memnet-llm 0.2.10

In-memory working-memory graph CLI for LLM agents (goldfish brain scratch space)

MemNet

Pure in-memory working-memory graph for LLM "goldfish brains" — structured state an agent writes once and re-reads each turn because the model forgets.

MemNet is scratch memory for the current task, not a durable knowledge base or vector store. Sessions live in RAM, expire by TTL, and disappear on session close. There is no automatic disk state; persistence is explicit and user-controlled via session save / session load.

The problem it solves

LLMs lose track of entities, state, and rules between tool calls. MemNet gives the agent a small, typed, atomised knowledge graph it can:

• atomise state into many small @TAG: rows and @EDG links (one idea per row — the most important discipline),
• add facts, tasks, and relations once; update them when state changes,
• pull back only the live slice on the next turn (query warm --anchor),
• settle or expire missions so they stop polluting the prompt,
• keep hard limits and produce machine-readable warnings instead of silent bloat.

The pipe wire format is intentionally token-efficient: warm reads inject a connected subgraph, not JSON dumps or prose blobs.

Installation

pip install memnet-llm

From source (development):

pip install -e ".[dev]"

The CLI command is still memnet. PyPI package name is memnet-llm (the name memnet on PyPI is a different project — memristive neural networks).

Requires Python ≥ 3.11.

Quick start

You need two terminals. One runs the in-memory server that holds the graph; the other runs the CLI client.

Terminal 1 (server):

memnet serve
# prints: MEMNET_SERVE=127.0.0.1:18765

Terminal 2 (client):

# See the cheat sheet
memnet guide --loose

# Start a session with the bundled schema
memnet session open --map-file src/memnet/examples/schema.example.txt
# stderr also prints: MEMNET_SESSION=mn_xxxxxxxx
$env:MEMNET_SESSION = "mn_xxxxxxxx"

# Ingest a small world (LAW rules + world state + missions)
memnet add --file src/memnet/examples/workflow.example.txt

# Preferred read for the next LLM turn: only live mission state
memnet query warm --anchor PLR01

# When done
memnet session close $env:MEMNET_SESSION

Without memnet serve running, any stateful command returns @ERR: serve_required.

For one-off scripting or tests you can set MEMNET_TEST_INLINE=1 to run in-process (no server), but this is not the normal multi-turn agent mode.

LLM agents: read LLM-GUIDE.md (in this repo) for the full agent playbook, the goldfish loop, settlement pattern, and disciplines. It is written to be consumed by models.

Atomisation (required)

MemNet is a knowledge graph, not a document store. Atomisation — breaking state into many small nodes and explicit @EDG edges — is the discipline that makes query warm token-efficient.

Do	Don't
One fact / entity / task per @TAG: row	Paragraphs or merged facts in one field
Wire relations with @EDG	"A helps B and also C" in a single row
Short fields: ids, codes, keys, paths	Prose, markdown, full file contents
Batch many lines in one add --stdin	One giant row instead of many atoms

# Good — three atoms + one edge
memnet add --stdin @"
@TSK: T01|Clear warehouse|1|in_progress|persistent
@NPC: N03|helper|labour|1|0|0|active|persistent
@EDG: E01|N03|helps|T01|labour|persistent
"@

See LLM-GUIDE.md for the full agent playbook. Application notes (novel, SysML, MUD) show domain-specific tag maps — all use the same atomisation rule.

The goldfish loop (recommended pattern)

A typical agent turn:

1. add new rows or update existing ones — atomised @TAG: lines (batch via --stdin or --file is best).
2. query warm --anchor <focus> — returns only active (non-recyclable) rows, always includes LAW.
3. Paste the wire lines into the prompt, reason, decide on next adds/updates or a mission settle.
4. On mission complete: update the TSK (or equivalent) with both status=settled and recycle=delete_on_settle. Mission edges usually use delete_on_expire or delete_on_settle.
5. Optionally housekeep prune stale --apply to physically remove settled rows.
6. Next turn starts again at step 1 with a (usually new) anchor.

query context (cold) returns everything and emits @WRN: stale_in_store|… on stderr when recyclable rows exist. Prefer warm.

Wire format

Token-efficient pipe rows — one graph atom per line (pair with atomisation above):

@TAG: field|field|...

