core/docs/providers/windsurf.mdx

---
title: "Windsurf"
description: "Connect your Windsurf IDE to CORE's memory system"
---

### Prerequisites

- Windsurf IDE installed
- CORE account - [Sign up at core.heysol.ai](https://core.heysol.ai)

### Step 1: Add CORE MCP in Windsurf

1. Open Windsurf IDE
2. Navigate to **Windsurf Settings** → **Cascade** section
3. Open **MCP Marketplace** -> **Settings** OR **View raw config** to open the configuration file
4. Add the following to your `mcp_config.json`:

```json
{
  "mcpServers": {
    "core-memory": {
      "serverUrl": "https://core.heysol.ai/api/v1/mcp?source=windsurf"
    }
  }
}
```

5. Save the file and restart Windsurf IDE

### Step 2: Authenticate with CORE

1. After saving the config, Windsurf will open a browser window for authentication
2. Grant Windsurf permission to access your CORE memory

### Step 3: Verify Connection

1. Go to **Cascade Editor** → **Plugin Icon** -> Hit **Refresh** icon
2. Confirm **core-memory** shows as **Active** with green indicator

Add your first memory:

> "Summarise the whole project in detail and add it to CORE Memory"

---

## Alternative: Using API Key Method

If the OAuth authentication doesn't work, use the API key method instead:

### Step 1: Get Your API Key

1. Log into your CORE dashboard at [core.heysol.ai](https://core.heysol.ai)
2. Navigate to **Settings** (bottom left)
3. Go to **API Key** → **Generate new key** → Name it "windsurf"
4. Copy the generated key

### Step 2: Update MCP Configuration

Replace your `mcp_config.json` configuration with:

```json
{
  "mcpServers": {
    "core-memory": {
      "serverUrl": "https://core.heysol.ai/api/v1/mcp/source=windsurf",
      "headers": {
        "Authorization": "Bearer <YOUR_TOKEN>"
      }
    }
  }
}
```

Replace `<YOUR_TOKEN>` with the API key you copied from Step 1.

### Step 3: Restart and Verify

1. Save the file and restart Windsurf IDE
2. Go to **Cascade Editor** → **Plugin Icon** → Hit **Refresh**
3. Confirm **core-memory** shows as **Active** with green indicator

---

## Enable Automatic Memory Integration (Recommended)

Create `AGENTS.md` in your project root (if it doesn't exist, just append if it already exists):

```bash
touch AGENTS.md
```

