# turn, the user got their answer; the error fired on a
|
|
# subsequent step (title gen, follow-up tool turn, etc.).
|
|
# Don't blast a "context exceeded" card over a completed reply.
|
|
p_streamed_substantive = bool(turn.stream_text_msg_id) and turn.current_turn_emitted
|
|
if p_streamed_substantive and is_long_context_error(e, extra_text=p_stderr_tail):
|
|
# Mark the session completed (not error), keep the assistant
|
|
# reply visible, and skip the overflow card. The next user
|
|
# turn will properly hit the pre-send guard if the chat is
|
|
# still over cap.
|
|
session.status = "completed"
|
|
if turn.stream_text_msg_id:
|
|
try:
|
|
await ws_manager.send_to_session(session_id, "agent:stream_end", {
|
|
"session_id": session_id,
|
|
"message_id": turn.stream_text_msg_id,
|
|
})
|
|
except Exception:
|
|
pass
|
|
return
|
|
if is_long_context_error(e, extra_text=p_stderr_tail):
|
|
friendly_msg = (
|
|
"This conversation has grown too large for your account's "
|
|
"standard context window. Long-context requests require an "
|
|
"upgraded tier, switch to Chat mode or start a fresh chat "
|
|
"to continue."
|
|
)
|
|
error_msg = Message(role="system", content=friendly_msg, branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
p_ovf_payload = {
|
|
"session_id": session_id,
|
|
"reason": "long_context_required",
|
|
"message": friendly_msg,
|
|
"model": session.model,
|
|
"provider": session.provider,
|
|
"context_window": session.context_window,
|
|
"framework_overhead_tokens": session.framework_overhead_tokens,
|
|
"input_tokens": session.tokens.get("input", 0),
|
|
"active_mcps": list(session.active_mcps),
|
|
"compact_threshold_pct": session.compact_threshold_pct,
|
|
"context_soft_cap_pct": session.context_soft_cap_pct,
|
|
}
|
|
await ws_manager.send_to_session(session_id, "agent:context_overflow", p_ovf_payload)
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
try:
|
|
from backend.apps.service.client import submit_diagnostic
|
|
submit_diagnostic({
|
|
"kind": "context_overflow",
|
|
"where": "agent_manager._run_streaming_turn",
|
|
"session_id": session_id,
|
|
"model": session.model,
|
|
"provider": session.provider,
|
|
"context_window": session.context_window,
|
|
"input_tokens": session.tokens.get("input", 0),
|
|
"framework_overhead_tokens": session.framework_overhead_tokens,
|
|
"active_mcps_count": len(session.active_mcps),
|
|
"messages_count": len(session.messages),
|
|
"error_preview": redact_for_telemetry(str(e), limit=500),
|
|
})
|
|
except Exception:
|
|
logger.debug("submit_diagnostic for context_overflow failed", exc_info=True)
|
|
elif is_transient_capacity_error(e, extra_text=p_stderr_tail):
|
|
# A genuine throttle (429/overload/capacity) that already burned
|
|
# the whole silent-backoff budget (the only way one reaches here).
|
|
# It's a limit, not a failure, so don't append a system-message
|
|
# card; emit a transient signal for the muted pill and mark the
|
|
# turn completed so it doesn't read as an error.
|
|
session.status = "completed"
|
|
if turn.stream_text_msg_id:
|
|
try:
|
|
await ws_manager.send_to_session(session_id, "agent:stream_end", {
|
|
"session_id": session_id,
|
|
"message_id": turn.stream_text_msg_id,
|
|
})
|
|
except Exception:
|
|
pass
|
|
await ws_manager.send_to_session(session_id, "agent:rate_limited", {
|
|
"session_id": session_id,
|
|
"retry_after_s": parse_retry_after(e, p_stderr_tail),
|
|
})
|
|
elif is_free_trial_exhausted(e, extra_text=p_stderr_tail):
|
|
# Free runs spent. Flip back to own_key and show a friendly
|
|
# "connect a model" upsell instead of a raw 402.
|
|
try:
|
|
from backend.apps.subscription.free_trial import clear_free_trial
|
|
await clear_free_trial(load_settings())
|
|
except Exception:
|
|
logger.debug("clear_free_trial after exhaustion failed", exc_info=True)
|
|
friendly_msg = (
|
|
"You've used your free runs. Connect a model to keep going: "
|
|
"your own API key, an AI subscription you already pay for, or "
|
|
"OpenSwarm Pro."
