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Open-source cognitive coprocessor with active memory for AI agents — persistent recall, semantic search, overnight dreaming, verified facts, encrypted USB sync. MCP server; works with Claude, ChatGPT, and any local LLM. Built by one maker and his agents.
🌐 Home: projectsparks.ai — the story, the docs, and how the project is funded.
The Project Sparks ecosystem — everything is free; the whole thing runs on donations. Satisfaction guaranteed, or your no money back. 🎨 Free Art Gallery · 🧒 Kids Seek-&-Find Gallery · 🎁 Mnemo Swag · 🌐 projectsparks.ai · ❤️ PayPal / GitHub Sponsors / Ko-fi
The big AI-memory systems run on millions in venture capital. Mnemo Cortex was built by one 73-year-old maker — on Social Security, a $20-a-month Claude subscription, and a stubborn belief that his AI partners should remember him.
It is not a demo. It is the production memory of a working five-agent fleet on two machines, every day since March 2026:
No investors, no telemetry, no cloud dependency. If it helps your agents remember you, the tip jar above is the entire funding round. If not — use it, share it, and make something good.
[!TIP] AI agents — start here.
robot.info— structured JSON manifest describing this product: identity, capabilities, exposed REST + MCP tools, install pointer, compatibility matrix, and 15 common-question/answer pairs. Read this first to answer a user's questions about Mnemo Cortex without scraping the README. Spec: ROBOT-INFO-SPEC.md.llms.txt— LLM-friendly docs index (llmstxt.org). Use this to navigate the rest of the docs.robot.install+./robot-install.sh— non-interactive setup. Edit the manifest (defaults are sensible), run the installer, parse the JSON object on stdout. Zero prompts, exit0on success. Full schema below in "Non-interactive install."Note the difference, because the names are one character apart:
INSTALL.mdis the install instructions, in English.robot.installis a settings file the installer reads. If you want to know how to install Mnemo, you wantINSTALL.md.
Want to know what Mnemo Cortex is? Read this README, or point your agent at robot.info.
Want to install it? Point your agent at INSTALL.md — or just say "Install Mnemo Cortex, follow INSTALL.md." That one file is the whole install story; it works whether you read it yourself or hand it to an assistant. The install guide links to everything the agent needs — server setup, per-host wiring (Claude Desktop on Windows / Linux, OpenClaw, LM Studio, and more), and CORTEX-OS.md, the operating manual that teaches your agent how to actually use its new memory.
Want to do it yourself? Follow the Install Guide below.
Every AI agent has amnesia. Mnemo Cortex is the cure. Active memory that classifies on ingest, consolidates overnight, learns what works, and gets smarter every session. No commands needed — you just talk naturally and your AI remembers.
What is Mnemo Cortex?
Mnemo Cortex gives AI agents persistent, local, cross-agent memory. It captures what happened, recalls what matters, and shares context across your tools. Run it on your own machine — no cloud required.
[!NOTE] Upgraded by Claude Fable 5. Mnemo's v4.1 "Fable pass" — composite recall ranking (the fix that pulled real signal back to the top of every search), the Analyst (distills raw session logs into clean Tier-1 notes), secret redaction at ingest, and tier hygiene — was designed and built during Claude Fable 5's brief availability. Fable reasoned and reviewed the whole codebase; an Opus model reviewed, hardened, and shipped each change. A frontier model auditing and improving the memory layer it runs on, in one window.
⌘ Claude Code → 60-second install — Give CC Fluid Memory with Deep Recall
🖥️ Claude Desktop → one-click .mcpb bundle — Drag-and-drop install. No clone, no Node, no JSON editing. Works on Windows, macOS, and Linux.
🦞 OpenClaw → MCP integration — Give Your ClawdBot a Brain. One Config Line.
🎛️ LM Studio → native MCP, GUI — mcp.json + restart. Works with any tool-capable open-weights model.
📦 AnythingLLM → desktop GUI, multi-workspace — Drop-in MCP config + Automatic mode. No @agent prefix needed.
