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Vlk MemAct

by aranajhonnyversion 0.3.0i★ –iRuns on your computer
Registry nameio.github.aranajhonny/vlk-core

Install

Overview

Description from the maker (original English)

Stateful memory for AI agents. Temporal awareness, error loop detection, and preventive constraints.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: DATABASE_URL

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

Fill in the empty values before running it.

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • DATABASE_URL env
    SQLite database path (default: sqlite:vlk.db?mode=rwc)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account aranajhonny, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 5680 servers in this category
  • Published to the registry more recently than 18% of the other 5680 servers in this category
  • Basic checks equal to or better than 100% of the other 5680 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Vlk MemAct safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.0) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 2 days ago
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Vlk² — Temporal State Machine for AI Agents

Vlk² gives AI agents a sense of time: what's happening now (PRESENT), what's already been tried (PAST), and what should guide future decisions (FUTURE). It runs as a single binary over MCP, backed by SQLite, and manages these temporal transitions autonomously.

The killer demo: when an agent repeats the same Rust/TS/Python/Go error 3 times, Vlk² auto-archives the loop and injects a preventive constraint so the agent stops retrying the same broken approach. But the architecture is general — the temporal state machine can track any kind of past→present→future transition across any agent framework.

What It Does

  • Temporal state machine — each memory has a state: PRESENT (active context), PAST (archived, hidden from context), FUTURE (preventive constraints), PURGED (forgotten). A background agent transitions entries between states without agent intervention.
  • Cross-language loop detection — fingerprints errors by language, not by string match. Recognizes Rust (error[E0277]), TypeScript/JS (TS2345, TypeError), Python (ValueError, KeyError), Go (panic: nil pointer), HTTP (503 Service Unavailable), test frameworks (expected: X, got: Y), with a timestamp-stripping fallback. When the same fingerprint appears ≥3 times, Vlk² auto-archives the loop to PAST and injects a FUTURE constraint.
  • Reconsolidation on retrieval — every recall checks current beliefs; if contradictory, the memory rewrites itself.
  • Token budget enforcement — every 30s, estimates total tokens in PRESENT + FUTURE. If over budget (8K default), archives lowest-importance entries until under budget.
  • Autonomous consolidation — background task (30s cycle) evaluates importance, archives stale entries, and forgets disconnected ones.

Why Not Just a Memory System?

General memory systems (agentmemory, Hindsight, Mem0, Supermemory) store facts, preferences, and context. Vlk² does one thing they don't: detect that the same Rust compile error repeated 3 times across different files and inject a constraint so the agent stops retrying the same broken approach. It's not a replacement — it's a specialized fault-detection layer that runs alongside them.

CapabilityVlk²agentmemoryHindsightSupermemory
Fact/persona memory—✅ 53 tools✅ hooks✅ keywords
Cross-language error fingerprinting✅ 7 strategies———
Autonomous loop breaking✅ ≥3 repeats auto-archive———
Reconsolidation on retrieval✅———
Token-bounded context✅ 8K default———

Quick Start

cd vlk-core

cargo build --release DATABASE_URL="sqlite:vlk.db?mode=rwc" ./target/release/vlk-core

Any MCP client connects via stdin/stdout — JSON-RPC 2.0, one line per message.

MCP Tools

ToolPurpose
vlk_record_statePush a log/error as a PRESENT slot for loop detection
vlk_fetch_contextClean context with reconsolidation + loop interception
vlk_time_travelArchive PRESENT → PAST and inject a FUTURE constraint
vlk_get_historyFull timeline audit
vlk_search_memoryKeyword search across logs and constraints
vlk_summarize_sessionState counts + token estimates
vlk_revoke_futureRemove incorrectly learned constraints
vlk_get_factsRetrieved distilled patterns
vlk_consolidation_statusShow consolidation agent state with token bar
vlk_pin_memoryProtect a pattern from forgetting
vlk_unpin_memoryRestore normal forgetting eligibility

How Loop Detection Works

  1. Each error is fingerprinted by type: Rust error[E...], TS TS..., Python TypeError:, Go panic:, HTTP 503, test assertion, or first-80-chars fallback
  2. Fingerprints group near-duplicates — same error code with different timestamps/line numbers count as one pattern
  3. At ≥3 repeats → auto-archives the loop + injects [SYSTEM ANCHOR] preventive constraint
  4. Future context fetches include the constraint, so the agent stops repeating

Architecture

memory_contents (immutable, team-agnostic)

└── FK ──► agent_timeline (PRESENT / PAST / FUTURE / PURGED) ├── memory_metadata (temporal vectors, importance, connectivity) └── semantic_facts (distilled patterns from errors) └── fact_contradictions (resolution tracking)

Consolidation Agent (background tokio, 30s cycle) SCAN → EVALUATE importance → BUDGET_CHECK → ARCHIVE low-value → FORGET stale

Verification

What's tested: the core loop detection path is verified: 3 identical Rust errors → auto-archive to PAST + FUTURE constraint injection. All 13 MCP tools respond correctly.

Not yet tested: pin/unpin with non-existent timeline_ids, token budget enforcement under load, conflict resolution.

Loop detection smoke test — sends the same Rust error[E0277] 3 times and verifies auto-archive + constraint injection:

import subprocess, json, tempfile, os, sys

db = tempfile.mktemp(suffix='.db') proc = subprocess.Popen(['./target/release/vlk-core'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, env={'DATABASE_URL': f'sqlite:{db}?mode=rwc'})

def rpc(msg): proc.stdin.write((json.dumps(msg) + '\n').encode()) proc.stdin.flush() return json.loads(proc.stdout.readline())

Init

rpc({'jsonrpc':'2.0','id':1,'method':'initialize','params':{ 'protocolVersion':'2024-11-05','capabilities':{}, 'clientInfo':{'name':'test','version':'1.0'}}})

Send the same Rust error 3 times to trigger loop detection

error = 'error[E0277]: cannot add str to str' for i in range(3): r = rpc({'jsonrpc':'2.0','id':i+2,'method':'tools/call','params':{ 'name':'vlk_record_state', 'arguments':{'raw_log': error, 'file_context': f'lib.rs:{i*10}'}}}) assert 'PRESENT state recorded' in r'result'0 print(f' Record {i+1}: OK')

fetch_context triggers loop detection → auto-archive → constraint injection

r = rpc({'jsonrpc':'2.0','id':5,'method':'tools/call','params':{ 'name':'vlk_fetch_context','arguments':{}}}) txt = r'result'0 assert 'E0277' in txt, 'error missing after fetch' assert any(kw in txt for kw in ['SYSTEM ANCHOR', 'PREVENTIVE', 'CONSTRAINT']), \ 'no constraint injected' print(f' Fetch: loop archived + constraint injected')

proc.terminate(); proc.wait() for f in (db, db + '-wal', db + '-shm'): try: os.remove(f) except FileNotFoundError: pass print('PASS: loop detection works')

Integration

Add to Hermes, OpenCode, Claude Code, Codex, or any MCP host:

# ~/.hermes/config.yaml or equivalent

mcp_servers: vlk2: command: "vlk2" env: DATABASE_URL: "sqlite:~/.hermes/vlk/vlk.db?mode=rwc" timeout: 30

Compatible with any MCP client. Works alongside agentmemory, Hindsight, Supermemory, or Hermes built-in memory.

Requirements

  • Rust 2021 edition
  • SQLite (bundled via sqlx)
  • 6.4MB binary

Current Status

Actively developed. The core loop detection and corrective memory cycle is implemented and E2E-tested. Vlk² is a specialized complement to general memory systems — not a replacement.

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