Octochains is a lightweight Python framework for parallel, and isolated multi-agent reasoning and consensus. It is purpose-built for complex, decomposable tasks that require independent, multi-perspective analysis without logical contamination.
Unlike traditional sequential agent chains where models bias each other through shared chat histories, Octochains executes domain specialists in Parallel Isolated Threads. Every angle of a high-stakes decision, from clinical diagnostics to financial risk and legal audits, is evaluated in pristine isolation before being synthesized by a centralized verification layer.
Standard multi-agent frameworks suffer from Cognitive Tunnel Vision and Groupthink, where early outputs dictate downstream reasoning. Octochains eliminates this through a robust, thread-safe architecture:
- Parallel Isolation: Expert agents operate in private threads with zero awareness of peer outputs, guaranteeing objective, unpolluted analysis.
- Centralized Consensus: A specialized aggregator audits isolated reports, resolving logical conflicts and highlighting evidence gaps before delivering a type-safe verdict.
- Audit-First Design: Every execution generates an immutable, 100% traceable log of expert rationale and error states, meeting EU AI Act requirements for monitorable enterprise AI.
- Portable Expertise: Agents can be equipped with Skills, markdown-based knowledge packs parsed with zero external dependencies, letting you inject domain procedure without writing a single line of prompt-engineering code.
| Feature | Octochains | Sequential Frameworks (e.g., CrewAI, AutoGen) | Routing Graph Frameworks (e.g., LangGraph) |
|---|---|---|---|
| Execution Model | Parallel Isolated Threads | Sequential / Turn-Based Chat | Directed Acyclic Graphs (DAGs) |
| Cognitive Bias Protection | 100% (Zero Peer Awareness) | Low (Agents read previous chat logs) | Moderate (Depends on node state) |
| Fault Tolerance | Thread-Level Isolation & Recovery | Global workflow fails on node crash | Complex custom retry logic required |
| Dependencies | Just one (Pydantic). Everything else, including Skills, is pure stdlib. | Heavy (LangChain, Pydantic, etc.) | Heavy |
| Primary Use Case | High-Stakes Consensus & Auditing | Conversational Task Automation | Complex Stateful Workflows |
Octochains is built on the architectural principles validated in the study "Towards a Science of Scaling Agent Systems" (Google Research / MIT). Research confirms that for analytical, decomposable tasks, a Parallel Isolated architecture delivers a massive performance delta over standard sequential 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) |
octochains-architecture.mp4
Octochains is designed to be developer-first, model-agnostic, and lightweight.
pip install octochainsOctochains is a "Pure Engine." It does not force you to install heavy SDKs or learn proprietary API wrappers. You maintain 100% control over your models and API keys.
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.contentThree domain specialists analyze a case in parallel, fully isolated from each other, then a Synthesizer merges their reports into one executive verdict.
from octochains.agents.presets import cfo_agent, cro_agent, data_sovereignty_auditor
from octochains.aggregators import Synthesizer
from octochains.engine import Engine
engine = Engine(
agents=[
cfo_agent(my_llm),
cro_agent(my_llm),
data_sovereignty_auditor(my_llm),
],
aggregator=Synthesizer(llm_callable=my_llm)
)
report = engine.run(
problem_data="Startup acquisition dossier: SaaS platform, $2M ARR, EU-based customer data..."
)
print(report.consensus.narrative)That's a full parallel-isolated reasoning pipeline — no custom classes, no prompt engineering. See the Official Preset Agents catalog for all 9 available specialists. Everything below shows how to build your own agents, aggregators, and Skills from scratch.
Agents inherit from Agent and implement an execute() method. The framework builds the strict "Forced Perspective" identity prompt for you, while you retain full control over the execution loop.
from octochains.base import Agent
class TechAnalyst(Agent):
def __init__(self, llm_callable):
super().__init__(
role="Chief Technology Officer",
goal="Evaluate technical feasibility and database scalability.",
llm_callable=llm_callable
)
def execute(self, problem_data: str) -> str:
# 1. Framework generates a strict, isolated identity prompt
system_prompt = self._build_prompt(problem_data)
# 2. You control the API execution (or tool injection!)
full_prompt = f"{system_prompt} Please provide your expert analysis."
return self.llm_callable(full_prompt)💡 Using Tools? You own the execute() loop! You can bypass the simple text wrapper and inject your provider's native tool schemas (e.g., OpenAI functions or external database hooks) directly into the API call.
