Richard Tang
2174 lines
80 KiB
Python
2174 lines
80 KiB
Python
"""Agent Runner - loads and runs exported agents."""
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import json
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import logging
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import os
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from collections.abc import Callable
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from dataclasses import dataclass, field
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from datetime import UTC
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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from framework.config import get_hive_config, get_max_context_tokens, get_preferred_model
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from framework.credentials.validation import (
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ensure_credential_key_env as _ensure_credential_key_env,
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)
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from framework.graph import Goal
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from framework.graph.edge import (
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DEFAULT_MAX_TOKENS,
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EdgeCondition,
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EdgeSpec,
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GraphSpec,
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)
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from framework.graph.executor import ExecutionResult
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from framework.graph.node import NodeSpec
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from framework.llm.provider import LLMProvider, Tool
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from framework.runner.preload_validation import run_preload_validation
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from framework.runner.tool_registry import ToolRegistry
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from framework.runtime.agent_runtime import AgentRuntime, AgentRuntimeConfig, create_agent_runtime
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from framework.runtime.execution_stream import EntryPointSpec
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from framework.runtime.runtime_log_store import RuntimeLogStore
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from framework.tools.flowchart_utils import generate_fallback_flowchart
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if TYPE_CHECKING:
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from framework.runner.protocol import AgentMessage, CapabilityResponse
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logger = logging.getLogger(__name__)
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CLAUDE_CREDENTIALS_FILE = Path.home() / ".claude" / ".credentials.json"
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CLAUDE_OAUTH_TOKEN_URL = "https://console.anthropic.com/v1/oauth/token"
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CLAUDE_OAUTH_CLIENT_ID = "9d1c250a-e61b-44d9-88ed-5944d1962f5e"
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CLAUDE_KEYCHAIN_SERVICE = "Claude Code-credentials"
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# Buffer in seconds before token expiry to trigger a proactive refresh
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_TOKEN_REFRESH_BUFFER_SECS = 300 # 5 minutes
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# Codex (OpenAI) subscription auth
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CODEX_AUTH_FILE = Path.home() / ".codex" / "auth.json"
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CODEX_OAUTH_TOKEN_URL = "https://auth.openai.com/oauth/token"
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CODEX_OAUTH_CLIENT_ID = "app_EMoamEEZ73f0CkXaXp7hrann"
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CODEX_KEYCHAIN_SERVICE = "Codex Auth"
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_CODEX_TOKEN_LIFETIME_SECS = 3600 # 1 hour (no explicit expiry field)
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def _read_claude_keychain() -> dict | None:
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"""Read Claude Code credentials from macOS Keychain.
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Returns the parsed JSON dict, or None if not on macOS or entry missing.
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"""
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import getpass
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import platform
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import subprocess
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if platform.system() != "Darwin":
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return None
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try:
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account = getpass.getuser()
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result = subprocess.run(
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[
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"security",
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"find-generic-password",
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"-s",
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CLAUDE_KEYCHAIN_SERVICE,
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"-a",
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account,
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"-w",
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],
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capture_output=True,
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encoding="utf-8",
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timeout=5,
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)
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if result.returncode != 0:
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return None
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raw = result.stdout.strip()
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if not raw:
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return None
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return json.loads(raw)
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except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError) as exc:
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logger.debug("Claude keychain read failed: %s", exc)
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return None
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def _save_claude_keychain(creds: dict) -> bool:
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"""Write Claude Code credentials to macOS Keychain. Returns True on success."""
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import getpass
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import platform
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import subprocess
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if platform.system() != "Darwin":
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return False
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try:
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account = getpass.getuser()
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data = json.dumps(creds)
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result = subprocess.run(
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[
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"security",
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"add-generic-password",
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"-U",
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"-s",
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CLAUDE_KEYCHAIN_SERVICE,
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"-a",
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account,
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"-w",
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data,
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],
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capture_output=True,
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timeout=5,
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)
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return result.returncode == 0
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except (subprocess.TimeoutExpired, OSError) as exc:
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logger.debug("Claude keychain write failed: %s", exc)
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return False
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def _read_claude_credentials() -> dict | None:
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"""Read Claude Code credentials from Keychain (macOS) or file (Linux/Windows)."""
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# Try macOS Keychain first
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creds = _read_claude_keychain()
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if creds:
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return creds
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# Fall back to file
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if not CLAUDE_CREDENTIALS_FILE.exists():
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return None
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try:
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with open(CLAUDE_CREDENTIALS_FILE, encoding="utf-8") as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return None
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def _refresh_claude_code_token(refresh_token: str) -> dict | None:
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"""Refresh the Claude Code OAuth token using the refresh token.
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POSTs to the Anthropic OAuth token endpoint with form-urlencoded data
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(per OAuth 2.0 RFC 6749 Section 4.1.3).
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Returns:
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Dict with new token data (access_token, refresh_token, expires_in)
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on success, None on failure.
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"""
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import urllib.error
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import urllib.parse
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import urllib.request
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data = urllib.parse.urlencode(
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{
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"grant_type": "refresh_token",
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"refresh_token": refresh_token,
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"client_id": CLAUDE_OAUTH_CLIENT_ID,
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}
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).encode("utf-8")
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req = urllib.request.Request(
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CLAUDE_OAUTH_TOKEN_URL,
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data=data,
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headers={"Content-Type": "application/x-www-form-urlencoded"},
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method="POST",
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)
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try:
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with urllib.request.urlopen(req, timeout=15) as resp:
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return json.loads(resp.read())
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except (urllib.error.URLError, json.JSONDecodeError, TimeoutError, OSError) as exc:
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logger.debug("Claude Code token refresh failed: %s", exc)
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return None
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def _save_refreshed_credentials(token_data: dict) -> None:
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"""Write refreshed token data back to Keychain (macOS) or credentials file."""
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import time
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creds = _read_claude_credentials()
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if not creds:
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return
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try:
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oauth = creds.get("claudeAiOauth", {})
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oauth["accessToken"] = token_data["access_token"]
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if "refresh_token" in token_data:
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oauth["refreshToken"] = token_data["refresh_token"]
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if "expires_in" in token_data:
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oauth["expiresAt"] = int((time.time() + token_data["expires_in"]) * 1000)
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creds["claudeAiOauth"] = oauth
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# Try Keychain first (macOS), fall back to file
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if _save_claude_keychain(creds):
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logger.debug("Claude Code credentials refreshed in Keychain")
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return
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if CLAUDE_CREDENTIALS_FILE.exists():
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with open(CLAUDE_CREDENTIALS_FILE, "w", encoding="utf-8") as f:
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json.dump(creds, f, indent=2)
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logger.debug("Claude Code credentials refreshed in file")
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except (json.JSONDecodeError, OSError, KeyError) as exc:
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logger.debug("Failed to save refreshed credentials: %s", exc)
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def get_claude_code_token() -> str | None:
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"""Get the OAuth token from Claude Code subscription with auto-refresh.
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Reads from macOS Keychain (on Darwin) or ~/.claude/.credentials.json
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(on Linux/Windows), as created by the Claude Code CLI.
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If the token is expired or close to expiry, attempts an automatic
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refresh using the stored refresh token.
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Returns:
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The access token if available, None otherwise.
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"""
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import time
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creds = _read_claude_credentials()
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if not creds:
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return None
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oauth = creds.get("claudeAiOauth", {})
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access_token = oauth.get("accessToken")
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if not access_token:
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return None
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# Check token expiry (expiresAt is in milliseconds)
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expires_at_ms = oauth.get("expiresAt", 0)
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now_ms = int(time.time() * 1000)
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buffer_ms = _TOKEN_REFRESH_BUFFER_SECS * 1000
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if expires_at_ms > now_ms + buffer_ms:
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# Token is still valid
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return access_token
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# Token is expired or near expiry — attempt refresh
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refresh_token = oauth.get("refreshToken")
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if not refresh_token:
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logger.warning("Claude Code token expired and no refresh token available")
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return access_token # Return expired token; it may still work briefly
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logger.info("Claude Code token expired or near expiry, refreshing...")
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token_data = _refresh_claude_code_token(refresh_token)
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if token_data and "access_token" in token_data:
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_save_refreshed_credentials(token_data)
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return token_data["access_token"]
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# Refresh failed — return the existing token and warn
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logger.warning("Claude Code token refresh failed. Run 'claude' to re-authenticate.")
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return access_token
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# ---------------------------------------------------------------------------
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# Codex (OpenAI) subscription token helpers
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# ---------------------------------------------------------------------------
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def _get_codex_keychain_account() -> str:
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"""Compute the macOS Keychain account name used by the Codex CLI.
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The Codex CLI stores credentials under the account
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``cli|<sha256(~/.codex)[:16]>`` in the ``Codex Auth`` service.
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"""
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import hashlib
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codex_dir = str(Path.home() / ".codex")
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digest = hashlib.sha256(codex_dir.encode()).hexdigest()[:16]
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return f"cli|{digest}"
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def _read_codex_keychain() -> dict | None:
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"""Read Codex auth data from macOS Keychain (macOS only).
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Returns the parsed JSON from the Keychain entry, or None if not
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available (wrong platform, entry missing, etc.).
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"""
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import platform
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import subprocess
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if platform.system() != "Darwin":
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return None
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try:
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account = _get_codex_keychain_account()
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result = subprocess.run(
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[
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"security",
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"find-generic-password",
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"-s",
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CODEX_KEYCHAIN_SERVICE,
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"-a",
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account,
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"-w",
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],
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capture_output=True,
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encoding="utf-8",
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timeout=5,
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)
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if result.returncode != 0:
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return None
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raw = result.stdout.strip()
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if not raw:
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return None
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return json.loads(raw)
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except (subprocess.TimeoutExpired, json.JSONDecodeError, OSError) as exc:
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logger.debug("Codex keychain read failed: %s", exc)
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return None
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def _read_codex_auth_file() -> dict | None:
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"""Read Codex auth data from ~/.codex/auth.json (fallback)."""
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if not CODEX_AUTH_FILE.exists():
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return None
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try:
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with open(CODEX_AUTH_FILE, encoding="utf-8") as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return None
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def _is_codex_token_expired(auth_data: dict) -> bool:
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"""Check whether the Codex token is expired or close to expiry.
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The Codex auth.json has no explicit ``expiresAt`` field, so we infer
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expiry as ``last_refresh + _CODEX_TOKEN_LIFETIME_SECS``. Falls back
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to the file mtime when ``last_refresh`` is absent.
