Heimdall provides a unified heimdall CLI that manages everything from project setup to MCP integration.

1. Install Heimdall

pip install heimdall-mcp

This installs the heimdall command-line tool with all necessary dependencies.

2. Initialize Your Project

Navigate to your project directory and set up Heimdall:

cd /path/to/your/project

# Initialize project memory (starts Qdrant, creates collections, sets up config)
heimdall project init

This single command interactively builds up everything asking user preferences:

• ✅ Starts Qdrant vector database automatically
• ✅ Creates project-specific memory collections
• ✅ Sets up .heimdall/ configuration directory
• ✅ Downloads required AI models
• ✅ File monitoring
• ✅ Git hooks
• ✅ MCP integration

Note: this creates a .heimdall/ directory in your project for configuration - you should NOT commit this - add to .gitignore!

Load Project Knowledge

Recommended: Use automatic file monitoring and place files in .heimdall/docs/:

# Copy or symlink your documentation to the monitored directory
ln -r -s my-project-docs ./.heimdall/docs/project-docs

# Start automatic monitoring (files are loaded instantly when changed)
heimdall monitor start

Alternative: Manual loading for one-time imports:

# Load documentation and files manually
heimdall load docs/ --recursive
heimdall load README.md

Your project's memory is now active and ready for your LLM.

Real-time Git Integration

You can parse your entire git history with:

# Load git commit history
heimdall git-load .

You can also install git hooks for automatic memory updates on commits:

# Install the post-commit hook (Python-based, cross-platform)
heimdall git-hooks install

Note: If you have existing post-commit hooks, they'll be safely chained and preserved - but proceed carefully.

🧹 Cleanup

To remove Heimdall from a project:

# Navigate to the project you want to clean up
cd /path/to/project

# Cleanup data, remove collections, uninstall git hooks
memory_system project clean

This cleanly removes project-specific data while preserving the shared Qdrant instance for other projects.

⚙️ How It Works Under the Hood

Heimdall extracts unstructured knowledge from your documentation and structured data from your git history. This information is vectorized and stored in a Qdrant database. The LLM can then query this database using a simple set of tools to retrieve relevant, context-aware information.

graph TD
    %% Main client outside the server architecture
    AI_Assistant["🤖 AI Assistant (e.g., Claude)"]

    %% Top-level subgraph for the entire server
    subgraph Heimdall MCP Server Architecture

        %% 1. Application Interface Layer
        subgraph Application Interface
            MCP_Server["MCP Server (heimdall-mcp)"]
            CLI["CognitiveCLI (heimdall/cli.py)"]
            style MCP_Server fill:#b2ebf2,stroke:#00acc1,color:#212121
            style CLI fill:#b2ebf2,stroke:#00acc1,color:#212121
        end

        %% 2. Core Logic Engine
        style Cognitive_System fill:#ccff90,stroke:#689f38,color:#212121
        Cognitive_System["🧠 CognitiveSystem (core/cognitive_system.py)<br/>"]

        %% 3. Storage Layer (components side-by-side)
        subgraph Storage Layer
            Qdrant["🗂️ Qdrant Storage<br/><hr/>- Vector Similarity Search<br/>- Multi-dimensional Encoding"]
            SQLite["🗃️ SQLite Persistence<br/><hr/>- Memory Metadata & Connections<br/>- Caching & Retrieval Stats"]
        end

        %% 4. Output Formatting
        style Formatted_Response fill:#fff9c4,stroke:#fbc02d,color:#212121
        Formatted_Response["📦 Formatted MCP Response<br/><i>{ core, peripheral }</i>"]

        %% Define internal flow
        MCP_Server -- calls --> CLI
        CLI -- calls --> Cognitive_System

