octochains 0.2.0

A framework for parallel, isolated multi-agent reasoning.

Octochains

Octochains is a lightweight, zero-dependency Python framework for Collaborative AI Reasoning.It is purpose-built for Decomposable Tasks, complex problems that require independent, multi-perspective analysis.

By shifting from monolithic responses to Parallel Isolated Reasoning, Octochains ensures that every angle of a decision, from clinical diagnostics to financial risk, is evaluated in threaded isolation, preventing logical contamination and "Expert Blindspots."

Scientifically Validated Performance

Octochains is built on the architectural principles validated in the 2026 study "Towards a Science of Scaling Agent Systems" (Google Research / MIT).

Research confirms that for analytical, decomposable tasks, a Parallel Isolated architecture (the core of Octochains) delivers a massive performance delta over standard sequential or single-agent models:

Benchmark	Task Domain	Performance Gain vs. Single-Agent
Finance-Agent (FAB)	Decomposable Financial Reasoning	+80.8% 🚀
Workbench	Structured Business Planning	+57.2%
PlanCraft	Sequential Automation	(Use Single-Agent Instead)

Why Octochains?

Standard AI chains suffer from "Cognitive Tunnel Vision", where a model commits to a logical path too early. Octochains eliminates this via:

• Parallel Isolation: Expert nodes operate in private threads with zero awareness of peers, preventing "logical contamination."
• Centralized Verification: A specialized "Chief Justice" aggregator synthesizes reports, identifying conflicts and evidence gaps before delivering a verdict.
• Audit-First Design: Every decision generates a 100% traceable log of expert rationale, meeting EU AI Act requirements for monitorable AI.

Octochains Anathomy

https://github.com/user-attachments/assets/ede601fd-0a08-451f-b783-67d854767bb8

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Quickstart

Octochains is designed to be developer-first and model-agnostic.

1. Install

pip install octochains

2. Bring Your Own LLM (Zero-Dependency)

Octochains requires an LLMCallable: a standard Python function that takes a prompt: str and returns an output (string, dictionary, or object).

import openai

client = openai.Client(api_key="sk-...")

def my_llm(prompt: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3
    )
    return response.choices[0].message.content

3. Define Agent

from octochains import Agent, tool

class Specialist(Agent):
    def __init__(self):
        super().__init__(
            role="Legal Expert", 
            goal="Identify liability risks",
            input_description="A business proposal document.",
            llm_callable=my_llm
        )

    @tool
    def check_compliance(self, text: str):
        """
        Analyzes text for regulatory non-compliance.
        """
        # Framework automatically generates JSON schema for this tool
        return "Compliant"

    def execute(self, data: str) -> str:
        # Base class automatically handles the double-blind isolation prompt
        # and dynamically injects your @tool schemas!
        prompt = self._build_prompt(data)
        return self.llm_callable(prompt)

4. Define an Aggregator

from octochains import Aggregator
from typing import Any

class ChiefConsensusOfficer(Aggregator):
    def __init__(self):
        super().__init__(
            role="Chief Aggregator",
            goal="Synthesize expert opinions into a final verdict",
            llm_callable=my_llm
        )

    def execute(self, agent_reports: dict[str, str]) -> Any:
        """
        Receives a dictionary of reports.
        Key: Agent Role, Value: Agent output string.
        Can return a string, or natively return a structured JSON/Pydantic object!
        """
        # Helper method cleanly formats the raw dictionary
        compiled_reports = self._format_reports(agent_reports)
        
        prompt = f"""
        Role: {self.role}
        Goal: {self.goal}
        Reports:{compiled_reports}
        FINAL VERDICT:
        """
        return self.llm_callable(prompt)

5. Run the Parallel Engine

from octochains import Engine

# Initialize your experts and the aggregator
legal_expert = Specialist()
# finance_expert = FinanceSpecialist()
# tech_expert = TechSpecialist()

engine = Engine(
    agents=[legal_expert], # Add as many agents as you need
    aggregator=ChiefConsensusOfficer()
)

# Broadcast the complex problem to all agents at once
report = engine.run("Full Project Alpha Investment Case File...")

print(f"Consensus: {report.consensus}")
print(f"Audit Trail: {report.traces}")

Architecture & Strategy

Octochains is designed for high-stakes environments where "vibe-based" AI isn't enough. It excels in Medical Diagnostics, Legal Audits, and Strategic Business and Financial Analysis.

Repository Structure

• /src/octochains/engine.py: The high-performance parallel execution engine.
• /src/octochains/agents/: A growing library of specialized experts (Finance, Legal, Medical and etc.).
• /src/octochains/aggregators/: Standardized synthesis logic (Majority Vote, Weighted Consensus, etc.).

Future Roadmap

We are expanding Octochains from a library into a comprehensive ecosystem for high-stakes reasoning:

• Community-driven marketplace for pre-tuned specialists Agents.

License

Octochains is Fair-code, distributed under the Business Source License 1.1.

• Individuals & Internal Use: Free to use for personal projects, research, and internal business workflows.
• Commercial Providers: You cannot offer Octochains as a managed SaaS or sell a commercial wrapper of the engine without a license.
• The Guarantee: On May 10, 2030, this version automatically becomes Apache 2.0 (Open Source).

To access the Enterprise Reasoning Features, contact: [e-mail]