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"""
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import time
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from datetime import datetime
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now = time.time()
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last_refresh = auth_data.get("last_refresh")
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if last_refresh is None:
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# Fall back to file modification time
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try:
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last_refresh = CODEX_AUTH_FILE.stat().st_mtime
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except OSError:
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# Cannot determine age — assume expired
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return True
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elif isinstance(last_refresh, str):
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# Codex stores last_refresh as an ISO 8601 timestamp string —
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# convert to Unix epoch float for arithmetic.
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try:
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last_refresh = datetime.fromisoformat(last_refresh.replace("Z", "+00:00")).timestamp()
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except (ValueError, TypeError):
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return True
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expires_at = last_refresh + _CODEX_TOKEN_LIFETIME_SECS
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return now >= (expires_at - _TOKEN_REFRESH_BUFFER_SECS)
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def _refresh_codex_token(refresh_token: str) -> dict | None:
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"""Refresh the Codex OAuth token using the refresh token.
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POSTs to the OpenAI auth endpoint with form-urlencoded data.
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Returns:
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Dict with new token data on success, None on failure.
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"""
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import urllib.error
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import urllib.parse
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import urllib.request
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data = urllib.parse.urlencode(
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{
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"grant_type": "refresh_token",
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"refresh_token": refresh_token,
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"client_id": CODEX_OAUTH_CLIENT_ID,
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}
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).encode("utf-8")
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req = urllib.request.Request(
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CODEX_OAUTH_TOKEN_URL,
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data=data,
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headers={"Content-Type": "application/x-www-form-urlencoded"},
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method="POST",
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)
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try:
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with urllib.request.urlopen(req, timeout=15) as resp:
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return json.loads(resp.read())
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except (urllib.error.URLError, json.JSONDecodeError, TimeoutError, OSError) as exc:
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logger.debug("Codex token refresh failed: %s", exc)
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return None
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def _save_refreshed_codex_credentials(auth_data: dict, token_data: dict) -> None:
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"""Write refreshed tokens back to ~/.codex/auth.json only (not Keychain).
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The Codex CLI manages its own Keychain entries, so we only update the
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file-based credentials.
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"""
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from datetime import datetime
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try:
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tokens = auth_data.get("tokens", {})
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tokens["access_token"] = token_data["access_token"]
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if "refresh_token" in token_data:
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tokens["refresh_token"] = token_data["refresh_token"]
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if "id_token" in token_data:
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tokens["id_token"] = token_data["id_token"]
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auth_data["tokens"] = tokens
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auth_data["last_refresh"] = datetime.now(UTC).isoformat()
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CODEX_AUTH_FILE.parent.mkdir(parents=True, exist_ok=True, mode=0o700)
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fd = os.open(CODEX_AUTH_FILE, os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o600)
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||
with os.fdopen(fd, "w", encoding="utf-8") as f:
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||
json.dump(auth_data, f, indent=2)
|
||
logger.debug("Codex credentials refreshed successfully")
|
||
except (OSError, KeyError) as exc:
|
||
logger.debug("Failed to save refreshed Codex credentials: %s", exc)
|
||
|
||
|
||
def get_codex_token() -> str | None:
|
||
"""Get the OAuth token from Codex subscription with auto-refresh.
|
||
|
||
Reads from macOS Keychain first, then falls back to
|
||
``~/.codex/auth.json``. If the token is expired or close to
|
||
expiry, attempts an automatic refresh.
|
||
|
||
Returns:
|
||
The access token if available, None otherwise.
|
||
"""
|
||
# Try Keychain first, then file
|
||
auth_data = _read_codex_keychain() or _read_codex_auth_file()
|
||
if not auth_data:
|
||
return None
|
||
|
||
tokens = auth_data.get("tokens", {})
|
||
access_token = tokens.get("access_token")
|
||
if not access_token:
|
||
return None
|
||
|
||
# Check if token is still valid
|
||
if not _is_codex_token_expired(auth_data):
|
||
return access_token
|
||
|
||
# Token is expired or near expiry — attempt refresh
|
||
refresh_token = tokens.get("refresh_token")
|
||
if not refresh_token:
|
||
logger.warning("Codex token expired and no refresh token available")
|
||
return access_token # Return expired token; it may still work briefly
|
||
|
||
logger.info("Codex token expired or near expiry, refreshing...")
|
||
token_data = _refresh_codex_token(refresh_token)
|
||
|
||
if token_data and "access_token" in token_data:
|
||
_save_refreshed_codex_credentials(auth_data, token_data)
|
||
return token_data["access_token"]
|
||
|
||
# Refresh failed — return the existing token and warn
|
||
logger.warning("Codex token refresh failed. Run 'codex' to re-authenticate.")
|
||
return access_token
|
||
|
||
|
||
def _get_account_id_from_jwt(access_token: str) -> str | None:
|
||
"""Extract the ChatGPT account_id from the access token JWT.
|
||
|
||
The OpenAI access token JWT contains a claim at
|
||
``https://api.openai.com/auth`` with a ``chatgpt_account_id`` field.
|
||
This is used as a fallback when the auth.json doesn't store the
|
||
account_id explicitly.
|
||
"""
|
||
import base64
|
||
|
||
try:
|
||
parts = access_token.split(".")
|
||
if len(parts) != 3:
|
||
return None
|
||
payload = parts[1]
|
||
# Add base64 padding
|
||
padding = 4 - len(payload) % 4
|
||
if padding != 4:
|
||
payload += "=" * padding
|
||
decoded = base64.urlsafe_b64decode(payload)
|
||
claims = json.loads(decoded)
|
||
auth = claims.get("https://api.openai.com/auth")
|
||
if isinstance(auth, dict):
|
||
account_id = auth.get("chatgpt_account_id")
|
||
if isinstance(account_id, str) and account_id:
|
||
return account_id
|
||
except Exception:
|
||
pass
|
||
return None
|
||
|
||
|
||
def get_codex_account_id() -> str | None:
|
||
"""Extract the account ID from Codex auth data for the ChatGPT-Account-Id header.
|
||
|
||
Checks the ``tokens.account_id`` field first, then falls back to
|
||
decoding the account ID from the access token JWT.
|
||
|
||
Returns:
|
||
The account_id string if available, None otherwise.
|
||
"""
|
||
auth_data = _read_codex_keychain() or _read_codex_auth_file()
|
||
if not auth_data:
|
||
return None
|
||
tokens = auth_data.get("tokens", {})
|
||
account_id = tokens.get("account_id")
|
||
if account_id:
|
||
return account_id
|
||
# Fallback: extract from JWT
|
||
access_token = tokens.get("access_token")
|
||
if access_token:
|
||
return _get_account_id_from_jwt(access_token)
|
||
return None
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Kimi Code subscription token helpers
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def get_kimi_code_token() -> str | None:
|
||
"""Get the API key from a Kimi Code CLI installation.
|
||
|
||
Reads the API key from ``~/.kimi/config.toml``, which is created when
|
||
the user runs ``kimi /login`` in the Kimi Code CLI.
|
||
|
||
Returns:
|
||
The API key if available, None otherwise.
|
||
"""
|
||
import tomllib
|
||
|
||
config_path = Path.home() / ".kimi" / "config.toml"
|
||
if not config_path.exists():
|
||
return None
|
||
|
||
try:
|
||
with open(config_path, "rb") as f:
|
||
config = tomllib.load(f)
|
||
providers = config.get("providers", {})
|
||
# kimi-cli stores credentials under providers.kimi-for-coding
|
||
for provider_cfg in providers.values():
|
||
if isinstance(provider_cfg, dict):
|
||
key = provider_cfg.get("api_key")
|
||
if key:
|
||
return key
|
||
except Exception:
|
||
pass
|
||
return None
|
||
|
||
|
||
@dataclass
|
||
class AgentInfo:
|
||
"""Information about an exported agent."""
|
||
|
||
name: str
|
||
description: str
|
||
goal_name: str
|
||
goal_description: str
|
||
node_count: int
|
||
edge_count: int
|
||
nodes: list[dict]
|
||
edges: list[dict]
|
||
entry_node: str
|
||
terminal_nodes: list[str]
|
||
success_criteria: list[dict]
|
||
constraints: list[dict]
|
||
required_tools: list[str]
|
||
has_tools_module: bool
|
||
|
||
|
||
@dataclass
|
||
class ValidationResult:
|
||
"""Result of agent validation."""
|
||
|
||
valid: bool
|
||
errors: list[str] = field(default_factory=list)
|
||
warnings: list[str] = field(default_factory=list)
|
||
missing_tools: list[str] = field(default_factory=list)
|
||
missing_credentials: list[str] = field(default_factory=list)
|
||
|
||
|
||
def load_agent_export(data: str | dict) -> tuple[GraphSpec, Goal]:
|
||
"""
|
||
Load GraphSpec and Goal from export_graph() output.