The Aggregator waits for all experts to finish, reads their parallel reports, and synthesizes the final executive decision.
from octochains.base import Aggregator
from typing import Any
class ChiefConsensusOfficer(Aggregator):
def __init__(self, llm_callable):
super().__init__(
role="Chief Aggregator",
goal="Synthesize expert opinions into a final verdict",
llm_callable=llm_callable
)
def execute(self, agent_reports: dict[str, str]) -> Any:
# Helper method cleanly formats valid reports and injects anti-hallucination guardrails
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)The engine launches all agents concurrently, traps individual thread failures without crashing the pool, and pipes clean data to the aggregator.
from octochains.engine import Engine
# 1. Initialize your workforce
tech_expert = TechAnalyst(llm_callable=my_llm)
# finance_expert = FinanceSpecialist(llm_callable=my_llm)
# legal_expert = LegalExpert(llm_callable=my_llm)
engine = Engine(
agents=[tech_expert],
aggregator=ChiefConsensusOfficer(llm_callable=my_llm)
)
# 2. Broadcast the problem data to all agents simultaneously
report = engine.run(
problem_data="Full Project Alpha Investment Case File...",
show_log=True # Enables real-time execution tracing in your terminal
)
print(f"Consensus:\n{report.consensus}")
print(f"Audit Trail:\n{report.traces}")[ENGINE] Booting Octochains Parallel Reasoning Workflow...
[ENGINE] Provisioned Agents: 1 | Assigned Aggregator: Chief Aggregator
├── [Dispatching] Thread launched for Chief Technology Officer...
└── [Success] Collected structured report from Chief Technology Officer.
[ENGINE] >>> PHASE 2: Aggregated Consensus
Consensus:
Technical feasibility is APPROVED. The architecture supports isolated scaling without bottlenecking database read operations.
Audit Trail:
[Trace(agent_role='Chief Technology Officer', status='success', error_message=None)]Beyond custom Agent subclasses, Octochains ships pre-built specialists — each a SkilledAgent bundled with a curated, versioned Skill. Import them directly from octochains.agents.presets.
For evaluating a business, product, or investment case from every executive angle in parallel.
| Preset | Role | Focus |
|---|---|---|
cfo_agent |
Chief Financial Officer | Runway, burn rate, margin threats |
cto_agent |
Chief Technology Officer | Scalability bottlenecks, tech debt, key-person risk |
cro_agent |
Chief Revenue Officer | Market synergy, sales cycle friction, integration timelines |
cpo_agent |
Chief Product Officer | Product-Market Fit, defensibility, churn risk |
cmo_agent |
Chief Marketing Officer | CAC:LTV ratios, acquisition channel sustainability |
For flagging legal and regulatory exposure before it reaches a human reviewer.
| Preset | Role | Focus |
|---|---|---|
data_sovereignty_auditor |
Data Sovereignty Auditor | GDPR Art. 5/17 — cross-border transfers, retention limits |
ai_risk_assessor |
AI Risk Assessor | EU AI Act regulatory tiering |
phi_sanitizer |
Health Data Compliance Officer | Special Category Data (PHI) handling & anonymization |
licensing_reviewer |
Open-Source Compliance Engineer | Copyleft (GPL/AGPL) contamination risk |
from octochains.agents.presets import cfo_agent, cto_agent, licensing_reviewer
from octochains.aggregators import Synthesizer
from octochains.engine import Engine
engine = Engine(
agents=[cfo_agent(my_llm), cto_agent(my_llm), licensing_reviewer(my_llm)],
aggregator=Synthesizer(llm_callable=my_llm)
)
report = engine.run(problem_data="Startup acquisition dossier...")Every preset accepts extra_skills=[...] to layer your own domain knowledge on top of the official one — no subclassing required:
from octochains.skills import Skill
from pathlib import Path
house_style = Skill.from_file(Path("skills/our_valuation_method/SKILL.md"))
finance_expert = cfo_agent(llm_callable=my_llm, extra_skills=[house_style])A Skill is a markdown file with a simple key: value frontmatter block — no YAML library, no nested lists or objects, just flat fields:
---
name: churn-risk-heuristics
description: Detects early churn signals from usage and support ticket data.
