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QonQrete Documentation

Version: v1.0.0-stable (See VERSION file for the canonical version).

This document provides a comprehensive overview of the QonQrete Secure AI Construction Loop System.

Table of Contents

Architecture

This section contains Mermaid diagrams illustrating the complete architecture of the QonQrete system v1.0.0.

High-Level System Overview

flowchart TB
    subgraph HOST["🖥️ Host System"]
        User([👤 User])
        Shell[./qonqrete.sh]
        EnvFile[.env<br/>API Keys]
        Version[VERSION<br/>v0.8.8]
    end

    subgraph CONTAINER["🐳 Container Runtime"]
        subgraph QRANE["🏗️ Qrane Orchestrator"]
            Loader[loader.py<br/>Config Parser]
            TUI[tui.py<br/>Display Filter]
            PathMgr[paths.py<br/>Path Manager]
            MainLoop[qrane.py<br/>Main Loop]
        end

        subgraph AGENTS["🤖 Agent Pipeline"]
            direction TB
            
            subgraph LOCAL["⚡ Local Agents (Zero Cost)"]
                Qompressor[🦴 Qompressor<br/>Skeletonizer]
                Qontextor[🔍 Qontextor<br/>AST + Jedi + PyCG]
                CalQulator[🧮 CalQulator<br/>Cost Estimator]
                LoQal[✅ LoQal Verifier<br/>Syntax + Imports]
            end

            subgraph AI["🧠 AI Agents"]
                InstruQtor[📋 InstruQtor<br/>Briq Planner]
                ConstruQtor[🔨 ConstruQtor<br/>Code Generator]
                InspeQtor[🔎 InspeQtor<br/>Batched Reviewer]
            end

            subgraph CACHE["💾 Cache Layer"]
                Qontrabender[🌀 Qontrabender<br/>Hybrid Cache]
            end
        end

        subgraph LIBAI["🔌 AI Provider Abstraction"]
            LibAI[lib_ai.py]
            OpenAI[(OpenAI<br/>gpt-4.1-*)]
            Gemini[(Gemini<br/>2.5-flash/pro)]
            Anthropic[(Anthropic<br/>claude-*)]
            DeepSeek[(DeepSeek<br/>V3.2)]
            Qwen[(Qwen<br/>qwen3-coder)]
        end
    end

    subgraph WORQSPACE["📁 Worqspace Volume"]
        Config[config.yaml]
        PipeConfig[pipeline_config.yaml]
        TasQ[tasq.md]
        
        subgraph QAGE["📦 qage_timestamp/"]
            BriqD[briq.d/]
            BloqD[bloq.d/]
            QontextD[qontext.d/]
            Qodeyard[qodeyard/]
            ExeqD[exeq.d/]
            ReqapD[reqap.d/]
            StruqtureD[struqture/<br/>Logs]
        end
    end

    User --> Shell
    Shell --> EnvFile
    Shell --> Version
    Shell --> CONTAINER
    
    MainLoop --> Loader
    MainLoop --> TUI
    MainLoop --> PathMgr
    
    Loader --> Config
    Loader --> PipeConfig
    
    MainLoop --> Qompressor
    MainLoop --> Qontextor
    MainLoop --> CalQulator
    MainLoop --> InstruQtor
    MainLoop --> ConstruQtor
    MainLoop --> InspeQtor
    MainLoop --> Qontrabender
    
    ConstruQtor --> LoQal
    
    InstruQtor --> LibAI
    ConstruQtor --> LibAI
    InspeQtor --> LibAI
    
    LibAI --> OpenAI
    LibAI --> Gemini
    LibAI --> Anthropic
    LibAI --> DeepSeek
    LibAI --> Qwen
    
    TasQ --> InstruQtor
    InstruQtor --> BriqD
    
    Qodeyard --> Qompressor
    Qompressor --> BloqD
    
    Qodeyard --> Qontextor
    Qontextor --> QontextD
    
    BriqD --> CalQulator
    BloqD --> CalQulator
    
    BriqD --> ConstruQtor
    BloqD --> ConstruQtor
    ConstruQtor --> Qodeyard
    ConstruQtor --> ExeqD
    
