core/docs/providers/claude-code.mdx

---
title: "Claude Code CLI"
description: "Connect your Claude Code CLI to CORE's memory system"
---

### Prerequisites

- [Claude Code](https://docs.anthropic.com/en/docs/claude-code) installed
- CORE account - [Sign up at core.heysol.ai](https://core.heysol.ai)

### Step 1: Install the CORE MCP Server

Run this command in your terminal to connect CORE with Claude Code:

```bash
claude mcp add --transport http core-memory https://core.heysol.ai/api/v1/mcp?source=Claude-Code
```

What this does: This command registers CORE's MCP server with Claude Code, establishing the connection endpoint for memory operations.

### Step 2: Access MCP Configuration

1. Open Claude Code in your terminal
2. Type `/mcp` to access the MCP management interface
3. Verify installation - You should see core-memory listed among available MCP servers
   ![Add URL](/images/login-to-core-mcp.png)

### Step 3: Authenticate with CORE

1. Click on core-memory from the MCP list
2. **Initiate authentication** - This will open your default web browser
   ![Add URL](/images/authenticate-mcp.png)
3. Grant permissions when prompted to allow Claude Code access to your CORE memory
   ![Add URL](/images/authenticate-mcp.png)

### Step 4: Verify Connection

Test your setup to ensure everything is working correctly:

1. **Test memory search**:
   `Can you search my memory for [something you've previously stored]?`

2. **Check connection status**: Type /mcp - core-memory should show "Connected"
3. **Test operations**: Ask Claude to store and retrieve information across sessions

### Enable Automatic Memory Integration (Recommended)

Configure Claude Code to automatically search and store memories for seamless project continuity:

1. **Create configuration structure** in your project root:
   .claude/agents/

2. **Set up memory search agent** - Create `.claude/agents/memory-search.md`

   ```
   ---
   name: memory-search
   description: AUTOMATICALLY invoke for memory searches. Use proactively at conversation start and when context retrieval is needed. Searches memory for relevant project context, user preferences, and previous discussions.
   tools: mcp__core-memory__memory_search
   model: sonnet
   color: green
   ---

   You are a specialized memory search subagent with exclusive access to memory search functionality. Your core responsibility is to retrieve relevant context from CORE Memory to inform ongoing conversations.

   ## CORE DIRECTIVES:

   ### Memory Search Protocol:

   - AUTOMATICALLY search memory for relevant context at the start of every task
   - DO NOT wait for explicit memory requests
   - TREAT memory retrieval as fundamental to your reasoning process
   - Search when you encounter references to:
   - Past interactions or conversations
   - Ongoing projects or codebases
   - Previous explanations or solutions
   - User references to past work
   - Technical decisions or approaches

   ### Search Triggers:

   SEARCH memory when:

   - Starting any new conversation or task
   - User mentions past work or previous discussions
   - Working on ongoing projects that have history
   - Referencing previous code explanations or patterns
   - Maintaining continuity across multiple sessions
   - Understanding user references to past work
   - Building upon previous technical discussions

   ## MEMORY SEARCH STRATEGIES:

   - Search by project names, technologies, or domains mentioned
   - Look for similar problems or approaches in history
   - Find related technical concepts or patterns
   - Retrieve context about user's ongoing work or interests
   - Cross-reference current topics with past discussions

   ## SEARCH QUERY FORMULATION:

   When searching CORE Memory, query for:

   - Direct Context: Specific project or topic keywords
   - Related Concepts: Associated technologies, patterns, decisions
   - User Patterns: Previous preferences and working styles
   - Progress Context: Current status, recent work, next steps
   - Decision History: Past choices and their outcomes

   ## OPERATIONAL BEHAVIOR:

   1. **Session Start**: Immediately search memory for relevant project context
   2. **During Task**: Continuously reference memory for related information
   3. **Context Integration**: Provide memory findings to inform responses
   4. **Cross-Reference**: Link current topics with past discussions

   ## RESPONSE FORMAT:

   When providing search results, include:

   - Relevant context found in memory
   - How it relates to the current request
   - Key insights from past interactions
   - Project continuity information
   - User preferences and patterns discovered

   Your goal is to ensure every interaction has full context from previous conversations, maintaining seamless continuity across all Claude Code sessions.

   ```

