2026-07-20 20:20 UTCIn-site rewrite5 min readUpdated: 2026-07-20 20:31 UTC

Show HN: Cognikernel- Local Memory for AI Coding Assistants

Cognikernel provides persistent, structured project memory for AI coding assistants like Claude Code and Codex. It captures decisions, constraints, and abandoned approaches from coding sessions using an event-sourced store, and injects compact context blocks in future sessions to prevent re-decision. Unlike cloud-based memory tools, Cognikernel runs a deterministic extraction pipeline locally with two small ONNX models (~130 MB total) on CPU, ensuring privacy and low latency. The system features four hook surfaces, hybrid retrieval (BM25 + optional dense embeddings), and a fail-open reliability spine. Benchmarks show 2-4× fewer file reads and up to ~20% token cost reduction on complex projects.

SourceHacker News AIAuthor: KanishkNoir

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Persistent, structured project memory for Claude Code — and Codex. CogniKernel watches a coding session through its hook surfaces, extracts the decisions, constraints, and abandoned approaches worth keeping, consolidates them into an event-sourced store, and injects them back as a compact context block the next time you work — so the agent stops re-deciding what you already decided. The store is keyed on the project path, so memory made in one tool travels to the other.

It is not a vector-database wrapper. It is an event-sourced log of typed memory with lexical-primary retrieval, write-time consolidation, and a fail-open reliability spine designed never to break your session.

And there is no LLM in the loop. Most memory tools work by sending your transcripts to a generative model to "summarize what mattered" — another API key, per-session token cost, added latency, and your session content leaving the machine. CogniKernel treats extraction as classification, not generation: a deterministic sanitize → classify → consolidate pipeline, with two small fine-tuned encoder models (~130 MB ONNX, run locally on CPU in milliseconds) scoring salience and detecting when a new decision supersedes an old one. No API calls, no tokens billed, nothing leaves your machine. The only LLM involved is the coding agent you already run — CogniKernel makes it remember.

Naming: CogniKernel is the project; memlora (package memlora-edge) is the Python module and CLI it ships as — the working name the code grew up under. One project, two names: memlora init, memlora doctor, etc.

The memory loop

Everything CogniKernel does is one loop: observe → extract → consolidate → store → retrieve → assemble → inject. The left rail captures; the right rail recalls; the spine underneath keeps both honest.

The four hook surfaces

CogniKernel attaches to a session at four points. Each is fail-open — if memory is unavailable or errors, the hook logs at WARNING, returns cleanly, and the session continues.

Surface Hook Authority What it does

Session block SessionStart advisory injects the canonical decisions/constraints/skeleton block

CK-1 recall UserPromptSubmit advisory surfaces prompt-relevant memory, dual-evidence gated, dedup'd via render ledger

Read/Edit gate PreToolUse hard / JIT read-efficiency gate on Read/Grep; just-in-time prohibition surfacing on Write/Edit (K2)

Capture Stop side-effect extracts and persists decisions — you never write memory to CLAUDE.md by hand

What gets remembered

Memory is typed, not free-text chunks. The type drives ranking, rendering, and supersession:

DECISION — a choice that was made ("use Redis for the rate limiter")

CONSTRAINT_HARD / CONSTRAINT_SOFT — rules, graded by deontic force

APPROACH_ABANDONED_DO_NOT_RETRY — a dead end, kept in the graveyard so the agent doesn't re-attempt it

conventions, config facts, schema decisions (canonical role keys)

A decision key lets a later restatement supersede an earlier one (latest-wins), so the store self-consolidates instead of accumulating contradictions. An optional cross-encoder adds semantic supersession when the encoder backend is installed.

Retrieval

Lexical-primary, with dense as a fused signal — never pure vector:

Hybrid core — FTS5 BM25 ∪ optional dense embeddings → Reciprocal Rank Fusion

prohibition_search — a type-restricted lexical pool so a "do not do X" rule can't be crowded out by topically-similar prose at the moment you're about to do X

Skeleton — an AST symbol graph ranked by PageRank; find_related unions semantic (embedding) neighbours with import-graph-adjacent events to surface what a change touches (the semantic axis needs the embedding extra)

Golden-record consolidation at read — latest-wins reconciliation so recall returns one coherent answer, not a pile of revisions

What it saves you

Benchmarked in a three-arm comparison — CogniKernel vs flat curated notes vs no memory — with real agent sessions across four multi-session projects:

File reads: the universal win. The CogniKernel arm made the fewest file reads in every project — typically 2–4× fewer (23 vs 63, 16 vs 47/53, 40 vs 89/83), and in the best case 3 reads vs 29 because the injected block + AST skeleton carried the whole repo's shape. Fewer reads means fewer tool round-trips and more of the context window left for actual work — your session gets longer before compaction, not just cheaper.