|
||
|
||
Args:
|
||
data: JSON string or dict from export_graph()
|
||
|
||
Returns:
|
||
Tuple of (GraphSpec, Goal)
|
||
"""
|
||
if isinstance(data, str):
|
||
data = json.loads(data)
|
||
|
||
# Extract graph and goal
|
||
graph_data = data.get("graph", {})
|
||
goal_data = data.get("goal", {})
|
||
|
||
# Build NodeSpec objects
|
||
nodes = []
|
||
for node_data in graph_data.get("nodes", []):
|
||
nodes.append(NodeSpec(**node_data))
|
||
|
||
# Build EdgeSpec objects
|
||
edges = []
|
||
for edge_data in graph_data.get("edges", []):
|
||
condition_str = edge_data.get("condition", "on_success")
|
||
condition_map = {
|
||
"always": EdgeCondition.ALWAYS,
|
||
"on_success": EdgeCondition.ON_SUCCESS,
|
||
"on_failure": EdgeCondition.ON_FAILURE,
|
||
"conditional": EdgeCondition.CONDITIONAL,
|
||
"llm_decide": EdgeCondition.LLM_DECIDE,
|
||
}
|
||
edge = EdgeSpec(
|
||
id=edge_data["id"],
|
||
source=edge_data["source"],
|
||
target=edge_data["target"],
|
||
condition=condition_map.get(condition_str, EdgeCondition.ON_SUCCESS),
|
||
condition_expr=edge_data.get("condition_expr"),
|
||
priority=edge_data.get("priority", 0),
|
||
input_mapping=edge_data.get("input_mapping", {}),
|
||
)
|
||
edges.append(edge)
|
||
|
||
# Build GraphSpec
|
||
graph = GraphSpec(
|
||
id=graph_data.get("id", "agent-graph"),
|
||
goal_id=graph_data.get("goal_id", ""),
|
||
version=graph_data.get("version", "1.0.0"),
|
||
entry_node=graph_data.get("entry_node", ""),
|
||
entry_points=graph_data.get("entry_points", {}), # Support pause/resume architecture
|
||
terminal_nodes=graph_data.get("terminal_nodes", []),
|
||
pause_nodes=graph_data.get("pause_nodes", []), # Support pause/resume architecture
|
||
nodes=nodes,
|
||
edges=edges,
|
||
max_steps=graph_data.get("max_steps", 100),
|
||
max_retries_per_node=graph_data.get("max_retries_per_node", 3),
|
||
description=graph_data.get("description", ""),
|
||
)
|
||
|
||
# Build Goal
|
||
from framework.graph.goal import Constraint, SuccessCriterion
|
||
|
||
success_criteria = []
|
||
for sc_data in goal_data.get("success_criteria", []):
|
||
success_criteria.append(
|
||
SuccessCriterion(
|
||
id=sc_data["id"],
|
||
description=sc_data["description"],
|
||
metric=sc_data.get("metric", ""),
|
||
target=sc_data.get("target", ""),
|
||
weight=sc_data.get("weight", 1.0),
|
||
)
|
||
)
|
||
|
||
constraints = []
|
||
for c_data in goal_data.get("constraints", []):
|
||
constraints.append(
|
||
Constraint(
|
||
id=c_data["id"],
|
||
description=c_data["description"],
|
||
constraint_type=c_data.get("constraint_type", "hard"),
|
||
category=c_data.get("category", "safety"),
|
||
check=c_data.get("check", ""),
|
||
)
|
||
)
|
||
|
||
goal = Goal(
|
||
id=goal_data.get("id", ""),
|
||
name=goal_data.get("name", ""),
|
||
description=goal_data.get("description", ""),
|
||
success_criteria=success_criteria,
|
||
constraints=constraints,
|
||
)
|
||
|
||
return graph, goal
|
||
|
||
|
||
class AgentRunner:
|
||
"""
|
||
Loads and runs exported agents with minimal boilerplate.
|
||
|
||
Handles:
|
||
- Loading graph and goal from agent.json
|
||
- Auto-discovering tools from tools.py
|
||
- Setting up Runtime, LLM, and executor
|
||
- Executing with dynamic edge traversal
|
||
|
||
Usage:
|
||
# Simple usage
|
||
runner = AgentRunner.load("exports/outbound-sales-agent")
|
||
result = await runner.run({"lead_id": "123"})
|
||
|
||
# With context manager
|
||
async with AgentRunner.load("exports/outbound-sales-agent") as runner:
|
||
result = await runner.run({"lead_id": "123"})
|
||
|
||
# With custom tools
|
||
runner = AgentRunner.load("exports/outbound-sales-agent")
|
||
runner.register_tool("my_tool", my_tool_func)
|
||
result = await runner.run({"lead_id": "123"})
|
||
"""
|
||
|
||
@staticmethod
|
||
def _resolve_default_model() -> str:
|
||
"""Resolve the default model from ~/.hive/configuration.json."""
|
||
return get_preferred_model()
|
||
|
||
def __init__(
|
||
self,
|
||
agent_path: Path,
|
||
graph: GraphSpec,
|
||
goal: Goal,
|
||
mock_mode: bool = False,
|
||
storage_path: Path | None = None,
|
||
model: str | None = None,
|
||
intro_message: str = "",
|
||
runtime_config: "AgentRuntimeConfig | None" = None,
|
||
interactive: bool = True,
|
||
skip_credential_validation: bool = False,
|
||
requires_account_selection: bool = False,
|
||
configure_for_account: Callable | None = None,
|
||
list_accounts: Callable | None = None,
|
||
credential_store: Any | None = None,
|
||
):
|
||
"""
|
||
Initialize the runner (use AgentRunner.load() instead).
|
||
|
||
Args:
|
||
agent_path: Path to agent folder
|
||
graph: Loaded GraphSpec object
|
||
goal: Loaded Goal object
|
||
mock_mode: If True, use mock LLM responses
|
||
storage_path: Path for runtime storage (defaults to temp)
|
||
model: Model to use (reads from agent config or ~/.hive/configuration.json if None)
|
||
intro_message: Optional greeting shown to user on TUI load
|
||
runtime_config: Optional AgentRuntimeConfig (webhook settings, etc.)
|
||
interactive: If True (default), offer interactive credential setup on failure.
|
||
Set to False when called from the TUI (which handles setup via its own screen).
|
||
skip_credential_validation: If True, skip credential checks at load time.
|
||
requires_account_selection: If True, TUI shows account picker before starting.
|
||
configure_for_account: Callback(runner, account_dict) to scope tools after selection.
|
||
list_accounts: Callback() -> list[dict] to fetch available accounts.
|
||
credential_store: Optional shared CredentialStore (avoids creating redundant stores).
|
||
"""
|
||
self.agent_path = agent_path
|
||
self.graph = graph
|
||
self.goal = goal
|
||
self.mock_mode = mock_mode
|
||
self.model = model or self._resolve_default_model()
|
||
self.intro_message = intro_message
|
||
self.runtime_config = runtime_config
|
||
self._interactive = interactive
|
||
self.skip_credential_validation = skip_credential_validation
|
||
self.requires_account_selection = requires_account_selection
|
||
self._configure_for_account = configure_for_account
|
||
self._list_accounts = list_accounts
|
||
self._credential_store = credential_store
|
||
|
||
# Set up storage
|
||
if storage_path:
|
||
self._storage_path = storage_path
|
||
self._temp_dir = None
|
||
else:
|
||
# Use persistent storage in ~/.hive/agents/{agent_name}/ per RUNTIME_LOGGING.md spec
|
||
home = Path.home()
|
||
default_storage = home / ".hive" / "agents" / agent_path.name
|
||
default_storage.mkdir(parents=True, exist_ok=True)
|
||
self._storage_path = default_storage
|
||
self._temp_dir = None
|
||
|
||
# Load HIVE_CREDENTIAL_KEY from shell config if not in env.
|
||
# Must happen before MCP subprocesses are spawned so they inherit it.
|
||
_ensure_credential_key_env()
|
||
|
||
# Initialize components
|
||
self._tool_registry = ToolRegistry()
|
||
self._llm: LLMProvider | None = None
|
||
self._approval_callback: Callable | None = None
|
||
|
||
# AgentRuntime — unified execution path for all agents
|
||
self._agent_runtime: AgentRuntime | None = None
|
||
# Pre-load validation: structural checks + credentials.
|
||
# Fails fast with actionable guidance — no MCP noise on screen.
|
||
run_preload_validation(
|
||
self.graph,
|
||
interactive=self._interactive,
|
||
skip_credential_validation=self.skip_credential_validation,
|
||
)
|
||
|
||
# Auto-discover tools from tools.py
|
||
tools_path = agent_path / "tools.py"
|
||
if tools_path.exists():
|
||
self._tool_registry.discover_from_module(tools_path)
|
||
|
||
# Set environment variables for MCP subprocesses
|
||
# These are inherited by MCP servers (e.g., GCU browser tools)
|
||
os.environ["HIVE_AGENT_NAME"] = agent_path.name
|
||
os.environ["HIVE_STORAGE_PATH"] = str(self._storage_path)
|
||
|
||
# Auto-discover MCP servers from mcp_servers.json
|
||
mcp_config_path = agent_path / "mcp_servers.json"
|
||
if mcp_config_path.exists():
|
||
self._load_mcp_servers_from_config(mcp_config_path)
|
||
|
||
@staticmethod
|
||
def _import_agent_module(agent_path: Path):
|
||
"""Import an agent package from its directory path.
|
||
|
||
Ensures the agent's parent directory is on sys.path so the package
|
||
can be imported normally (supports relative imports within the agent).
|
||
|
||
Always reloads the package and its submodules so that code changes
|
||
made since the last import (or since a previous session load in the
|
||
same server process) are picked up.
|
||
"""
|
||
import importlib
|
||
import sys
|
||
|
||
package_name = agent_path.name
|
||
parent_dir = str(agent_path.resolve().parent)
|
||
|
||
# Always place the correct parent directory first on sys.path.
|
||
# Multiple agent dirs can contain packages with the same name
|
||
# (e.g. exports/deep_research_agent and examples/deep_research_agent).
|
||
# Without this, a previously-added parent dir could shadow the
|
||
# agent we actually want to load.
|
||
if parent_dir in sys.path:
|
||
sys.path.remove(parent_dir)
|
||
sys.path.insert(0, parent_dir)
|
||
|
||
# Evict cached submodules first (e.g. deep_research_agent.nodes,
|
||
# deep_research_agent.agent) so the top-level reload picks up
|
||
# changes in the entire package — not just __init__.py.
|
||
stale = [
|
||
name
|
||
for name in sys.modules
|
||
if name == package_name or name.startswith(f"{package_name}.")
|
||
]
|
||
for name in stale:
|
||
del sys.modules[name]
|
||
|
||
return importlib.import_module(package_name)
|
||
|
||
@classmethod
|
||
def load(
|
||
cls,
|
||
agent_path: str | Path,
|
||
mock_mode: bool = False,
|
||
storage_path: Path | None = None,
|
||
model: str | None = None,
|
||
interactive: bool = True,
|
||
skip_credential_validation: bool | None = None,
|
||
credential_store: Any | None = None,
|
||
) -> "AgentRunner":
|
||
"""
|
||
Load an agent from an export folder.
|
||
|
||
Imports the agent's Python package and reads module-level variables
|
||
(goal, nodes, edges, etc.) to build a GraphSpec. Falls back to
|
||
agent.json if no Python module is found.
|
||
|
||
Args:
|
||
agent_path: Path to agent folder
|
||
mock_mode: If True, use mock LLM responses
|
||
storage_path: Path for runtime storage (defaults to ~/.hive/agents/{name})
|
||
model: LLM model to use (reads from agent's default_config if None)
|
||
interactive: If True (default), offer interactive credential setup.