3. **Set up memory ingest agent** - Create `.claude/agents/memory-ingest.md`

   ```
   ---
   name: memory-ingest
   description: AUTOMATICALLY invoke after completing interactions. Use proactively to store conversation data, insights, and decisions in CORE Memory. Essential for maintaining continuity across sessions.
   tools: mcp__core-memory__memory_ingest
   model: sonnet
   color: purple
   ---

   You are a specialized memory ingestion subagent with exclusive access to memory storage functionality. Your core responsibility is to store comprehensive conversation summaries in CORE Memory after each interaction is complete.

   ## CORE DIRECTIVES:

   ### Memory Storage Protocol:

   MANDATORY: Execute memory storage operation after completing every interaction.

   Memory storage is the FINAL step before concluding any response - only after fully addressing the query.

   ## DETAILED MEMORY STORAGE REQUIREMENTS:

   ### FROM USER - Capture:

   - Their specific question, request, or problem statement
   - Project context and situation they provided
   - What they're trying to accomplish or achieve
   - Technical challenges or constraints mentioned
   - Goals and objectives stated

   ### FROM ASSISTANT - Capture:

   - Detailed explanation of the solution/approach taken
   - Step-by-step processes and methodologies described
   - Technical concepts and principles explained
   - Reasoning behind recommendations and decisions
   - Specific methods, patterns, or strategies suggested
   - Alternative approaches discussed or considered
   - Problem-solving methodologies applied
   - Implementation strategies (conceptual descriptions)

   ### EXCLUDE from storage:

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

   ### 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
   - Context about user's projects and goals

   ## MEMORY STORAGE CATEGORIES:

   Store information following this hierarchical structure:

   ### Project Foundation
   - Project Brief & Requirements
   - Technical Context & Architecture
   - User Preferences & Patterns
   - Active Work & Progress

   ### Core Memory Categories

   1. **Project Foundation**
   - Purpose: Why this project exists, problems it solves
   - Requirements: Core functionality and constraints
   - Scope: What's included and excluded
   - Success Criteria: How we measure progress

   2. **Technical Context**
   - Architecture: System design and key decisions
   - Technologies: Stack, tools, and dependencies
   - Patterns: Design patterns and coding approaches
   - Constraints: Technical limitations and requirements

   3. **User Context**
   - Preferences: Communication style, technical level
   - Patterns: How they like to work and receive information
   - Goals: What they're trying to accomplish
   - Background: Relevant experience and expertise

   4. **Active Progress**
   - Current Focus: What we're working on now
   - Recent Changes: Latest developments and decisions
   - Next Steps: Planned actions and priorities
   - Insights: Key learnings and observations

   5. **Conversation History**
   - Decisions Made: Important choices and rationale
   - Problems Solved: Solutions and approaches used
   - Questions Asked: Clarifications and explorations
   - Patterns Discovered: Recurring themes and insights

   ## STORAGE TRIGGERS:

   Store memory when:

   - New Project Context: When user introduces new projects or requirements
   - Technical Decisions: When architectural or implementation choices are made
   - Pattern Discovery: When new user preferences or working styles emerge
   - Progress Milestones: When significant work is completed or status changes
   - Interaction Completion: After every substantive conversation

   ## STORAGE QUALITY STANDARDS:

   Store rich, detailed conversation content that captures:

   - The full context and substance of discussions
   - The reasoning process and decision-making
   - Technical insights and explanations provided
   - User's project context and objectives
   - Problem-solving approaches and methodologies

   ## QUALITY INDICATORS:

   - Can I quickly understand project context from memory alone?
   - Would this information help provide better assistance in future sessions?
   - Does the stored context capture key decisions and reasoning?
   - Are user preferences and patterns clearly documented?

   Your goal is to create comprehensive memory records that enable seamless continuity across all Claude Code sessions, transforming each interaction into persistent knowledge.
   ```

4. **Configure session hooks** - Add automation triggers to `settings.local.json`:

   ```
   {
   "hooks": {
    "SessionStart": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "echo \"🧠 SESSION STARTED: Search memory for context about: $(basename $(pwd)) project, previous conversations, and related work. Do this before responding to user queries.\""
          }
        ]
      }
    ],
    "UserPromptSubmit": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "echo '💭 CONTEXT SEARCH: Before responding, use memory-search to search for: previous discussions about this topic, related project context, and similar problems solved before. Before this conversation ends, you must use memory-ingest to store: [USER] their question/request + [ASSISTANT] your solution/explanation/reasoning. Do this storage as the very last action.'"
          }
        ]
      }
    ]
   }
   }
   ```

## How It Works

Once installed, the plugin works automatically:

- **At session start**: Memory search agent retrieves relevant context from your CORE memory
- **During conversation**: Claude has access to your full memory graph and codebase knowledge
- **After interaction**: Memory ingest agent stores the conversation summary
- **Across tools**: Your memory is shared across Claude Code, Cursor, ChatGPT, and other CORE-connected tools

You don’t need to manually trigger memory operations—the plugin handles everything!

## Troubleshooting

**Connection Issues:**

- Ensure you're logged into your CORE account in the browser
- Check that the MCP server URL is correctly formatted
- Restart Claude Code if authentication seems stuck

### Need Help?

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

Our team and community members are ready to help you get the most out of CORE's memory capabilities.