ContextLab / ContextLab/orchestrator

Build governed cross-run memory, retrieval, and solution reuse

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architecture enhancement
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Python
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Description

## Origin

Deferred from the MVP proposed as a fresh realization of #485.

## Goal

Turn the MVP's run-local journal, artifact store, summary DAG, and FTS retrieval into a durable cross-run knowledge system.

## Scope

- Cross-run/project/user scopes for insights and reusable problem solutions.
- Positive and negative solution records; typed I/O compatibility plus semantic matching.
- Hybrid retrieval and reranking only after benchmarks show the SQLite FTS baseline is insufficient.
- Contradiction detection, confidence, expiry/decay, provenance, retraction, and impact tracing.
- Incremental sealing/summarization without quadratic recomputation.
- Retrieval and summary-routing evaluation on seeded-needle and task-grounded benchmarks.
- Garbage collection, retention, legal deletion/redaction, encryption, tenant isolation, and secret/PII classification.

## Acceptance criteria

- [ ] Cross-run reuse improves measured success/cost without increasing corrected decomposition branching above the safe threshold.
- [ ] Negative results prevent repeated failed decomposition paths.
- [ ] Every retrieved claim resolves to immutable source spans and exact provenance.
- [ ] Retraction identifies downstream artifacts influenced by the removed insight.
- [ ] Retention/deletion behavior is documented and tested, including content-addressed data.

Contributor guide

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