xyglue 0.1.0

A thinking-first agent framework built on Progressive Capture Theory

XYGLUE

A thinking-first agent framework that inserts a thinking layer between goal and plan, generating three ranked execution paths before any work begins.

---

The Problem

Every agent framework does this:

Goal → Plan → Execute → Output

The plan is a single path. If it fails, you get retries. If the approach was wrong, you get a faster wrong answer.

Nobody stops to ask: is the plan itself any good?

What XYGLUE Does Differently

XYGLUE inserts a thinking layer before planning. Three agents analyze the task, classify what's actually required vs. what's just convention, and generate three alternative paths — ranked from most creative to most safe.

                          ┌─────────────────────────────────────────┐
                          │          THINKING LAYER                 │
                          │                                         │
                          │  ┌─────────┐   Every task has a         │
                Goal ────►│  │  Scout  │   "standard" way.          │
                          │  └────┬────┘   Scout maps it.           │
                          │       │                                  │
                          │  ┌────▼──────┐ Which parts are physics  │
                          │  │ Physicist │ (must do) vs habit        │
                          │  └────┬──────┘ (just convention)?       │
                          │       │                                  │
                          │  ┌────▼──────┐ Three paths:             │
                          │  │ Architect │ A = break all habits     │
                          │  └──┬──┬──┬──┘ B = break some           │
                          │     │  │  │    C = optimized standard   │
                          └─────┼──┼──┼─────────────────────────────┘
                                │  │  │
                ┌───────────────┼──┼──┼──────────────────────┐
                │           EXECUTION LAYER                   │
                │               │  │  │                       │
                │          Path A  B  C  (with fallthrough)   │
                │               │  │  │                       │
                │           Self-healing pipeline              │
                │           20 seeded failure patterns         │
                │           Checkpointing + rollback           │
                └───────────────┼──┼──┼──────────────────────┘
                                │  │  │
                ┌───────────────┼──┼──┼──────────────────────┐
                │            CAPTURE LAYER                    │
                │                                             │
                │   Ground Tracker — records every run        │
                │   Template Library — quality ≥ 0.95,        │
                │     proven 2-3×, single failure resets      │
                └─────────────────────────────────────────────┘
                                  │
                                  ▼
                               Brief

The key insight: Most of what developers assume is "required" is actually just convention. A REST API doesn't need Express. Auth doesn't need JWT. XYGLUE's Physicist separates what's actually constrained (physics) from what's just popular (habit) — then the Architect generates paths that break the habits.

Path A breaks everything. Path C plays it safe. Path B is the pragmatic middle. The engine runs A first, and if it fails, self-heals and falls through to B, then C. You get the upside of creative approaches with the safety net of conventional ones.

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Installation

pip install xyglue

For AI-powered thinking (optional):

pip install xyglue[ai]

Quick Start

import asyncio
from xyglue import Engine

async def main():
    engine = Engine()
    result = await engine.run("Build a REST API for user management", mode="auto")
    print(result.brief.format())

asyncio.run(main())

✅ Complete. Path A succeeded.
🔍 Scout benchmark: web. Standard: code, database.
   2 habits broken. 0 physics respected.

No API key needed — every AI component has a rule-based fallback that works out of the box.

Two Modes

Mode	What happens
semi (default)	Jarvis-style. Shows you the Brief with all three paths. You pick one.
auto	Full autonomous. Runs Path A → heals → falls through to B → C.

# Semi: review before executing
engine = Engine()
result = await engine.run("Build a REST API")
print(result.brief.format())            # See the thinking
final = await engine.approve(result, path="A")

# Auto: just run it
result = await engine.run("Build a REST API", mode="auto")

Three Controls

Control	Options	What it does
mode	semi, auto	Pause for approval or run autonomously
divergence	low, medium, high	How aggressively Path A breaks from convention
learning	supervised, auto	Whether proven templates auto-promote or need your approval

engine = Engine(
    api_key="sk-ant-...",       # Optional: AI-powered thinking
    mode="semi",
    divergence="high",
    learning="supervised",
    agents={                     # Custom agent names (shown in Brief)
        "scout": "Recon",
        "physicist": "Newton",
        "architect": "Blueprint",
    },
)

The Thinking Layer

Three agents run in sequence before any execution happens:

Scout

Maps the benchmark — how is this task typically done? What tools, patterns, and sequence does everyone use? Also identifies cross-domain components that could apply but typically don't.

Physicist

Classifies every element the Scout found as either:

• Physics — can't change. Auth is required. TCP handshakes happen. Rate limits exist.
• Habit — just convention. REST is a choice. Express is a choice. PostgreSQL is a choice.

The Physicist is aggressive. Most things developers assume are necessary are just conventional.

Architect

Generates three paths from the classified map:

Path	Strategy	Risk	Confidence
A	Maximum divergence. Break every habit. Pull cross-domain solutions.	High	Lower
B	Informed hybrid. Break habits selectively where the payoff is clear.	Medium	Medium
C	Optimized benchmark. The conventional approach, but enhanced. The floor.	Low	Higher

The Healing Pipeline

When a step fails during execution, XYGLUE doesn't just retry blindly:

Failure → Detect → Classify → Match Pattern → Apply Fix → Retry

1. Detector classifies the error (timeout, auth, rate limit, network, etc.)
2. Pattern Library checks 20 seeded patterns for a known fix (fast path)
3. Diagnoser runs rule-based or AI diagnosis if no pattern matches (slow path)
4. Fix is applied and the step retries

Successful AI-diagnosed fixes get learned as new patterns. Patterns that fail lose confidence and eventually get evicted. The library improves over time.

The Capture Layer

Every run is recorded by the Ground Tracker — what was the benchmark, which constraints were classified, which path won, what failed and why.

The Template Library is intentionally strict:

• Quality threshold: 0.95 (non-negotiable)
• Must score 0.95+ on 2-3 separate runs to be promoted
• A single failure resets the validation counter

A library of 50 proven templates beats a library of 5,000 unvalidated ones.

Custom Tools

from xyglue import Tool, ToolResult

class MyTool(Tool):
    @property
    def name(self) -> str:
        return "my_tool"

    async def execute(self, params: dict) -> ToolResult:
        return ToolResult(success=True, output="done")

engine = Engine()
engine.register_tool(MyTool())

Project Structure

xyglue/
  __init__.py          # Public API: Engine, RunResult, Brief, Tool, etc.
  config.py            # EngineConfig with all settings
  engine.py            # Main orchestrator (thinking → execution → capture)
  sdk.py               # Public Engine wrapper (one import, one class)
  storage.py           # SQLite persistence layer
  thinking/            # Scout, Physicist, Architect agents
  goal/                # Goal processing, normalization, entity extraction
  plan/                # Plan models + checkpoint management
  execute/             # Step executor with timeout, retry, fallback
  heal/                # Failure detection → diagnosis → pattern match → fix
  learn/               # Pattern library + 20 seed patterns
  capture/             # Ground tracker + template library
  brief/               # Brief output formatting
  tools/               # Tool ABC, ToolResult, MockTool, ToolRegistry

Requirements

• Python 3.11+
• aiosqlite>=0.19.0 (only required dependency)
• anthropic>=0.25.0 (optional, for AI-powered thinking)

Contributing

1. Fork the repo and create a feature branch
2. Install dev dependencies: pip install -e ".[dev]"
3. Make changes
4. Run checks:

   pytest                    # 263 tests
   ruff check xyglue/       # Lint
   mypy xyglue/             # Type check
   

5. Open a pull request

See CLAUDE.md for code conventions and how to add features.

License

MIT -- Copyright (c) 2026 Luke H