378d599125
Multi-agent AI automation system with shared message bus, specialized roles (coder/researcher/reviewer), and deny-by-default security. - Config system with Pydantic validation and YAML loading - Async message bus with inter-agent delegation - LLM providers: Anthropic (Claude) and LiteLLM (DeepSeek/Kimi/MiniMax) - Tool system: registry, builtins (file/bash/web), approval engine, MCP client - Agent engine with tool-calling loop and orchestrator for multi-agent management - CLI channel (REPL) and Discord channel - Docker + Dockge deployment config - Typer CLI: chat, serve, status, agents commands Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
115 lines
3.2 KiB
Python
115 lines
3.2 KiB
Python
"""Configuration system — YAML config + Pydantic validation."""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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import yaml
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from pydantic import BaseModel, Field
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class ProviderConfig(BaseModel):
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"""Single LLM provider configuration."""
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provider: str = "anthropic"
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model: str = "claude-sonnet-4-5-20250929"
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max_tokens: int = 8192
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temperature: float = 0.3
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api_key_env: str = ""
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class LLMConfig(BaseModel):
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"""LLM providers section."""
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providers: dict[str, ProviderConfig] = Field(default_factory=dict)
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class CLIChannelConfig(BaseModel):
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enabled: bool = True
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default_agent: str = "coder"
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class DiscordChannelConfig(BaseModel):
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enabled: bool = False
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token_env: str = "DISCORD_BOT_TOKEN"
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default_agent: str = "coder"
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allowed_users: list[str] = Field(default_factory=list)
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class ChannelsConfig(BaseModel):
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cli: CLIChannelConfig = Field(default_factory=CLIChannelConfig)
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discord: DiscordChannelConfig = Field(default_factory=DiscordChannelConfig)
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class ToolsConfig(BaseModel):
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workspace: str = "./data"
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auto_approve: list[str] = Field(
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default_factory=lambda: ["read_file", "list_dir", "web_fetch", "delegate"]
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)
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require_approval: list[str] = Field(
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default_factory=lambda: ["bash", "write_file", "edit_file"]
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)
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class MCPServerConfig(BaseModel):
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"""Single MCP server configuration."""
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command: str = ""
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args: list[str] = Field(default_factory=list)
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env: dict[str, str] = Field(default_factory=dict)
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url: str = ""
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class OrchestratorConfig(BaseModel):
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max_concurrent: int = 5
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delegation_timeout: int = 120
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class AgentFileConfig(BaseModel):
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"""Parsed from agent markdown frontmatter."""
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name: str = ""
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provider: str = "anthropic"
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model: str = ""
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temperature: float = 0.3
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max_iterations: int = 30
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tools: list[str] = Field(default_factory=list)
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instructions: str = ""
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class Config(BaseModel):
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"""Top-level application config."""
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llm: LLMConfig = Field(default_factory=LLMConfig)
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channels: ChannelsConfig = Field(default_factory=ChannelsConfig)
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tools: ToolsConfig = Field(default_factory=ToolsConfig)
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mcp_servers: dict[str, MCPServerConfig] = Field(default_factory=dict)
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agents: dict[str, str] = Field(default_factory=dict)
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orchestrator: OrchestratorConfig = Field(default_factory=OrchestratorConfig)
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def load_config(path: str | Path = "config.yaml") -> Config:
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"""Load and validate config from YAML file."""
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p = Path(path)
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if not p.exists():
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return Config()
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raw = yaml.safe_load(p.read_text()) or {}
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return Config.model_validate(raw)
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def parse_agent_file(path: str | Path) -> AgentFileConfig:
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"""Parse a markdown agent definition with YAML frontmatter."""
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text = Path(path).read_text()
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if not text.startswith("---"):
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return AgentFileConfig(instructions=text)
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parts = text.split("---", 2)
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if len(parts) < 3:
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return AgentFileConfig(instructions=text)
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frontmatter = yaml.safe_load(parts[1]) or {}
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body = parts[2].strip()
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frontmatter["instructions"] = body
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return AgentFileConfig.model_validate(frontmatter)
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