    Qodeyard --> InspeQtor
    ExeqD --> InspeQtor
    InspeQtor --> ReqapD
    
    Qontrabender --> BloqD
    
    classDef host fill:#2d2d2d,stroke:#888,color:#fff
    classDef container fill:#1a3a1a,stroke:#4a4,color:#fff
    classDef local fill:#1a1a3a,stroke:#44a,color:#fff
    classDef ai fill:#3a1a1a,stroke:#a44,color:#fff
    classDef cache fill:#3a3a1a,stroke:#aa4,color:#fff
    classDef volume fill:#2a1a2a,stroke:#a4a,color:#fff
    classDef provider fill:#1a2a2a,stroke:#4aa,color:#fff
    
    class User,Shell,EnvFile,Version host
    class Qompressor,Qontextor,CalQulator,LoQal local
    class InstruQtor,ConstruQtor,InspeQtor ai
    class Qontrabender cache
    class Config,PipeConfig,TasQ,BriqD,BloqD,QontextD,Qodeyard,ExeqD,ReqapD,StruqtureD volume
    class OpenAI,Gemini,Anthropic,DeepSeek,Qwen provider

Detailed Pipeline Flow

flowchart LR
    subgraph CYCLE["🔄 CyQle N"]
        direction TB
        
        Start([Start CyQle]) --> Qompressor
        
        subgraph PREP["1️⃣ Preparation Phase"]
            Qompressor[🦴 Qompressor<br/>Generate Skeletons]
            Qontextor[🔍 Qontextor<br/>Build Symbol Map]
            Qompressor --> Qontextor
        end
        
        subgraph PLAN["2️⃣ Planning Phase"]
            InstruQtor[📋 InstruQtor<br/>Decompose TasQ → BriQs]
            CalQulator[🧮 CalQulator<br/>Estimate Costs]
            Qontextor --> InstruQtor
            InstruQtor --> CalQulator
        end
        
        subgraph BUILD["3️⃣ Build Phase (Interleaved)"]
            direction TB
            ConstruQtor[🔨 ConstruQtor<br/>Generate Code]
            LoQal[✅ LoQal<br/>Verify Syntax]
            Retry{Retry?}
            
            CalQulator --> ConstruQtor
            ConstruQtor --> LoQal
            LoQal -->|FAIL| Retry
            Retry -->|Yes| ConstruQtor
            Retry -->|No| NextBriq
            LoQal -->|PASS| NextBriq[Next BriQ]
            NextBriq -->|More BriQs| ConstruQtor
        end
        
        subgraph REVIEW["4️⃣ Review Phase (Batched)"]
            InspeQtor[🔎 InspeQtor<br/>Batch Review All BriQs]
            MetaReview[📊 Meta-Review<br/>Consolidate Results]
            NextBriq -->|All Done| InspeQtor
            InspeQtor --> MetaReview
        end
        
        subgraph CACHE["5️⃣ Cache Phase"]
            Qontrabender[🌀 Qontrabender<br/>Save to Qache]
            MetaReview --> Qontrabender
        end
        
        subgraph CHECKPOINT["6️⃣ CheQpoint"]
            CheQpoint{User Decision}
            Qontrabender --> CheQpoint
            CheQpoint -->|Continue| NextCycle([CyQle N+1])
            CheQpoint -->|TweaQ| TweaQ[Modify TasQ]
            CheQpoint -->|Quit| Done([Exit])
            TweaQ --> NextCycle
        end
    end
    
    style PREP fill:#1a1a3a,stroke:#44a
    style PLAN fill:#2a1a2a,stroke:#a4a
    style BUILD fill:#3a1a1a,stroke:#a44
    style REVIEW fill:#1a3a1a,stroke:#4a4
    style CACHE fill:#3a3a1a,stroke:#aa4
    style CHECKPOINT fill:#2a2a2a,stroke:#888

ConstruQtor Build Loop (Interleaved Mode)

flowchart TB
    subgraph CONSTRUQTOR["🔨 ConstruQtor v0.8.8 - Per-BriQ Processing"]
        Start([Start]) --> LoadBriqs[Load BriQ Files]
        LoadBriqs --> ForEach{For Each BriQ}
        
        ForEach --> Attempt[Attempt 1/3]
        
        subgraph ATTEMPT["Build Attempt"]
            Attempt --> Context[Gather Context<br/>bloq.d + qontext.d]
            Context --> Prompt[Build AI Prompt]
            Prompt --> AICall[🧠 AI Generate Code]
            AICall --> Parse[Parse Response<br/>Extract Files]
            Parse --> Write[Write to qodeyard/]
        end
        
        subgraph VERIFY["LoQal Verification"]
            Write --> Syntax[Python compile]
            Syntax -->|Error| SyntaxFail[❌ Syntax Error]
            Syntax -->|OK| Imports[Check Imports]
            Imports -->|Warning| ImportWarn[⚠️ Import Warning]
            Imports -->|OK| Pass[✅ Passed]
        end
        
