Agent with tiered working memory and cross-session learning — architecture, gaps, and what the research didn't cover
I've been building PRAANA — a coding agent with two systems I couldn't find combined in one self-contained binary: an Adaptive Context Engine (within-session) and Cognitive Memory (cross-session). Posting because the architectural decisions may be useful independent of the coding use case.
The core problem:
Every agent session is a context window management problem. Append-until-full plus reactive compaction is lossy — by the time you compact, you've already paid the drift cost, and you've lost track of which information was load-bearing.
PRAANA's ACE curates on every turn. A deterministic compiler assembles the prompt in 5 sections:
1. System Frame — identity + tools
2. Memory Digest — ranked cross-session learnings
3. Active State — current work objects, full resolution
4. Peripheral Stubs — everything inactive, one-line anchors
5. Recent Turns — last N turns, budget-capped
State objects demote Active → Soft → Hard based on idle turns. Two-pass auto-hydration before each turn: substring keyword match, then BM25 for fuzzy overlap. Scores are density-weighted: decisions score 1.0, narrative scores 0.6, errors score 0.8. The compiler knows what kind of information is filling up, not just token count.
Cognitive Memory:
At /exit, a summariser extracts structured learnings from the transcript. Six kinds: fact, preference, decision, pattern, mistake, constraint — domain-agnostic; coding-specific knowledge lives in content, not schema. Stored in SQLite with sqlite-vec + Transformers.js (in-process, 384-dim). Confidence decays 5%/day. Entries confirmed across two or more sessions promote to Consolidated Memory (10x slower decay). Ranked recall: cosine × confidence × recency × pin_boost.
Where the research fell short:
I surveyed 20+ agent-memory repos. What I found:
Mem0, LangChain, and most memory backends are retrieval systems. They store and recall but have no outcome-based feedback loops. No architecture for "this memory was used and confirmed, increase confidence" vs "this memory was contradicted, reduce it." Letta has the most interesting consolidation work (sleep-time agents) but it's a platform, not extractable, and consolidation is partial.
Nobody combined proactive context curation with learning memory in one self-contained process. The compression tools — Headroom, ACON — are SDK/proxy layers that sit between you and the LLM. They don't own agent state.
The gap I missed: the research covered storage architecture, not learning signal. The reinforcement path in PRAANA — boost confidence when a session succeeds, decay when contradicted — is wired but the session-success signal hasn't shipped yet (#162). I designed a complete feedback loop and then discovered the trigger was the hard part.
The larger plan:
Four systems — Adaptive Context, Cognitive Memory, Background Consolidation, Intelligent Router — all domain-agnostic. No system encodes anything about code. The coding agent is the proving ground; coding outcomes are measurable. Once Phase 1 validates the architecture, Phase 2 extracts the runtime as @praana/runtime. I'm not extracting it until the coding agent proves it works.
Gaps:
Reinforcement path dormant (#162). No A/B eval harness — scorecard ships, headless task runner is next, no published benchmark claims. Background Consolidation Processor schema exists, not scalable yet. Runtime extraction is Phase 2, not started.
GitHub: amitkumardubey/praana — MIT, TypeScript, Bun.
If you're working on agent memory or context management architecturally, I'd welcome the comparison. What are you seeing in production that the research repos didn't surface?
