aleph-rlm 0.7.0

MCP server for recursive LLM reasoning—load context, iterate with search/code/think tools, converge on answers

Aleph

Your RAM is the new context window.

Aleph is an MCP server that gives any LLM access to gigabytes of local data without consuming context. Load massive files into a Python process—the model explores them via search, slicing, and sandboxed code execution. Only results enter the context window, never the raw content.

Based on the Recursive Language Model (RLM) architecture.

Use Cases

Scenario	What Aleph Does
Large log analysis	Load 500MB of logs, search for patterns, correlate across time ranges
Codebase navigation	Load entire repos, find definitions, trace call chains, extract architecture
Data exploration	JSON exports, CSV files, API responses—explore interactively with Python
Mixed document ingestion	Load PDFs, Word docs, HTML, and logs like plain text
Semantic search	Find relevant sections by meaning, then zoom in with peek
Research sessions	Save/resume sessions, track evidence with citations, spawn sub-queries

Requirements

• Python 3.10+
• An MCP-compatible client: Claude Code, Cursor, VS Code, Windsurf, Codex CLI, or Claude Desktop

Quickstart

1. Install

pip install "aleph-rlm[mcp]"

2. Configure your MCP client

Automatic (recommended):

aleph-rlm install

This auto-detects your installed clients and configures them.

Manual (any MCP client):

{
  "mcpServers": {
    "aleph": {
      "command": "aleph",
      "args": ["--enable-actions", "--workspace-mode", "any"]
    }
  }
}

Config file locations

Client	macOS/Linux	Windows
Claude Code	~/.claude/settings.json	%USERPROFILE%\.claude\settings.json
Claude Desktop	~/Library/Application Support/Claude/claude_desktop_config.json	%APPDATA%\Claude\claude_desktop_config.json
Cursor	~/.cursor/mcp.json	%USERPROFILE%\.cursor\mcp.json
VS Code	~/.vscode/mcp.json	%USERPROFILE%\.vscode\mcp.json
Codex CLI	~/.codex/config.toml	%USERPROFILE%\.codex\config.toml

See MCP_SETUP.md for detailed instructions.

3. Verify

In your assistant, run:

get_status()

If using Claude Code, tools are prefixed: mcp__aleph__get_status.

AI Assistant Setup (MCP + /aleph Skill) — Copy/Paste

Paste this into any AI coding assistant or agentic coder to add Aleph (MCP server + /aleph skill):

You are an AI coding assistant. Please set up Aleph (Model Context Protocol / MCP).

1) Add the Aleph MCP server config:
{
  "mcpServers": {
    "aleph": {
      "command": "aleph",
      "args": ["--enable-actions", "--workspace-mode", "any"]
    }
  }
}

2) Install the /aleph skill prompt:
- Claude Code: copy docs/prompts/aleph.md -> ~/.claude/commands/aleph.md
- Codex CLI: copy docs/prompts/aleph.md -> ~/.codex/skills/aleph/SKILL.md
- Gemini CLI: copy docs/prompts/aleph.md -> ~/.gemini/skills/aleph/SKILL.md
  Ensure ~/.gemini/settings.json has "experimental": { "skills": true } and restart.
If this client uses a different skill/command folder, ask me where to place it.

3) Verify: run get_status() or list_contexts().
If tools are namespaced, use mcp__aleph__get_status or mcp__aleph__list_contexts.

4) (Optional) Enable sub_query (recursive sub-agent):
- Quick: just say "use claude backend" — the LLM will run set_backend("claude")
- Env var: set ALEPH_SUB_QUERY_BACKEND=claude|codex|gemini|api
- API backend: set ALEPH_SUB_QUERY_API_KEY + ALEPH_SUB_QUERY_MODEL
Runtime switching: the LLM can call set_backend() or configure() anytime—no restart needed.

5) Use the skill: /aleph (Claude Code) or $aleph (Codex CLI).
Gemini CLI: /skills list (use /skills enable aleph if disabled).

The /aleph Skill

The /aleph skill is a prompt that teaches your LLM how to use Aleph effectively. It provides workflow patterns, tool guidance, and troubleshooting tips.

Note: Aleph works best when paired with the skill prompt + MCP server together.

What it does

• Loads files into searchable in-memory contexts
• Tracks evidence with citations as you reason
• Supports semantic search and fast rg-based codebase search
• Enables recursive sub-queries for deep analysis
• Persists sessions for later resumption (memory packs)

Simplest Use Case

Just point at a file:

/aleph path/to/huge_log.txt

The LLM will load it into Aleph's external memory and immediately start analyzing using RLM patterns—no extra setup needed.

How to invoke

Client	Command
Claude Code	/aleph
Codex CLI	$aleph

For other clients, copy docs/prompts/aleph.md and paste it at session start.

