bryan
1202 lines
42 KiB
Python
1202 lines
42 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.graph import Goal
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from framework.graph.edge import AsyncEntryPointSpec, EdgeCondition, EdgeSpec, GraphSpec
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from framework.graph.executor import ExecutionResult, GraphExecutor
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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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# Multi-entry-point runtime imports
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from framework.runtime.agent_runtime import AgentRuntime, create_agent_runtime
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from framework.runtime.core import Runtime
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from framework.runtime.execution_stream import EntryPointSpec
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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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# Configuration paths
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HIVE_CONFIG_FILE = Path.home() / ".hive" / "configuration.json"
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CLAUDE_CREDENTIALS_FILE = Path.home() / ".claude" / ".credentials.json"
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def get_hive_config() -> dict[str, Any]:
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"""Load hive configuration from ~/.hive/configuration.json."""
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if not HIVE_CONFIG_FILE.exists():
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return {}
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try:
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with open(HIVE_CONFIG_FILE) as f:
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return json.load(f)
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except (json.JSONDecodeError, OSError):
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return {}
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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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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 = "cerebras/zai-glm-4.7",
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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 - any LiteLLM-compatible model name
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(e.g., "claude-sonnet-4-20250514", "gpt-4o-mini", "gemini/gemini-pro")
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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
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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 by default
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home = Path.home()
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default_storage = home / ".hive" / "storage" / 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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# Initialize components
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self._tool_registry = ToolRegistry()
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self._runtime: Runtime | None = None
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self._llm: LLMProvider | None = None
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self._executor: GraphExecutor | None = None
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self._approval_callback: Callable | None = None
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# Multi-entry-point support (AgentRuntime)
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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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# 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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@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 = "cerebras/zai-glm-4.7",
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) -> "AgentRunner":
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"""
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Load an agent from an export folder.
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Args:
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agent_path: Path to agent folder (containing agent.json)
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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: LLM model to use (any LiteLLM-compatible model name)
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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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# Load agent.json
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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"agent.json not 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:
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# Register STDIO MCP server
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runner.register_mcp_server(
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name="tools",
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transport="stdio",
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command="python",
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args=["-m", "aden_tools.mcp_server", "--stdio"],
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cwd="/path/to/tools"
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)
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# Register HTTP MCP server
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runner.register_mcp_server(
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name="tools",
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transport="http",
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url="http://localhost:4001"
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)
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"""
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server_config = {
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"name": name,
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"transport": transport,
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**config_kwargs,
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}
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return self._tool_registry.register_mcp_server(server_config)
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def _load_mcp_servers_from_config(self, config_path: Path) -> None:
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"""Load and register MCP servers from a configuration file."""
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self._tool_registry.load_mcp_config(config_path)
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def set_approval_callback(self, callback: Callable) -> None:
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"""
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Set a callback for human-in-the-loop approval during execution.
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Args:
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callback: Function to call for approval (receives node info, returns bool)
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"""
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self._approval_callback = callback
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# If executor already exists, update it
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if self._executor is not None:
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self._executor.approval_callback = callback
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def _setup(self) -> None:
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"""Set up runtime, LLM, and executor."""
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# Set up session context for tools (workspace_id, agent_id, session_id)
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workspace_id = "default" # Could be derived from storage path
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agent_id = self.graph.id or "unknown"
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# Use "current" as a stable session_id for persistent memory
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session_id = "current"
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self._tool_registry.set_session_context(
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workspace_id=workspace_id,
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agent_id=agent_id,
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session_id=session_id,
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)
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# Create LLM provider
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# Uses LiteLLM which auto-detects the provider from model name
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if self.mock_mode:
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# Use mock LLM for testing without real API calls
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from framework.llm.mock import MockLLMProvider
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self._llm = MockLLMProvider(model=self.model)
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else:
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from framework.llm.litellm import LiteLLMProvider
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# Check if Claude Code subscription is configured
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config = get_hive_config()
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llm_config = config.get("llm", {})
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use_claude_code = llm_config.get("use_claude_code_subscription", False)
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api_key = None
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if use_claude_code:
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# Get OAuth token from Claude Code subscription
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api_key = get_claude_code_token()
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if not api_key:
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print("Warning: Claude Code subscription configured but no token found.")
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print("Run 'claude' to authenticate, then try again.")