Tokens: up to ~20% cheaper where memory matters. Price-weighted token cost (cache-write 1.25×, cache-read 0.1×, output 5×) came out 18–23% lower on projects with evolving decisions and cross-file dependencies — and roughly a wash on small implementation-heavy projects where the code itself is cheap to re-read. We publish the honest number, not the raw-token one (raw sums look ~30–40% better, but ~95% of any session's bill is discounted cache-read).

Recall instead of re-derivation. Where memory earns its keep is projects whose state is too large, too evolving, or too long-lived to re-derive cheaply: the agent starts already knowing the decisions, constraints, and dead ends, instead of spending the first quarter of the session rediscovering them.

Reliability — the spine

The system is designed to degrade legibly, never silently:

Atomic migrations — each numbered migration applies its body + version bump in one transaction; safe to crash mid-script

Idempotent replay — a re-run worker job can't double-count or drift decay (evidence-provenance guard)

Fail-open hooks — every surface swallows its own failure and logs at WARNING; silence never reads as success

memlora doctor --strict — per-subsystem health (schema, FTS5, embeddings, symbols, worker queue); non-zero exit when degraded

Architecture enforcement — import-linter layered contracts, guarded by a meta-test so a typo can't silently disable them

CI promotion gate — lint + full suite (incl. tests/reliability/ failure-injection) on every PR; see CONTRIBUTING.md

Cross-platform (Codex)

The store is platform-neutral — one SQLite DB per logical project, so Claude Code and Codex working in the same directory share one memory. Project resolution is alias-aware: C:\repo and /mnt/c/repo resolve to the same store, so memory follows the checkout across Windows, WSL, and native mounts; for genuinely different checkout paths, an opt-in project_identity key in .memlora/config.toml pins them to one shared store. Codex reads memory through the registered MCP server (get_session_state / recall); the capture direction is pull-based, because Codex has no Stop-hook equivalent:

memlora codex-sync scans ~/.codex/sessions for rollouts whose recorded cwd maps to the project and captures the delta through the same extraction pipeline (a rollout→transcript adapter is the only Codex-specific code; delta/dedup/idempotency are shared and unchanged).

Automatic at the handoff — Claude's SessionStart drains pending Codex rollouts before building the block, and the MCP server's queue drainer pulls new rollouts each cycle, so a live Claude session picks up Codex-side decisions without waiting for the next session; on the Codex side, init writes an AGENTS.md instruction + a ck-sync skill so Codex pulls at session start.

init provisions both — .mcp.json (Claude) and .codex/config.toml (with the server's cwd + project env pinned) + AGENTS.md (Codex), idempotently and without clobbering existing settings.

memlora doctor reports a codex health line (sessions dir + rollout count, or "nothing to sync" — Codex is optional, so its absence is healthy).

A decision made in Codex reaches the next Claude session's block, and vice versa. The action-point surfaces (CK-1, PreToolUse gate) are Claude-only — Codex has no per-prompt/per-tool hook — so on Codex the loop degrades to the shared block + MCP recall.

Interfaces

MCP tools (the session block is injected automatically; these are for targeted use): recall · find_related · skeleton · get_session_state

CLI:

memlora init — register the project and install the session hooks

memlora doctor [--strict] — subsystem health report

memlora codex-sync — capture Codex CLI sessions for this project

memlora install-heads — install the trained encoder artifacts (salience + cross-encoder ONNX bodies): downloaded from the heads-v1 release and sha256-verified, or copied from a local models/ export when present

memlora show / memlora reset — inspect / clear stored memory

Quickstart

uv sync # core (lexical-only) uv sync --extra embedding # + dense retrieval (fastembed + numpy)

uv run memlora init . # register this project + install hooks uv run memlora doctor . # subsystem health

uv run memlora install-heads # optional: trained encoder heads (~270 MB download);

without them extraction/supersession fall back to

the legacy/lexical path — everything still works

Then start a Claude Code session in the project — the memory block appears automatically at session start, and decisions are captured when the session ends.

Project layout

src/memlora/ integration/ hooks, CLI, MCP server, session/worker orchestration extraction/ sanitize -> classify -> salience -> decision-key pipeline delta/ delta-merge + supersession (latest-wins; cross-encoder optional) retrieval/ hybrid BM25 + dense -> RRF storage/ event-sourced SQLite, FTS5, migrations, render ledger embedding/ optional dense vectors (fastembed) symbols/ AST skeleton + PageRank graph compression/ authority-weighted drop-to-fit budget injection/ block template assembly model.py Event — the dependency-free domain primitive tests/ unit/ per-subsystem reliability/ crash-replay · worker-contention · corrupt-input injection

Status

Schema v18 (includes the Codex cross-platform capture). Architecture contracts: 3 kept / 0 broken. CI gate: lint + full suite on Ubuntu (3.11/3.12) and Windows. See CONTRIBUTING.md for the Definition of Done that gates every change.

About

Persi

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