|
||
Set to False from TUI callers that handle setup via their own UI.
|
||
skip_credential_validation: If True, skip credential checks at load time.
|
||
When None (default), uses the agent module's setting.
|
||
credential_store: Optional shared CredentialStore (avoids creating redundant stores).
|
||
|
||
Returns:
|
||
AgentRunner instance ready to run
|
||
"""
|
||
agent_path = Path(agent_path)
|
||
|
||
# Try loading from Python module first (code-based agents)
|
||
agent_py = agent_path / "agent.py"
|
||
if agent_py.exists():
|
||
agent_module = cls._import_agent_module(agent_path)
|
||
|
||
goal = getattr(agent_module, "goal", None)
|
||
nodes = getattr(agent_module, "nodes", None)
|
||
edges = getattr(agent_module, "edges", None)
|
||
|
||
if goal is None or nodes is None or edges is None:
|
||
raise ValueError(
|
||
f"Agent at {agent_path} must define 'goal', 'nodes', and 'edges' "
|
||
f"in agent.py (or __init__.py)"
|
||
)
|
||
|
||
# Read model and max_tokens from agent's config if not explicitly provided
|
||
agent_config = getattr(agent_module, "default_config", None)
|
||
if model is None:
|
||
if agent_config and hasattr(agent_config, "model"):
|
||
model = agent_config.model
|
||
|
||
if agent_config and hasattr(agent_config, "max_tokens"):
|
||
max_tokens = agent_config.max_tokens
|
||
logger.info(
|
||
"Agent default_config overrides max_tokens: %d "
|
||
"(configuration.json value ignored)",
|
||
max_tokens,
|
||
)
|
||
else:
|
||
hive_config = get_hive_config()
|
||
max_tokens = hive_config.get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
|
||
|
||
# Resolve max_context_tokens with priority:
|
||
# 1. agent loop_config["max_context_tokens"] (explicit, wins silently)
|
||
# 2. agent default_config.max_context_tokens (logged)
|
||
# 3. configuration.json llm.max_context_tokens
|
||
# 4. hardcoded default (32_000)
|
||
agent_loop_config: dict = dict(getattr(agent_module, "loop_config", {}))
|
||
if "max_context_tokens" not in agent_loop_config:
|
||
if agent_config and hasattr(agent_config, "max_context_tokens"):
|
||
agent_loop_config["max_context_tokens"] = agent_config.max_context_tokens
|
||
logger.info(
|
||
"Agent default_config overrides max_context_tokens: %d"
|
||
" (configuration.json value ignored)",
|
||
agent_config.max_context_tokens,
|
||
)
|
||
else:
|
||
agent_loop_config["max_context_tokens"] = get_max_context_tokens()
|
||
|
||
# Read intro_message from agent metadata (shown on TUI load)
|
||
agent_metadata = getattr(agent_module, "metadata", None)
|
||
intro_message = ""
|
||
if agent_metadata and hasattr(agent_metadata, "intro_message"):
|
||
intro_message = agent_metadata.intro_message
|
||
|
||
# Build GraphSpec from module-level variables
|
||
graph_kwargs: dict = {
|
||
"id": f"{agent_path.name}-graph",
|
||
"goal_id": goal.id,
|
||
"version": "1.0.0",
|
||
"entry_node": getattr(agent_module, "entry_node", nodes[0].id),
|
||
"entry_points": getattr(agent_module, "entry_points", {}),
|
||
"terminal_nodes": getattr(agent_module, "terminal_nodes", []),
|
||
"pause_nodes": getattr(agent_module, "pause_nodes", []),
|
||
"nodes": nodes,
|
||
"edges": edges,
|
||
"max_tokens": max_tokens,
|
||
"loop_config": agent_loop_config,
|
||
}
|
||
# Only pass optional fields if explicitly defined by the agent module
|
||
conversation_mode = getattr(agent_module, "conversation_mode", None)
|
||
if conversation_mode is not None:
|
||
graph_kwargs["conversation_mode"] = conversation_mode
|
||
identity_prompt = getattr(agent_module, "identity_prompt", None)
|
||
if identity_prompt is not None:
|
||
graph_kwargs["identity_prompt"] = identity_prompt
|
||
|
||
graph = GraphSpec(**graph_kwargs)
|
||
|
||
# Generate flowchart.json if missing (for template/legacy agents)
|
||
generate_fallback_flowchart(graph, goal, agent_path)
|
||
# Read skill configuration from agent module
|
||
agent_default_skills = getattr(agent_module, "default_skills", None)
|
||
agent_skills = getattr(agent_module, "skills", None)
|
||
|
||
# Read runtime config (webhook settings, etc.) if defined
|
||
agent_runtime_config = getattr(agent_module, "runtime_config", None)
|
||
|
||
# Read pre-run hooks (e.g., credential_tester needs account selection)
|
||
skip_cred = getattr(agent_module, "skip_credential_validation", False)
|
||
if skip_credential_validation is not None:
|
||
skip_cred = skip_credential_validation
|
||
needs_acct = getattr(agent_module, "requires_account_selection", False)
|
||
configure_fn = getattr(agent_module, "configure_for_account", None)
|
||
list_accts_fn = getattr(agent_module, "list_connected_accounts", None)
|
||
|
||
runner = cls(
|
||
agent_path=agent_path,
|
||
graph=graph,
|
||
goal=goal,
|
||
mock_mode=mock_mode,
|
||
storage_path=storage_path,
|
||
model=model,
|
||
intro_message=intro_message,
|
||
runtime_config=agent_runtime_config,
|
||
interactive=interactive,
|
||
skip_credential_validation=skip_cred,
|
||
requires_account_selection=needs_acct,
|
||
configure_for_account=configure_fn,
|
||
list_accounts=list_accts_fn,
|
||
credential_store=credential_store,
|
||
)
|
||
# Stash skill config for use in _setup()
|
||
runner._agent_default_skills = agent_default_skills
|
||
runner._agent_skills = agent_skills
|
||
return runner
|
||
|
||
# Fallback: load from agent.json (legacy JSON-based agents)
|
||
agent_json_path = agent_path / "agent.json"
|
||
if not agent_json_path.is_file():
|
||
raise FileNotFoundError(f"No agent.py or agent.json found in {agent_path}")
|
||
|
||
with open(agent_json_path, encoding="utf-8") as f:
|
||
export_data = f.read()
|
||
|
||
if not export_data.strip():
|
||
raise ValueError(f"Empty agent export file: {agent_json_path}")
|
||
|
||
try:
|
||
graph, goal = load_agent_export(export_data)
|
||
except json.JSONDecodeError as exc:
|
||
raise ValueError(f"Invalid JSON in agent export file: {agent_json_path}") from exc
|
||
|
||
# Generate flowchart.json if missing (for legacy JSON-based agents)
|
||
generate_fallback_flowchart(graph, goal, agent_path)
|
||
|
||
runner = cls(
|
||
agent_path=agent_path,
|
||
graph=graph,
|
||
goal=goal,
|
||
mock_mode=mock_mode,
|
||
storage_path=storage_path,
|
||
model=model,
|
||
interactive=interactive,
|
||
skip_credential_validation=skip_credential_validation or False,
|
||
credential_store=credential_store,
|
||
)
|
||
runner._agent_default_skills = None
|
||
runner._agent_skills = None
|
||
return runner
|
||
|
||
def register_tool(
|
||
self,
|
||
name: str,
|
||
tool_or_func: Tool | Callable,
|
||
executor: Callable | None = None,
|
||
) -> None:
|
||
"""
|
||
Register a tool for use by the agent.
|
||
|
||
Args:
|
||
name: Tool name
|
||
tool_or_func: Either a Tool object or a callable function
|
||
executor: Executor function (required if tool_or_func is a Tool)
|
||
"""
|
||
if isinstance(tool_or_func, Tool):
|
||
if executor is None:
|
||
raise ValueError("executor required when registering a Tool object")
|
||
self._tool_registry.register(name, tool_or_func, executor)
|
||
else:
|
||
# It's a function, auto-generate Tool
|
||
self._tool_registry.register_function(tool_or_func, name=name)
|
||
|
||
def register_tools_from_module(self, module_path: Path) -> int:
|
||
"""
|
||
Auto-discover and register tools from a Python module.
|
||
|
||
Args:
|
||
module_path: Path to tools.py file
|
||
|
||
Returns:
|
||
Number of tools discovered
|
||
"""
|
||
return self._tool_registry.discover_from_module(module_path)
|
||
|
||
def register_mcp_server(
|
||
self,
|
||
name: str,
|
||
transport: str,
|
||
**config_kwargs,
|
||
) -> int:
|
||
"""
|
||
Register an MCP server and discover its tools.
|
||
|
||
Args:
|
||
name: Server name
|
||
transport: "stdio" or "http"
|
||
**config_kwargs: Additional configuration (command, args, url, etc.)
|
||
|
||
Returns:
|
||
Number of tools registered from this server
|
||
|
||
Example:
|
||
# Register STDIO MCP server
|
||
runner.register_mcp_server(
|
||
name="tools",
|
||
transport="stdio",
|
||
command="python",
|
||
args=["-m", "aden_tools.mcp_server", "--stdio"],
|
||
cwd="/path/to/tools"
|
||
)
|
||
|
||
# Register HTTP MCP server
|
||
runner.register_mcp_server(
|
||
name="tools",
|
||
transport="http",
|
||
url="http://localhost:4001"
|
||
)
|
||
"""
|
||
server_config = {
|
||
"name": name,
|
||
"transport": transport,
|
||
**config_kwargs,
|
||
}
|
||
return self._tool_registry.register_mcp_server(server_config)
|
||
|
||
def _load_mcp_servers_from_config(self, config_path: Path) -> None:
|
||
"""Load and register MCP servers from a configuration file."""
|
||
self._tool_registry.load_mcp_config(config_path)
|
||
|
||
def set_approval_callback(self, callback: Callable) -> None:
|
||
"""
|
||
Set a callback for human-in-the-loop approval during execution.
|
||
|
||
Args:
|
||
callback: Function to call for approval (receives node info, returns bool)
|
||
"""
|
||
self._approval_callback = callback
|
||
|
||
def _setup(self, event_bus=None) -> None:
|
||
"""Set up runtime, LLM, and executor."""