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if api_key:
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# Use Claude Code subscription token
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self._llm = LiteLLMProvider(model=self.model, api_key=api_key)
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else:
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# Fall back to environment variable
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api_key_env = self._get_api_key_env_var(self.model)
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if api_key_env and os.environ.get(api_key_env):
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self._llm = LiteLLMProvider(model=self.model)
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elif api_key_env:
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print(f"Warning: {api_key_env} not set. LLM calls will fail.")
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print(f"Set it with: export {api_key_env}=your-api-key")
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# Get tools for executor/runtime
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tools = list(self._tool_registry.get_tools().values())
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tool_executor = self._tool_registry.get_executor()
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if self._uses_async_entry_points:
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# Multi-entry-point mode: use AgentRuntime
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self._setup_agent_runtime(tools, tool_executor)
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else:
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# Single-entry-point mode: use legacy GraphExecutor
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self._setup_legacy_executor(tools, tool_executor)
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def _get_api_key_env_var(self, model: str) -> str | None:
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"""Get the environment variable name for the API key based on model name."""
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model_lower = model.lower()
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# Map model prefixes to API key environment variables
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# LiteLLM uses these conventions
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if model_lower.startswith("cerebras/"):
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return "CEREBRAS_API_KEY"
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elif model_lower.startswith("openai/") or model_lower.startswith("gpt-"):
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return "OPENAI_API_KEY"
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elif model_lower.startswith("anthropic/") or model_lower.startswith("claude"):
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return "ANTHROPIC_API_KEY"
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elif model_lower.startswith("gemini/") or model_lower.startswith("google/"):
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return "GOOGLE_API_KEY"
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elif model_lower.startswith("mistral/"):
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return "MISTRAL_API_KEY"
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elif model_lower.startswith("groq/"):
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return "GROQ_API_KEY"
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elif model_lower.startswith("ollama/"):
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return None # Ollama doesn't need an API key (local)
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elif model_lower.startswith("azure/"):
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return "AZURE_API_KEY"
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elif model_lower.startswith("cohere/"):
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return "COHERE_API_KEY"
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elif model_lower.startswith("replicate/"):
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return "REPLICATE_API_KEY"
|
|
elif model_lower.startswith("together/"):
|
|
return "TOGETHER_API_KEY"
|
|
else:
|
|
# Default: assume OpenAI-compatible
|
|
return "OPENAI_API_KEY"
|
|
|
|
def _setup_legacy_executor(self, tools: list, tool_executor: Callable | None) -> None:
|
|
"""Set up legacy single-entry-point execution using GraphExecutor."""
|
|
# Create runtime
|
|
self._runtime = Runtime(storage_path=self._storage_path)
|
|
|
|
# Create executor
|
|
self._executor = GraphExecutor(
|
|
runtime=self._runtime,
|
|
llm=self._llm,
|
|
tools=tools,
|
|
tool_executor=tool_executor,
|
|
approval_callback=self._approval_callback,
|
|
)
|
|
|
|
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)
|
|
|
|
# Create AgentRuntime with all entry points
|
|
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,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
if self._uses_async_entry_points:
|
|
# Multi-entry-point mode: use AgentRuntime
|
|
return await self._run_with_agent_runtime(
|
|
input_data=input_data or {},
|
|
entry_point_id=entry_point_id,
|
|
)
|
|
else:
|
|
# Legacy single-entry-point mode
|
|
return await self._run_with_executor(
|
|
input_data=input_data or {},
|
|
session_state=session_state,
|
|
)
|
|
|
|
async def _run_with_executor(
|
|
self,
|
|
input_data: dict,
|
|
session_state: dict | None = None,
|
|
) -> ExecutionResult:
|
|
"""Run using legacy GraphExecutor (single entry point)."""
|
|
if self._executor is None:
|
|
self._setup()
|
|
|
|
return await self._executor.execute(
|
|
graph=self.graph,
|
|
goal=self.goal,
|
|
input_data=input_data,
|
|
session_state=session_state,
|
|
)
|
|
|
|
async def _run_with_agent_runtime(
|
|
self,
|
|
input_data: dict,
|
|
entry_point_id: str | None = None,
|
|
) -> ExecutionResult:
|
|
"""Run using AgentRuntime (multi-entry-point)."""
