ain-state-compiler 0.9.0

The G-Brain Company Brain Primitive: continuously compiles Slack, Jira, and Gmail into an executable, conflict-resolved operational state for AI agents.

ain-state-compiler

The G-Brain Company Brain Primitive: continuously compiles Slack, Jira, and Gmail into an executable, conflict-resolved operational state for AI agents.

Overview

The ain-state-compiler operates 100% offline at the source level, parsing and aggregating enterprise communication and issue-tracking streams to produce internally consistent state representations. It prevents AI agents from executing against stale, fragmented, or conflicting corporate knowledge.

Key Outputs

• Institutional Memory Modules (IMMs): Markdown-based, human- and machine-readable state snapshots.
• Operational Execution Graphs (OEGs): Optimized YAML topologies representing resolution paths.
• Active Conflict Reports: Discrepancy matrices identifying contradictions between engineering, marketing, and support data.

Installation

pip install ain-state-compiler

Usage

from ain_state_compiler.compiler import StateCompiler

# The project directory should contain a 'mock_data' folder with slack_history.json, jira_issues.json, and emails.json
compiler = StateCompiler(project_dir="/path/to/project")
summary = compiler.compile()

print(f"Processed {summary['processed_slack_events']} Slack events.")
print(f"Detected {summary['detected_conflicts']} active state conflicts.")

Core Architecture

"Ponytail" Lazy Gatekeepers

Internalizes the spirit of the "lazy senior dev" reductionist mindset directly into the core architecture:

• LazyStateFilter: A strict deterministic "No-Op" filter that drops incoming data if it does not meaningfully mutate the operational state.
• StateReuseEngine: Scans a historical cache of previously resolved conflicts. If a highly similar transformation exists, the compiler clones and adapts rather than generating from scratch.
• StateCompilerEngine: Enforces rigid bounds (max_tokens, length limits) on LLM compilation passes, aborting cleanly to naive primitives if structural code bloat occurs.

Conflict Detection & Optimization

• ConflictDetector: Runs rule-based, deterministic logic to spot discrepancies before invoking generation.
• TokenOptimizer: Compresses verbose JSON state outputs into highly dense YAML representations, minimizing token footprint for downstream agent ingestion.

Changelog

v0.9.0 (Current)

• LLM-Native Retrieval Revamp: Shifted away from raw Markdown context dumps to token-efficient Retrieval-Augmented Generation (RAG).
• FTS5 Fast Search: Extracted tight, context-rich snippets (truncated to scale) instead of unbounded document loads.
• MCP Server: Added mcp_server.py using FastMCP exposing search_ain_context and search_ain_by_tag to tools like Claude Desktop and Codex.
• Native Ollama Tools: Added ollama_plugin.py to route local queries securely through tool-calling pipelines.

v0.8.3

• Rebranding: Updated GitHub URLs and package author metadata to That-Tech-Geek.

v0.8.1

• Ponytail Architecture: Introduced LazyStateFilter, StateReuseEngine, and StateCompilerEngine programmatic gatekeepers.
• State Minimization: Implemented deterministic noise filtering and semantic hash comparisons to prevent unnecessary compilation.
• Token/Complexity Penalties: Set rigid metric constraints for generative compilation steps to minimize complexity.