Timothy @aden
1344 lines
48 KiB
Python
1344 lines
48 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 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_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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validate_agent_credentials,
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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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AsyncEntryPointSpec,
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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.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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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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def get_claude_code_token() -> str | None:
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"""
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Get the OAuth token from Claude Code subscription.
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Reads from ~/.claude/.credentials.json which is created by the
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Claude Code CLI when users authenticate with their subscription.
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Returns:
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The access token if available, None otherwise.
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"""
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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) as f:
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creds = json.load(f)
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return creds.get("claudeAiOauth", {}).get("accessToken")
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except (json.JSONDecodeError, OSError):
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return None
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@dataclass
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class AgentInfo:
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"""Information about an exported agent."""
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name: str
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description: str
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goal_name: str
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goal_description: str
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node_count: int
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edge_count: int
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nodes: list[dict]
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edges: list[dict]
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entry_node: str
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terminal_nodes: list[str]
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success_criteria: list[dict]
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constraints: list[dict]
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required_tools: list[str]
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has_tools_module: bool
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# Multi-entry-point support
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async_entry_points: list[dict] = field(default_factory=list)
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is_multi_entry_point: bool = False
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@dataclass
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class ValidationResult:
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"""Result of agent validation."""
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valid: bool
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errors: list[str] = field(default_factory=list)
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warnings: list[str] = field(default_factory=list)
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missing_tools: list[str] = field(default_factory=list)
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missing_credentials: list[str] = field(default_factory=list)
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def load_agent_export(data: str | dict) -> tuple[GraphSpec, Goal]:
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"""
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Load GraphSpec and Goal from export_graph() output.
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Args:
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data: JSON string or dict from export_graph()
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Returns:
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Tuple of (GraphSpec, Goal)
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"""
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if isinstance(data, str):
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data = json.loads(data)
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# Extract graph and goal
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graph_data = data.get("graph", {})
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goal_data = data.get("goal", {})
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# Build NodeSpec objects
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nodes = []
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for node_data in graph_data.get("nodes", []):
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nodes.append(NodeSpec(**node_data))
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# Build EdgeSpec objects
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edges = []
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for edge_data in graph_data.get("edges", []):
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condition_str = edge_data.get("condition", "on_success")
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condition_map = {
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"always": EdgeCondition.ALWAYS,
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"on_success": EdgeCondition.ON_SUCCESS,
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"on_failure": EdgeCondition.ON_FAILURE,
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"conditional": EdgeCondition.CONDITIONAL,
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"llm_decide": EdgeCondition.LLM_DECIDE,
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}
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edge = EdgeSpec(
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id=edge_data["id"],
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source=edge_data["source"],
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target=edge_data["target"],
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condition=condition_map.get(condition_str, EdgeCondition.ON_SUCCESS),
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condition_expr=edge_data.get("condition_expr"),
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priority=edge_data.get("priority", 0),
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input_mapping=edge_data.get("input_mapping", {}),
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)
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edges.append(edge)
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# Build AsyncEntryPointSpec objects for multi-entry-point support
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async_entry_points = []
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for aep_data in graph_data.get("async_entry_points", []):
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async_entry_points.append(
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AsyncEntryPointSpec(
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id=aep_data["id"],
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name=aep_data.get("name", aep_data["id"]),
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entry_node=aep_data["entry_node"],
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trigger_type=aep_data.get("trigger_type", "manual"),
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trigger_config=aep_data.get("trigger_config", {}),
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isolation_level=aep_data.get("isolation_level", "shared"),
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priority=aep_data.get("priority", 0),
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max_concurrent=aep_data.get("max_concurrent", 10),
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)
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)
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# Build GraphSpec
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graph = GraphSpec(
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id=graph_data.get("id", "agent-graph"),
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goal_id=graph_data.get("goal_id", ""),
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version=graph_data.get("version", "1.0.0"),
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entry_node=graph_data.get("entry_node", ""),
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entry_points=graph_data.get("entry_points", {}), # Support pause/resume architecture
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async_entry_points=async_entry_points, # Support multi-entry-point agents
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terminal_nodes=graph_data.get("terminal_nodes", []),
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pause_nodes=graph_data.get("pause_nodes", []), # Support pause/resume architecture
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nodes=nodes,
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edges=edges,
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max_steps=graph_data.get("max_steps", 100),
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max_retries_per_node=graph_data.get("max_retries_per_node", 3),
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description=graph_data.get("description", ""),
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)
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# Build Goal
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from framework.graph.goal import Constraint, SuccessCriterion
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success_criteria = []
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for sc_data in goal_data.get("success_criteria", []):
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success_criteria.append(
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SuccessCriterion(
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id=sc_data["id"],
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description=sc_data["description"],
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metric=sc_data.get("metric", ""),
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target=sc_data.get("target", ""),
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weight=sc_data.get("weight", 1.0),
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)
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)
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constraints = []
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for c_data in goal_data.get("constraints", []):
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constraints.append(
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Constraint(
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id=c_data["id"],
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description=c_data["description"],
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constraint_type=c_data.get("constraint_type", "hard"),
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category=c_data.get("category", "safety"),
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check=c_data.get("check", ""),
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)
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)
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goal = Goal(
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id=goal_data.get("id", ""),
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name=goal_data.get("name", ""),
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description=goal_data.get("description", ""),
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success_criteria=success_criteria,
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constraints=constraints,
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)
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return graph, goal
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class AgentRunner:
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"""
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Loads and runs exported agents with minimal boilerplate.