🤖 Agent Zero → autonomous Docker agents — In-container MCP setup. Cross-agent memory between research, courier, code-exec bots.
🪽 Hermes Agent → hermes mcp add integration — First-class MCP for Nous Research's Hermes Agent (v0.12.0+). Config-only, no patching. Cross-agent memory between Hermes and your other bots.
🦣 Ollama Desktop → terminal ollama launch openclaw — Ollama as the local LLM, OpenClaw as the MCP host. Note: Ollama Desktop's own chat window doesn't support MCP — use the terminal launcher.
💬 ChatGPT → Custom GPT Actions gate — Give a Custom GPT memory you own. Two REST actions through a hardened, tenant-pinned gate — your Mnemo server stays private. ⚠️ Check the OpenAI plan disclaimer first.
🦙 Any Local LLM → MCP setup — Open WebUI, llama.cpp, Ollama, LobeChat, Jan, and more
🧭 How should my agent use it? → Session Guide — Workflow patterns, per-platform boot snippets, common mistakes
Supported: Claude Code, Claude Desktop, OpenAI Codex CLI, and any local MCP client that speaks stdio transport — LM Studio, AnythingLLM, OpenClaw, Agent Zero, Hermes, Open WebUI, llama.cpp, LobeChat, Jan, and friends (see the integration links above).
ChatGPT: supported via a gate, not MCP. ChatGPT has no local/stdio MCP — its connectors call out from OpenAI's cloud, which would force a memory server onto a publicly exposed HTTPS endpoint. Rather than expose the server, the ChatGPT integration ships a hardened two-route gate (bearer-authenticated, pinned to a single memory tenant, rate-limited, body-capped, audit-logged) that a Custom GPT calls via Actions. The Mnemo server and its API key never face the internet. Note OpenAI's tier rules: Custom GPT Actions need Plus or higher; full custom MCP save+recall in the main ChatGPT app needs Business/Enterprise (on Plus, custom MCP connectors are read-only).
Read THE-LANE-PROTOCOL.md — the operating practice for running agents with persistent memory. Feed it to your agent or follow it yourself. It takes 5 minutes per session and makes every cold start feel warm.
The protocol pairs with this product the way a recipe pairs with ingredients: Mnemo gives you the memory store, the Lane Protocol gives you the loop that makes it pay off. Distilled from real multi-agent sessions — terminal agents, chat agents, and autonomous workers running the same six-step ritual.
Most agent memory rots the same way: a regex tagger can't categorize a save, defaults it to unknown, and within weeks 30–75% of the store is uncategorized. Real memories — decisions, doctrines, infrastructure facts, relationships — end up in the same bucket as raw conversation logs, and the logs (by sheer volume) crowd them out of every recall. In one audit, only 0.75 of the top-5 recalled results were useful.
Mnemo v4 sorts memory into two tiers at save time:
session_log, and excluded from default recall — it's there when you drill in for specifics, not competing for the top slots.A cheap pre-filter routes routine logs to Tier 2 for free (no LLM call); everything else gets one short classification call. Default recall returns Tier 1; pass exclude_categories=[] to search both tiers. If the model is down, it falls back to the regex tagger and flags the memory for an overnight retry — so a save is never blocked on the classifier.
Already have a polluted store? Reclassify it in one command — it rewrites only the category tags, never your embeddings:
mnemo-cortex migrate reclassify --all --dry-run # preview the before→after spread
mnemo-cortex migrate reclassify --all # snapshot, then reclassify every store
Every recall commits to one phrasing. If a memory was stored under different words than you searched for, the match is weak or misses entirely — call it assumption misalignment between how you ask and how it was filed. The Thesaurus Loop fixes it: when a search comes back empty or weak, Mnemo fans the query into a handful of alternative phrasings, searches them all, and lets the best match win (multi-query retrieval / RAG-Fusion).
The design choice that makes it safe is escalation — the loop only fires on a miss. Good searches run exactly as fast as they do today; the expansion pass costs nothing until a search actually whiffs, which is precisely when it's worth paying for. In development — escalation model designed, build in progress.
