Xplore-LAB/dsh-plugin-asmemory

Give your agent a time memory: record what happened and what changed, then analyze trends, anomalies, and causality — not just what was said.

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What it does

asmemory stores two kinds of typed events, not raw text:

• State — a value of some entity/metric at a point in time (gpu.temperature = 78°C)
• Action — something that happened (agent ran training, operator adjusted a valve)

On top of this memory it provides four analyses:

Analysis	Question it answers
Trend	Is my metric going up or down? (slope + direction)
Anomaly	Which readings are outliers? (z-score)
Causal	Did action X move metric Y? (before/after delta)
Summary	What's in my memory? (counts + entities)

Why asmemory

Most memory plugins store conversations or documents, so they answer "what did you say". asmemory stores actions and states, so it answers "what happened, and why":

"Did GPU temperature rise after training started?" → causal "Is my sleep trending down this week?" → trend "Which readings are outliers?" → anomaly

It is the memory layer for the physical and operational world — agents observing themselves, industrial processes, and personal metrics.

Example: agent self-tracking

Record your agent's own actions and resource states, then ask why the GPU got hot:

from asmemory import StateEvent, ActionEvent, MemoryStore, analysis

store = MemoryStore("memory.db")
store.add_state(StateEvent("gpu", "temperature", 78.5, "celsius"))
store.add_action(ActionEvent("agent", "run_training", "qwen3.6", ts=1723500000))

# Did training actually heat the GPU?
causal = analysis.causal_effect(store, "run_training", "gpu", "temperature")
print(causal["before_mean"], "->", causal["after_mean"], f"(Δ={causal['delta']})")

Real output (24h simulated agent, 72 states + 20 actions):

【因果】run_training → gpu.temperature:  45.3 → 78.7  (Δ=33.4, up)   ← significant
【因果对照】git_commit → gpu.temperature: 53.7 → 56.4  (Δ=2.7, up)    ← no effect
【异常】ram.usage: 1 outlier (z=-2.4)

The engine cleanly separates real causality (training) from coincidence (git commits) — no LLM guessing involved, just time-series math.

Example: industrial monitoring → DataLens

Air-separation plant: oxygen purity (monitored metric) vs. valve opening (control action). asmemory remembers the causality, then exports to DataLens for over-control optimization:

from asmemory.export import export_datalens

export_datalens(store, entity="oxygen", metric="purity",
                action_verb="valve_adjust",
                pollutant="氧纯度", regulator="导叶开度",
                regulatory_limit=99.5)
# → data_datalens.csv + data_datalens.config.json

Real output (240 min, 240 states + 240 actions):

【因果】valve_adjust → oxygen.purity: Δ=0.0009 (up)
✅ CSV → data_datalens.csv          (时间,指标值,控制量,整点标记)
✅ config → data_datalens.config.json (pollutant/regulator/limit)

Open data_datalens.csv in DataLens to visualize the "still over-controlling in the safe zone" savings space.

Tools

Seven MCP tools, exposed to the model as mcp__asmemory__<tool>:

Tool	What it does
memory_store_state	Record a state event (entity / metric / value / unit / tags)
memory_store_action	Record an action event (actor / verb / object / amount)
memory_trend	Trend direction + slope of a metric
memory_anomaly	z-score outlier detection
memory_causal	Mean change of a metric before/after an action
memory_summary	Library statistics
memory_export_datalens	Export CSV + config for DataLens visualization

Installation

The server runs from the asmemory-mcp command (or an absolute path via ASMEMORY_MCP_PATH). Install the command first, then register the MCP bridge with DSH.

1. Install the asmemory-mcp command:

   pip install .
   

   (Or skip the install and set ASMEMORY_MCP_PATH=/path/to/bin/asmemory-mcp instead.)

2. Launch DSH with the plugin patch:

   dsh web --patch "$PWD/cordis.yml"
   

   (Once published, you can also run dsh plugin add dsh-plugin-asmemory.)

3. Done. The server is a single stdio process using only the Python 3.10+ standard library.

Persistence defaults to ~/.asmemory/memory.db (override with ASMEMORY_DB_PATH).

Verified

The full loop is tested end-to-end on a real DSH instance (headless profile + a local Qwen3.6 model): the agent called memory_store_state, memory_store_action, and memory_summary, and the events landed in SQLite — exactly the data it was asked to record.

Quick start

python3 examples/demo_agent_self_tracking.py   # agent self-tracking demo
python3 examples/demo_datalens_export.py       # industrial → DataLens export demo

Use cases

• Agent self-tracking — record the agent's own actions and resource states
• Industrial monitoring — process variables and operator actions (air separation, emission control)
• Personal data — sleep, weight, spending, exercise trends

License

MIT — use it, fork it, ship it. And if it earns you a star-shaped reward in return, all the better. ⭐