|
|
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()
|
|
|
|
# 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"
|
|
|
|
# Trigger and wait for result
|
|
result = await self._agent_runtime.trigger_and_wait(
|
|
entry_point_id=entry_point_id,
|
|
input_data=input_data,
|
|
)
|
|
|
|
# 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",
|
|
)
|
|
|
|
# === Multi-Entry-Point API (for agents with async_entry_points) ===
|
|
|
|
async def start(self) -> None:
|
|
"""
|
|
Start the agent runtime (for multi-entry-point agents).
|
|
|
|
This starts all registered entry points and allows concurrent execution.
|
|
For single-entry-point agents, this is a no-op.
|
|
"""
|
|
if not self._uses_async_entry_points:
|
|
return
|
|
|
|
if self._agent_runtime is None:
|
|
self._setup()
|
|
|
|
await self._agent_runtime.start()
|
|
|
|
async def stop(self) -> None:
|
|
"""
|
|
Stop the agent runtime (for multi-entry-point agents).
|
|
|
|
For single-entry-point agents, this is a no-op.
|
|
"""
|
|
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).
|
|
|
|
For multi-entry-point agents only. 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
|
|
|
|
Raises:
|
|
RuntimeError: If agent doesn't use async entry points
|
|
"""
|
|
if not self._uses_async_entry_points:
|
|
raise RuntimeError(
|
|
"trigger() is only available for multi-entry-point agents. "
|
|
"Use run() for single-entry-point agents."
|
|
)
|
|
|
|
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.
|
|
|
|
For multi-entry-point agents only.
|
|
|
|
Returns:
|
|
Dict with overall_progress, criteria_status, constraint_violations, etc.
|
|
|
|
Raises:
|
|
RuntimeError: If agent doesn't use async entry points
|
|
"""
|
|
if not self._uses_async_entry_points:
|
|
raise RuntimeError(
|
|
"get_goal_progress() is only available for multi-entry-point agents."
|
|
)
|
|
|
|
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 (for multi-entry-point agents).
|
|
|
|
Returns:
|
|
List of EntryPointSpec objects
|
|
"""
|
|
if not self._uses_async_entry_points:
|
|
return []
|
|
|
|
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 CredentialStore (encrypted files + env var fallback)
|
|
missing_credentials = []
|
|
try:
|
|
from aden_tools.credentials import CREDENTIAL_SPECS
|
|
|
|
from framework.credentials import CredentialStore
|
|
from framework.credentials.storage import (
|
|
CompositeStorage,
|
|
EncryptedFileStorage,
|
|
EnvVarStorage,
|
|
)
|
|
|
|
# Build env mapping for fallback
|
|
env_mapping = {
|
|
(spec.credential_id or name): spec.env_var
|
|
for name, spec in CREDENTIAL_SPECS.items()
|
|
}
|
|
storage = CompositeStorage(
|
|
primary=EncryptedFileStorage(),
|
|
fallbacks=[EnvVarStorage(env_mapping=env_mapping)],
|
|
)
|
|
store = CredentialStore(storage=storage)
|
|
|
|
# Build reverse mappings
|
|
tool_to_cred: dict[str, str] = {}
|
|
node_type_to_cred: dict[str, str] = {}
|
|
for cred_name, spec in CREDENTIAL_SPECS.items():
|
|
for tool_name in spec.tools:
|
|
tool_to_cred[tool_name] = cred_name
|
|
for nt in spec.node_types:
|
|
node_type_to_cred[nt] = cred_name
|
|
|
|
# Check tool credentials
|
|
checked: set[str] = set()
|
|
for tool_name in info.required_tools:
|
|
cred_name = tool_to_cred.get(tool_name)
|
|
if cred_name is None or cred_name in checked:
|
|
continue
|
|
checked.add(cred_name)
|
|
spec = CREDENTIAL_SPECS[cred_name]
|
|
cred_id = spec.credential_id or cred_name
|
|
if spec.required and not store.is_available(cred_id):
|
|
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 nt in node_types:
|
|
cred_name = node_type_to_cred.get(nt)
|
|
if cred_name is None or cred_name in checked:
|
|
continue
|
|
checked.add(cred_name)
|
|
spec = CREDENTIAL_SPECS[cred_name]
|
|
cred_id = spec.credential_id or cred_name
|
|
if spec.required and not store.is_available(cred_id):
|
|
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 ("llm_generate", "llm_tool_use") 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 = eval_llm.complete(
|
|
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 - for multi-entry-point agents)."""
|
|
# 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()
|
|
# Start runtime for multi-entry-point agents
|
|
if self._uses_async_entry_points and 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()
|