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MINDSTACK: Suggestions for QonQrete’s Local AI Agent Brain Stack

This document outlines suggested changes, potential fixes for discrepancies, and general improvements to enhance the efficiency, performance, reliability, and security of QonQrete’s local AI agent brain stack. It also explains how this architectural approach addresses common LLM challenges.

Implemented Suggestions

1. Strict Instruction Following Prompts

System prompts have been refined to explicitly instruct LLMs on output format and constraints. For example, the construQtor agent’s prompt now includes a MANDATORY NAMING CONVENTIONS (STRICT) section that forces the AI to use specific verb prefixes for function names, making the generated code more deterministic and easier to parse.

2. qontextor CLI Helpers

The qontextor agent can now be invoked directly from the command line to query the generated context, providing powerful tools for developers to understand the codebase:

  • python3 worqer/qontextor.py --query "<search_term>": Performs a semantic search for symbols using sentence-transformers.
  • python3 worqer/qontextor.py --verb "<verb_pattern>": Finds symbols matching a specific verb pattern (e.g., get_.*).
  • python3 worqer/qontextor.py --ripple "<symbol_name>": Analyzes the ripple effect of changing a symbol, showing which parts of the codebase would be affected.

Suggested Changes & Improvements

1. Enhanced Contextual Retrieval for lib_ai.py

  • Current: lib_ai.py currently appends all provided context_files to the prompt.
  • Suggestion: Implement a more intelligent, semantic retrieval mechanism. Instead of sending all bloq.d and qontext.d files, lib_ai.py (or a new retriever agent) should:
    • Analyze the current base_prompt and tasq.md.
    • Query the qontext.d (semantic context) to identify files most semantically relevant to the task using the new --query functionality.
    • Only include the top-N most relevant bloq.d (structural) and qontext.d (semantic) files in the context_files list passed to the LLM. This significantly reduces token usage and noise.

2. Dynamic Skeletonization Sensitivity

  • Current: qompressor applies a fixed stripping logic.
  • Suggestion: Allow qompressor to operate with different “sensitivities” or “levels of detail” based on the QONQ_SENSITIVITY environment variable. For example:
    • Level 0 (low sensitivity): Only keep function/class names.
    • Level 5 (medium): Current behavior (signatures, docstrings).
    • Level 9 (high sensitivity): Keep small utility functions bodies, or critical boilerplate.
    • This would enable even finer-grained token optimization based on the task’s needs.

3. Structured reQap for Better Parsing

  • Current: reQap is largely free-form Markdown. While it contains “Assessment: SUCCESS/PARTIAL/FAILURE”, parsing and automated interpretation of the full content might be brittle.
  • Suggestion: Encourage (or enforce via prompt engineering for instruqtor to generate) a more structured YAML/JSON section within the reQap.md for key metrics, identified issues, and proposed next steps. This would allow qrane.py or a gateQeeper agent to programmatically evaluate the reQap more effectively.

4. Consolidated Error Reporting

  • Current: Errors are printed to sys.stderr and also logged to events_*.log. qrane.py handles some critical exceptions.
  • Suggestion: Implement a centralized error and warning reporting mechanism (e.g., a dedicated error.log file or a structured logging system). This would make it easier to aggregate, monitor, and analyze issues across agents and cycles.

Errors/Discrepancies Noted

  • lib_ai.py DeepSeek Streaming: The _run_deepseek function in lib_ai.py notes that the DeepSeek provider “currently doesn’t support streaming to stderr.” This is a discrepancy with the streaming behavior of other providers and leads to a less interactive experience for DeepSeek users.
    • Fix: Investigate if the DeepSeekProvider from sqeleton/deepseek_provider.py can be updated to support streaming, or if the openai library’s streaming feature can be leveraged directly for DeepSeek if their API supports it.

Efficiency & Speed Upgrades

  1. Parallel Context Generation: For qontextor.py’s initial scan, processing files sequentially can be slow. Implement parallel processing (e.g., using multiprocessing or ThreadPoolExecutor) for generate_context_local calls.
  2. Cached AI Responses: For qontextor.py, consider caching AI-generated context for files that haven’t changed. This would reduce redundant LLM calls and speed up subsequent scans.
  3. Selective qompressor Runs: If only a subset of qodeyard files have changed, qompressor could be optimized to only re-process those specific files, rather than always clearing and rebuilding bloq.d entirely. This requires change detection.

Tricks to Hallucinate Less, Stay More on Track

  1. Chain-of-Thought (CoT) Integration: Encourage agents to output their reasoning steps (CoT) within reQap.md. This makes their internal logic transparent and helps identify where reasoning might have gone astray.
  2. Critic/Reviewer Agent: Introduce a dedicated inspeqtor (or similar) agent whose sole job is to critically review the output of other agents against the current tasq.md and provided bloq.d/qontext.d. This agent would then generate a reQap that is more robust and actionable.