|
||
# Configure structured logging (auto-detects JSON vs human-readable)
|
||
from framework.observability import configure_logging
|
||
|
||
configure_logging(level="INFO", format="auto")
|
||
|
||
# Set up session context for tools (workspace_id, agent_id, session_id)
|
||
workspace_id = "default" # Could be derived from storage path
|
||
agent_id = self.graph.id or "unknown"
|
||
# Use "current" as a stable session_id for persistent memory
|
||
session_id = "current"
|
||
|
||
self._tool_registry.set_session_context(
|
||
workspace_id=workspace_id,
|
||
agent_id=agent_id,
|
||
session_id=session_id,
|
||
)
|
||
|
||
# Create LLM provider
|
||
# Uses LiteLLM which auto-detects the provider from model name
|
||
# Skip if already injected (e.g. worker agents with a pre-built LLM)
|
||
if self._llm is not None:
|
||
pass # LLM already configured externally
|
||
elif self.mock_mode:
|
||
# Use mock LLM for testing without real API calls
|
||
from framework.llm.mock import MockLLMProvider
|
||
|
||
self._llm = MockLLMProvider(model=self.model)
|
||
else:
|
||
from framework.llm.litellm import LiteLLMProvider
|
||
|
||
# Check if a subscription mode is configured
|
||
config = get_hive_config()
|
||
llm_config = config.get("llm", {})
|
||
use_claude_code = llm_config.get("use_claude_code_subscription", False)
|
||
use_codex = llm_config.get("use_codex_subscription", False)
|
||
use_kimi_code = llm_config.get("use_kimi_code_subscription", False)
|
||
api_base = llm_config.get("api_base")
|
||
|
||
api_key = None
|
||
if use_claude_code:
|
||
# Get OAuth token from Claude Code subscription
|
||
api_key = get_claude_code_token()
|
||
if not api_key:
|
||
print("Warning: Claude Code subscription configured but no token found.")
|
||
print("Run 'claude' to authenticate, then try again.")
|
||
elif use_codex:
|
||
# Get OAuth token from Codex subscription
|
||
api_key = get_codex_token()
|
||
if not api_key:
|
||
print("Warning: Codex subscription configured but no token found.")
|
||
print("Run 'codex' to authenticate, then try again.")
|
||
elif use_kimi_code:
|
||
# Get API key from Kimi Code CLI config (~/.kimi/config.toml)
|
||
api_key = get_kimi_code_token()
|
||
if not api_key:
|
||
print("Warning: Kimi Code subscription configured but no key found.")
|
||
print("Run 'kimi /login' to authenticate, then try again.")
|
||
|
||
if api_key and use_claude_code:
|
||
# Use litellm's built-in Anthropic OAuth support.
|
||
# The lowercase "authorization" key triggers OAuth detection which
|
||
# adds the required anthropic-beta and browser-access headers.
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model,
|
||
api_key=api_key,
|
||
api_base=api_base,
|
||
extra_headers={"authorization": f"Bearer {api_key}"},
|
||
)
|
||
elif api_key and use_codex:
|
||
# OpenAI Codex subscription routes through the ChatGPT backend
|
||
# (chatgpt.com/backend-api/codex/responses), NOT the standard
|
||
# OpenAI API. The consumer OAuth token lacks platform API scopes.
|
||
extra_headers: dict[str, str] = {
|
||
"Authorization": f"Bearer {api_key}",
|
||
"User-Agent": "CodexBar",
|
||
}
|
||
account_id = get_codex_account_id()
|
||
if account_id:
|
||
extra_headers["ChatGPT-Account-Id"] = account_id
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model,
|
||
api_key=api_key,
|
||
api_base="https://chatgpt.com/backend-api/codex",
|
||
extra_headers=extra_headers,
|
||
store=False,
|
||
allowed_openai_params=["store"],
|
||
)
|
||
elif api_key and use_kimi_code:
|
||
# Kimi Code subscription uses the Kimi coding API (OpenAI-compatible).
|
||
# The api_base is set automatically by LiteLLMProvider for kimi/ models.
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model,
|
||
api_key=api_key,
|
||
api_base=api_base,
|
||
)
|
||
else:
|
||
# Local models (e.g. Ollama) don't need an API key
|
||
if self._is_local_model(self.model):
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model,
|
||
api_base=api_base,
|
||
)
|
||
else:
|
||
# Fall back to environment variable
|
||
# First check api_key_env_var from config (set by quickstart)
|
||
api_key_env = llm_config.get("api_key_env_var") or self._get_api_key_env_var(
|
||
self.model
|
||
)
|
||
if api_key_env and os.environ.get(api_key_env):
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model,
|
||
api_key=os.environ[api_key_env],
|
||
api_base=api_base,
|
||
)
|
||
else:
|
||
# Fall back to credential store
|
||
api_key = self._get_api_key_from_credential_store()
|
||
if api_key:
|
||
self._llm = LiteLLMProvider(
|
||
model=self.model, api_key=api_key, api_base=api_base
|
||
)
|
||
# Set env var so downstream code (e.g. cleanup LLM in
|
||
# node._extract_json) can also find it
|
||
if api_key_env:
|
||
os.environ[api_key_env] = api_key
|
||
elif api_key_env:
|
||
print(f"Warning: {api_key_env} not set. LLM calls will fail.")
|
||
print(f"Set it with: export {api_key_env}=your-api-key")
|
||
|
||
# Fail fast if the agent needs an LLM but none was configured
|
||
if self._llm is None:
|
||
has_llm_nodes = any(
|
||
node.node_type in ("event_loop", "gcu") for node in self.graph.nodes
|
||
)
|
||
if has_llm_nodes:
|
||
from framework.credentials.models import CredentialError
|
||
|
||
if self._is_local_model(self.model):
|
||
raise CredentialError(
|
||
f"Failed to initialize LLM for local model '{self.model}'. "
|
||
f"Ensure your local LLM server is running "
|
||
f"(e.g. 'ollama serve' for Ollama)."
|
||
)
|
||
api_key_env = self._get_api_key_env_var(self.model)
|
||
hint = (
|
||
f"Set it with: export {api_key_env}=your-api-key"
|
||
if api_key_env
|
||
else "Configure an API key for your LLM provider."
|
||
)
|
||
raise CredentialError(f"LLM API key not found for model '{self.model}'. {hint}")
|
||
|
||
# For GCU nodes: auto-register GCU MCP server if needed, then expand tool lists
|
||
has_gcu_nodes = any(node.node_type == "gcu" for node in self.graph.nodes)
|
||
if has_gcu_nodes:
|
||
from framework.graph.gcu import GCU_MCP_SERVER_CONFIG, GCU_SERVER_NAME
|
||
|
||
# Auto-register GCU MCP server if tools aren't loaded yet
|
||
gcu_tool_names = self._tool_registry.get_server_tool_names(GCU_SERVER_NAME)
|
||
if not gcu_tool_names:
|
||
# Resolve cwd to repo-level tools/ (not relative to agent_path)
|
||
gcu_config = dict(GCU_MCP_SERVER_CONFIG)
|
||
_repo_root = Path(__file__).resolve().parent.parent.parent.parent
|
||
gcu_config["cwd"] = str(_repo_root / "tools")
|
||
self._tool_registry.register_mcp_server(gcu_config)
|
||
gcu_tool_names = self._tool_registry.get_server_tool_names(GCU_SERVER_NAME)
|
||
|
||
# Expand each GCU node's tools list to include all GCU server tools
|
||
if gcu_tool_names:
|
||
for node in self.graph.nodes:
|
||
if node.node_type == "gcu":
|
||
existing = set(node.tools)
|
||
for tool_name in sorted(gcu_tool_names):
|
||
if tool_name not in existing:
|
||
node.tools.append(tool_name)
|
||
|
||
# For event_loop/gcu nodes: auto-register file tools MCP server, then expand tool lists
|
||
has_loop_nodes = any(node.node_type in ("event_loop", "gcu") for node in self.graph.nodes)
|
||
if has_loop_nodes:
|
||
from framework.graph.files import FILES_MCP_SERVER_CONFIG, FILES_MCP_SERVER_NAME
|
||
|
||
files_tool_names = self._tool_registry.get_server_tool_names(FILES_MCP_SERVER_NAME)
|
||
if not files_tool_names:
|
||
# Resolve cwd to repo-level tools/ (not relative to agent_path)
|
||
files_config = dict(FILES_MCP_SERVER_CONFIG)
|
||
_repo_root = Path(__file__).resolve().parent.parent.parent.parent
|
||
files_config["cwd"] = str(_repo_root / "tools")
|
||
self._tool_registry.register_mcp_server(files_config)
|
||
files_tool_names = self._tool_registry.get_server_tool_names(FILES_MCP_SERVER_NAME)
|
||
|
||
if files_tool_names:
|
||
for node in self.graph.nodes:
|
||
if node.node_type in ("event_loop", "gcu"):
|
||
existing = set(node.tools)
|
||
for tool_name in sorted(files_tool_names):
|
||
if tool_name not in existing:
|
||
node.tools.append(tool_name)
|
||
|
||
# Get tools for runtime
|
||
tools = list(self._tool_registry.get_tools().values())
|
||
tool_executor = self._tool_registry.get_executor()
|
||
|
||
# Collect connected account info for system prompt injection
|
||
accounts_prompt = ""
|
||
accounts_data: list[dict] | None = None
|
||
tool_provider_map: dict[str, str] | None = None
|
||
try:
|
||
from aden_tools.credentials.store_adapter import CredentialStoreAdapter
|
||
|
||
if self._credential_store is not None:
|
||
adapter = CredentialStoreAdapter(store=self._credential_store)
|
||
else:
|
||
adapter = CredentialStoreAdapter.default()
|
||
accounts_data = adapter.get_all_account_info()
|
||
tool_provider_map = adapter.get_tool_provider_map()
|
||
if accounts_data:
|
||
from framework.graph.prompt_composer import build_accounts_prompt
|
||
|
||
accounts_prompt = build_accounts_prompt(accounts_data, tool_provider_map)
|
||
except Exception:
|
||
pass # Best-effort — agent works without account info
|
||
|
||
# Skill configuration — the runtime handles discovery, loading, and
|
||
# prompt rasterization. The runner just builds the config.
|
||
from framework.skills.config import SkillsConfig
|
||
from framework.skills.manager import SkillsManagerConfig
|
||
|
||
skills_manager_config = SkillsManagerConfig(
|
||
skills_config=SkillsConfig.from_agent_vars(
|
||
default_skills=getattr(self, "_agent_default_skills", None),
|
||
skills=getattr(self, "_agent_skills", None),
|
||
),
|
||
project_root=self.agent_path,
|
||
)
|
||
|
||
self._setup_agent_runtime(
|
||
tools,
|
||
tool_executor,
|
||
accounts_prompt=accounts_prompt,
|
||
accounts_data=accounts_data,
|
||
tool_provider_map=tool_provider_map,
|
||
event_bus=event_bus,
|
||
skills_manager_config=skills_manager_config,
|
||
)
|
||
|
||
def _get_api_key_env_var(self, model: str) -> str | None:
|
||
"""Get the environment variable name for the API key based on model name."""