        SyntaxFail --> RetryCheck{Attempts < 3?}
        RetryCheck -->|Yes| Attempt
        RetryCheck -->|No| MarkFail[Mark FAILURE]
        
        ImportWarn --> WriteExeq
        Pass --> WriteExeq[Write exeQ Summary]
        MarkFail --> WriteExeq
        
        WriteExeq --> ForEach
        ForEach -->|Done| Complete([All BriQs Complete])
    end
    
    style ATTEMPT fill:#3a1a1a,stroke:#a44
    style VERIFY fill:#1a3a1a,stroke:#4a4

InspeQtor Batched Review Flow

flowchart TB
    subgraph INSPEQTOR["🔎 InspeQtor v0.8.8 - Two-Stage Batched Review"]
        Start([Start]) --> Gather[Gather All ExeQs<br/>from exeq.d/cyqleN/]
        
        subgraph STAGE1["Stage 1: Batched Per-BriQ Reviews"]
            Gather --> CalcBatches[Calculate Batches<br/>max 12 briqs, 60K tokens]
            CalcBatches --> ForBatch{For Each Batch}
            
            ForBatch --> BuildPrompt[Build Batch Prompt<br/>All BriQs + Code]
            BuildPrompt --> AIReview[🧠 AI Batch Review]
            AIReview --> ParseResults[Parse Results<br/>Extract Per-BriQ Status]
            ParseResults --> WriteReqaps[Write Individual<br/>reqap.md Files]
            WriteReqaps --> ForBatch
        end
        
        subgraph STAGE2["Stage 2: Meta-Review"]
            ForBatch -->|All Batches Done| GatherReqaps[Gather All ReQaps]
            GatherReqaps --> MetaPrompt[Build Meta Prompt]
            MetaPrompt --> MetaAI[🧠 AI Consolidate]
            MetaAI --> FinalReqap[Write Final ReQap<br/>Cycle Summary]
        end
        
        subgraph LOQAL["LoQal Verification"]
            FinalReqap --> LoQalCheck[Run Full Verification<br/>All qodeyard/ Files]
            LoQalCheck --> Report[Append to ReQap]
        end
        
        Report --> Done([Complete])
    end
    
    style STAGE1 fill:#1a3a1a,stroke:#4a4
    style STAGE2 fill:#3a3a1a,stroke:#aa4
    style LOQAL fill:#1a1a3a,stroke:#44a

Directory Structure

flowchart TB
    subgraph DIRS["📁 QonQrete Directory Structure"]
        direction TB
        
        subgraph ROOT["qonqrete/"]
            qonqrete_sh[qonqrete.sh<br/>Entry Point]
            VERSION[VERSION<br/>0.8.8]
            env[.env<br/>API Keys]
            Dockerfile[Dockerfile]
        end
        
        subgraph QRANE["qrane/"]
            qrane_py[qrane.py<br/>Orchestrator]
            loader[loader.py<br/>Config]
            paths[paths.py<br/>Paths]
            tui[tui.py<br/>Display]
            lib_funq[lib_funqtions.py<br/>Pricing]
        end
        
        subgraph WORQER["worqer/"]
            lib_ai[lib_ai.py<br/>AI Abstraction]
            instruqtor[instruqtor.py]
            construqtor[construqtor.py]
            inspeqtor[inspeqtor.py]
            qompressor[qompressor.py]
            qontextor[qontextor.py]
            qontrabender[qontrabender.py]
            calqulator[calqulator.py]
            loqal_verifier[loqal_verifier.py]
        end
        
        subgraph WORQSPACE["worqspace/"]
            config[config.yaml]
            pipeline[pipeline_config.yaml]
            tasq[tasq.md]
            sqrapyard[sqrapyard/<br/>Persistent Storage]
            
            subgraph QAGE["qage_YYYYMMDD_HHMMSS/"]
                briq_d[briq.d/<br/>Planned Steps]
                bloq_d[bloq.d/<br/>Skeletons]
                qontext_d[qontext.d/<br/>Symbol Maps]
                qodeyard_g[qodeyard/<br/>Generated Code]
                exeq_d[exeq.d/<br/>Build Summaries]
                reqap_d[reqap.d/<br/>Reviews]
                struqture[struqture/<br/>Logs]
            end
        end
        