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Handles:
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- Loading graph and goal from agent.json
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- Auto-discovering tools from tools.py
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- Setting up Runtime, LLM, and executor
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- Executing with dynamic edge traversal
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Usage:
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# Simple usage
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runner = AgentRunner.load("exports/outbound-sales-agent")
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result = await runner.run({"lead_id": "123"})
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# With context manager
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async with AgentRunner.load("exports/outbound-sales-agent") as runner:
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result = await runner.run({"lead_id": "123"})
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# With custom tools
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runner = AgentRunner.load("exports/outbound-sales-agent")
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runner.register_tool("my_tool", my_tool_func)
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result = await runner.run({"lead_id": "123"})
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"""
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@staticmethod
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def _resolve_default_model() -> str:
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"""Resolve the default model from ~/.hive/configuration.json."""
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return get_preferred_model()
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def __init__(
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self,
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agent_path: Path,
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graph: GraphSpec,
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goal: Goal,
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mock_mode: bool = False,
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storage_path: Path | None = None,
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model: str | None = None,
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intro_message: str = "",
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runtime_config: "AgentRuntimeConfig | None" = None,
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):
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"""
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Initialize the runner (use AgentRunner.load() instead).
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Args:
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agent_path: Path to agent folder
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graph: Loaded GraphSpec object
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goal: Loaded Goal object
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mock_mode: If True, use mock LLM responses
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storage_path: Path for runtime storage (defaults to temp)
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model: Model to use (reads from agent config or ~/.hive/configuration.json if None)
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intro_message: Optional greeting shown to user on TUI load
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runtime_config: Optional AgentRuntimeConfig (webhook settings, etc.)
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"""
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self.agent_path = agent_path
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self.graph = graph
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self.goal = goal
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self.mock_mode = mock_mode
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self.model = model or self._resolve_default_model()
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self.intro_message = intro_message
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self.runtime_config = runtime_config
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# Set up storage
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if storage_path:
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self._storage_path = storage_path
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self._temp_dir = None
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else:
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# Use persistent storage in ~/.hive/agents/{agent_name}/ per RUNTIME_LOGGING.md spec
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home = Path.home()
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default_storage = home / ".hive" / "agents" / agent_path.name
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default_storage.mkdir(parents=True, exist_ok=True)
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self._storage_path = default_storage
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self._temp_dir = None
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# Load HIVE_CREDENTIAL_KEY from shell config if not in env.
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# Must happen before MCP subprocesses are spawned so they inherit it.
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_ensure_credential_key_env()
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# Initialize components
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self._tool_registry = ToolRegistry()
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self._llm: LLMProvider | None = None
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self._approval_callback: Callable | None = None
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# AgentRuntime — unified execution path for all agents
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self._agent_runtime: AgentRuntime | None = None
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self._uses_async_entry_points = self.graph.has_async_entry_points()
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# Validate credentials before spawning MCP servers.
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# Fails fast with actionable guidance — no MCP noise on screen.
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self._validate_credentials()
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# Auto-discover tools from tools.py
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tools_path = agent_path / "tools.py"
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if tools_path.exists():
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self._tool_registry.discover_from_module(tools_path)
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# Auto-discover MCP servers from mcp_servers.json
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mcp_config_path = agent_path / "mcp_servers.json"
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if mcp_config_path.exists():
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self._load_mcp_servers_from_config(mcp_config_path)
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def _validate_credentials(self) -> None:
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"""Check that required credentials are available before spawning MCP servers.
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Raises CredentialError with actionable guidance if any are missing.
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"""
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validate_agent_credentials(self.graph.nodes)
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@staticmethod
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def _import_agent_module(agent_path: Path):
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"""Import an agent package from its directory path.
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Tries package import first (works when exports/ is on sys.path,
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which cli.py:_configure_paths() ensures). Falls back to direct
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file import of agent.py via importlib.util.
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"""
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import importlib
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package_name = agent_path.name
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# Try importing as a package (works when exports/ is on sys.path)
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try:
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return importlib.import_module(package_name)
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except ImportError:
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pass
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# Fallback: import agent.py directly via file path
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import importlib.util
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agent_py = agent_path / "agent.py"
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if not agent_py.exists():
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raise FileNotFoundError(
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f"No importable agent found at {agent_path}. "
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f"Expected a Python package with agent.py."
