rpcs1 0.1.0

RPCS-1 Agent Tuner — configuration framework for AI agents grounded in receiver dynamics

rpcs1 — AI Agent Parameter Tuner

Configure AI agents that don't oscillate, overload, or freeze.

Stop debugging agent failures case-by-case. The RPCS-1 SDK translates your agent's task and environment into specific platform parameters — grounded in receiver dynamics from cognitive systems research.

Built on the matching principle: agents in fast-changing environments need short attention windows; agents in stable environments benefit from longer integration.

Installation

pip install rpcs1
# With Anthropic integration:
pip install rpcs1[anthropic]
# With OpenAI integration:
pip install rpcs1[openai]

Quick Start

from rpcs1 import recommend_params

config = recommend_params(
    task_description="Customer support agent handling refund requests",
    environment_entropy="dynamic",           # stable | moderate | dynamic | chaotic
    environment_predictability="somewhat_predictable",
    stakes="high",                           # low | medium | high | catastrophic
    context_relevance="medium",              # short | medium | long
    commitment_style="cautious",             # decisive | balanced | cautious
    target_platform="anthropic",             # anthropic | openai | open_source | generic
    domain="customer_support",              # optional
)

print(config.platform_parameters.temperature)        # e.g. 0.52
print(config.platform_parameters.model_recommendation)  # e.g. 'claude-sonnet-4-6'
print(config.predicted_regime)                       # 'stable'
print(config.reasoning)                              # cites Matching Principle (Pred-09-5)

The Five Receiver Primitives

Every recommendation is driven by five receiver primitives from RPCS-1:

Primitive	Name	What it controls
TI	Temporal Integration	How much history to integrate (context window strategy)
SG	Signal Gain	How strongly to amplify signals (temperature)
FT	Filtering Threshold	How conservatively to gate action (tool use strategy)
UE	Update Elasticity	How readily to revise the model (retry strategy)
AR	Ambiguity Resolution	How aggressively to resolve uncertainty

Stability Regimes

The SDK predicts and warns about four stability regimes:

• stable — balanced operation, well-matched to environment
• near_oscillation — high TI + high SG → agent revisits decisions, won't commit
• near_overload — low TI + high SG → agent acts on insufficient information
• near_freeze — low UE + high FT → agent hedges endlessly, won't act

Anthropic Integration

from rpcs1 import recommend_params
from rpcs1.integrations.anthropic import to_anthropic_params
import anthropic

rec = recommend_params(
    task_description="Medical triage assistant",
    environment_entropy="moderate",
    environment_predictability="somewhat_predictable",
    stakes="catastrophic",
    commitment_style="cautious",
    target_platform="anthropic",
)

params = to_anthropic_params(
    rec,
    system_prompt="You are a medical intake assistant.",
    user_message="I've had chest pain for an hour.",
)

client = anthropic.Anthropic()
message = client.messages.create(**params)

Theoretical Foundation

The RPCS-1 SDK implements the matching principle (Pred-09-5) from IMM Paper 9:

Stable receivers in an environment with entropy H satisfy TI ~ 1/H.

This translates directly: agents in chaotic environments need short attention windows (low TI, frequent grounding); agents in stable environments benefit from long integration (high TI, large context windows).

All parameter recommendations are deterministic, explainable, and traceable to specific IMM principles. No ML models, no black-box recommendations.

License

MIT — see LICENSE file.

Web app and full documentation: https://rpcs1.dev