|
|
)
|
|
error_msg = Message(role="system", content=friendly_msg, branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:free_trial_exhausted", {
|
|
"session_id": session_id,
|
|
"message": friendly_msg,
|
|
})
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
elif is_auth_error(e, extra_text=p_stderr_tail):
|
|
# Three sub-cases the user can hit, with distinct fixes:
|
|
# 1. "No credentials for provider: claude", user picked a
|
|
# -cc route but doesn't have Claude Pro/Max connected
|
|
# via 9Router. Tell them to either connect Claude
|
|
# Pro/Max OR pick a non--cc model.
|
|
# 2. OpenSwarm Pro 401, bearer expired. Reconnect.
|
|
# 3. Anthropic API key 401, wrong key. Re-enter.
|
|
p_model = (session.model or "").lower()
|
|
p_combined = f"{e!s}\n{p_stderr_tail}".lower()
|
|
# Codex/OpenAI subscription tokens rotate every ~2-3
|
|
# minutes, the user sees the rotation window as a 401
|
|
# with "reset after 1m 59s" or similar. Don't ask them to
|
|
# reconnect; just tell them to wait it out and retry.
|
|
if (
|
|
("codex/" in p_combined or "[codex/" in p_combined or p_model.startswith(("cx/", "gpt-")))
|
|
and ("authentication token is expired" in p_combined or "authentication token has expired" in p_combined or "401" in p_combined)
|
|
):
|
|
friendly_msg = (
|
|
"GPT subscription token just rotated, this is "
|
|
"automatic and resets every couple minutes. Send "
|
|
"your message again in ~1 minute and it'll go "
|
|
"through. (No need to reconnect anything.)"
|
|
)
|
|
reason = "codex_token_rotating"
|
|
elif "no credentials for provider" in p_combined:
|
|
friendly_msg = (
|
|
"Selected route requires Claude Pro / Max, but it's "
|
|
"not connected. Open Settings → Models and either "
|
|
"connect Claude Pro / Max, or switch the model to a "
|
|
"non-`-cc` variant (e.g. Claude Sonnet 4.6 instead "
|
|
"of Sonnet 4.6 -cc)."
|
|
)
|
|
reason = "claude_sub_not_connected"
|
|
elif (
|
|
"-cc" not in p_model
|
|
and getattr(load_settings(), "connection_mode", "own_key") == "openswarm-pro"
|
|
):
|
|
friendly_msg = (
|
|
"OpenSwarm Pro authentication failed. Your subscription "
|
|
"token may have expired even though the connection still "
|
|
"shows green. Open Settings → Models and click "
|
|
"Disconnect / Reconnect on Claude Pro / Max to refresh "
|
|
"the token."
|
|
)
|
|
reason = "openswarm_pro_auth_expired"
|
|
else:
|
|
friendly_msg = (
|
|
"Anthropic authentication failed. The API key or "
|
|
"subscription token for this model is invalid. Open "
|
|
"Settings → Models and re-enter the API key, or "
|
|
"reconnect Claude Pro / Max."
|
|
)
|
|
reason = "anthropic_auth_invalid"
|
|
error_msg = Message(role="system", content=friendly_msg, branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:auth_error", {
|
|
"session_id": session_id,
|
|
"reason": reason,
|
|
"message": friendly_msg,
|
|
"model": session.model,
|
|
})
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
elif is_unknown_model_error(e, extra_text=p_stderr_tail):
