GCRI

Generalized Cognitive Refinement Iteration

Overview

GCRI (Generalized Cognitive Refinement Iteration) Single Unit is a Hierarchical Multi-Agent System where central coordination and field execution are separated. Rather than simply generating code, strategy formulation-execution-verification-evaluation stages are performed by different specialized agents, and this process occurs in isolated sandbox environments.

This is not just an LLM wrapper—it's an agent-centric architecture where multiple teams compete, critique, and converge to produce verified solutions.

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GCRI as a Thinking Unit

Single Unit = Intelligent Resource Controller

A single GCRI loop functions as one unified thinking unit that can replace traditional LLM calls. Unlike simple prompt-response patterns, each GCRI unit:

• Controls Resources: Manages its own workspace, file system, and execution environment
• Self-Verifies: Internal red team challenges every solution before returning results
• Learns from Failures: Builds constraints that prevent repeated mistakes
• Returns Verified Output: Only outputs that survive internal criticism are released

Think of it as a "super-LLM" where a single function call triggers an entire competitive ecosystem of agents working toward the same goal.

Composability: GCRI Units as Building Blocks

Because GCRI is a complete graph with clear input/output contracts, it can be composed into larger systems:

The Meta-Planner (gcri plan) decomposes complex goals into sequential tasks and delegates each to a fresh GCRI unit. Each unit:

1. Receives context from previous units
2. Executes its specialized task with full agent competition
3. Returns verified results to the planner
4. Passes accumulated knowledge to the next unit

This enables modular reasoning where each step is internally verified before proceeding.

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Architecture: The Coordinators vs. The Workers

The Coordinators (Central Command - Main Graph)

The management layer that sets system direction, audits results, and makes final decisions.

Agent	Role	Key Responsibilities	Input/Output
Strategy Generator (Strategy Planner)	Tactician	• Multi-angle Approach: Analyzes user requirements to establish N different solution strategies, not a single solution. • Diversity Assurance: Sets different initial directions so that all branches don't write identical code.	• Input: Task, Memory (Constraints) • Output: Strategies (List of strings)
Decision Maker (Final Authority)	Judge	• Gatekeeping: Coldly evaluates the validity of results from each execution branch. • Winner Selection: If the task is accomplished, identifies the 'winning branch' that wrote the most perfect code. • Deployment Approval: Sends approval signal for the system to merge (commit) sandbox results to the original project.	• Input: Aggregated Results, File Contexts • Output: Decision (Bool), best_branch_index
Memory Manager (Memory Keeper)	Analyst	• Failure Analysis: Analyzes errors and logical flaws from failed loops. • Constraint Generation: Converts "what should never be done next time" into ActiveConstraints to continuously update agent intelligence.	• Input: Global Feedback • Output: ActiveConstraints (Rules)

The Workers (Field Execution Team - Branch Subgraph)

The practitioner layer that performs actual coding and verification within isolated sandboxes created per branch.

Agent	Role	Key Responsibilities	Input/Output
Hypothesis Generator (Code Generator)	Coder	• Execution: Implements assigned strategy into actual working code. • File Manipulation: Directly accesses sandbox filesystem to create (write_file) or modify files.	• Input: Task, Strategy • Output: Hypothesis (Code Artifacts)
Reasoning Agent (Refiner)	Reviewer	• Self-Critique: Doesn't immediately execute coder's hypothesis, but first reviews for logical leaps or missing requirements. • Refinement: Reinforces logic and refines (concretizes) hypothesis.	• Input: Hypothesis • Output: Reasoning (Refined Logic)
Verification Agent (Verifier)	Red Team	• Vulnerability Search: Finds logical flaws or execution errors in written code. • Counter-Example Generation: Presents specific 'counter-examples' that can break the code to test solution robustness. • Survival Judgment: If code cannot withstand this counter-example, that branch fails.	• Input: Refined Hypothesis • Output: Verification (Counter-Example)

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Collaboration Flow

Process Steps

1. Command: Strategy Generator analyzes the problem and issues 3 infiltration routes (strategies): A, B, C.
2. Isolation: System builds 3 mutually invisible sandboxes (workspaces) for teams A, B, C. (Smart Copy & Link)
3. Execution:
   • Each team's Hypothesis Generator writes code.
   • Reasoning Agent refines it.
   • Verification Agent attacks and attempts to break it.
4. Report: Survival status and results from each team are reported to Decision Maker.
5. Verdict & Merge: If Decision Maker judges Team B's results as best, the system reflects only Team B's sandbox contents to the original server.

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Core Architecture Values

🎯 Clear R&R Separation

Planners (Strategy), executors (Hypothesis), verifiers (Verification), and evaluators (Decision) are separated, minimizing bias and hallucination.

🏆 Competitive Evolution

Multiple agent teams compete in parallel to find optimal solutions.

🔒 Safe Execution

All execution occurs in environments isolated from the main system, with only verified results exported.

🧠 Continuous Learning

Failed attempts are converted into constraints, making the system smarter with each iteration.

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Quick Start

Prerequisites

• Python 3.12+ required
• Docker (for sandbox isolation)
• Set up environment variables in .env file (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY)

Installation

# Install from PyPI
pip install gcri

# Or clone repository for development
git clone https://github.com/Dirac-Robot/GCRI.git
cd GCRI
pip install -e .

Basic Usage

Single Task Mode - Execute one task with multiple competing strategies:

gcri

Enter your task at the prompt. GCRI will spawn multiple agent teams that compete to solve it.