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)
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spec = importlib.util.spec_from_file_location(
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f"{package_name}.agent",
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agent_py,
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submodule_search_locations=[str(agent_path)],
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)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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@classmethod
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def load(
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cls,
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agent_path: str | Path,
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mock_mode: bool = False,
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storage_path: Path | None = None,
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model: str | None = None,
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) -> "AgentRunner":
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"""
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Load an agent from an export folder.
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Imports the agent's Python package and reads module-level variables
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(goal, nodes, edges, etc.) to build a GraphSpec. Falls back to
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agent.json if no Python module is found.
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Args:
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agent_path: Path to agent folder
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mock_mode: If True, use mock LLM responses
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storage_path: Path for runtime storage (defaults to ~/.hive/agents/{name})
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model: LLM model to use (reads from agent's default_config if None)
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Returns:
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AgentRunner instance ready to run
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"""
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agent_path = Path(agent_path)
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# Try loading from Python module first (code-based agents)
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agent_py = agent_path / "agent.py"
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if agent_py.exists():
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agent_module = cls._import_agent_module(agent_path)
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goal = getattr(agent_module, "goal", None)
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nodes = getattr(agent_module, "nodes", None)
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edges = getattr(agent_module, "edges", None)
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if goal is None or nodes is None or edges is None:
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raise ValueError(
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f"Agent at {agent_path} must define 'goal', 'nodes', and 'edges' "
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f"in agent.py (or __init__.py)"
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)
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# Read model and max_tokens from agent's config if not explicitly provided
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agent_config = getattr(agent_module, "default_config", None)
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if model is None:
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if agent_config and hasattr(agent_config, "model"):
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model = agent_config.model
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if agent_config and hasattr(agent_config, "max_tokens"):
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max_tokens = agent_config.max_tokens
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else:
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hive_config = get_hive_config()
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max_tokens = hive_config.get("llm", {}).get("max_tokens", DEFAULT_MAX_TOKENS)
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# Read intro_message from agent metadata (shown on TUI load)
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agent_metadata = getattr(agent_module, "metadata", None)
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intro_message = ""
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if agent_metadata and hasattr(agent_metadata, "intro_message"):
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intro_message = agent_metadata.intro_message
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# Build GraphSpec from module-level variables
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graph_kwargs: dict = {
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"id": f"{agent_path.name}-graph",
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"goal_id": goal.id,
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"version": "1.0.0",
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"entry_node": getattr(agent_module, "entry_node", nodes[0].id),
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"entry_points": getattr(agent_module, "entry_points", {}),
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"async_entry_points": getattr(agent_module, "async_entry_points", []),
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"terminal_nodes": getattr(agent_module, "terminal_nodes", []),
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"pause_nodes": getattr(agent_module, "pause_nodes", []),
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"nodes": nodes,
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"edges": edges,
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"max_tokens": max_tokens,
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"loop_config": getattr(agent_module, "loop_config", {}),
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}
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# Only pass optional fields if explicitly defined by the agent module
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conversation_mode = getattr(agent_module, "conversation_mode", None)
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if conversation_mode is not None:
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graph_kwargs["conversation_mode"] = conversation_mode
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identity_prompt = getattr(agent_module, "identity_prompt", None)
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if identity_prompt is not None:
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graph_kwargs["identity_prompt"] = identity_prompt
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graph = GraphSpec(**graph_kwargs)
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# Read runtime config (webhook settings, etc.) if defined
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agent_runtime_config = getattr(agent_module, "runtime_config", None)
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return cls(
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agent_path=agent_path,
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graph=graph,
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goal=goal,
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mock_mode=mock_mode,
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storage_path=storage_path,
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model=model,
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intro_message=intro_message,
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runtime_config=agent_runtime_config,
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)
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# Fallback: load from agent.json (legacy JSON-based agents)
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agent_json_path = agent_path / "agent.json"
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if not agent_json_path.exists():
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raise FileNotFoundError(f"No agent.py or agent.json found in {agent_path}")
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with open(agent_json_path) as f:
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graph, goal = load_agent_export(f.read())
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return cls(
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agent_path=agent_path,
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graph=graph,
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goal=goal,
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mock_mode=mock_mode,
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storage_path=storage_path,
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model=model,
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)
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def register_tool(
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self,
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name: str,
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tool_or_func: Tool | Callable,
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executor: Callable | None = None,
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) -> None:
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"""
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Register a tool for use by the agent.
