Transformation Spine

Provider-neutral transformation platform. Models are workers; the spine owns context, memory, decisions, governance, and provider selection — so outcome convergence holds no matter which engine runs beneath (llama.cpp, Codex, openenry, Claude Code, any OpenAI-compatible endpoint).

Core Idea

Context is contextual to its usage. Like variables in a compiled language, the spine assigns every piece of context a lifetime:

Scope	Analogy	Durability	Persisted
TRANSIENT	temporaries	one call	no
SESSION	stack frame	one task/session	while open
PROJECT	module state	project lifecycle	yes
PERSISTENT	globals in code	forever	yes

The spine assembles the minimum sufficient in-scope context for each prompt, promotes proven facts up a scope, demotes superseded decisions down, and retires the ephemeral — without the model ever owning memory.

Outcome Convergence

From the OCS projects: every transformation is a gated cycle. The spine computes an error signal per provider result; a result converges when the gap to the committed artifact is zero. This is what makes a 8B llama.cpp model and a 70B Codex model converge to the same outcome — swapping an engine is a configuration change, not a data loss event.

Package

src/spine/
  context.py    ContextScope enum, ScopePolicy table, ContextFact
  store.py      ContextStore (promote/demote/consolidate/expire)
  provider.py   Provider protocol, ModelSpec, RouterRule
  result.py     ProviderResult (error_signal, convergence)
  adapters.py   LlamaCppProvider, OllamaProvider, OpenAIProvider
  connectors.py MCP/ACP connectors (GitHub, Azure DevOps, Jira)
  ctst.py       CTSTLedger (append-only JSONL with hash chain)
  api.py        FastAPI spine application
tests/
  test_scope.py, test_store.py, test_convergence.py

Architecture

See docs/spine-architecture.mmd for the full architecture diagram.

Build Plan

See BUILD_PLAN.md for the phased v0.2.0 upgrade plan covering:

• Multi-backend adapters (HuggingFace, skill-level)
• Expanded MCP/ACP connectors (ServiceNow, Databricks, Confluence)
• Audit logs & telemetry
• Local/cloud/hybrid profiles
• Graphical workflow authoring
• Prebuilt agent libraries
• Enterprise integrations roadmap
• Tool-call abstraction
• OS-level safeguarding

All phases maintain ASF gate compliance (G0-G8) with verified evidence.

Install

python -m pip install -e ".[dev]"

Test / verify

python -m pytest tests
python -m ruff check src tests
python -m mypy src

Status

v0.1.0 — Core spine with provider abstraction, context lifecycle, and a CTST ledger.

v0.2.0 — Released 2026-10-09 after an independent audit and a full remediation program (see CHANGELOG.md and ASFQC/GATES.md): 178 tests, coverage held to a minimum of 80% in CI, CI green on Python 3.11 and 3.12. Serving surfaces: REST API, MCP (HTTP and stdio), an A2A agent card, ACP runs, a browser UI, and the spine CLI. Connector auto-discovery via entry points and plugin directories; a CTST ledger with a verified hash chain; durable context persistence.

v0.3.0 — Released 2026-10-10. Closes the current development plan: estimator calibration (the token engine now learns per-route correction factors from estimate-vs-actual telemetry), a certification package under certification/ (manifest, certificate TS-CERT-2026-001, test record — verify with python3 scripts/certify.py --check), and a public demo running this release at spine.aimlds.org.

Support

License

TransformationSpine is dual-licensed by its copyright holder, Nrupal Akolkar:

• Open source: GNU Affero General Public License, version 3 or later (AGPL-3.0-or-later) — see LICENSE.
• Commercial: a commercial license is available for proprietary or hosted use without AGPL obligations — see LICENSE-COMMERCIAL.md.

Releases up to and including v0.2.0 were published under the Apache License 2.0; the AGPL-3.0-or-later + commercial dual licensing applies from the change recorded in CHANGELOG.md onward.