arcana-agent 0.1.0b9

Agent Runtime for production — LLM-native execution with tools, budget, and trace built in.

Arcana

Agent runtime that lets LLMs think, not just execute.

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The Problem

Most agent frameworks treat LLMs as unreliable workers that need a manager watching every step. They force rigid formats -- ReAct loops, JSON command schemas, mechanical retries -- and dump entire tool catalogs into every prompt. The result is high ceremony, low capability. The framework spends its complexity constraining the LLM instead of releasing it.

The Arcana Approach

Arcana is an operating system for LLM agents, not a pipeline. The LLM decides strategy; the runtime provides services -- budget enforcement, tool dispatch, trace recording, context management. The framework never interprets LLM output: raw facts (TurnFacts) and runtime assessment (TurnAssessment) are kept visibly separate. Eight design principles and four prohibitions, codified in a Constitution, govern every line of code.

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Quick Start

pip install arcana-agent

import arcana

result = await arcana.run("Summarize this article", api_key="sk-xxx")
print(result.output)
print(f"Cost: ${result.cost_usd:.4f} | Tokens: {result.tokens_used}")

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Core Features

Tools with Affordances

Tools declare when and why, not just how. The LLM reasons about whether to call a tool, not just how.

@arcana.tool(
    when_to_use="When you need to do math calculations",
    what_to_expect="Returns the numeric result as a string",
    failure_meaning="The expression was malformed",
)
def calc(expression: str) -> str:
    return str(eval(expression))

result = await arcana.run("What is 15 * 37 + 89?", tools=[calc], api_key="sk-xxx")

Runtime

Create once at startup, use across your entire application. Holds providers, tools, budget, and trace as long-lived resources.

runtime = arcana.Runtime(
    providers={"deepseek": "sk-xxx", "openai": "sk-proj-xxx"},
    tools=[calc, web_search],
    budget=arcana.Budget(max_cost_usd=5.0),
    trace=True,
)

result = await runtime.run("Analyze recent trends in quantum computing")

Interactive Chat

Multi-turn sessions with persistent history, shared budget, and context compression.

async with runtime.chat() as c:
    r = await c.send("What are the main themes in this dataset?")
    r = await c.send("Expand on the second theme")
    print(c.total_cost_usd)

Ask User

The LLM can ask clarifying questions mid-execution. If no handler is provided, it proceeds with best judgment -- interaction is a capability, not a dependency.

result = await runtime.run(
    "Book a restaurant for dinner",
    input_handler=lambda q: input(f"Agent asks: {q}\n> "),
)

Structured Output

Return validated Pydantic instances instead of raw text.

from pydantic import BaseModel

class Summary(BaseModel):
    title: str
    key_points: list[str]
    sentiment: str

result = await arcana.run(
    "Summarize this article",
    response_format=Summary,
    api_key="sk-xxx",
)
print(result.output.title)       # str
print(result.output.key_points)  # list[str]

Multimodal

Pass images alongside text. URLs, local file paths, and data URIs all work.

result = await arcana.run(
    "Describe what you see in this image",
    images=["https://example.com/photo.jpg"],
    provider="openai",
    api_key="sk-proj-xxx",
)

Multi-Agent Teams

Runtime provides communication and budget. Agents decide strategy -- no forced hierarchy.

result = await runtime.team(
    "Design a landing page for an AI product",
    agents=[
        arcana.AgentConfig(name="designer", prompt="You are a senior UX designer."),
        arcana.AgentConfig(name="copywriter", prompt="You are a conversion copywriter."),
        arcana.AgentConfig(name="critic", prompt="You find weaknesses and suggest improvements."),
    ],
    max_rounds=3,
)

Graph Orchestration

For workflows that need explicit state machines. Available when you need it, never forced.

from arcana import StateGraph, START, END

graph = runtime.graph(state_schema=MyState)
graph.add_node("research", research_fn)
graph.add_node("write", write_fn)
graph.add_edge(START, "research")
graph.add_edge("research", "write")
graph.add_edge("write", END)

app = graph.compile()
result = await app.ainvoke(initial_state)

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What Makes Arcana Different

	LangChain	CrewAI	AutoGPT	Arcana
LLM autonomy	Framework-driven chains	Role-locked agents	Fully autonomous, no guardrails	LLM decides strategy within runtime boundaries
Token efficiency	Full context every call	Full prompt per agent	Unbounded context growth	Working-set discipline -- only what this step needs
Thinking signals	Ignored	Ignored	Ignored	Runtime listens to thinking for confidence, never constrains
Tool management	All tools in every prompt	Per-agent tool sets	All tools always	Dynamic per-turn exposure with affordances
User interaction	Not built in	Not built in	Not built in	ask_user built-in, graceful fallback if no handler
Default path	Chain/graph required	Crew required	Agent loop always	Direct answer when possible, agent loop when needed

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Providers

DeepSeek | OpenAI | Anthropic | Google Gemini | Kimi (Moonshot) | GLM (Zhipu) | MiniMax | Ollama

All providers use a single OpenAI-compatible adapter. Adding a new provider is one function call.

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Documentation

Guide	Description
Quick Start	Installation through deployment
Configuration	Full configuration reference
Providers	Provider setup and fallback chains
API Reference	Public API documentation
Architecture	System design and internals
Constitution	Design principles and prohibitions
Examples	Runnable code examples
Changelog	Release history

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Installation

pip install arcana-agent                   # Core (DeepSeek, OpenAI)
pip install arcana-agent[anthropic]        # + Claude support
pip install arcana-agent[gemini]           # + Gemini support
pip install arcana-agent[all-providers]    # All providers
pip install arcana-agent[ui]              # + Trace Web UI

Or with uv:

uv add arcana-agent
uv add arcana-agent --extra all-providers

Requires Python 3.11+.

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License

MIT