mirror of
https://github.com/hanzoai/agents.git
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zeekayandGitHub
5b5cf9e9db
Transforms hanzoai/agents from a marketplace config repo into a full agent orchestration platform by merging the AgentField infrastructure: - Go control plane (K8s-like agent orchestration, service mesh, DAG workflows) - Python SDK (FastAPI-based agent framework, LiteLLM, memory, DID/VC) - TypeScript SDK (@hanzo/agents - Vercel AI SDK, MCP, workflows) - Go SDK (idiomatic agent builder + client) - React 19 embedded dashboard (XyFlow DAGs, Recharts, Radix UI) - Docker/K8s/Helm deployment configs - Examples: hello world, deep research, RAG, simulation, benchmarks - Functional test suite across Python/Go/TypeScript All references rebranded: AgentField → Hanzo Agents - Module: github.com/hanzoai/agents/control-plane - Python: hanzo_agents - TypeScript: @hanzo/agents - Go: github.com/hanzoai/agents/sdk/go - Env vars: HANZO_AGENTS_* - Config: hanzo-agents.yaml Previous marketplace configs moved to config/ directory.
77 lines
2.6 KiB
TypeScript
77 lines
2.6 KiB
TypeScript
import { z } from 'zod';
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// Helper: coerce string numbers to actual numbers (handles "0.8" -> 0.8)
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const coercedNumber = z.coerce.number();
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// Helper: coerce record values to numbers (for outcomeDistribution)
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const coercedNumberRecord = z.record(z.coerce.number());
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export const ScenarioInputSchema = z.object({
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scenario: z.string(),
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context: z.array(z.string()).optional()
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});
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export type ScenarioInput = z.infer<typeof ScenarioInputSchema>;
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export const ScenarioAnalysisSchema = z.object({
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entityType: z.string(),
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decisionType: z.string(),
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decisionOptions: z.array(z.string()).min(1), // At least one option
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analysis: z.string(),
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keyAttributes: z.array(z.string()).default([]) // Default to empty if missing
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});
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export type ScenarioAnalysis = z.infer<typeof ScenarioAnalysisSchema>;
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export const FactorGraphSchema = z.object({
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attributes: z.record(z.string()),
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attributeGraph: z.string(),
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samplingStrategy: z.string()
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});
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export type FactorGraph = z.infer<typeof FactorGraphSchema>;
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export const EntityProfileSchema = z.object({
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entityId: z.string(),
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attributes: z.record(z.any()),
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profileSummary: z.string()
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});
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export type EntityProfile = z.infer<typeof EntityProfileSchema>;
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export const EntityDecisionSchema = z.object({
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entityId: z.string(),
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decision: z.string(),
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confidence: coercedNumber.min(0).max(1), // Coerce "0.8" -> 0.8, clamp 0-1
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keyFactor: z.string(),
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tradeOff: z.string(),
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reasoning: z.string().optional().default('') // Default empty if missing
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});
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export type EntityDecision = z.infer<typeof EntityDecisionSchema>;
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export const SimulationInsightsSchema = z.object({
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outcomeDistribution: coercedNumberRecord, // Coerce string numbers in record
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keyInsight: z.string(),
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detailedAnalysis: z.string(),
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segmentPatterns: z.string(),
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causalDrivers: z.string()
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});
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export type SimulationInsights = z.infer<typeof SimulationInsightsSchema>;
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export const SimulationResultSchema = z.object({
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scenario: z.string(),
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context: z.array(z.string()),
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populationSize: z.number(),
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scenarioAnalysis: ScenarioAnalysisSchema,
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factorGraph: FactorGraphSchema,
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entities: z.array(EntityProfileSchema),
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decisions: z.array(EntityDecisionSchema),
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insights: SimulationInsightsSchema
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});
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export type SimulationResult = z.infer<typeof SimulationResultSchema>;
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export const RunSimulationInputSchema = z.object({
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scenario: z.string(),
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populationSize: z.number().int().positive(),
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context: z.array(z.string()).default([]),
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parallelBatchSize: z.number().int().positive().default(20),
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explorationRatio: z.number().min(0).max(1).default(0.1)
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});
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export type RunSimulationInput = z.infer<typeof RunSimulationInputSchema>;
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