| FMRS | 46 / 100 · D | 75 / 100 · B |
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| Reliability | 4 / 20 | 12 / 20 |
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| Security and permissions | 9 / 20 | 16 / 20 |
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| Maintenance | 11 / 20 | 14 / 20 |
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| Documentation | 12 / 20 | 18 / 20 |
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| Setup experience | 10 / 20 | 15 / 20 |
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| Best for | - AI agent developers needing fast and accurate image search capabilities.
- Vision applications that require text, image, and hybrid search in one library.
- Production environments needing scalable vector search (FAISS, ChromaDB, Qdrant) and metadata management.
| - Individuals or teams who prioritise privacy and offline operation and do not want data leaving the machine
- Users who share memory across multiple MCP clients such as Claude Code, Claude Desktop, Hermes Agent, OpenClaw, Cursor, Codex and Gemini CLI
- Scenarios that need encrypted storage, a hash-chained audit log and verifiable vault migration
- Users who want low-latency recall, a bundled embedding model and no extra LLM calls inside the memory layer
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| Not for | - Users needing real-time video search or object detection.
- End-users without Python or machine learning background.
- Specific features requiring non-OpenAI-compatible LLM providers (though most OpenAI SDK endpoints work).
| - Users who need cloud multi-device sync or a hosted team memory service; moving between machines means locking the vault and copying the file
- Users who expect the server itself to run an LLM that extracts facts and decides what to remember; Compartment explicitly keeps no LLM inside and leaves that to the host model
- Users who cannot keep a passphrase safe or who need a recovery path if it is lost, since Compartment never generates a password, seed or recovery phrase
- Deployments that require network transports such as SSE or streamable-http; this server is stdio only and opens no ports
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| Required permissions | - Filesystem access: for indexing image folders or files.
- Network access: for downloading model weights (e.g., CLIP) and calling LLM APIs (e.g., OpenAI).
- Local resources: GPU (CUDA/MPS) acceleration if available.
| - Read and write the vault file under the user's home directory, by default ~/.compartment/memory.vault, and its sibling settings file
- Access the session directory holding the unlock credential (configurable with COMPARTMENT_SESSION_DIR), which also carries the shared embedding process's Unix socket
- Optionally use the macOS keychain (compartment unlock --keychain is an explicit opt-in, and it survives reboots)
- Write or merge an mcpServers entry into each MCP client's own configuration file, taking a byte-exact backup first
- Install a PostToolUse hook for Claude Code that captures memory files the agent writes
- Serve a read-only dashboard on 127.0.0.1 behind a one-time random URL token
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| Risks and side effects | - Privacy: image data may be sent to third-party LLM services when generating captions.
- Performance: large-scale indexing can consume significant CPU/GPU and memory resources.
- Dependency: relies on external models and vector databases, which may fail due to network or version changes.
| - A leaked passphrase means the encrypted content can be decrypted; Compartment generates no recovery phrase and cannot recover a lost passphrase
- Enabling the memory_unlock tool places the passphrase in the model's context, which is why it is off by default
- If a 2FA keyfile is lost, for example a USB stick, the vault cannot be opened even with the passphrase
- `export --plaintext` writes the vault as unencrypted JSONL, so that file must be handled carefully
- Memory content can come from untrusted sources; recall wraps memories with a notice that they are stored data and content can be marked quarantined, but the host agent must still treat memory as data to limit prompt-injection risk
- The capture hook and client integration write into the user's own settings files, although the implementation backs up and merges rather than replacing
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| Supported clients | Claude Desktop | Claude Desktop, Claude Code, Hermes Agent, OpenClaw, Cursor, VS Code, Codex, Gemini CLI, Cline, Roo Code, Zed, OpenCode, LM Studio, AnythingLLM, BoltAI, Goose, Kiro |
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| Tools | 1 | 14 |
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