---

name: agent-config-validator description: Validate AgentConfig definitions for the Agent Framework. Use when creating or modifying agent configurations to ensure correct structure, valid tool references, and proper sub-agent composition. Validates TypeScript interfaces and Python Pydantic models. allowed-tools: Read, Grep, Glob

Agent Configuration Validation

Validates AgentConfig definitions against the Agent Framework schema.

AgentConfig Schema

Copyinterface ModelConfig {
  provider: 'gemini' | 'openai' | 'anthropic' | 'ollama' | 'custom';
  model: string;           // e.g., "gpt-4o", "claude-3-haiku", "llama3.2"
  baseUrl?: string;        // For custom/Ollama endpoints
  apiKeyEnvVar?: string;   // Environment variable name for API key
}

interface AgentConfig {
  id: string;                              // Required: unique identifier
  name: string;                            // Required: display name
  type: 'llm' | 'sequential' | 'parallel' | 'loop' | 'custom';  // Required
  modelConfig: ModelConfig;                // Required for type='llm'
  description: string;                     // Required: for orchestrator routing
  instruction: string;                     // Required: system prompt
  tools: string[];                         // MCP tool IDs
  subAgents: string[];                     // Sub-agent IDs
  outputKey?: string;                      // For state passing
  maxIterations?: number;                  // For LoopAgent (default: 3)
  stateSchema?: Record<string, any>;       // Optional state definition
  createdAt: Date;
  isActive: boolean;
}

Validation Rules

Required Fields

Type-Specific Rules

LLM Agent

• Must have modelConfig with valid provider and model
• Must have instruction (system prompt)
• outputKey recommended for state passing

Sequential Agent

• Must have at least 2 agents in subAgents
• Order matters (first to last execution)

Parallel Agent

• Must have at least 2 agents in subAgents
• Each sub-agent should have unique outputKey

Loop Agent

• Must have subAgents
• Should specify maxIterations (default: 3)

Reference Validation

• Tool references: Each tool ID in tools[] must exist in MCP registry
• Sub-agent references: Each ID in subAgents[] must be a valid agent ID
• Model provider: Must be a supported provider with valid credentials

Example Validations

Valid LLM Agent

Copy{
  "id": "research-agent",
  "name": "Research Agent",
  "type": "llm",
  "modelConfig": {
    "provider": "gemini",
    "model": "gemini-2.5-flash"
  },
  "description": "Gathers and summarizes information from various sources",
  "instruction": "You are a research assistant...",
  "tools": ["web_search", "document_reader"],
  "subAgents": [],
  "outputKey": "research_results",
  "isActive": true
}

Invalid Configuration (Missing Fields)

Copy{
  "id": "broken-agent",
  "name": "Broken",
  "type": "llm"
  // MISSING: modelConfig, description, instruction
}

Validation Commands

Copy# Validate Python Pydantic model
uv run python -c "from agent.state.models import AgentConfig; AgentConfig.model_validate(config)"

# Check TypeScript interface
bun run typecheck

Common Issues

1. Missing description: Orchestrator can't route to agent
2. Invalid tool references: Tools won't load at runtime
3. Circular sub-agent references: Causes infinite loops
4. Missing outputKey in parallel: Results overwrite each other