Installing the skill

Option 1: Direct download (simplest)

Download docs/prompts/aleph.md and save it to:

• Claude Code: ~/.claude/commands/aleph.md (macOS/Linux) or %USERPROFILE%\.claude\commands\aleph.md (Windows)
• Codex CLI: ~/.codex/skills/aleph/SKILL.md (macOS/Linux) or %USERPROFILE%\.codex\skills\aleph\SKILL.md (Windows)

Option 2: From installed package

macOS/Linux

# Claude Code
mkdir -p ~/.claude/commands
cp "$(python -c "import aleph; print(aleph.__path__[0])")/../docs/prompts/aleph.md" ~/.claude/commands/aleph.md

# Codex CLI
mkdir -p ~/.codex/skills/aleph
cp "$(python -c "import aleph; print(aleph.__path__[0])")/../docs/prompts/aleph.md" ~/.codex/skills/aleph/SKILL.md

Windows (PowerShell)

# Claude Code
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.claude\commands"
$alephPath = python -c "import aleph; print(aleph.__path__[0])"
Copy-Item "$alephPath\..\docs\prompts\aleph.md" "$env:USERPROFILE\.claude\commands\aleph.md"

# Codex CLI  
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.codex\skills\aleph"
Copy-Item "$alephPath\..\docs\prompts\aleph.md" "$env:USERPROFILE\.codex\skills\aleph\SKILL.md"

How It Works

┌───────────────┐    tool calls     ┌────────────────────────┐
│   LLM client  │ ────────────────► │  Aleph (Python, RAM)   │
│ (limited ctx) │ ◄──────────────── │  search/peek/exec      │
└───────────────┘    small results  └────────────────────────┘

1. Load — load_context (paste text) or load_file (from disk)
2. Explore — search_context, semantic_search, peek_context
3. Compute — exec_python with 100+ built-in helpers
4. Reason — think, evaluate_progress, get_evidence
5. Persist — save_session to resume later

Quick Example

# Load log data
load_context(content=logs, context_id="logs")
# → "Context loaded 'logs': 445 chars, 7 lines, ~111 tokens"

# Search for errors
search_context(pattern="ERROR", context_id="logs")
# → Found 2 match(es):
#   Line 1: 2026-01-15 10:23:45 ERROR [auth] Failed login...
#   Line 4: 2026-01-15 10:24:15 ERROR [db] Connection timeout...

# Extract structured data
exec_python(code="emails = extract_emails(); print(emails)", context_id="logs")
# → [{'value': '[e-mail]', 'line_num': 0, 'start': 50, 'end': 66}, ...]

Advanced Workflows

Multi-Context Workflow (code + docs + diffs)

Load multiple sources, then compare or reconcile them:

# Load a design doc and a repo snapshot (or any two sources)
load_context(content=design_doc_text, context_id="spec")
rg_search(pattern="AuthService|JWT|token", paths=["."], load_context_id="repo_hits", confirm=true)

# Compare or reconcile
diff_contexts(a="spec", b="repo_hits")
search_context(pattern="missing|TODO|mismatch", context_id="repo_hits")

Advanced Querying with exec_python

Treat exec_python as a reasoning tool, not just code execution:

# Example: extract class names or key sections programmatically
exec_python(code="print(extract_classes())", context_id="repo_hits")

Tools

Core (always available):

• load_context, list_contexts, diff_contexts — manage in-memory data
• search_context, semantic_search, peek_context, chunk_context — explore data; use semantic_search for concepts/fuzzy queries, search_context for precise regex
• exec_python, get_variable — compute in sandbox (100+ built-in helpers)
• think, evaluate_progress, summarize_so_far, get_evidence, finalize — structured reasoning
• tasks — lightweight task tracking per context
• get_status — session state
• sub_query — spawn recursive sub-agents (CLI or API backend)

exec_python helpers

The sandbox includes 100+ helpers that operate on the loaded context:

Category	Examples
Extractors (25)	extract_emails(), extract_urls(), extract_dates(), extract_ips(), extract_functions()
Statistics (8)	word_count(), line_count(), word_frequency(), ngrams()
Line operations (12)	head(), tail(), grep(), sort_lines(), columns()
Text manipulation (15)	replace_all(), between(), truncate(), slugify()
Validation (7)	is_email(), is_url(), is_json(), is_numeric()
Core	peek(), lines(), search(), chunk(), cite(), sub_query(), sub_query_map(), sub_query_batch(), sub_query_strict()

Extractors return list[dict] with keys: value, line_num, start, end.

Action tools (requires --enable-actions):

• load_file, read_file, write_file — filesystem (PDFs, Word, HTML, .gz supported)
• run_command, run_tests, rg_search — shell + fast repo search
• save_session, load_session — persist state (memory packs)
• add_remote_server, list_remote_tools, call_remote_tool — MCP orchestration

Configuration

Workspace controls:

• --workspace-root <path> — root for relative paths (default: git root from invocation cwd)
• --workspace-mode <fixed|git|any> — path restrictions
• --require-confirmation — require confirm=true on action calls
• ALEPH_WORKSPACE_ROOT — override workspace root via environment

Limits:

• --max-file-size — max file read (default: 1GB)
• --max-write-bytes — max file write (default: 100MB)
• --timeout — sandbox/command timeout (default: 60s)
• --max-output — max command output (default: 50,000 chars)

See docs/CONFIGURATION.md for all options.

Documentation

• MCP_SETUP.md — client configuration
• docs/CONFIGURATION.md — CLI flags and environment variables
• docs/prompts/aleph.md — skill prompt and tool reference
• CHANGELOG.md — release history
• DEVELOPMENT.md — contributing guide

Development

git clone https://github.com/Hmbown/aleph.git
cd aleph
pip install -e ".[dev,mcp]"
pytest

References

Recursive Language Models
Zhang, A. L., Kraska, T., & Khattab, O. (2025)
arXiv:2512.24601

License

MIT