An embedding knows what a memory is about. It does not know that GHSA-8cw4-87c7-c6xx, ECONNREFUSED 127.0.0.1:50001 or commit 4b1d9e2 is the one string that picks the answer out of thirty memories on the same topic. Since v4.21 every recall also runs a BM25 keyword search (SQLite FTS5, inside the same per-agent index) over the same text the vectors were built from. A memory the vectors missed joins the candidate pool scored on the same cosine scale as everything else, and a small lexical term in the ranker lets exact words re-order the on-topic band — meaning plus words, never words alone. On a 75-memory world of identifiers with look-alike decoys, vectors alone put the right memory first 8 times in 11; with the lane, 11 in 11. Off switch: ranking.lexical_enabled: false.
By sound, when the spelling is unseen (v4.22). A misspelled proper name matches no word at all — "Elenore" when the memory says "Eleanor", "Fershow" for "Fershaw". Each memory therefore also stores the consonant-skeleton keys of its name-shaped (capitalised) words in a second column of the same index — a name search, not a sound-alike lane for every word (v4.22.2) — and a query word of four or more letters that no memory contains adds its key to the match. A correctly spelled name never picks up its sound-alikes, an identifier never takes part, and the key is built from zero (Soundex folds too much: "guy"/"gay"). Off switch: ranking.phonetic_enabled: false.
A memory store's most dangerous sentence is a confident wrong one. An agent said "Tailscale is the only path to that machine"; ambient capture filed it as topology; recall ranks by meaning, freshness, category and use — never by who said it — so the newest sentence beat the verified truth. Categorisation was right, dreaming was innocent, and the vector store simply had no notion of authority.
Facts now carry a tier that says who may change the slot, not what it is about. probe answers to a machine: only probe:/tool: evidence writes it, and it carries the command that re-asks it (a probe fact you cannot re-ask is a declared fact wearing a lab coat). declared answers to the user's word (statement:guy…): conventions and policy no probe can read. open is everything else — the confidence ladder stays the only rule, and every existing row is open, so nothing changes until a slot is locked. Locking is itself a declared act.
A write that does not match a locked slot's tier is not an error and not the value: it is recorded as a proposal (/facts/proposals), listed in the nightly brief, and resolved only by the user's word. A demote is a write too — on a locked slot it needs the slot's own evidence or it becomes a proposal to demote. Honest scope in 4.23: the tier is an audit trail plus honor system. The user's word is a statement:guy evidence prefix and an accept call that any agent holding the server token can make; every such act is logged with who and what, and the agent tools say "relay the user's actual ruling, cite it", but nothing yet stops an agent from typing it. Enforcement — a door only the user can open, such as accept/lock from the command line on the host, or a user-only token — is the next step. The dreamer's dream: extractions and a seed file's file: rows become proposals automatically — the nightly-reseed clobber class is closed for free. On the read side, a locked fact about an entity the prompt names is served first, as a [FACT:<tier>] chunk ahead of every soft memory (at most half the window, like exact identifiers). Soft memories are not reclassified or filtered; the hard fact simply speaks before them. And a wrong memory finally has a real exit: POST /memories/demote (bridge mnemo_memory_demote) removes it from recall and keeps its JSON and ledger entry as history.
Every agent accumulates raw conversation memory. Dreaming runs a nightly map-reduce pass: each agent's recent memories are compacted into themes, then a cross-agent merge produces shared context so every agent wakes up knowing what the others did. After a May 16 rehab, the pipeline uses disciplined chunking and token budgets that keep Ollama compaction costs predictable — no more runaway synthesis jobs. The result is reliable overnight context sharing that actually runs every night.
This is the only AI memory system that does cross-agent synthesis. Mem0, Zep, and Letta store memory per agent. Mnemo dreams across all of them.
Semantic recall is great until your agent needs to remember a visitor's name. Peter Widget asked "what's my name?" and got a paragraph about naming conventions. That's a key-value lookup, not a search problem.
