| FMRS | 55 / 100 · C | 75 / 100 · B |
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| Reliability | 8 / 20 | 12 / 20 |
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| Security and permissions | 12 / 20 | 16 / 20 |
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| Maintenance | 9 / 20 | 14 / 20 |
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| Documentation | 14 / 20 | 18 / 20 |
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| Setup experience | 12 / 20 | 15 / 20 |
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| Best for | - Job seekers who want to search and apply for remote jobs via AI
- Recruiters and employers who need to post jobs, find candidates, and benchmark salaries
- Developers who want to integrate remote job data into AI workflows
- Anyone who wants to manage their job search or hiring pipeline conversationally
| - 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 | - Those looking for on-site or non-remote job listings
- Developers needing more than 100,000 job/candidate records or high-volume API access
- Users concerned about completing OAuth login in a browser (needed for authenticated tools)
- Those expecting all tools to be entirely free without any account (employer tools require a company-linked account)
| - 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 | - Public tools (search, salary, company research) require no authentication
- Authenticated tools require a Himalayas account; OAuth 2.1 flow opens a browser for login
- Employer tools require the account to be linked to a company
- The server can fetch and post data to Himalayas; data is stored on the Himalayas platform
| - 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 | - Data is dependent on the Himalayas platform; listings and salary data may not be perfectly current
- OAuth login for authenticated tools presents standard authorization risks
- Posting jobs may incur costs (e.g., sticky placement) via Stripe
- Tools may break if the Himalayas API changes or rate limits are hit
- Searches and posted data may contain incomplete or inaccurate information
| - 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 Code, ChatGPT, Gemini, Cursor, Windsurf, VS Code, Microsoft Copilot Studio, OpenClaw | 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 | 34 | 14 |
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