Agents

import helix

agent = helix.Agent(
    name="Analyst",
    role="Senior data analyst",
    goal="Analyze datasets and produce concise summaries.",

    # Optional: rich background context that shapes agent behaviour
    backstory=(
        "You have 8 years of experience in financial data analysis. "
        "You prefer bullet-point summaries over long prose."
    ),

    # Model selection with automatic fallback
    model=helix.ModelConfig(
        primary="gpt-4o",
        fallback_chain=["gpt-4o-mini", "gemini-2.0-flash"],
        temperature=0.3,
    ),

    # Hard cost limit
    budget=helix.BudgetConfig(budget_usd=1.00),
    mode=helix.AgentMode.PRODUCTION,

    # Memory
    memory=helix.MemoryConfig(short_term_limit=20),

    # Semantic caching (cost reduction on repeated/similar queries)
    cache=helix.CacheConfig(enabled=True, semantic_threshold=0.92),
)

result = helix.run(agent, "Summarize last quarter's sales trends.")

AgentResult fields: output, cost_usd, steps, model_used, cache_hits, cache_savings_usd, tool_calls, run_id, duration_s, trace.

Agents also expose LangChain-compatible aliases: agent.invoke(task) (sync) and await agent.ainvoke(task) (async), both equivalent to helix.run() / await agent.run().