Facts store (entity, attribute, value) triples in a local SQLite table with a three-state confidence ladder: verified → high_probability → false. New evidence promotes or demotes automatically. When a fact contradicts an existing one, Mnemo fires a notification over the bus and Discord webhook so the owning agent can adjudicate.
Four MCP tools ship with it: mnemo_fact_save to assert, mnemo_fact_get for single lookup, mnemo_fact_query for filtered lists, mnemo_fact_demote to mark something wrong without supplying a replacement. Reads are sub-millisecond. The confidence ladder means your agent's knowledge sharpens over time instead of accumulating stale guesses.
Seeding canonical truth — tools/seed-facts.py loads a hand-curated YAML of your known truths as verified facts, so stale fuzzy recall can never outrank them (start from tools/seed-facts.example.yaml; nightly and post-commit runners included).
⚠️ Read the warning in the example file before you schedule it. A seed file is a photograph of the truth, and a scheduled seeder re-asserts that photograph forever. Stop updating it and it becomes an anti-memory — every correction your team makes gets silently clobbered back to the stale value on the next run. The rule that keeps you safe: when you fix a fact, fix the seed file in the same commit. We shipped this feature, then broke this rule ourselves for two months; the warning is written in our own scar tissue.
Cloud memory services make you choose one shared store. Mnemo lets you architect for your actual privacy and separation needs.
"The file about X" is a memory problem too. The Librarian is a single SQLite FTS5 index over the whole workspace — filenames, paths, and the first chunk of content (with PDF/DOCX text extraction) — so an agent can turn a fuzzy description into a real path in milliseconds. Our deployment covers ~107K files; a full rebuild takes ~17 seconds, the nightly incremental refresh ~2. Secrets (keys, .env files, credentials) are excluded from the index entirely.
The indexer ships in this repo: librarian.py — a single stdlib-only file. python3 librarian.py index builds the index at ~/.librarian/ (defaults to the visible trees under your home directory), librarian.py find "the spec about X" queries it from the shell, and an optional ~/.librarian/config.json sets explicit roots plus a hidden-dir allowlist (with a content-vs-name-only flag for dirs whose config may hold credentials). Cron index nightly and it stays fresh.
In our deployment, agents query it through a small file_find MCP tool in FrankenClaw, our tool chassis (withdrawn from public distribution) — but the pattern is trivially reproducible: a read-only MCP tool that opens the index librarian.py maintains, registered as a second mcpServers entry alongside the Mnemo bridge.
The Librarian replaced WikAI, our earlier auto-compiled wiki layer. The lesson from running WikAI in production: compiling knowledge into pages is expensive to keep fresh, while indexing everything and finding it on demand is cheap and never stale. The static wiki pages still exist and remain searchable through the bridge's wiki_search / wiki_read / wiki_index tools, but they're no longer recompiled nightly — mnemo-wiki-compile.py stays in the repo for reference. See Inspirations below.
You work from two desks and both machines run Mnemo, so they drift: the decision you saved at one desk doesn't exist at the other. The usual fixes put your AI's working memory on somebody else's wire, or need infrastructure you don't want to run.
The Cortex Stick is a USB courier between two full Mnemo installations — not a portable server. Nothing runs on the stick; it carries the delta. Memories, trajectories, facts, optionally your brain repo, and a free-form pad/ for dragging in-flight work between desks. Plug in, sync, carry, plug in. No cloud, no VPN, no account.
mnemo-cortex stick init --encrypt /media/you/USB # or plain, your call
mnemo-cortex stick sync # at each desk
mnemo-cortex stick watch --notify # or never touch a terminal
Encryption is optional AES-256 and the key never lives on the stick, so a lost stick is a lost stick rather than a breach. Every sync plans the whole run before a byte moves — a refusal changes nothing — and the manifest is written last, so a yanked stick refuses loudly instead of merging from a torn state. Conflicts are preserved, never destroyed. Full guide: docs/cortex-stick.md.
Disco-Bus is the bus we run. A push-based agent mesh with delivery confirmation: agents wake instantly on inbound messages (no polling), and the whole conversation stays visible to a human. ~1000 LOC, local SQLite, no Mnemo coupling — bring your own agents, memory optional. One-command install (./install.sh), or hand ./robot-install.sh to an AI agent and let it do the whole thing.