• One record = one idea — split compound state into more rows + @EDG, not longer fields.
• Pipe inside a value must be escaped: note\|extra (or \\| in some shells).
• Always quote the whole line in PowerShell or bash when it contains special characters.
• Reserved output tags: @SESSION, @ERR, @WRN, @STAT, @REL, @DEL.
• Errors and advisories go to stderr; data rows to stdout.

Example multi-line ingest (PowerShell):

memnet add --stdin @"
@NPC: N01|Shen Tiexin|female(12)|0|traditional|80|active|persistent
@EDG: E01|N01|seeks_help|PLR01|unlock|delete_on_expire
"@

memnet update --stdin @"
@NPC: N01|Shen Tiexin|female(12)|0|traditional|90|active|persistent
"@

See memnet guide, memnet examples map, and memnet tagmap fields --tag <TAG> for the current schema.

Commands

Run any command with --help for full flags.

Session lifecycle

Command	Purpose
session open --map-file	Create a new session (prints @SESSION: and MEMNET_SESSION=... on stderr)
session resume <id>	Attach to an existing session
session current	Show the id from $env:MEMNET_SESSION (or "none")
session list	List live sessions (id, expires, minutes left, last modified)
session save --file	Optional export of the current graph to a user-chosen snapshot file (wire format)
session load --file	Restore a snapshot into RAM (new session id by default; --keep-id to reuse)
session close <id>	Destroy the session (graph is gone)

Default TTL is 60 minutes (MEMNET_SESSION_TTL_MINUTES or --ttl).

Add, update & delete

• add [line] [--file PATH] [--stdin] [--dry-run] [--allow-new-relation] [--agent NAME] [--session]
  • Create new rows only. Fails with id_exists if the id is already in the graph.
• update [line] [--file PATH] [--stdin] [--dry-run] [--allow-new-relation] [--agent NAME] [--session]
  • Replace existing rows only. Fails with not_found if the id is missing (catches update typos).
• Batch via --stdin or --file is strongly preferred. --dry-run parses without mutating.
• delete --id ID

Query (graph reads)

• query warm --anchor ID [--depth N] [--max-rows M] — the normal read for agents. active_only is forced; anchor is required. LAW rows are always included.
• query context [--anchor] [--depth] [--max-rows] [--active-only] — cold/full view (use for audit; warns on stale rows).
• query neighbors <id> [--depth]
• query path <src> <dst>

Direct reads

• read list [--tag T] [--active-only] [--where field=value ...]
• read get --id ID [--tag T]

--where filters by field value (exact match). Repeat for AND. Use * or ? wildcards for glob match (e.g. --where name=*Tiexin*).

Housekeeping

• housekeep stats — @STAT rows + caps for rows/edges/relations/orphans/dangling/recyclable/modified.
• housekeep stale|orphans|dangling|recyclable — list the respective sets.
• housekeep prune stale|... --apply — actually delete (emits @DEL lines and a summary on stderr).

stale = recyclable + dangling + orphans.

Schema & relations

• tagmap fields [--tag T] / map fields — reference field lists.
• tagmap show / map show — current session's effective tag map.
• relations list — allowed EDG relation names for this session.
• New relations are rejected unless add or update uses --allow-new-relation (subject to max_relations).

Examples & server

• examples map|workflow|add <tag>|path
• serve [--host] [--port] — the in-memory graph host. Required for normal CLI use across processes.
• version, guide, guide --loose

Session model & optional snapshots

• One session open per task. Agents should resume rather than open duplicates.
• The graph is RAM only while the session is live.
• session save --file my.snap writes a plain-text wire-format snapshot that you own. session load --file my.snap brings it back into a (usually new) RAM session.
• On close or TTL expiry the session is dropped from the server; no server-side files remain.
• Snapshots are a user convenience, not MemNet's durability layer.

Housekeeping, warnings, and signals

On every stateful command the server emits advisory @WRN lines on stderr (capped per call):

• near_cap*, ttl_expiring
• stale_in_store, stale_dangling, stale_orphans, stale_graph
• mission_settled
• fanout_clamped, dangling_endpoint, etc.

@STAT lines report counts vs caps. @DEL lines are emitted after successful prune --apply or delete.