Add the following to `AGENTS.md`:

```markdown
⚠️ **CRITICAL: READ THIS FIRST - MANDATORY MEMORY PROTOCOL** ⚠️

You are an AI coding assistant with access to CORE Memory - a persistent knowledge system that maintains project context, learnings, and continuity across all coding sessions.

## 🔴 MANDATORY STARTUP SEQUENCE - DO NOT SKIP 🔴

**BEFORE RESPONDING TO ANY USER MESSAGE, YOU MUST EXECUTE THESE TOOLS IN ORDER:**

### STEP 1 (REQUIRED): Search for Relevant Context

EXECUTE THIS TOOL FIRST:
`memory_search`

- Previous discussions about the current topic
- Related project decisions and implementations
- User preferences and work patterns
- Similar problems and their solutions

**Additional search triggers:**

- User mentions "previously", "before", "last time", or "we discussed"
- User references past work or project history
- Working on the CORE project (this repository)
- User asks about preferences, patterns, or past decisions
- Starting work on any feature or bug that might have history

**How to search effectively:**

- Write complete semantic queries, NOT keyword fragments
- Good: `"Manoj's preferences for API design and error handling"`
- Bad: `"manoj api preferences"`
- Ask: "What context am I missing that would help?"
- Consider: "What has the user told me before that I should remember?"

### Query Patterns for Memory Search

**Entity-Centric Queries** (Best for graph search):

- ✅ GOOD: `"Manoj's preferences for product positioning and messaging"`
- ✅ GOOD: `"CORE project authentication implementation decisions"`
- ❌ BAD: `"manoj product positioning"`
- Format: `[Person/Project] + [relationship/attribute] + [context]`

**Multi-Entity Relationship Queries** (Excellent for episode graph):

- ✅ GOOD: `"Manoj and Harshith discussions about BFS search implementation"`
- ✅ GOOD: `"relationship between entity extraction and recall quality in CORE"`
- ❌ BAD: `"manoj harshith bfs"`
- Format: `[Entity1] + [relationship type] + [Entity2] + [context]`

**Semantic Question Queries** (Good for vector search):

- ✅ GOOD: `"What causes BFS search to return empty results? What are the requirements for BFS traversal?"`
- ✅ GOOD: `"How does episode graph search improve recall quality compared to traditional search?"`
- ❌ BAD: `"bfs empty results"`
- Format: Complete natural questions with full context

**Concept Exploration Queries** (Good for BFS traversal):

- ✅ GOOD: `"concepts and ideas related to semantic relevance in knowledge graph search"`
- ✅ GOOD: `"topics connected to hop distance weighting and graph topology in BFS"`
- ❌ BAD: `"semantic relevance concepts"`
- Format: `[concept] + related/connected + [domain/context]`

**Temporal Queries** (Good for recent work):

- ✅ GOOD: `"recent changes to search implementation and reranking logic"`
- ✅ GOOD: `"latest discussions about entity extraction and semantic relevance"`
- ❌ BAD: `"recent search changes"`
- Format: `[temporal marker] + [specific topic] + [additional context]`

## 🔴 MANDATORY SHUTDOWN SEQUENCE - DO NOT SKIP 🔴

**AFTER FULLY RESPONDING TO THE USER, YOU MUST EXECUTE THIS TOOL:**

### FINAL STEP (REQUIRED): Store Conversation Memory

EXECUTE THIS TOOL LAST:
`memory_ingest`
Include the spaceId parameter using the ID from your initial memory_get_space call.

⚠️ **THIS IS NON-NEGOTIABLE** - You must ALWAYS store conversation context as your final action.

**What to capture in the message parameter:**

From User:

- Specific question, request, or problem statement
- Project context and situation provided
- What they're trying to accomplish
- Technical challenges or constraints mentioned

From Assistant:

- Detailed explanation of solution/approach taken
- Step-by-step processes and methodologies
- Technical concepts and principles explained
- Reasoning behind recommendations and decisions
- Alternative approaches discussed
- Problem-solving methodologies applied

**Include in storage:**

- All conceptual explanations and theory
- Technical discussions and analysis
- Problem-solving approaches and reasoning
- Decision rationale and trade-offs
- Implementation strategies (described conceptually)
- Learning insights and patterns

**Exclude from storage:**

- Code blocks and code snippets
- File contents or file listings
- Command examples or CLI commands
- Raw data or logs

**Quality check before storing:**

- Can someone quickly understand project context from memory alone?
- Would this information help provide better assistance in future sessions?
- Does stored context capture key decisions and reasoning?

---

## Summary: Your Mandatory Protocol

1. **FIRST ACTION**: Execute `memory_search` with semantic query about the user's request
2. **RESPOND**: Help the user with their request
3. **FINAL ACTION**: Execute `memory_ingest` with conversation summary and spaceId

**If you skip any of these steps, you are not following the project requirements.**
```

## How It Works

Once connected, CORE memory integrates with Windsurf's Cascade:

- **Auto-recall**: Cascade searches your memory at conversation start
- **Auto-store**: Key insights saved automatically after conversations
- **Cross-platform**: Memory shared across Windsurf, Cursor, Claude Code, ChatGPT
- **Project continuity**: Context persists across all coding sessions

## Troubleshooting

**Connection Issues:**

- Ensure core-memory MCP is active (green indicator)
- Try toggling the MCP off and on
- Restart Windsurf IDE completely

**Authentication Problems:**

- Make sure you completed the OAuth flow in browser
- Check that your CORE account is active at core.heysol.ai

**MCP Not Appearing:**

- Verify `mcp_config.json` syntax is valid JSON
- Restart Windsurf after config changes

### Need Help?

Join our [Discord community](https://discord.gg/YGUZcvDjUa) - ask in **#core-support** channel.