|
|
# Upstream rejected the model code itself (e.g. Codex 1211 on a
|
|
# ChatGPT plan that lacks our GPT ids). Track it; the friendly
|
|
# "add an API key / pick another model" card is rendered frontend-side.
|
|
try:
|
|
from backend.apps.service.client import submit_diagnostic
|
|
submit_diagnostic({
|
|
"kind": "model_error",
|
|
"subkind": "unknown_model",
|
|
"model": session.model,
|
|
"provider": session.provider,
|
|
"connection_mode": getattr(load_settings(), "connection_mode", "own_key"),
|
|
"error_preview": redact_for_telemetry(str(e), limit=400),
|
|
"stderr_tail": redact_for_telemetry(p_stderr_tail),
|
|
})
|
|
except Exception:
|
|
logger.debug("submit_diagnostic model_error failed", exc_info=True)
|
|
error_msg = Message(role="system", content=f"Error: {str(e)}", branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
else:
|
|
# Track unclassified agent failures too so we stop flying blind on them.
|
|
try:
|
|
from backend.apps.service.client import submit_diagnostic
|
|
submit_diagnostic({
|
|
"kind": "model_error",
|
|
"subkind": "unclassified",
|
|
"model": session.model,
|
|
"provider": session.provider,
|
|
"connection_mode": getattr(load_settings(), "connection_mode", "own_key"),
|
|
"error_preview": redact_for_telemetry(str(e), limit=400),
|
|
"stderr_tail": redact_for_telemetry(p_stderr_tail),
|
|
})
|
|
except Exception:
|
|
logger.debug("submit_diagnostic model_error failed", exc_info=True)
|
|
error_msg = Message(role="system", content=f"Error: {str(e)}", branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
except BaseException as e:
|
|
# Catch BaseExceptionGroup from anyio task groups (e.g. concurrent
|
|
# CLI crash + pending approval cancellation) so it doesn't escape
|
|
# and kill the uvicorn process.
|
|
logger.exception(f"Agent {session_id} fatal error: {e}")
|
|
session.status = "error"
|
|
error_msg = Message(role="system", content=f"Error: {str(e)}", branch_id=session.active_branch_id)
|
|
session.messages.append(error_msg)
|
|
await ws_manager.send_to_session(session_id, "agent:message", {
|
|
"session_id": session_id,
|
|
"message": error_msg.model_dump(mode="json"),
|
|
})
|
|
finally:
|
|
# Only the session's live task finalizes. A stopped task (popped by
|
|
# stop_agent, which already finalized status + saved) or one
|
|
# superseded by a newer turn must not pop the new turn's partial
|
|
# mirror, broadcast a stale terminal status, or overwrite the
|
|
# snapshot the live turn is writing.
|
|
p_is_live_task = self.tasks.get(session_id) is asyncio.current_task()
|
|
if p_is_live_task:
|
|
self._live_partial.pop(session_id, None)
|
|
if session_id in self.sessions and p_is_live_task:
|
|
# For canvas-launched App Builder sessions, the workspace
|
|
# folder IS the session_id (see launch_agent), so meta.json
|
|
# lives at outputs_workspace/<session_id>/meta.json. Read it
|
|
# and propagate name/description into the Output row before
|
|
# the terminal status fires; without this, the row stays
|
|
# "Untitled App" forever because no React component polls
|
|
# the file on the canvas path. Best-effort, only acts when
|
|
# the row's name is still the default placeholder.
|
|
if session.mode == "view-builder":
|
|
try:
|
|
from backend.apps.outputs.outputs import sync_output_from_meta_json, _load_all
|
|
if sync_output_from_meta_json(session_id, fallback_name=session.name):
|
|
# Broadcast the renamed row so the sidebar
|
|
# flips from "Untitled App" to the real name
|
|
# without waiting for the next mount.
|
|
try:
|
|
matching = [o for o in p_load_all() if o.workspace_id == session_id]
|
|
if matching:
|
|
await ws_manager.broadcast_global("agent:output_upserted", {
|
|
"output": matching[0].model_dump(mode="json"),
|
|
})
|
|
except Exception:
|
|
logger.exception("post-sync output_upserted broadcast failed")
|
|
except Exception:
|
|
logger.exception("post-session meta sync failed")
|
|
await ws_manager.send_to_session(session_id, "agent:status", {
|
|
"session_id": session_id,
|
|
"status": session.status,
|
|
"session": session.model_dump(mode="json"),
|
|
})
|
|
try:
|
|
save_session(session_id, session.model_dump(mode="json"))
|
|
except Exception as e:
|
|
logger.warning(f"Failed to snapshot session {session_id}: {e}")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
agent_manager = AgentManager()
|