Planner Mode - Break down complex goals into sequential tasks:

gcri plan

The meta-planner will decompose your goal into subtasks and execute them systematically.

Programmatic Usage

from gcri.config import scope
from gcri.graphs.gcri_unit import GCRI

config = scope()  # returns config object
unit = GCRI(config)
result = unit('Write a Python script to analyze CSV files')
print(result['final_output'])

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Configuration Presets

GCRI provides pre-configured presets in the presets/ directory for convenience. These are ready-to-use configurations for different use cases and model providers.

Available Presets

• Balanced: General-purpose configuration with good speed/quality tradeoff
• Coding Specialist: Optimized for code generation tasks
• Deep Research: Maximum thoroughness for complex problems
• Lightweight: Fast execution with minimal resource usage

Supported Providers

• OpenAI (GPT-4o, GPT-4o-mini)
• Anthropic (Claude 4 Sonnet, Claude 4.5 Haiku)
• Google (Gemini 2.5 Pro, Gemini 2.5 Flash)
• Mixed providers (mixed_*.json)
• Local models (local_*.json)

Using a Preset

Use the custom_config_path parameter when running GCRI:

# Single task mode with preset
gcri custom_config_path=presets/gpt_5_balanced.json

# Planner mode with preset
gcri plan custom_config_path=presets/claude_deep_research.json

# Use your own custom configuration
gcri custom_config_path=/path/to/your/custom_config.json

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Project Structure

GCRI/
├── assets/                 # Project assets (logos, images)
├── gcri/
│   ├── config.py          # Configuration management
│   ├── entry.py           # CLI entry point
│   ├── graphs/
│   │   ├── gcri_unit.py   # Core GCRI workflow
│   │   ├── planner.py     # Meta-planner for multi-task
│   │   ├── schemas.py     # Pydantic data models
│   │   └── states.py      # Workflow state definitions
│   ├── templates/         # Prompt templates
│   │   ├── strategy_generator.txt
│   │   ├── hypothesis.txt
│   │   ├── reasoning.txt
│   │   ├── verification.txt
│   │   ├── decision.txt
│   │   └── memory.txt
│   └── tools/
│       └── cli.py         # Local execution tools
├── presets/               # Pre-configured model setups
└── README.md

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Advanced Features

Workspace Isolation

Each branch executes in its own isolated workspace directory:

• Pattern: logs/{timestamp}/workspaces/iter_{N}_branch_{M}/
• Files created by agents are scoped to their workspace
• Decision agent can inspect and verify outputs before deployment

File Verification

Decision agent performs mandatory audits:

• Checks if claimed files actually exist
• Executes code to verify it runs without errors
• Only deploys verified results to project root

Memory System

• Active Constraints: Rules extracted from failures
• Iteration History: Complete log of all attempts
• Feedback Loop: Failed strategies inform future iterations

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Public API Reference

Core Classes

gcri.graphs.gcri_unit.GCRI

Main workflow executor.

GCRI(config)(
    task: str,
    initial_memory: StructuredMemory = None,
    commit_mode: str = 'manual'  # 'manual' or 'auto-accept'
) -> dict

Returns:

• decision: Boolean indicating if task was completed
• final_output: Solution text (if decision=True)
• memory: Updated memory state
• results: Detailed branch results

gcri.graphs.planner.GCRIMetaPlanner

Multi-task planner.

GCRIMetaPlanner(config)(goal: str) -> dict

gcri.tools.cli.build_model

LLM agent builder with optional tool access.

build_model(
    model_id: str,
    gcri_options: dict = None,
    work_dir: str = None,
    **parameters
) -> CodeAgentBuilder

CLI Tools

• execute_shell_command(command: str): Execute shell commands in workspace
• read_file(filepath: str): Read files from workspace
• write_file(filepath: str, content: str): Write files to workspace
• local_python_interpreter(code: str): Execute Python code

All tools operate within isolated workspace contexts and include interactive safety guards.

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Testing & Development

Running Tests

pip install pytest
pytest -q

Static Analysis

pip install pylint
pylint gcri

Code Style

pip install black isort
black gcri/
isort gcri/

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Troubleshooting

Agent initialization failure

• Check if required authentication keys exist in .env
• Verify model ID and parameters in gcri/config.py are correct

Template file not found

• Check config.templates path
• Relative paths depend on working directory

Tool execution stops at terminal

• Local tools require user confirmation via InteractiveToolGuard
• Enable auto-mode or set gcri_options.use_code_tools=False

Logs/output not saved

• Check write permissions for config.log_dir
• Verify path exists and is writable

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Contributing

We welcome contributions! Please follow these guidelines:

1. Fork & Branch: Create a feature branch from main
2. Code Style: Use black and isort for formatting
3. Commit Messages: Clearly state purpose of changes
4. Tests: Add tests for new features
5. Documentation: Update relevant docs and templates

Adding New Presets

1. Create JSON file in presets/ directory
2. Follow existing preset structure
3. Document model requirements and use cases

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License

This project is licensed under the MIT License. See LICENSE for details.

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Contact & Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Acknowledgments

Built with:

• LangGraph - Workflow orchestration
• LangChain - LLM integration
• Pydantic - Data validation
• Rich - Terminal formatting

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GCRI: Where Multiple Minds Converge to Code