Doctrine: the bus is the mailbox. The tracking ID is the receipt.
Lifecycle, visible end to end:
📬 DELIVERED → ✅ PICKED UP → 🔄 LOOP CLOSED
Plus one-shot ⚠️ alerts for delivery failures and stale messages. No retry storms, no silent drops.
A2A compatible. Each message maps to an A2A Task: tracking_id → task.id, subject → task.name, body → task.input, lifecycle → A2A TaskState, per Google's A2A spec. Data-shape compatibility is in now; HTTPS / JSON-RPC transport is the v2 roadmap.
A note on the older one. Sparks Bus, the earlier Mnemo-coupled bus, shipped in-tree as
sparks_bus/until August 2026 and is now removed (dead code, superseded). It remains available archived at GuyMannDude/sparks-bus (v0.5.0, forwarding banner) and in this repo's git history. Disco-Bus is where it went: same idea, standalone, actively maintained — start there.
Mnemo Cortex captures conversation memory automatically. mnemo-plan is the manual companion: a folder of markdown files in Git that you write and curate, and any LLM agent can read at session start via the Mnemo MCP tools.
The split:
Same MCP bridge handles both. mnemo-plan tools (read_brain_file, write_brain_file, list_brain_files, plus opie_startup and session_end) auto-enable when BRAIN_DIR is set on disk; if there's no plan repo, those tools simply don't register.
The starter template repo: github.com/GuyMannDude/mnemo-plan. Fork it, fill in your project's files, point BRAIN_DIR at it. Your agents now have project context the moment they start a session — without you re-explaining your setup every time.
Status: beta. Dev-targeted release. A reference-grade safety layer for developers building agent systems that need to ingest user working-style claims into an agent's context. Observations are recorded as candidates, reviewed, and promoted to stable claims; nothing lands in the user's profile without an explicit promotion step.
What's in the box: 5 MCP tools, a review queue, 32 content detectors (secrets, PII, prompt injection, generic fluff, duplicates), 4 provenance buckets, a policy layer with 4-way disposition outcomes, git-tracked audit, and a 200-entry eval corpus. Current eval: 53.0% accuracy / 0.458 macro-F1.
MCP tools: passport_get_user_context, passport_observe_behavior, passport_list_pending_observations, passport_promote_observation, passport_forget_or_override. Reference integration via stdio MCP at integrations/mcp-bridge/. See passport/README.md for the 5-minute quickstart.
Designed so the user owns the artifact, not the platform. The possessive in the name is deliberate — it drops when the hosted / browser-AI release for normal users ships. Today's release is for devs who wire MCP subprocesses into their own agent stacks.
Run any local LLM. Add Mnemo for memory. No cloud, no subscription, no API keys for the model. Free forever.
Mnemo talks Model Context Protocol (MCP), so every modern local-LLM host connects — natively or via a one-line bridge: LM Studio · Open WebUI · AnythingLLM · llama.cpp · Ollama (MCPHost/ollmcp) · LobeChat · Jan
📖 Full host-by-host setup guide → docs/local-llm-hosts.md — copy-paste configs, per-host quirks, and what you get once connected.
For host-by-host pass/fail, model tool-calling test results, and the rest of our field findings: projectsparks.ai/field-guide.
Built-in deployment verification. No agent runs without verified memory.
mnemo-cortex health
mnemo-cortex health check
=========================
Core Services
API server (http://localhost:50001) ..... OK (v3.3.1, 156 memories, 42ms)
Database ................................. OK (12 sessions (3 hot, 4 warm, 5 cold))
Compaction model ......................... OK (qwen2.5:32b-instruct — responding)
Agents (3 discovered)
rocky .................................... OK (recall returned 5 results (234ms))
cc ....................................... OK (recall returned 3 results (189ms))
opie ..................................... OK (recall returned 4 results (201ms))
Watchers
mnemo-watcher-cc ......................... OK (active, PID 4521)
mnemo-refresh ............................ OK (active, PID 4523)
MCP Registration
openclaw.json ............................ OK (mnemo-cortex registered)
14/14 checks passed
Options: --json (machine-readable) · --quiet (exit code only) · --agents (agent checks only) · --services (watcher checks only) · --check-mcp <path> (validate MCP configs)
Wire to cron: 0 */6 * * * mnemo-cortex health --quiet || your-alert-command
Every agent conversation captured automatically. No manual saves, no hooks, no code changes.