|
||
model_lower = model.lower()
|
||
|
||
# Map model prefixes to API key environment variables
|
||
# LiteLLM uses these conventions
|
||
if model_lower.startswith("cerebras/"):
|
||
return "CEREBRAS_API_KEY"
|
||
elif model_lower.startswith("openai/") or model_lower.startswith("gpt-"):
|
||
return "OPENAI_API_KEY"
|
||
elif model_lower.startswith("anthropic/") or model_lower.startswith("claude"):
|
||
return "ANTHROPIC_API_KEY"
|
||
elif model_lower.startswith("gemini/") or model_lower.startswith("google/"):
|
||
return "GEMINI_API_KEY"
|
||
elif model_lower.startswith("mistral/"):
|
||
return "MISTRAL_API_KEY"
|
||
elif model_lower.startswith("groq/"):
|
||
return "GROQ_API_KEY"
|
||
elif model_lower.startswith("openrouter/"):
|
||
return "OPENROUTER_API_KEY"
|
||
elif self._is_local_model(model_lower):
|
||
return None # Local models don't need an API key
|
||
elif model_lower.startswith("azure/"):
|
||
return "AZURE_API_KEY"
|
||
elif model_lower.startswith("cohere/"):
|
||
return "COHERE_API_KEY"
|
||
elif model_lower.startswith("replicate/"):
|
||
return "REPLICATE_API_KEY"
|
||
elif model_lower.startswith("together/"):
|
||
return "TOGETHER_API_KEY"
|
||
elif model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
|
||
return "MINIMAX_API_KEY"
|
||
elif model_lower.startswith("kimi/"):
|
||
return "KIMI_API_KEY"
|
||
elif model_lower.startswith("hive/"):
|
||
return "HIVE_API_KEY"
|
||
else:
|
||
# Default: assume OpenAI-compatible
|
||
return "OPENAI_API_KEY"
|
||
|
||
def _get_api_key_from_credential_store(self) -> str | None:
|
||
"""Get the LLM API key from the encrypted credential store.
|
||
|
||
Maps model name to credential store ID (e.g. "anthropic/..." -> "anthropic")
|
||
and retrieves the key via CredentialStore.get().
|
||
"""
|
||
if not os.environ.get("HIVE_CREDENTIAL_KEY"):
|
||
return None
|
||
|
||
# Map model prefix to credential store ID
|
||
model_lower = self.model.lower()
|
||
cred_id = None
|
||
if model_lower.startswith("anthropic/") or model_lower.startswith("claude"):
|
||
cred_id = "anthropic"
|
||
elif model_lower.startswith("minimax/") or model_lower.startswith("minimax-"):
|
||
cred_id = "minimax"
|
||
elif model_lower.startswith("kimi/"):
|
||
cred_id = "kimi"
|
||
elif model_lower.startswith("hive/"):
|
||
cred_id = "hive"
|
||
# Add more mappings as providers are added to LLM_CREDENTIALS
|
||
|
||
if cred_id is None:
|
||
return None
|
||
|
||
try:
|
||
store = self._credential_store
|
||
if store is None:
|
||
from framework.credentials import CredentialStore
|
||
|
||
store = CredentialStore.with_encrypted_storage()
|
||
return store.get(cred_id)
|
||
except Exception:
|
||
return None
|
||
|
||
@staticmethod
|
||
def _is_local_model(model: str) -> bool:
|
||
"""Check if a model is a local model that doesn't require an API key.
|
||
|
||
Local providers like Ollama run on the user's machine and do not
|
||
need any authentication credentials.
|
||
"""
|
||
LOCAL_PREFIXES = (
|
||
"ollama/",
|
||
"ollama_chat/",
|
||
"vllm/",
|
||
"lm_studio/",
|
||
"llamacpp/",
|
||
)
|
||
return model.lower().startswith(LOCAL_PREFIXES)
|
||
|
||
def _setup_agent_runtime(
|
||
self,
|
||
tools: list,
|
||
tool_executor: Callable | None,
|
||
accounts_prompt: str = "",
|
||
accounts_data: list[dict] | None = None,
|
||
tool_provider_map: dict[str, str] | None = None,
|
||
event_bus=None,
|
||
skills_manager_config=None,
|
||
) -> None:
|
||
"""Set up multi-entry-point execution using AgentRuntime."""
|
||
entry_points = []
|
||
|
||
# Always create a primary entry point for the graph's entry node.
|
||
# For multi-entry-point agents this ensures the primary path (e.g.
|
||
# user-facing rule setup) is reachable alongside async entry points.
|
||
if self.graph.entry_node:
|
||
entry_points.insert(
|
||
0,
|
||
EntryPointSpec(
|
||
id="default",
|
||
name="Default",
|
||
entry_node=self.graph.entry_node,
|
||
trigger_type="manual",
|
||
isolation_level="shared",
|
||
),
|
||
)
|
||
|
||
# Create AgentRuntime with all entry points
|
||
log_store = RuntimeLogStore(base_path=self._storage_path / "runtime_logs")
|
||
|
||
# Enable checkpointing by default for resumable sessions
|
||
from framework.graph.checkpoint_config import CheckpointConfig
|
||
|
||
checkpoint_config = CheckpointConfig(
|
||
enabled=True,
|
||
checkpoint_on_node_start=False, # Only checkpoint after nodes complete
|
||
checkpoint_on_node_complete=True,
|
||
checkpoint_max_age_days=7,
|
||
async_checkpoint=True, # Non-blocking
|
||
)
|
||
|
||
# Handle runtime_config - only pass through if it's actually an AgentRuntimeConfig.
|
||
# Agents may export a RuntimeConfig (LLM settings) or queen-generated custom classes
|
||
# that would crash AgentRuntime if passed through.
|
||
runtime_config = None
|
||
if self.runtime_config is not None:
|
||
from framework.runtime.agent_runtime import AgentRuntimeConfig
|
||
|
||
if isinstance(self.runtime_config, AgentRuntimeConfig):
|
||
runtime_config = self.runtime_config
|
||
|
||
self._agent_runtime = create_agent_runtime(
|
||
graph=self.graph,
|
||
goal=self.goal,
|
||
storage_path=self._storage_path,
|
||
entry_points=entry_points,
|
||
llm=self._llm,
|
||
tools=tools,
|
||
tool_executor=tool_executor,
|
||
runtime_log_store=log_store,
|
||
checkpoint_config=checkpoint_config,
|
||
config=runtime_config,
|
||
graph_id=self.graph.id or self.agent_path.name,
|
||
accounts_prompt=accounts_prompt,
|
||
accounts_data=accounts_data,
|
||
tool_provider_map=tool_provider_map,
|
||
event_bus=event_bus,
|
||
skills_manager_config=skills_manager_config,
|
||
)
|
||
|
||
# Pass intro_message through for TUI display
|
||
self._agent_runtime.intro_message = self.intro_message
|
||
|
||
# ------------------------------------------------------------------
|
||
# Execution modes
|
||
#
|
||
# run() – One-shot, blocking execution for worker agents
|
||
# (headless CLI via ``hive run``). Validates, runs
|
||
# the graph to completion, and returns the result.
|
||
#
|
||
# start() / trigger() – Long-lived runtime for the frontend (queen).
|
||
# start() boots the runtime; trigger() sends
|
||
# non-blocking execution requests. Used by the
|
||
# server session manager and API routes.
|
||
# ------------------------------------------------------------------
|
||
|
||
async def run(
|
||
self,
|
||
input_data: dict | None = None,
|
||
session_state: dict | None = None,
|
||
entry_point_id: str | None = None,
|
||
) -> ExecutionResult:
|
||
"""One-shot execution for worker agents (headless CLI).
|
||
|
||
Validates credentials, runs the graph to completion, and returns
|
||
the result. Used by ``hive run`` and programmatic callers.
|
||
|
||
For the frontend (queen), use start() + trigger() instead.
|
||
|
||
Args:
|
||
input_data: Input data for the agent (e.g., {"lead_id": "123"})
|
||
session_state: Optional session state to resume from
|
||
entry_point_id: For multi-entry-point agents, which entry point to trigger
|
||
(defaults to first entry point or "default")
|
||
|
||
Returns:
|
||
ExecutionResult with output, path, and metrics
|
||
"""
|
||
# Validate credentials before execution (fail-fast)
|
||
validation = self.validate()
|
||
if validation.missing_credentials:
|
||
error_lines = ["Cannot run agent: missing required credentials\n"]
|
||
for warning in validation.warnings:
|
||
if "Missing " in warning:
|
||
error_lines.append(f" {warning}")
|
||
error_lines.append("\nSet the required environment variables and re-run the agent.")
|
||
error_msg = "\n".join(error_lines)
|
||
return ExecutionResult(
|
||
success=False,
|
||
error=error_msg,
|
||
)
|
||
|
||
return await self._run_with_agent_runtime(
|
||
input_data=input_data or {},
|
||
entry_point_id=entry_point_id,
|
||
session_state=session_state,
|
||
)
|
||
|
||
async def _run_with_agent_runtime(
|
||
self,
|
||
input_data: dict,
|
||
entry_point_id: str | None = None,
|
||
session_state: dict | None = None,
|
||
) -> ExecutionResult:
|
||
"""Run using AgentRuntime."""