Security Enhancements

  1. API Key Management: While DEEPSEEK_API_KEY is from env vars, ensure config.yaml never stores sensitive keys directly. Always prioritize environment variables or secure vault integrations.
  2. Input Validation/Sanitization: Implement stricter input validation for prompts and generated code/configurations to prevent injection attacks or unexpected behavior, especially when interacting with external systems or executing code.
  3. Least Privilege: Ensure agents operate with the minimum necessary file system and network permissions. For example, qompressor only needs read access to qodeyard and write access to bloq.d.

Why this Solves Gemini, OpenAI, DeepSeek, and Qwen’s Memory/Context/Hallucination Issues

The QonQrete architecture, particularly its layered memory and context mechanisms, directly addresses the inherent limitations of large language models from various providers:

  1. Limited Context Window: All current LLMs have a finite context window. By using qompressor to create token-efficient skeletons (bloq.d) and qontextor to generate concise semantic summaries (qontext.d), QonQrete maximizes the amount of relevant information an LLM can receive. It prioritizes structural and semantic information over verbose implementation details, ensuring the LLM gets the most “bang for its token.”

  2. Context Drifting and Hallucination: LLMs often “forget” instructions or drift off-topic, leading to hallucinations. QonQrete mitigates this through:

    • Hierarchical Context: The tasq.md (current directive) provides a constant, explicit anchor for the LLM’s attention.
    • Operational History (QONQ_PREVIOUS_LOG): By explicitly injecting the previous agent’s output, the LLM has a clear trail of recent actions, preventing it from losing track of the immediate conversation or task flow.
    • Externalized Knowledge: Instead of relying solely on the LLM’s internal (and fallible) memory, QonQrete externalizes critical knowledge into file-based structures (bloq.d, qontext.d, tasq.d). This acts as an “external brain” that can be consistently referenced and updated, rather than being re-generated or “remembered” by the LLM itself in every turn. This drastically reduces the cognitive load on the LLM.

  3. Token Cost and Speed: Repeatedly sending entire codebases to LLMs is prohibitively expensive and slow. QonQrete’s approach:

    • Zero-Cost Skeletonization: qompressor effectively provides architectural understanding at near-zero token cost for the stripped code.
    • Semantic Compression: qontextor compresses complex code into digestible, structured YAML, further reducing token count for deep semantic understanding.
    • Selective Retrieval (Proposed): With intelligent retrieval, only the most relevant context is sent, optimizing both cost and latency.

  4. Managing Complex Projects: LLMs struggle with large, multi-file projects. QonQrete’s modular agent architecture and structured context allows it to break down complex tasks into manageable sub-tasks, each handled by a specialized agent with a focused context. This aligns well with how humans tackle large problems: by breaking them down and focusing on relevant parts at a time.

  5. Addressing Qwen’s 90k Token Hallucination Issue with QonQrete’s Structured Approach: Qwen, like other large context models, offers impressive token windows, but they are particularly susceptible to the “lost in the middle” phenomenon and an increased risk of hallucination when dealing with extremely long and unstructured inputs. QonQrete’s architecture provides a robust solution by:

    • Maximizing Signal-to-Noise Ratio: Instead of feeding raw, undifferentiated large inputs, QonQrete’s pre-processing (skeletonization via qompressor and semantic indexing via qontextor) ensures that the information presented within Qwen’s vast context window is highly distilled and relevant. This significantly increases the signal-to-noise ratio, making it harder for Qwen to “get lost” in a sea of less important tokens.
    • Structured Information Delivery: QonQrete doesn’t just reduce tokens; it structures them. By providing bloq.d (structural blueprints) and qontext.d (semantic summaries) as distinct, organized blocks, Qwen receives information in a format that mirrors a human’s organized understanding of a project. This scaffolding helps Qwen integrate information more effectively and reduces the cognitive load of inferring structure.
    • Preventing “Lost in the Middle”: Important context (like the tasq.md directive, specific code skeletons, or semantic summaries) is actively injected into the prompt based on immediate relevance. This strategic placement, combined with a highly structured input, counteracts the tendency for LLMs to overlook information not at the beginning or end of a very long prompt. The LLM isn’t asked to find the needle in the haystack; it’s given the needle in a clear, labeled box.
    • Grounding with Externalized Knowledge: The entire memory system (bloq.d, qontext.d, tasq.d, reqap.d) acts as a persistent, external knowledge base. This external grounding reduces Qwen’s reliance on its internal, potentially fallible, parametric memory for project details. When Qwen needs to recall a function signature or a project’s purpose, it consults a reliably structured and continuously updated external source, drastically cutting down on hallucination.

In essence, QonQrete creates a powerful feedback loop and an externalized, tiered memory system that offloads cognitive burden from the LLM, enabling it to focus its reasoning power on problem-solving rather than rote memorization or sifting through irrelevant data. This architectural pattern fundamentally strengthens the AI agent’s ability to stay on track, reason effectively, and generate accurate results across different LLM backends.