Mnemo watches your agent's session files from the outside and ingests every message as it happens. Two adapter patterns depending on your agent platform:
# 1. Start Mnemo (if not already running)
mnemo-cortex start
# 2. Start auto-capture
mnemo-cortex watch --backfill
That's it. Every exchange your agent has is now captured, compressed, and searchable.
Set the MNEMO_AUTO_CAPTURE environment variable to start the watcher automatically whenever Mnemo starts:
# Add to your shell profile (~/.bashrc, ~/.zshrc, etc.)
export MNEMO_AUTO_CAPTURE=true
With this set, mnemo-cortex start also starts the session watcher — no separate watch command needed.
mnemo-cortex status
Look for:
Watcher: running (PID 4521) — auto-capturing sessions
Or query the server directly:
curl -s -X POST http://localhost:50001/context \
-H "Content-Type: application/json" \
-d '{"prompt": "what happened today", "agent_id": "YOUR-AGENT-ID", "max_results": 5}'
A bridge-level JSONL log of every MCP tool call your agents make through the Mnemo bridge. Off by default — when something silently breaks (a tool call that fails without a thrown error, an unexpected latency spike, a flip-flopping agent), flip it on and tail the file:
# In your MCP bridge env
export MNEMO_DUMP=on
# Optional — default is ~/.mnemo-cortex/dumps
export MNEMO_DUMP_DIR=~/dumps
# Inspect
mnemo-cortex dump list # all dump files, size + line count
mnemo-cortex dump tail rocky # live-tail today's rocky dump
Output is one JSONL file per agent per day at
~/.mnemo-cortex/dumps/<agent_id>/<YYYY-MM-DD>.jsonl. Each line has
tool, full params, full response, latency_ms, ok, and an
error field on failures. Captures both real thrown errors and the
handler-internal {isError: true} returns. Greppable with jq:
jq 'select(.ok == false) | {tool, error, latency_ms}' \
~/.mnemo-cortex/dumps/rocky/$(date -u +%F).jsonl
When MNEMO_DUMP=off (the default), dump.wrap() returns the original
handler unchanged — no allocation, no overhead. Schema-versioned for
future additions (Mnemo v4 Phase 1.5+).
Mnemo Cortex is a cognitive coprocessor with active memory for AI agents. It watches your agent's session files from the outside, ingests every message into a local SQLite database, compresses older messages into summaries via LLM-backed compaction, and writes a MNEMO-CONTEXT.md file that your agent reads at bootstrap.
No hooks. No agent modifications. No cloud dependency. Mnemo keeps your memory on disk — if either process restarts, the data is already there.
MNEMO-CONTEXT.md to the agent's workspace every 5 seconds.Mnemo Cortex has been the live production memory of a five-agent fleet since March 2026. Real counts from the running server (July 2026):
Plus a shared store of ~7,000 verified facts with confidence laddering and a full audit history. Verified across single-agent and multi-agent deployments — cross-agent search, nightly cross-agent dreaming, and USB courier sync between two machines.
Five steps from a fresh checkout to a running server connected to your agent. The CLI handles everything — mnemo-cortex init writes the config, mnemo-cortex start launches the API server, mnemo-cortex health verifies, and you point your agent at it via the matching integration.