        subgraph DOC["doc/"]
            docs[DOCUMENTATION.md<br/>RELEASE-NOTES.md<br/>QUICKSTART.md<br/>etc.]
        end
    end
    
    qonqrete_sh --> qrane_py
    qrane_py --> loader
    qrane_py --> paths
    qrane_py --> tui
    loader --> config
    loader --> pipeline
    
    qrane_py --> instruqtor
    qrane_py --> construqtor
    qrane_py --> inspeqtor
    
    instruqtor --> lib_ai
    construqtor --> lib_ai
    construqtor --> loqal_verifier
    inspeqtor --> lib_ai
    
    instruqtor --> briq_d
    qompressor --> bloq_d
    qontextor --> qontext_d
    construqtor --> qodeyard_g
    construqtor --> exeq_d
    inspeqtor --> reqap_d
    
    style ROOT fill:#2d2d2d,stroke:#888
    style QRANE fill:#1a3a1a,stroke:#4a4
    style WORQER fill:#3a1a1a,stroke:#a44
    style WORQSPACE fill:#2a1a2a,stroke:#a4a
    style QAGE fill:#3a3a1a,stroke:#aa4
    style DOC fill:#1a1a3a,stroke:#44a

Cost Flow Visualization

flowchart LR
    subgraph COSTS["💰 Token Cost Flow (v0.8.8)"]
        
        subgraph FREE["🆓 Zero Cost"]
            Qompressor[Qompressor<br/>Local Python]
            Qontextor[Qontextor<br/>AST + Jedi]
            CalQulator[CalQulator<br/>Local Math]
            LoQal[LoQal Verifier<br/>compile + imports]
            Qontrabender[Qontrabender<br/>Local Cache]
        end
        
        subgraph CHEAP["💵 Low Cost"]
            InstruQtor[InstruQtor<br/>gpt-4.1-nano<br/>$0.10/1M in]
            InspeQtorBatch[InspeQtor Batched<br/>gpt-4.1-mini<br/>$0.40/1M in]
        end
        
        subgraph MAIN["💰 Main Cost"]
            ConstruQtor[ConstruQtor<br/>gemini-2.5-flash<br/>$0.30/1M in]
        end
        
        subgraph TOTAL["📊 7 CyQle Estimate"]
            Est[InstruQtor: ~$0.01<br/>ConstruQtor: ~$1.50<br/>InspeQtor: ~$0.30<br/>───────────<br/>TOTAL: ~$2.00]
        end
    end
    
    FREE --> CHEAP
    CHEAP --> MAIN
    MAIN --> TOTAL
    
    style FREE fill:#1a3a1a,stroke:#4a4
    style CHEAP fill:#3a3a1a,stroke:#aa4
    style MAIN fill:#3a1a1a,stroke:#a44
    style TOTAL fill:#1a1a3a,stroke:#44a

Execution Flows

This section traces the end-to-end execution flows of the QonQrete system, from user command to the completion of a cycle.

1. Initialization Flow (./qonqrete.sh init)

  1. User Input: User executes ./qonqrete.sh init. An optional --msb or --docker flag can be provided.
  2. qonqrete.sh: The script parses the init command.
  3. Runtime Detection: It checks for the --msb or --docker flags. If none are provided, it checks pipeline_config.yaml for a microsandbox: true setting. If not found, it defaults to Docker.
  4. Container Build:
    • If the runtime is docker, it executes docker build -t qonqrete-qage -f Dockerfile ..
    • If the runtime is msb, it executes msb build . -t qonqrete-qage (or mbx).
  5. Result: A container image named qonqrete-qage is created in the local registry of the selected runtime, ready for execution.

