indifferentketchup
c935687725
Drop 9 batch proposals that are superseded by the boocode-lift-analysis (boocontext-audit, conductor upgrades, self-healing/verify-gate skills): add-3tier-memory, import-llm-evaluator, import-pregel-engine, plugin-platform, conductor-evolution, code-intelligence-upgrade, dev-workflow, ui-overhaul, agent-reliability. Delete 11 stub archive files (49-66B each, 'Status: Shipped. Archived.' only) that provide zero documentation value over the existing CHANGELOG.md + git tags.
36 lines
2.7 KiB
Markdown
36 lines
2.7 KiB
Markdown
## Why
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Current AI agents lack structured, durable memory beyond the immediate context window. Conversations are stateless, preferences are forgotten, and long-term learning is nonexistent. Three OSS repos (LangMem, DeerFlow, CowAgent) demonstrate production patterns for agent memory — but no unified, portable engine exists that combines short-term context management, long-term semantic memory, tiered consolidation, and hybrid retrieval. This change builds that engine by extracting and adapting the best patterns from all three.
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## What Changes
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- **New `memory-engine/` module** in the codebase providing a unified memory & context API
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- **Short-term context summarization** — token-budget-aware conversation windowing (LangMem pattern)
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- **Long-term semantic memory** — LLM-extracted facts stored with optional vector embeddings (LangMem/DeerFlow hybrid)
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- **Tiered memory architecture** — Context tier (ephemeral session) → Daily tier (summarized records) → Core tier (distilled long-term) (CowAgent pattern)
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- **Hybrid search** — Keyword (FTS5) + Vector (cosine similarity on embeddings) with weighted merge (CowAgent pattern)
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- **Background consolidation** — Debounced, async memory extraction pipeline (DeerFlow queue + LangMem ReflectionExecutor)
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- **Deep Dream distillation** — Periodic overnight LLM consolidation of daily records into core memory (CowAgent pattern)
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- **Memory tools for agents** — `manage_memory` and `search_memory` tool interfaces (LangMem pattern)
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## Capabilities
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### New Capabilities
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- `short-term-context`: Token-budget window management, conversation summarization, and context trimming for LLM interactions
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- `long-term-memory`: Persistent fact extraction, storage, and retrieval with Pydantic-typed schemas
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- `tiered-consolidation`: Three-tier memory pipeline (context→daily→core) with promotion rules and Deep Dream distillation
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- `hybrid-search`: Combined keyword (FTS5) + vector (embedding cosine similarity) search with weighted scoring and temporal decay
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- `memory-tools`: `manage_memory` (CRUD) and `search_memory` (semantic query) tools for agent integration
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- `background-processing`: Debounced async memory update queue with thread-pool execution
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### Modified Capabilities
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<!-- No existing specs to modify — this is a greenfield module -->
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## Impact
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- New `memory-engine/` directory tree (no existing code modified)
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- Dependencies: `sqlite3` (stdlib), `numpy` (optional, for vector search), `pydantic` (schemas), `tiktoken` (token counting)
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- LLM provider integration via abstract `ChatModel` interface (not coupled to any provider)
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- Embedding provider integration via abstract `EmbeddingProvider` interface (supports OpenAI, local models)
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- Agent integration via simple tool interface (not coupled to any agent framework)
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