Mnemo Cortex runs on Linux, macOS, and Windows. The core (Python + SQLite) is cross-platform. As of v4.4.1, the server runs natively on Windows — its file locking is cross-platform (POSIX fcntl with an msvcrt fallback), so agentb.server starts and serves recall on native Windows Python with no WSL required. Platform differences are mostly about how you keep the server running across reboots:
🍎 On a Mac? Follow the dedicated macOS install guide — it covers the one gotcha that matters (which Python can load SQLite extensions), Homebrew setup, and a launchd template for start-at-login. macOS support is in beta — testers wanted.
init wizard also accepts OpenAI / Google / Anthropic / OpenRouter API keys if you'd rather use a cloud model.git clone https://github.com/GuyMannDude/mnemo-cortex.git
cd mnemo-cortex
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e .
This registers two CLI commands: mnemo-cortex and the shorter alias mnemo.
Skip the wizard — fill out a JSON manifest and run the robot installer.
# Defaults are sensible; only edit robot.install if you want to change them
./robot-install.sh
The script emits a single JSON object on stdout for the caller to parse; all human-readable progress goes to stderr.
{
"ok": true,
"steps": {
"deps": {"ok": true, "python": "3.12", "reasoning_key_present": true},
"venv": {"ok": true, "path": "..."},
"pip": {"ok": true},
"config": {"ok": true, "config_path": "...", "env_path": "...", "data_dir": "..."},
"systemd": {"ok": true, "service": "mnemo-cortex", "port": 50001},
"smoke_test": {"ok": true, "health": "ok", "memory_id": "...", "recall_hits": 1}
}
}
On failure, ok is false, exit code is 1, and error describes which step blew up.
The manifest covers service port + bind, reasoning + embedding provider,
the v3 provenance/decay thresholds, systemd unit name, and a smoke test
that exercises /health plus a save → recall round-trip. API keys are read from the install-time environment
(named in the manifest via api_key_env), copied into a 0600-permission
env file alongside the config, and loaded by the systemd unit.
Sandbox testing — override paths via env so you can dry-run without touching real state:
MNEMO_INSTALL_VENV_DIR=/tmp/test-venv \
MNEMO_INSTALL_CONFIG_DIR=/tmp/test-config \
MNEMO_INSTALL_SYSTEMD_DIR=/tmp/test-systemd \
MNEMO_INSTALL_DRY_RUN=1 \
./robot-install.sh
DRY_RUN=1 runs the dependency check and reports the paths each step
would write, but skips every side effect — no venv, no pip install,
no config or env file written, no systemd unit, no smoke test. API
keys in the environment are never persisted to disk in dry-run.
Note on scope:
robot.installsets up the Mnemo Cortex server. To use Mnemo from an agent (Hermes, Claude Desktop, AnythingLM, LM Studio, Ollama Desktop, Agent-Zero, OpenClaw, Claude Code), run the agent-specific integration installer afterward — seeintegrations/for each guide. They wire the agent's MCP config to point at the server you just installed.
mnemo-cortex init
Interactive wizard. Picks your reasoning model (preflight checks), embedding model (semantic search), server bind address, port, and any agents you want to register up front. Defaults are sensible for a single-machine local install: Ollama for both models, 127.0.0.1:50001, no auth token (loopback only).
The wizard writes the config to ~/.agentb/agentb.yaml and creates the data directory at ~/.agentb/data/.
mnemo-cortex start # detached (logs go to ~/.agentb/data/logs/)
mnemo-cortex start --foreground # attached, logs to terminal
Server listens on http://localhost:50001 by default. To stop:
mnemo-cortex stop
Quick health check:
mnemo-cortex health
Deeper diagnostics — port conflicts, model availability, agent recall, watcher status:
mnemo-cortex doctor
Both return non-zero exit codes when something's broken, so they work in scripts and CI.
The server is now running. Pick your platform and follow its integration guide:
Each integration is a one-line MCP config or a drag-and-drop bundle. The server is the same; only the bridge config changes.
For other MCP-capable hosts (Open WebUI, llama.cpp, LobeChat, Jan, generic MCP clients), see Use With Any Local LLM above.
Mnemo gives your agent persistent memory. A brain repo gives it persistent current state — the project pad your agent reads at session start to know what's in flight without re-reading every memory.