|
||
import sys
|
||
|
||
if self._agent_runtime is None:
|
||
self._setup()
|
||
|
||
# Start runtime if not running
|
||
if not self._agent_runtime.is_running:
|
||
await self._agent_runtime.start()
|
||
|
||
# Set up stdin-based I/O for client-facing nodes in headless mode.
|
||
# When a client_facing EventLoopNode calls ask_user(), it emits
|
||
# CLIENT_INPUT_REQUESTED on the event bus and blocks. We subscribe
|
||
# a handler that prints the prompt and reads from stdin, then injects
|
||
# the user's response back into the node to unblock it.
|
||
has_client_facing = any(n.client_facing for n in self.graph.nodes)
|
||
sub_ids: list[str] = []
|
||
|
||
if has_client_facing and sys.stdin.isatty():
|
||
from framework.runtime.event_bus import EventType
|
||
|
||
runtime = self._agent_runtime
|
||
|
||
async def _handle_client_output(event):
|
||
"""Print agent output to stdout as it streams."""
|
||
content = event.data.get("content", "")
|
||
if content:
|
||
print(content, end="", flush=True)
|
||
|
||
async def _handle_input_requested(event):
|
||
"""Read user input from stdin and inject it into the node."""
|
||
import asyncio
|
||
|
||
node_id = event.node_id
|
||
try:
|
||
loop = asyncio.get_event_loop()
|
||
user_input = await loop.run_in_executor(None, input, "\n>>> ")
|
||
except EOFError:
|
||
user_input = ""
|
||
|
||
# Inject into the waiting EventLoopNode via runtime
|
||
await runtime.inject_input(node_id, user_input)
|
||
|
||
sub_ids.append(
|
||
runtime.subscribe_to_events(
|
||
event_types=[EventType.CLIENT_OUTPUT_DELTA],
|
||
handler=_handle_client_output,
|
||
)
|
||
)
|
||
sub_ids.append(
|
||
runtime.subscribe_to_events(
|
||
event_types=[EventType.CLIENT_INPUT_REQUESTED],
|
||
handler=_handle_input_requested,
|
||
)
|
||
)
|
||
|
||
# Determine entry point
|
||
if entry_point_id is None:
|
||
# Use first entry point or "default" if no entry points defined
|
||
entry_points = self._agent_runtime.get_entry_points()
|
||
if entry_points:
|
||
entry_point_id = entry_points[0].id
|
||
else:
|
||
entry_point_id = "default"
|
||
|
||
try:
|
||
# Trigger and wait for result
|
||
result = await self._agent_runtime.trigger_and_wait(
|
||
entry_point_id=entry_point_id,
|
||
input_data=input_data,
|
||
session_state=session_state,
|
||
)
|
||
|
||
# Return result or create error result
|
||
if result is not None:
|
||
return result
|
||
else:
|
||
return ExecutionResult(
|
||
success=False,
|
||
error="Execution timed out or failed to complete",
|
||
)
|
||
finally:
|
||
# Clean up subscriptions
|
||
for sub_id in sub_ids:
|
||
self._agent_runtime.unsubscribe_from_events(sub_id)
|
||
|
||
# === Runtime API ===
|
||
|
||
async def start(self) -> None:
|
||
"""Boot the agent runtime for the frontend (queen).
|
||
|
||
Pair with trigger() to send execution requests. Used by the
|
||
server session manager. For headless worker agents, use run()
|
||
instead.
|
||
"""
|
||
if self._agent_runtime is None:
|
||
self._setup()
|
||
|
||
await self._agent_runtime.start()
|
||
|
||
async def stop(self) -> None:
|
||
"""Stop the agent runtime."""
|
||
if self._agent_runtime is not None:
|
||
await self._agent_runtime.stop()
|
||
|
||
async def trigger(
|
||
self,
|
||
entry_point_id: str,
|
||
input_data: dict[str, Any],
|
||
correlation_id: str | None = None,
|
||
) -> str:
|
||
"""Send a non-blocking execution request to a running runtime.
|
||
|
||
Used by the server API routes after start(). For headless
|
||
worker agents, use run() instead.
|
||
|
||
Args:
|
||
entry_point_id: Which entry point to trigger
|
||
input_data: Input data for the execution
|
||
correlation_id: Optional ID to correlate related executions
|
||
|
||
Returns:
|
||
Execution ID for tracking
|
||
"""
|
||
if self._agent_runtime is None:
|
||
self._setup()
|
||
|
||
if not self._agent_runtime.is_running:
|
||
await self._agent_runtime.start()
|
||
|
||
return await self._agent_runtime.trigger(
|
||
entry_point_id=entry_point_id,
|
||
input_data=input_data,
|
||
correlation_id=correlation_id,
|
||
)
|
||
|
||
async def get_goal_progress(self) -> dict[str, Any]:
|
||
"""
|
||
Get goal progress across all execution streams.
|
||
|
||
Returns:
|
||
Dict with overall_progress, criteria_status, constraint_violations, etc.
|
||
"""
|
||
if self._agent_runtime is None:
|
||
self._setup()
|
||
|
||
return await self._agent_runtime.get_goal_progress()
|
||
|
||
def get_entry_points(self) -> list[EntryPointSpec]:
|
||
"""
|
||
Get all registered entry points.
|
||
|
||
Returns:
|
||
List of EntryPointSpec objects
|
||
"""
|
||
if self._agent_runtime is None:
|
||
self._setup()
|
||
|
||
return self._agent_runtime.get_entry_points()
|
||
|
||
@property
|
||
def is_running(self) -> bool:
|
||
"""Check if the agent runtime is running (for multi-entry-point agents)."""
|
||
if self._agent_runtime is None:
|
||
return False
|
||
return self._agent_runtime.is_running
|
||
|
||
def info(self) -> AgentInfo:
|
||
"""Return agent metadata (nodes, edges, goal, required tools)."""
|
||
# Extract required tools from nodes
|
||
required_tools = set()
|
||
nodes_info = []
|
||
|
||
for node in self.graph.nodes:
|
||
node_info = {
|
||
"id": node.id,
|
||
"name": node.name,
|
||
"description": node.description,
|
||
"type": node.node_type,
|
||
"input_keys": node.input_keys,
|
||
"output_keys": node.output_keys,
|
||
}
|
||
|
||
if node.tools:
|
||
required_tools.update(node.tools)
|
||
node_info["tools"] = node.tools
|
||
|
||
nodes_info.append(node_info)
|
||
|
||
edges_info = [
|
||
{
|
||
"id": edge.id,
|
||
"source": edge.source,
|
||
"target": edge.target,
|
||
"condition": edge.condition.value,
|
||
}
|
||
for edge in self.graph.edges
|
||
]
|
||
|
||
return AgentInfo(
|
||
name=self.graph.id,
|
||
description=self.graph.description,
|
||
goal_name=self.goal.name,
|
||
goal_description=self.goal.description,
|
||
node_count=len(self.graph.nodes),
|
||
edge_count=len(self.graph.edges),
|
||
nodes=nodes_info,
|
||
edges=edges_info,
|
||
entry_node=self.graph.entry_node,
|
||
terminal_nodes=self.graph.terminal_nodes,
|
||
success_criteria=[
|
||
{
|
||
"id": sc.id,
|
||
"description": sc.description,
|
||
"metric": sc.metric,
|
||
"target": sc.target,
|
||
}
|
||
for sc in self.goal.success_criteria
|
||
],
|
||
constraints=[
|
||
{"id": c.id, "description": c.description, "type": c.constraint_type}
|
||
for c in self.goal.constraints
|
||
],
|
||
required_tools=sorted(required_tools),
|
||
has_tools_module=(self.agent_path / "tools.py").exists(),
|
||
)
|
||
|
||
def validate(self) -> ValidationResult:
|
||
"""
|
||
Check agent is valid and all required tools are registered.
|
||
|
||
Returns:
|
||
ValidationResult with errors, warnings, and missing tools
|
||
"""
|
||
errors = []
|
||
warnings = []
|
||
missing_tools = []
|
||
|
||
# Validate graph structure
|
||
graph_result = self.graph.validate()
|
||
errors.extend(graph_result["errors"])
|
||
warnings.extend(graph_result["warnings"])
|
||
|
||
# Check goal has success criteria
|
||
if not self.goal.success_criteria:
|
||
warnings.append("Goal has no success criteria defined")
|
||
|
||
# Check required tools are registered
|
||
info = self.info()
|
||
for tool_name in info.required_tools:
|
||
if not self._tool_registry.has_tool(tool_name):
|
||
missing_tools.append(tool_name)
|
||
|
||
if missing_tools:
|
||
warnings.append(f"Missing tool implementations: {', '.join(missing_tools)}")
|
||
|
||
# Check credentials for required tools and node types
|
||
# Uses CredentialStoreAdapter.default() which includes Aden sync support
|
||
missing_credentials = []
|
||
try:
|
||
from aden_tools.credentials.store_adapter import CredentialStoreAdapter
|
||
|
||
adapter = CredentialStoreAdapter.default()
|
||
|
||
# Check tool credentials
|
||
for _cred_name, spec in adapter.get_missing_for_tools(list(info.required_tools)):
|
||
missing_credentials.append(spec.env_var)
|
||
affected_tools = [t for t in info.required_tools if t in spec.tools]
|
||
tools_str = ", ".join(affected_tools)
|
||
warning_msg = f"Missing {spec.env_var} for {tools_str}"
|
||
if spec.help_url:
|
||
warning_msg += f"\n Get it at: {spec.help_url}"
|
||
warnings.append(warning_msg)
|
||
|
||
# Check node type credentials (e.g., ANTHROPIC_API_KEY for LLM nodes)
|
||
node_types = list({node.node_type for node in self.graph.nodes})
|
||
for _cred_name, spec in adapter.get_missing_for_node_types(node_types):
|
||
missing_credentials.append(spec.env_var)
|
||
affected_types = [t for t in node_types if t in spec.node_types]
|
||
types_str = ", ".join(affected_types)
|
||
warning_msg = f"Missing {spec.env_var} for {types_str} nodes"
|
||
if spec.help_url:
|
||
warning_msg += f"\n Get it at: {spec.help_url}"
|
||
warnings.append(warning_msg)
|
||
except ImportError:
|
||
# aden_tools not installed - fall back to direct check
|
||
has_llm_nodes = any(
|
||
node.node_type in ("event_loop", "gcu") for node in self.graph.nodes
|
||
)
|
||
if has_llm_nodes:
|
||
api_key_env = self._get_api_key_env_var(self.model)
|
||
if api_key_env and not os.environ.get(api_key_env):
|
||
if api_key_env not in missing_credentials:
|
||
missing_credentials.append(api_key_env)
|
||
warnings.append(
|
||
f"Agent has LLM nodes but {api_key_env} not set (model: {self.model})"
|
||
)
|
||
|
||
return ValidationResult(
|
||
valid=len(errors) == 0,
|
||
errors=errors,
|
||
warnings=warnings,
|
||
missing_tools=missing_tools,
|
||
missing_credentials=missing_credentials,
|
||
)
|
||
|
||
async def can_handle(
|
||
self, request: dict, llm: LLMProvider | None = None
|
||
) -> "CapabilityResponse":
|
||
"""
|
||
Ask the agent if it can handle this request.
