NeuralMemory — Associative Memory for AI Agents

A biologically-inspired memory system that uses spreading activation instead of keyword/vector search. Memories form a neural graph where neurons connect via 20 typed synapses. Frequently co-accessed memories strengthen their connections (Hebbian learning). Stale memories decay naturally. Contradictions are auto-detected.

Why not just vector search? Vector search finds documents similar to your query. NeuralMemory finds conceptually related memories through graph traversal — even when there's no keyword or embedding overlap. "What decision did we make about auth?" activates time + entity + concept neurons simultaneously and finds the intersection.

Setup

1. Install NeuralMemory

pip install neural-memory

The brain and config at

~/.neuralmemory/

are auto-created on first use.

2. Install the OpenClaw Plugin (Recommended)

The plugin occupies the exclusive memory slot — auto-injects context before each agent run and auto-captures memories after.

# Install from npm
npm install -g neuralmemory

Add to

~/.openclaw/openclaw.json

{
  "plugins": {
    "load": {
      "paths": ["<path-to-installed-plugin>"]
    },
    "entries": {
      "neuralmemory": {
        "enabled": true,
        "config": {
          "pythonPath": "python",
          "brain": "default",
          "autoContext": true,
          "autoCapture": true
        }
      }
    },
    "slots": {
      "memory": "neuralmemory"
    }
  }
}

Plugin features:

6 tools registered automatically (nmem_remember, nmem_recall, nmem_context, nmem_todo, nmem_stats, nmem_health)
```
before_agent_start
```
hook: injects tool instructions + relevant memories as context (persists across
```
/new
```
)
```
agent_end
```
hook: auto-extracts facts, decisions, and TODOs from the conversation
Configurable:
```
contextDepth
```
(0-3),
```
maxContextTokens
```
(100-10000)

After installing, build the plugin:

cd <path-to-installed-plugin>
npm run build

This compiles TypeScript to JavaScript in

dist/

. The plugin entry point is

dist/index.js

Windows Installation

On Windows, use forward slashes or escaped backslashes in

openclaw.json

paths:

{
  "plugins": {
    "load": {
      "paths": ["C:/Users/<you>/AppData/Roaming/npm/node_modules/neuralmemory"]
    }
  }
}

To find the installed path:

npm list -g neuralmemory --parseable

openclaw plugins list

doesn't show the plugin:

Verify the path in
```
openclaw.json
```
points to the package root (where
```
package.json
```
is)
Ensure
```
npm run build
```
was run (the
```
dist/
```
folder must exist with compiled
```
.js
```
files)
Use
```
python
```
instead of
```
python3
```
in the plugin config (Windows default)

Alternative: MCP Configuration (Manual)

If you prefer MCP over the plugin, add to

~/.openclaw/mcp.json

{
  "mcpServers": {
    "neural-memory": {
      "command": "python",
      "args": ["-m", "neural_memory.mcp"],
      "env": {
        "NEURALMEMORY_BRAIN": "default"
      }
    }
  }
}

On Windows, use

"python"

(not

"python3"

). This gives you all 60 MCP tools but without the auto-context/auto-capture hooks.

3. Verify

nmem stats

You should see brain statistics (neurons, synapses, fibers).

Troubleshooting

Symptom	Cause	Fix
`openclaw plugins list` doesn't show plugin	Plugin path wrong or not built	Run `npm run build` , verify path in `openclaw.json`
Agent runs `nmem remember` in terminal	Agent confused CLI vs tool	Plugin now auto-injects tool instructions via `systemPrompt`
Agent forgets tools after `/new`	No tool instructions in new session	Plugin now injects `systemPrompt` on every `before_agent_start`
`python3 not found` (Windows)	Windows uses `python` not `python3`	Set `pythonPath: "python"` in plugin config
Timeout errors	Slow machine or large brain	Increase `timeout` in plugin config (max 120000ms)

Tools Reference

Core Memory Tools

Tool	Purpose	When to Use
`nmem_remember`	Store a memory	After decisions, errors, facts, insights, user preferences
`nmem_recall`	Query memories	Before tasks, when user references past context, "do you remember..."
`nmem_context`	Get recent memories	At session start, inject fresh context
`nmem_todo`	Quick TODO with 30-day expiry	Task tracking