2. Main Execution Flow (./qonqrete.sh run)

  1. User Input: User executes ./qonqrete.sh run. Optional flags like --auto, --user, and --tui can be included.
  2. qonqrete.sh:
    • Parses the run command and any additional flags.
    • Reads the VERSION file and exports it as QONQ_VERSION.
    • Creates a unique timestamped run directory (qage_<timestamp>) inside worqspace/.
    • Sqrapyard Initialization: It checks the persistent worqspace/sqrapyard directory. If it contains files, they are copied into the new qage_<timestamp>/qodeyard. If sqrapyard/tasq.md exists, it’s copied to become the initial tasq for the first cycle.
    • Copies configuration files into the new run directory.
    • Constructs the docker run or msb run command, mounting the qage_<timestamp> directory and passing the necessary environment variables.
  3. qrane.py (Inside the Container):
    • The orchestrator starts.
    • API Key Validation: It reads config.yaml to identify all unique AI providers being used for the current run. It then checks that the corresponding environment variables (e.g., OPENAI_API_KEY, DEEPSEEK_API_KEY) are set. If a required key for a configured provider is missing, it exits with a clear error message.
    • It determines the UI mode (TUI/headless) and enters the main cyQle loop.
  4. The cyQle Loop:
    • The Qrane dynamically loads the agent pipeline from pipeline_config.yaml.
    • It executes each agent in sequence.
  5. The CheQpoint:
    • qrane.py reads the final reQap.md of the cycle. . It pauses and prompts the user for input ([Q]ontinue, [T]weaQ, [X]Quit), unless in autonomous mode. The default behavior (autonomous vs. user-gated) is controlled by the cheqpoint option in config.yaml, and can be overridden by the --auto or --user flags.
  6. Loop Continuation:
    • If approved, the reQap.md is promoted to become the task for the next cycle.
    • The cycle counter increments, and the loop repeats.
  7. Exit: If the user quits, the loop breaks, the container exits, and the script finishes.

3. Cleanup Flow (./qonqrete.sh clean)

  1. User Input: User executes ./qonqrete.sh clean.
  2. qonqrete.sh:
    • Searches for qage_* directories in worqspace/.
    • Prompts the user for confirmation.
    • If confirmed, it executes rm -rf worqspace/qage_*.
  3. Result: The worqspace is cleared of all previous run data.

Agent & Orchestrator Logic

This section details the operational logic for the QonQrete system.

Orchestrator Logic (qrane/qrane.py)

The Qrane is the heart of the system.

  • Dynamic Pipeline Loading: On startup, the Qrane reads the worqspace/pipeline_config.yaml file. It iterates through the agents list defined in this file to build the execution pipeline for the cycle.
  • Generic Execution: For each agent in the pipeline, the orchestrator constructs the appropriate command-line arguments based on the script, input, and output fields in the config.
  • Centralized Paths: It utilizes the PathManager class to resolve all file and directory paths.

Default Agent Logic

The following describes the logic of the three default agents that constitute the standard QonQrete pipeline.

Core Abstraction: worqer/lib_ai.py

All agents utilize this central library to interact with AI models. It uses a hybrid approach:

  • Official Python Libraries: Used for OpenAI, Google Gemini, and Anthropic. These libraries read their respective API keys (OPENAI_API_KEY, GOOGLE_API_KEY, ANTHROPIC_API_KEY) directly from the environment.
  • Custom Provider: A custom, OpenAI-compatible provider located in sqeleton/deepseek_provider.py is used for DeepSeek. This method is more reliable and uses the DEEPSEEK_API_KEY from the environment.
  • CLI Wrapper: A wrapper around the @qwen-code/qwen-code CLI tool is used for Qwen models.

This provides a consistent and modular interface for all AI interactions.

0. tasqLeveler (The Enhancer) - NEW in v0.9.0

  • Purpose: To automatically enhance tasq.md with golden path tests, dependency graphs, and mock infrastructure.
  • Runs Once: Only executes on Cycle 1, before InstruQtor.
  • Enhancements Added:
    • 📦 Dependency Graph: Explicit module import hierarchy to prevent circular imports
    • 🎯 Golden Path Tests: Code snippets that MUST work after each module is implemented
    • 🧪 Mock Infrastructure: Mock servers/services for testing integrations
    • 📋 Success Criteria: Global definition of what SUCCESS means
    • ⏱️ Phase Priority: Guidance on what to focus on if running low on cycles
    • 🔗 Base Classes: Abstract base classes for similar modules
  • Impact: +15-20% improvement in output quality by giving the AI explicit success criteria.
  • Backup: Original tasq is preserved as tasq_original.md.

1. instruQtor (The Planner)

  • Purpose: To decompose a high-level task (tasQ.md) into a series of small, actionable steps (briQ.md files).

  • Logic: It reads the task, constructs a detailed prompt for the AI, invokes the AI via lib_ai.py, and then parses the markdown response into individual briQ.md files.

  • Sensitivity: The level of detail in the breakdown can be controlled with the QONQ_SENSITIVITY environment variable, which corresponds to 10 predefined levels (0-9).