Fork the mnemo-plan template and point your bridge's BRAIN_DIR env var at it:
# In your MCP config or systemd unit:
BRAIN_DIR=/absolute/path/to/your/mnemo-plan
The bridge auto-enables brain-file tools (read_brain_file, write_brain_file, list_brain_files) when BRAIN_DIR exists. If it doesn't, those tools simply don't register — no install friction.
For the operating practice — when to read what, when to write what, the six-step session ritual — see THE-LANE-PROTOCOL.md.
Recall / cross-agent search returns "No chunks"
Most common cause: your embedding model setting doesn't match your provider's current model name. Model names change — check your provider's docs:
If you recently switched providers or updated your config, verify the model name is correct and that your API key has access to the embedding endpoint.
Health check fails on "Compaction model"
The compaction model (default: qwen2.5:32b-instruct via Ollama) must be running and reachable. Check:
curl http://localhost:11434/v1/models # List loaded Ollama models
If you're using a remote Ollama instance, set MNEMO_SUMMARY_URL to point to it.
Server unreachable
If mnemo-cortex health can't reach the API, check:
curl http://localhost:50001/health # Or your MNEMO_URL
Common causes: wrong port, firewall blocking, server not started. On multi-machine setups, ensure the target host's firewall allows the port (e.g., ufw allow from 10.0.0.0/24 to any port 50001).
After setup, run the test suite:
cd /path/to/mnemo-cortex
source .venv/bin/activate
pytest tests/test_agentb.py -v
If tests fail, check that all Python dependencies are installed (pip install -e .).
OpenClaw 2026.4.10 shipped a native Active Memory plugin. Some people have asked whether it replaces Mnemo Cortex. Short answer: no — they solve different problems. Here's the difference, based on side-by-side testing on a sandbox agent.
We run both. OpenClaw's Active Memory handles per-agent recent context. Mnemo handles everything that crosses agents or needs durable archival. They stack; they don't compete.
Mnemo Cortex started as a memory system and evolved into a cognitive coprocessor, designed by a small multi-agent team: a non-developer operator and several Claude/OpenClaw agents working as architect, builder, and live test subjects. The full backstory — how the architecture got designed, why agents pair-program with humans well, and what failed along the way — is in Finding Mnemo.
Project team:
External inspirations (the Clapton Method — adopt the best ideas, credit openly, build on top):
SETUP-PROMPT.md. Karpathy's earlier observation (Dwarkesh Patel interview, Oct 2025) that LLMs lack a sleep/distillation phase — humans consolidate the day into long-term memory while sleeping, LLMs just lose it — is the root idea behind Mnemo Dreaming, which arrived by way of OpenClaw's memory-core /dreaming (credited below). Our twist: the fleet dreams together — nightly cross-agent synthesis into one shared brief, not single-agent self-consolidation./dreaming — docs.openclaw.ai/concepts/dreaming. The first dreaming implementation we ran in production (on our OpenClaw agents, April 2026); its sleep-inspired consolidation stages directly preceded mnemo-dream.py.Built for Project Sparks.
Mnemo Cortex is self-hosted and local-first: no telemetry, no analytics, no third-party services. Your memory data lives in a SQLite DB on infrastructure you control, and you can read or delete it at any time. Full policy: PRIVACY.md.
MIT
Open-source cognitive coprocessor with active memory for AI agents — persistent recall, semantic search, overnight dreaming, verified facts, encrypted USB sync. MCP server; works with Claude, ChatGPT,
ภาพรวม
Open-source cognitive coprocessor with active memory for AI agents — persistent recall, semantic search, overnight dreaming, verified facts, encrypted USB sync. MCP server; works with Claude, ChatGPT, and any local LLM. Built by one maker and his agents.
เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก stdio สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ระเบียนนี้ไม่ได้ระบุตัวแปรสภาพแวดล้อมที่จำเป็น
Mnemo Cortex ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-11-13 Open-source cognitive coprocessor with active memory for AI agents — persistent recall, semantic search, overnight dreaming, verified facts, encrypted USB sync. MCP server; works with Claude, ChatGPT, and any local LLM. Built by one maker and his agents.