Intelligence Tools

Tool	Purpose	When to Use
`nmem_auto`	Auto-extract memories from text	After important conversations — captures decisions, errors, TODOs automatically
`nmem_recall` (depth=3)	Deep associative recall	Complex questions requiring cross-domain connections
`nmem_habits`	Workflow pattern suggestions	When user repeats similar action sequences

Management Tools

Tool	Purpose	When to Use
`nmem_health`	Brain health diagnostics	Periodic checkup, before sharing brain
`nmem_stats`	Brain statistics	Quick overview of memory counts
`nmem_version`	Brain snapshots and rollback	Before risky operations, version checkpoints
`nmem_transplant`	Transfer memories between brains	Cross-project knowledge sharing

Workflow

At Session Start

Call
```
nmem_context
```
to inject recent memories into your awareness
If user mentions a specific topic, call
```
nmem_recall
```
with that topic

During Conversation

When a decision is made:
```
nmem_remember
```
with type="decision"
When an error occurs:
```
nmem_remember
```
with type="error"
When user states a preference:
```
nmem_remember
```
with type="preference"
When asked about past events:
```
nmem_recall
```
with appropriate depth

At Session End

Call
```
nmem_auto
```
with action="process" on important conversation segments
This auto-extracts facts, decisions, errors, and TODOs

Examples

Remember a decision

nmem_remember(
  content="Use PostgreSQL for production, SQLite for development",
  type="decision",
  tags=["database", "infrastructure"],
  priority=8
)

Recall with spreading activation

nmem_recall(
  query="database configuration for production",
  depth=1,
  max_tokens=500
)

Returns memories found via graph traversal, not keyword matching. Related memories (e.g., "deploy uses Docker with pg_dump backups") surface even without shared keywords.

Trace causal chains

nmem_recall(
  query="why did the deployment fail last week?",
  depth=2
)

Follows CAUSED_BY and LEADS_TO synapses to trace cause-and-effect chains.

Auto-capture from conversation

nmem_auto(
  action="process",
  text="We decided to switch from REST to GraphQL because the frontend needs flexible queries. The migration will take 2 sprints. TODO: update API docs."
)

Automatically extracts: 1 decision, 1 fact, 1 TODO.

Key Features

Zero LLM dependency — Pure algorithmic: regex, graph traversal, Hebbian learning
Spreading activation — Associative recall through neural graph, not keyword/vector search
20 synapse types — Temporal (BEFORE/AFTER), causal (CAUSED_BY/LEADS_TO), semantic (IS_A/HAS_PROPERTY), emotional (FELT/EVOKES), conflict (CONTRADICTS)
Memory lifecycle — Short-term → Working → Episodic → Semantic with Ebbinghaus decay
Contradiction detection — Auto-detects conflicting memories, deprioritizes outdated ones
Hebbian learning — "Neurons that fire together wire together" — memory improves with use
Temporal reasoning — Causal chain traversal, event sequences, temporal range queries
Brain versioning — Snapshot, rollback, diff brain state
Brain transplant — Transfer filtered knowledge between brains
Vietnamese + English — Full bilingual support for extraction and sentiment

Depth Levels

Depth	Name	Speed	Use Case
0	Instant	<10ms	Quick facts, recent context
1	Context	~50ms	Standard recall (default)
2	Habit	~200ms	Pattern matching, workflow suggestions
3	Deep	~500ms	Cross-domain associations, causal chains

Notes

Memories are stored locally in SQLite at
```
~/.neuralmemory/brains/<brain>.db
```
No data is sent to external services (unless optional embedding provider is configured)
Brain isolation: each brain is independent, no cross-contamination
```
nmem_remember
```
returns fiber_id for reference tracking
Priority scale: 0 (trivial) to 10 (critical), default 5
Memory types: fact, decision, preference, todo, insight, context, instruction, error, workflow, reference

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NeuralMemory — Associative Memory for AI Agents

Setup

1. Install NeuralMemory

2. Install the OpenClaw Plugin (Recommended)

Windows Installation

Alternative: MCP Configuration (Manual)

3. Verify

Troubleshooting

Tools Reference

Core Memory Tools

Intelligence Tools

Management Tools

Workflow

At Session Start

During Conversation

At Session End

Examples

Remember a decision

Recall with spreading activation

Trace causal chains

Auto-capture from conversation

Key Features

Depth Levels

Notes

Quick Start

Manual Installation

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Tags

Multi-Agent Patterns

Memory Systems Design

Context Degradation Detection

Context Optimization

Tool Design for Agents

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