|
||
|
||
Uses LLM to evaluate the request against the agent's goal and capabilities.
|
||
|
||
Args:
|
||
request: The request to evaluate
|
||
llm: LLM provider to use (uses self._llm if not provided)
|
||
|
||
Returns:
|
||
CapabilityResponse with level, confidence, and reasoning
|
||
"""
|
||
from framework.runner.protocol import CapabilityLevel, CapabilityResponse
|
||
|
||
# Use provided LLM or set up our own
|
||
eval_llm = llm
|
||
if eval_llm is None:
|
||
if self._llm is None:
|
||
self._setup()
|
||
eval_llm = self._llm
|
||
|
||
# If still no LLM (mock mode), do keyword matching
|
||
if eval_llm is None:
|
||
return self._keyword_capability_check(request)
|
||
|
||
# Build context about this agent
|
||
info = self.info()
|
||
agent_context = f"""Agent: {info.name}
|
||
Goal: {info.goal_name}
|
||
Description: {info.goal_description}
|
||
|
||
What this agent does:
|
||
{info.description}
|
||
|
||
Nodes in the workflow:
|
||
{chr(10).join(f"- {n['name']}: {n['description']}" for n in info.nodes[:5])}
|
||
{"..." if len(info.nodes) > 5 else ""}
|
||
"""
|
||
|
||
# Ask LLM to evaluate
|
||
prompt = f"""You are evaluating whether an agent can handle a request.
|
||
|
||
{agent_context}
|
||
|
||
Request to evaluate:
|
||
{json.dumps(request, indent=2)}
|
||
|
||
Evaluate how well this agent can handle this request. Consider:
|
||
1. Does the request match what this agent is designed to do?
|
||
2. Does the agent have the required capabilities?
|
||
3. How confident are you in this assessment?
|
||
|
||
Respond with JSON only:
|
||
{{
|
||
"level": "best_fit" | "can_handle" | "uncertain" | "cannot_handle",
|
||
"confidence": 0.0 to 1.0,
|
||
"reasoning": "Brief explanation",
|
||
"estimated_steps": number or null
|
||
}}"""
|
||
|
||
try:
|
||
response = await eval_llm.acomplete(
|
||
messages=[{"role": "user", "content": prompt}],
|
||
system="You are a capability evaluator. Respond with JSON only.",
|
||
max_tokens=256,
|
||
)
|
||
|
||
# Parse response
|
||
import re
|
||
|
||
json_match = re.search(r"\{[^{}]*\}", response.content, re.DOTALL)
|
||
if json_match:
|
||
data = json.loads(json_match.group())
|
||
level_map = {
|
||
"best_fit": CapabilityLevel.BEST_FIT,
|
||
"can_handle": CapabilityLevel.CAN_HANDLE,
|
||
"uncertain": CapabilityLevel.UNCERTAIN,
|
||
"cannot_handle": CapabilityLevel.CANNOT_HANDLE,
|
||
}
|
||
return CapabilityResponse(
|
||
agent_name=info.name,
|
||
level=level_map.get(data.get("level", "uncertain"), CapabilityLevel.UNCERTAIN),
|
||
confidence=float(data.get("confidence", 0.5)),
|
||
reasoning=data.get("reasoning", ""),
|
||
estimated_steps=data.get("estimated_steps"),
|
||
)
|
||
except Exception:
|
||
# Fall back to keyword matching on error
|
||
pass
|
||
|
||
return self._keyword_capability_check(request)
|
||
|
||
def _keyword_capability_check(self, request: dict) -> "CapabilityResponse":
|
||
"""Simple keyword-based capability check (fallback when no LLM)."""
|
||
from framework.runner.protocol import CapabilityLevel, CapabilityResponse
|
||
|
||
info = self.info()
|
||
request_str = json.dumps(request).lower()
|
||
description_lower = info.description.lower()
|
||
goal_lower = info.goal_description.lower()
|
||
|
||
# Check for keyword matches
|
||
matches = 0
|
||
keywords = request_str.split()
|
||
for keyword in keywords:
|
||
if len(keyword) > 3: # Skip short words
|
||
if keyword in description_lower or keyword in goal_lower:
|
||
matches += 1
|
||
|
||
# Determine level based on matches
|
||
match_ratio = matches / max(len(keywords), 1)
|
||
if match_ratio > 0.3:
|
||
level = CapabilityLevel.CAN_HANDLE
|
||
confidence = min(0.7, match_ratio + 0.3)
|
||
elif match_ratio > 0.1:
|
||
level = CapabilityLevel.UNCERTAIN
|
||
confidence = 0.4
|
||
else:
|
||
level = CapabilityLevel.CANNOT_HANDLE
|
||
confidence = 0.6
|
||
|
||
return CapabilityResponse(
|
||
agent_name=info.name,
|
||
level=level,
|
||
confidence=confidence,
|
||
reasoning=f"Keyword match ratio: {match_ratio:.2f}",
|
||
estimated_steps=info.node_count if level != CapabilityLevel.CANNOT_HANDLE else None,
|
||
)
|
||
|
||
async def receive_message(self, message: "AgentMessage") -> "AgentMessage":
|
||
"""
|
||
Handle a message from the orchestrator or another agent.
|
||
|
||
Args:
|
||
message: The incoming message
|
||
|
||
Returns:
|
||
Response message
|
||
"""
|
||
from framework.runner.protocol import MessageType
|
||
|
||
info = self.info()
|
||
|
||
# Handle capability check
|
||
if message.type == MessageType.CAPABILITY_CHECK:
|
||
capability = await self.can_handle(message.content)
|
||
return message.reply(
|
||
from_agent=info.name,
|
||
content={
|
||
"level": capability.level.value,
|
||
"confidence": capability.confidence,
|
||
"reasoning": capability.reasoning,
|
||
"estimated_steps": capability.estimated_steps,
|
||
},
|
||
type=MessageType.CAPABILITY_RESPONSE,
|
||
)
|
||
|
||
# Handle request - run the agent
|
||
if message.type == MessageType.REQUEST:
|
||
result = await self.run(message.content)
|
||
return message.reply(
|
||
from_agent=info.name,
|
||
content={
|
||
"success": result.success,
|
||
"output": result.output,
|
||
"path": result.path,
|
||
"error": result.error,
|
||
},
|
||
type=MessageType.RESPONSE,
|
||
)
|
||
|
||
# Handle handoff - another agent is passing work
|
||
if message.type == MessageType.HANDOFF:
|
||
# Extract context from handoff and run
|
||
context = message.content.get("context", {})
|
||
context["_handoff_from"] = message.from_agent
|
||
context["_handoff_reason"] = message.content.get("reason", "")
|
||
result = await self.run(context)
|
||
return message.reply(
|
||
from_agent=info.name,
|
||
content={
|
||
"success": result.success,
|
||
"output": result.output,
|
||
"handoff_handled": True,
|
||
},
|
||
type=MessageType.RESPONSE,
|
||
)
|
||
|
||
# Unknown message type
|
||
return message.reply(
|
||
from_agent=info.name,
|
||
content={"error": f"Unknown message type: {message.type}"},
|
||
type=MessageType.RESPONSE,
|
||
)
|
||
|
||
@classmethod
|
||
async def setup_as_secondary(
|
||
cls,
|
||
agent_path: str | Path,
|
||
runtime: AgentRuntime,
|
||
graph_id: str | None = None,
|
||
) -> str:
|
||
"""Load an agent and register it as a secondary graph on *runtime*.
|
||
|
||
Uses :meth:`AgentRunner.load` to parse the agent, then calls
|
||
:meth:`AgentRuntime.add_graph` with the extracted graph, goal,
|
||
and entry points.
|
||
|
||
Args:
|
||
agent_path: Path to the agent directory
|
||
runtime: The running AgentRuntime to attach to
|
||
graph_id: Optional graph identifier (defaults to directory name)
|
||
|
||
Returns:
|
||
The graph_id used for registration
|
||
"""
|
||
agent_path = Path(agent_path)
|
||
runner = cls.load(agent_path)
|
||
gid = graph_id or agent_path.name
|
||
|
||
# Build entry points
|
||
entry_points: dict[str, EntryPointSpec] = {}
|
||
if runner.graph.entry_node:
|
||
entry_points["default"] = EntryPointSpec(
|
||
id="default",
|
||
name="Default",
|
||
entry_node=runner.graph.entry_node,
|
||
trigger_type="manual",
|
||
isolation_level="shared",
|
||
)
|
||
await runtime.add_graph(
|
||
graph_id=gid,
|
||
graph=runner.graph,
|
||
goal=runner.goal,
|
||
entry_points=entry_points,
|
||
)
|
||
return gid
|
||
|
||
def cleanup(self) -> None:
|
||
"""Clean up resources (synchronous)."""
|
||
# Clean up MCP client connections
|
||
self._tool_registry.cleanup()
|
||
|
||
if self._temp_dir:
|
||
self._temp_dir.cleanup()
|
||
self._temp_dir = None
|
||
|
||
async def cleanup_async(self) -> None:
|
||
"""Clean up resources (asynchronous)."""
|
||
# Stop agent runtime if running
|
||
if self._agent_runtime is not None and self._agent_runtime.is_running:
|
||
await self._agent_runtime.stop()
|
||
|
||
# Run synchronous cleanup
|
||
self.cleanup()
|
||
|
||
async def __aenter__(self) -> "AgentRunner":
|
||
"""Context manager entry."""
|
||
self._setup()
|
||
if self._agent_runtime is not None:
|
||
await self._agent_runtime.start()
|
||
return self
|
||
|
||
async def __aexit__(self, *args) -> None:
|
||
"""Context manager exit."""
|
||
await self.cleanup_async()
|
||
|
||
def __del__(self) -> None:
|
||
"""Destructor - cleanup temp dir."""
|
||
self.cleanup()
|