        Cognitive_System -- "1\. Vector search for candidates" --> Qdrant
        Cognitive_System -- "2\. Hydrates with metadata" --> SQLite
        Cognitive_System -- "3\. Performs Bridge Discovery" --> Formatted_Response

    end

    %% Define overall request/response flow between client and server
    AI_Assistant -- "recall_memorie" --> MCP_Server
    Formatted_Response -- "Returns structured memories" --> AI_Assistant

    %% --- Styling Block ---

    %% 1. Node Styling using Class Definitions
    classDef aiClientStyle fill:#dbeafe,stroke:#3b82f6,color:#1e3a8a
    classDef interfaceNodeStyle fill:#cffafe,stroke:#22d3ee,color:#0e7490
    classDef coreLogicStyle fill:#dcfce7,stroke:#4ade80,color:#166534
    classDef qdrantNodeStyle fill:#ede9fe,stroke:#a78bfa,color:#5b21b6
    classDef sqliteNodeStyle fill:#fee2e2,stroke:#f87171,color:#991b1b
    classDef responseNodeStyle fill:#fef9c3,stroke:#facc15,color:#854d0e

    %% 2. Assigning Classes to Nodes
    class AI_Assistant aiClientStyle
    class MCP_Server,CLI interfaceNodeStyle
    class Cognitive_System coreLogicStyle
    class Qdrant qdrantNodeStyle
    class SQLite sqliteNodeStyle
    class Formatted_Response responseNodeStyle

    %% 3. Link (Arrow) Styling
    %% Note: Styling edge label text is not reliably supported. This styles the arrow lines themselves.
    %% Primary request/response flow (links 0 and 1)
    linkStyle 0,1 stroke:#3b82f6,stroke-width:2px
    %% Internal application calls (links 2 and 3)
    linkStyle 2,3 stroke:#22d3ee,stroke-width:2px,stroke-dasharray: 5 5
    %% Internal data access calls (links 4 and 5)
    linkStyle 4,5 stroke:#9ca3af,stroke-width:2px
    %% Final processing call (link 6)
    linkStyle 6 stroke:#4ade80,stroke-width:2px

LLM Tool Reference

You can instruct your LLM to use the following six tools to interact with its memory:

| Tool | Description | | :---------------- | :------------------------------------------------------------------- | | store_memory | Stores a new piece of information, such as an insight or a solution. | | recall_memories | Performs a semantic search for relevant memories based on a query. | | session_lessons | Records a key takeaway from the current session for future use. | | memory_status | Checks the health and statistics of the memory system. | | delete_memory | Delete a specific memory by its unique ID. | | delete_memories_by_tags | Delete all memories that have any of the specified tags. |

💡 Best Practices

To maximize the effectiveness of Heimdall:

• Provide Quality Documentation: Think architecture decision records, style guides, and API documentation.
• Keep documents updated: Heilmdall will use documents in .heimdall/docs to provide memories - if they are outdated, so will be the memories. We suggest you use symbolic links to your actual docs directory in .heimdall/docs so Heimdall automatically refreshes memories with latest document versions.
• Maintain Good Git Hygiene: Write clear and descriptive commit messages. A message like feat(api): add user authentication endpoint is far more valuable than more stuff.
• Set Up Automation: Use heimdall monitor start and heimdall git-hooks install for hands-free memory updates.
• Guide Your Assistant: Use a system prompt (like a CLAUDE.md file) to instruct your LLM on how and when to use the available memory tools.
• Use Strategic Tagging: Establish rules for your LLM to tag memories consistently. Use temporary tags like temp-analysis, task-specific, or cleanup-after-project for memories that should be deleted after completion, enabling easy cleanup with delete_memories_by_tags.

🛠️ Command Reference

Core Commands

| Command | Description | | :------ | :---------- | | heimdall store <text> | Store experience in cognitive memory | | heimdall recall <query> | Retrieve relevant memories based on query | | heimdall load <path> | Load files/directories into memory | | heimdall git-load [repo] | Load git commit patterns into memory | | heimdall status | Show system status and memory statistics | | `heimdal