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Args:
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name: Tool name
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tool_or_func: Either a Tool object or a callable function
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executor: Executor function (required if tool_or_func is a Tool)
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"""
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if isinstance(tool_or_func, Tool):
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if executor is None:
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raise ValueError("executor required when registering a Tool object")
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self._tool_registry.register(name, tool_or_func, executor)
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else:
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# It's a function, auto-generate Tool
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self._tool_registry.register_function(tool_or_func, name=name)
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def register_tools_from_module(self, module_path: Path) -> int:
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"""
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Auto-discover and register tools from a Python module.
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Args:
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module_path: Path to tools.py file
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Returns:
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Number of tools discovered
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"""
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return self._tool_registry.discover_from_module(module_path)
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def register_mcp_server(
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self,
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name: str,
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transport: str,
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**config_kwargs,
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) -> int:
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"""
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Register an MCP server and discover its tools.
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|
|
Args:
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name: Server name
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transport: "stdio" or "http"
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**config_kwargs: Additional configuration (command, args, url, etc.)
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Returns:
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Number of tools registered from this server
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|
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) -> 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
|
|
if 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 Claude Code subscription is configured
|
|
config = get_hive_config()
|
|
llm_config = config.get("llm", {})
|
|
use_claude_code = llm_config.get("use_claude_code_subscription", False)
|
|
|
|
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.")
|
|
|
|
if api_key:
|
|
# Use Claude Code subscription token
|
|
self._llm = LiteLLMProvider(model=self.model, api_key=api_key)
|
|
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)
|
|
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)
|
|
# 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")
|
|
|
|
# Get tools for runtime
|
|
tools = list(self._tool_registry.get_tools().values())
|
|
tool_executor = self._tool_registry.get_executor()
|
|
|
|
self._setup_agent_runtime(tools, tool_executor)
|
|
|
|
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("ollama/"):
|
|
return None # Ollama doesn't need an API key (local)
|
|
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"
|
|
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"
|
|
# Add more mappings as providers are added to LLM_CREDENTIALS
|
|
|
|
if cred_id is None:
|
|
return None
|
|
|
|
try:
|
|
from framework.credentials import CredentialStore
|
|
|
|
store = CredentialStore.with_encrypted_storage()
|
|
return store.get(cred_id)
|
|
except Exception:
|
|
return None
|
|
|
|
def _setup_agent_runtime(self, tools: list, tool_executor: Callable | None) -> None:
|
|
"""Set up multi-entry-point execution using AgentRuntime."""
|
|
# Convert AsyncEntryPointSpec to EntryPointSpec for AgentRuntime
|
|
entry_points = []
|
|
for async_ep in self.graph.async_entry_points:
|
|
ep = EntryPointSpec(
|
|
id=async_ep.id,
|
|
name=async_ep.name,
|
|
entry_node=async_ep.entry_node,
|
|
trigger_type=async_ep.trigger_type,
|
|
trigger_config=async_ep.trigger_config,
|
|
isolation_level=async_ep.isolation_level,
|
|
priority=async_ep.priority,
|
|
max_concurrent=async_ep.max_concurrent,
|
|
)
|
|
entry_points.append(ep)
|
|
|
|
# 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
|
|
)
|
|
|
|
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=self.runtime_config,
|
|
)
|
|
|
|
# Pass intro_message through for TUI display
|
|
self._agent_runtime.intro_message = self.intro_message
|
|
|
|
async def run(
|
|
self,
|
|
input_data: dict | None = None,
|
|
session_state: dict | None = None,
|
|
entry_point_id: str | None = None,
|
|
) -> ExecutionResult:
|
|
"""
|
|
Execute the agent with given input data.
|
|
|
|
Validates credentials before execution. If any required credentials
|
|
are missing, returns an error result with instructions on how to
|
|
provide them.
|
|
|
|
For single-entry-point agents, this is the standard execution path.
|
|
For multi-entry-point agents, you can optionally specify which entry point to use.
|
|
|
|
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:
|
|
"""Start the agent runtime."""
|
|
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:
|
|
"""
|
|
Trigger execution at a specific entry point (non-blocking).
|
|
|
|
Returns execution ID for tracking.
|
|
|
|
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
|
|
]
|
|
|
|
# Build async entry points info
|
|
async_entry_points_info = [
|
|
{
|
|
"id": ep.id,
|
|
"name": ep.name,
|
|
"entry_node": ep.entry_node,
|
|
"trigger_type": ep.trigger_type,
|
|
"isolation_level": ep.isolation_level,
|
|
"max_concurrent": ep.max_concurrent,
|
|
}
|
|
for ep in self.graph.async_entry_points
|
|
]
|
|
|
|
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(),
|
|
async_entry_points=async_entry_points_info,
|
|
is_multi_entry_point=self._uses_async_entry_points,
|
|
)
|
|
|
|
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_errors = self.graph.validate()
|
|
errors.extend(graph_errors)
|
|
|
|
# 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 == "event_loop" 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,
|
|
)
|
|
|
|
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()
|