Antoine/ATOCore
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
95daa5c0400815443e26afdd49b3f7bd906ce7b4
ATOCore/docs/master-plan-status.md
Anto01 4da81c9e4e feat: retrieval eval harness + doc sync 
scripts/retrieval_eval.py walks a fixture file of project-hinted
questions, runs each against POST /context/build, and scores the
returned formatted_context against per-fixture expect_present and
expect_absent substring checklists. Exit 0 on all-pass, 1 on any
miss. Human-readable by default, --json for automation.

First live run against Dalidou at SHA 1161645: 4/6 pass. The two
failures are real findings, not harness bugs:

- p05-configuration FAIL: "GigaBIT M1" appears in the p05 pack.
  Cross-project bleed from a shared p05 doc that legitimately
  mentions the p04 mirror under test. Fixture kept strict so
  future ranker tuning can close the gap.
- p05-vendor-signal FAIL: "Zygo" missing. The vendor memory exists
  with confidence 0.9 but get_memories_for_context walks memories
  in fixed order (effectively by updated_at / confidence), so lower-
  ranked memories get pushed out of the per-project budget slice by
  higher-confidence ones even when the query is specifically about
  the lower-ranked content. Query-relevance ordering of memories is
  the natural next fix.

Docs sync:

- master-plan-status.md: Phase 9 reflection entry now notes that
  capture→reinforce runs automatically and project memories reach
  the context pack, while extract remains batch/manual. First batch-
  extract pass surfaced 1 candidate from 42 interactions — extractor
  rule tuning is a known follow-up.
- next-steps.md: the 2026-04-11 retrieval quality review entry now
  shows the project-memory-band work as DONE, and a new
  "Reflection Loop Live Check" subsection records the extractor-
  coverage finding from the first batch run.
- Both files now agree with the code; follow-up reviewers
  (Codex, future Claude) should no longer see narrative drift.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-11 12:39:03 -04:00

7.4 KiB
Raw Blame History

AtoCore Master Plan Status

Current Position

AtoCore is currently between Phase 7 and Phase 8.

The platform is no longer just a proof of concept. The local engine exists, the core correctness pass is complete, Dalidou hosts the canonical runtime and machine database, and OpenClaw on the T420 can consume AtoCore safely in read-only additive mode.

Phase Status

Completed

  • Phase 0 - Foundation
  • Phase 0.5 - Proof of Concept
  • Phase 1 - Ingestion

Baseline Complete

  • Phase 2 - Memory Core
  • Phase 3 - Retrieval
  • Phase 5 - Project State
  • Phase 7 - Context Builder

Partial

  • Phase 4 - Identity / Preferences
  • Phase 8 - OpenClaw Integration

Baseline Complete

  • Phase 9 - Reflection (all three foundation commits landed: A capture, B reinforcement, C candidate extraction + review queue). As of 2026-04-11 the capture → reinforce half runs automatically on every Stop-hook capture (length-aware token-overlap matcher handles paragraph-length memories), and project-scoped memories now reach the context pack via a dedicated --- Project Memories --- band between identity/preference and retrieved chunks. The extract half is still a manual / batch flow by design (scripts/atocore_client.py batch-extract + triage). First live batch-extract run over 42 captured interactions produced 1 candidate (rule extractor is conservative and keys on structural cues like ## Decision: headings that rarely appear in conversational LLM responses) — extractor tuning is a known follow-up.

Not Yet Complete In The Intended Sense

  • Phase 6 - AtoDrive
  • Phase 10 - Write-back
  • Phase 11 - Multi-model
  • Phase 12 - Evaluation
  • Phase 13 - Hardening

Engineering Layer Planning Sprint

Status: complete. All 8 architecture docs are drafted. The engineering layer is now ready for V1 implementation against the active project set.

The next concrete next step is the V1 implementation sprint, which should follow engineering-v1-acceptance.md as its checklist, and must apply the project-identity-canonicalization contract at every new service-layer entry point.

LLM Client Integration

A separate but related architectural concern: how AtoCore is reachable from many different LLM client contexts (OpenClaw, Claude Code, future Codex skills, future MCP server). The layering rule is documented in:

  • llm-client-integration.md — three-layer shape: HTTP API → shared operator client (scripts/atocore_client.py) → per-agent thin frontends; the shared client is the canonical backbone every new client should shell out to instead of reimplementing HTTP calls

This sits implicitly between Phase 8 (OpenClaw) and Phase 11 (multi-model). Memory-review and engineering-entity commands are deferred from the shared client until their workflows are exercised.

What Is Real Today

  • canonical AtoCore runtime on Dalidou
  • canonical machine DB and vector store on Dalidou
  • project registry with:
    • template
    • proposal preview
    • register
    • update
    • refresh
  • read-only additive OpenClaw helper on the T420
  • seeded project corpus for:
    • p04-gigabit
    • p05-interferometer
    • p06-polisher
  • conservative Trusted Project State for those active projects
  • first operational backup foundation for SQLite + project registry
  • implementation-facing architecture notes for future engineering knowledge work
  • first organic routing layer in OpenClaw via:
    • detect-project
    • auto-context

Now

These are the current practical priorities.

  1. Finish practical OpenClaw integration
    • make the helper lifecycle feel natural in daily use
    • use the new organic routing layer for project-knowledge questions
    • confirm fail-open behavior remains acceptable
    • keep AtoCore clearly additive
  2. Tighten retrieval quality
    • reduce cross-project competition
    • improve ranking on short or ambiguous prompts
    • add only a few anchor docs where retrieval is still weak
  3. Continue controlled ingestion
    • deepen active projects selectively
    • avoid noisy bulk corpus growth
  4. Strengthen operational boringness
    • backup and restore procedure
    • Chroma rebuild / backup policy
    • retention and restore validation

Next

These are the next major layers after the current practical pass.

  1. Clarify AtoDrive as a real operational truth layer
  2. Mature identity / preferences handling
  3. Improve observability for:
    • retrieval quality
    • context-pack inspection
    • comparison of behavior with and without AtoCore

Later

These are the deliberate future expansions already supported by the architecture direction, but not yet ready for immediate implementation.

  1. Minimal engineering knowledge layer
    • driven by docs/architecture/engineering-knowledge-hybrid-architecture.md
    • guided by docs/architecture/engineering-ontology-v1.md
  2. Minimal typed objects and relationships
  3. Evidence-linking and provenance-rich structured records
  4. Human mirror generation from structured state

Not Yet

These remain intentionally deferred.

  • automatic write-back from OpenClaw into AtoCore
  • automatic memory promotion
  • reflection loop integration — baseline now in (capture→reinforce auto, extract batch/manual). Extractor tuning and scheduled batch extraction still open.
  • replacing OpenClaw's own memory system
  • live machine-DB sync between machines
  • full ontology / graph expansion before the current baseline is stable

Working Rule

The next sensible implementation threshold for the engineering ontology work is:

  • after the current ingestion, retrieval, registry, OpenClaw helper, organic routing, and backup baseline feels boring and dependable

Until then, the architecture docs should shape decisions, not force premature schema work.

Reference in New Issue View Git Blame Copy Permalink