# Neo4j Memory Recall

> Retrieve relevant memory from the Neo4j graph when you need context about a person, project, topic, or past event. Use before answering questions that reference past conversations or named entities.

- Skill: `johnymontana/neo4j-memory-recall` (Agent Skill)
- Install (CLI): `npx skillmds@latest add johnymontana/neo4j-memory-recall`
- Raw SKILL.md: https://api.skillmd.com/api/skills/johnymontana/neo4j-memory-recall/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: johnymontana (https://skillmd.com/u/johnymontana)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/johnymontana/neo4j-memory-recall

---


# Neo4j Memory Recall

## When to use

- User references a person, project, or topic you may have discussed before
- You need facts from previous sessions not in the current conversation
- User asks "do you remember...", "last time we talked about...", or "what do you know about..."
- You are about to compose a message or email to someone and need relationship context
- User asks a question that might be answered by previously stored knowledge
- You want to check if you already have information before searching externally

## Workflow

### Preferred native tools

Use the built-in Neo4j-backed OpenClaw tools first:

1. Run `memory_search` with the user question or the key entity/topic
2. If a hit looks relevant, run `memory_get` on the returned pseudo-path for more detail
3. Use `entity_lookup` when the user is asking directly about one graph entity
4. Use `graph_query` only for relationship-heavy questions that need custom traversal

### Basic recall

1. Extract the key entity or topic from the user message
2. Prefer `memory_search`; use the bridge endpoint below only as a fallback or for low-level debugging
3. Inject the returned context into your response
4. If nothing is found, proceed normally and note you have no prior context

```bash
curl -s -X POST http://localhost:7575/memory/recall \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Sarah Kim",
    "limit": 10
  }'
```

### Recall with reasoning traces

Include reasoning history (tool calls, decisions) for audit or debugging:

```bash
curl -s -X POST http://localhost:7575/memory/recall \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Q3 roadmap decision",
    "limit": 10,
    "include_reasoning": true
  }'
```

### Selective context injection

For the most token-efficient context injection, use the `/memory/context` endpoint which returns a pre-formatted, relevance-ranked context block:

```bash
curl -s -X POST http://localhost:7575/memory/context \
  -H "Content-Type: application/json" \
  -d '{
    "message": "The full user message to find context for",
    "max_tokens": 2000
  }'
```

This returns a Markdown-formatted context block with:
- Matched entities and their properties
- Relationship graph (1-hop traversal)
- Relevant observations
- Recent reasoning traces

## Response format

### /memory/recall response

```json
{
  "results": [
    {
      "name": "Sarah Kim",
      "role": "Product Manager",
      "company": "Acme Corp",
      "_labels": ["Person"],
      "_score": 2.45,
      "_relationships": [
        { "type": "WORKS_AT", "target_labels": ["Organization"], "target_name": "Acme Corp" },
        { "type": "PARTICIPATED_IN", "target_labels": ["Event"], "target_name": "ProductConf 2026" }
      ]
    }
  ],
  "count": 1,
  "query": "Sarah Kim"
}
```

### /memory/context response

```json
{
  "context": "## Known Entities\n[Person] Sarah Kim: {'role': 'PM', 'company': 'Acme'}\n  Sarah Kim -WORKS_AT-> Acme Corp\n\n## Observations\n- Prefers async communication",
  "entities_used": 3,
  "reasoning_traces": 1,
  "token_estimate": 145
}
```

## Guidelines

- Recall BEFORE answering questions about people, projects, or past events
- Prefer `memory_search` + `memory_get` over raw `curl` when the native tools are available
- Use `/memory/context` for system prompt injection (most token-efficient)
- Use `/memory/recall` when you need raw structured data for processing
- If recall returns nothing, say so — don't hallucinate prior context
- Use `include_reasoning: true` only when the user asks about past decisions or audit trails
- Keep `limit` reasonable (5–15) to avoid overwhelming context
- The `_score` field indicates relevance — higher is better