Environment variables

Variable	Effect
MEMNET_SESSION	Default session id when --session is omitted
MEMNET_SESSION_TTL_MINUTES	Default TTL for new sessions (1..1440)
MEMNET_AGENT	Default agent name stamped on written records
MEMNET_SERVE_HOST, MEMNET_SERVE_PORT	Bind address for memnet serve (client discovery is implicit via the same vars)
MEMNET_MAX_ROWS / MAX_LAW / MAX_RELATIONS / ...	Hard caps (see Caps in config)
MEMNET_TEST_INLINE	When set, CLI runs in-process (tests, one-off scripts). Not for normal agent use.

All caps have MEMNET_MAX_* names; see source for the full list and defaults.

Architecture (one minute)

• tagMap — merged fixed + user schema loaded at session open. Drives parsing and field order on output.
• memStore — in-memory nodes + directed edges (EDG). Write-order index, simple BFS for neighbours / paths / warm packs.
• LAW rows — special, usually exempt from orphan/dangling accounting and always surface in warm reads.
• recycle field — persistent (default for active world) vs delete_on_settle / delete_on_expire (missions). query warm hides the latter.
• Server holds a registry of live SessionEntrys. CLI is a stateless client that talks over localhost TCP (or in-process in tests).

No JSON on the wire for LLM consumption — only the @TAG: lines plus a handful of control records on stderr.

Application notes

Rich, document-style worked examples live under application-notes/. These are self-contained narratives (one .md per note) showing complete usage patterns with all data — including background, configurations, bibles, rules, and user preferences — kept inside MemNet.

See application-notes/llm-novel-writer.md for the first example: an interactive LLM novel writer driven by the explicit 6-step read → context → user-input-as-data → analyse → update → loop pipeline.

See application-notes/llm-sysml-v2-modeling.md for the second: LLM-assisted SysML v2 textual modeling (6U CubeSat PDU controller) with the same 6-step pipeline, a SysML v2 syntax reference table, a deployable "whole model as rows" block, explicit demonstration that models living in different .sysml files are easily referred to together via @PKG rows + declaredIn / cross-package EDG, and concrete examples of using the graph to help build the system (allocations, ports and connections driving wiring, implementation tasks and deployment), write the system model docs (interconnection views, behaviour specs, traceability matrices generated from the rows), and evaluate runtime behaviour (BEHD + timing/power + allocations for analysis, latency and power budgeting). Warm + EDG + settlement still keeps every turn's slice small, granular and on-demand.

See application-notes/llm-mud.md for the third: a multiplayer text MUD pattern (Alice in Wonderland sample world) with a server-side MUD agent (active rooms, ticks, NPC actions on the shared graph) and client-side player agents (LLM generates room prose from query warm slices; no descriptions stored on @ROM rows). Covers tiered atomisation for large maps, deterministic go/get, server deltas, and load-test capacity notes via scripts/load_test_mud.py.

See application-notes/llm-daily-news.md for the fourth: a daily RSS digest pipeline (~100 articles/day) where MemNet is run-scoped working memory (120-minute TTL sessions). Covers minimal @KYWD hub nodes with degree metrics, cross-article linkage via shared tokens, layered @CLU/@SYN summarisation, prompt formatters as bounded views over query warm, batched upserts from Python via send_command, and graceful fallback when memnet serve is unavailable.

Development

.\scripts\dev.ps1          # setup / test / lint / fmt / cli
pytest

Tests run with MEMNET_TEST_INLINE=1 so they do not require a separate serve process.

MCP (optional)

Install the MCP adapter:

pip install memnet-llm[mcp]

Prerequisites: memnet serve running in another terminal (same as normal multi-command CLI use).

# Terminal 1
memnet serve

# Terminal 2 — stdio MCP server for Cursor / other hosts
memnet-mcp

Set MEMNET_SESSION to your open session id. Example Cursor MCP config:

{
  "mcpServers": {
    "memnet": {
      "command": "memnet-mcp",
      "env": {
        "MEMNET_SESSION": "mn_your_session_id"
      }
    }
  }
}

Tools (v1): serve_status, session_open, session_current, query_warm, add, update, read_get, housekeep_stats. Each returns a JSON envelope with stdout / stderr wire lines, exit_code, session_id, and errors[] (from @ERR: lines). session_open auto-seeds LAW01–LAW05 on every new session (prepended on each query_warm); optional seed_lines adds @CFG anchors and domain @LAW rows in the same call. See LLM-GUIDE.md.

Licence

MIT