version: 1.0.0
---
## Churn Risk Heuristics
1. Flag accounts with >30% MoM usage decline...
2. Cross-reference support ticket sentiment...Load it into any agent:
from pathlib import Path
from octochains.skills import Skill
from octochains.agents.skilled_agent import SkilledAgent
my_skill = Skill.from_file(Path("skills/churn-risk-heuristics/SKILL.md"))
agent = SkilledAgent(role="Growth Analyst", goal="...", llm_callable=my_llm, skills=[my_skill])Skills attached to an agent are surfaced by name and description in every prompt by default (cheap, always-on). Full skill content is only loaded on demand — see Agent.get_skill() and Agent.load_relevant_skills() in base.py for manual vs. automatic selection.
While Octochains allows you to build custom aggregators, we provide out-of-the-box modules designed for enterprise-grade verification and consensus.
The deterministic "Chief Justice" of your architecture. It audits expert reports for logical contradictions, timeline mismatches, and incompatible claims.
- Strategy 1 (Prompt-Matrix): Single-call audit using a structured internal comparative matrix.
- Strategy 2 (Parallel Pairwise): Multi-threaded execution that programmatically spawns isolated bilateral threads across all unique agent pairs for absolute, reproducible auditability.
- Mathematical Safety Gate: Automatically aborts audits without wasting API tokens if upstream failures reduce surviving reports to fewer than 2.
from octochains.aggregators import ConflictChecker
boss = ConflictChecker(
llm_callable=my_llm,
pairwise_audit=True, # Toggle to True for multi-threaded O(N^2) pairwise isolation
max_threads=5,
show_log=True
)The "Chief Integration Officer." It merges multiple isolated expert reports into a single cohesive executive narrative, automatically resolving redundancies, mapping citations strictly to responding agents, and zeroing out confidence scores if upstream pipelines fail.
from octochains.aggregators import Synthesizer
writer = Synthesizer(
llm_callable=my_llm,
show_log=True
)Check out the /cookbook/ directory for full examples of these aggregators in action.
/src/octochains/engine.py: High-performance parallel orchestrator with thread-level exception trapping./src/octochains/base.py: Superior abstract base classes with automated threshold gates and anti-hallucination prompt injection./src/octochains/skills.py: TheSkillclass — parses SKILL.md frontmatter + content with zero external dependencies./src/octochains/agents/presets.py: Official ready-to-use domain specialists (CFO, CTO, GDPR auditor, etc.)./src/octochains/agents/skilled_agent.py:SkilledAgent— the concrete, ready-to-use Agent that powers all official presets./src/octochains/agents/skills/: Bundled SKILL.md knowledge packs powering the official presets./src/octochains/aggregators/: Standardized synthesis and deterministic auditing logic.
We are actively expanding Octochains from a library into a comprehensive ecosystem for high-stakes reasoning:
- Expanded Preset Catalog: Community-contributed Skills reviewed and merged into the official preset library.
- Expanded Aggregator Suite: Out-of-the-box integration for democratic Majority Vote streams, strict Minimax boundary-testing gates, and categorical Classifiers.
- Octonodes: A production-grade visual application interface allowing architects to drag-and-drop parallel topologies, map data hooks, and export automated Python/Rust deployment code.
- HITL Gateways: Native Human-in-the-Loop intercept protocols allowing domain experts to step in at critical decision forks or review aggregated conflict logs before execution.
Octochains is Fair-Code, distributed under the Business Source License 1.1.
- Individuals & Internal Use: Free to use, modify, and scale for personal projects, academic research, and internal company workflows.
- Commercial Providers: You cannot offer Octochains as a managed SaaS reasoning infrastructure or sell a commercial wrapper of the core engine without an enterprise license.
- The Open-Source Guarantee: To protect the codebase as a permanent public good, this version contains a deterministic sunset clause: on May 10, 2030, the license automatically transitions to Apache 2.0 (Open Source).
To access Enterprise Reasoning Features or commercial licensing, contact: ahmad.vh7@gmail.com
Octochains is currently in Beta. We are iterating on the orchestration engine API. Breaking changes may occur in minor releases until v1.0.0.