  • Context-Aware: It reads the contents of the qodeyard to provide the AI with the current state of the codebase.

  • Universal File Rule (v0.9.0+): InstruQtor enforces a simple rule that applies to ALL cycles:

    • If a file EXISTS in qodeyard → create briqs to MODIFY/EXTEND it (never recreate)
    • If a file DOESN’T EXIST → create briqs to CREATE it (new modules welcome)

    This prevents the rebuild-from-scratch bug while maintaining full creative freedom.

2. construQtor (The Executor)

  • Purpose: To execute the steps from the briQ.md files and generate code.
  • Logic: It iterates through the briQ files sequentially. For each, it builds a prompt that includes the step’s instructions and the current state of the qodeyard directory. It then calls the AI to execute the step and writes the generated code to the qodeyard.

3. inspeQtor (The Reviewer)

  • Purpose: To review the construQtor’s work and provide feedback for the next cycle.
  • Logic: It gathers all generated code from the qodeyard, constructs a prompt instructing the AI to act as a senior code reviewer, and saves the AI’s assessment and suggestions to a reQap.md file.

Specialized Agents

The following agents can be added to the pipeline in pipeline_config.yaml to provide additional functionality.

1. qompressor (The Skeletonizer)

  • Purpose: To create a low-token, high-context representation of the codebase.
  • Logic: It mirrors the qodeyard directory into a new bloq.d directory. During the mirroring process, it strips the implementation bodies from source code files, keeping only the architectural elements like class/function signatures, imports, and docstrings. This “skeleton” provides the AI with the overall structure of the code at a fraction of the token cost.
  • Cost: Zero token cost. It’s a local pre-processing step.

2. qontextor (The Symbol Mapper)

  • Purpose: To generate a detailed, machine-readable map of the codebase’s symbols and their relationships.
  • Dual-Mode Logic:
    • Local Mode (provider: local): This is the default mode. qontextor performs a deterministic analysis of Python files using a sophisticated stack of local tools. It extracts classes, functions, signatures, and purposes with high accuracy and speed, incurring zero token cost.
      • Python AST: Extracts the fundamental structure of the code.
      • Docstrings & Verb Heuristics: Determines the purpose of functions and classes.
      • Jedi: Provides type inference and cross-file understanding.
      • PyCG: Generates a call graph for dependency analysis.
      • Fast vs. Complex Mode: Can be configured in worqspace/config.yaml to run in a 'fast' (AST, Jedi, Heuristics) or 'complex' (adds deep semantic analysis with sentence-transformers) mode.
    • AI Mode (provider: [ai_provider]): In this legacy mode, it uses the “skeletonized” output from the qompressor to analyze each file. It then uses an AI call to generate a YAML file for each source file, detailing its symbols (classes, functions, etc.), their signatures, their purpose, and their dependencies.
  • Cost: Incurs AI token costs only when an AI provider is specified. It’s best used for initial scans or when major architectural changes occur.

3. calqulator (The Cost Estimator)

  • Purpose: To provide a token and cost estimate for the upcoming construqtor cycle.
  • Logic: It analyzes the briQ.md files for the current cycle. Its calculation includes:
    1. A base cost for the “skeletonized” project context from bloq.d.
    2. The cost of the instructions in each briQ.md file.
    3. A “deep read” cost for any specific files that are explicitly referenced within a briQ.
  • Output: It annotates each briQ.md file with its estimated token count and cost, and prints a detailed report to the console.
  • Cost: Zero token cost. It performs local calculations.

Configuration

The behavior of the QonQrete system can be configured in the worqspace/ directory.

  • config.yaml:
    • cheqpoint: A boolean that sets the default execution mode. true (the default) enables user-gated cheqpoints. false makes the system autonomous by default. This can be overridden by the --user and --auto command-line flags.
    • auto_cycle_limit: The maximum number of cyQles to run in autonomous mode. 0 means infinite.
    • agents: The AI models to be used by each agent. You can also set the provider to local for agents that do not use AI.
  • pipeline_config.yaml:
    • microsandbox: Set to true to make Microsandbox (msb) the default container runtime.
    • agents: Defines the sequence of agents in the pipeline.

Getting Started

To get started with QonQrete, please see the QUICKSTART.md guide.

Terminology

For a complete list of the terminology used in the QonQrete system, please see the TERMINOLOGY.md file.