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Skill + MCP server for recursive LLM reasoning. Load context, iterate with search/code/think tools, converge on answers.

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Aleph

License: MIT Python 3.10+ PyPI version

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

Quickstart

1. Install

pip install "aleph-rlm[mcp]"

This installs three commands:

Command Purpose
aleph MCP server — connect from any MCP client
alef Standalone CLI — run RLM loops directly from your terminal
aleph-rlm Setup utility — auto-configure MCP clients

2. Choose your mode

Option A: MCP Mode (recommended for AI assistants)

Configure your MCP client to use the aleph server, then interact via tool calls.

Option B: CLI Mode (standalone terminal use)

Run alef directly from the command line — no MCP setup required.

MCP Mode Setup

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.

Verify

In your assistant, run:

get_status()

If using Claude Code, tools are prefixed: mcp__aleph__get_status.

CLI Mode (alef)

The alef command runs the full RLM reasoning loop directly from your terminal. It uses local CLI tools (claude, codex, or gemini) as the LLM backend — no separate Aleph API keys needed, just the CLI tool's own authentication.

Prerequisites: Have claude, codex, or gemini CLI installed and authenticated.

Basic Usage

# Simple query
alef run "What is 2+2?" --provider cli --model claude

# With context from a file
alef run "Summarize this log" --provider cli --model claude --context-file app.log

# JSON context
alef run "Extract all names" --provider cli --model claude --context '{"users": [{"name": "Alice"}, {"name": "Bob"}]}'

# Full JSON output with trajectory
alef run "Analyze this data" --provider cli --model claude --context-file data.json --json --include-trajectory

With Sub-Queries (Multi-Claude Recursion)

Enable recursive sub-queries where the LLM spawns additional Claude calls:

# Enable Claude CLI for sub-queries
export ALEPH_SUB_QUERY_BACKEND=claude

# Run a complex analysis that uses sub_query()
alef run "For each item in the context, use sub_query to summarize it, then combine results" \
  --provider cli --model claude \
  --context '{"items": [{"name": "Alice", "score": 95}, {"name": "Bob", "score": 87}]}' \
  --max-iterations 10

The RLM loop will:

  1. Execute Python code blocks to explore the context
  2. Call sub_query() which spawns additional Claude CLI processes
  3. Iterate until FINAL(answer) is reached

CLI Options

Flag Description
--provider cli Use local CLI tools instead of API
--model claude|codex|gemini Which CLI backend to use
--context "..." Inline context string
--context-file path Load context from file
--context-stdin Read context from stdin
--json Output JSON response
--include-trajectory Include full reasoning trace in JSON
--max-iterations N Limit RLM loop iterations

Environment Variables

Variable Description
ALEPH_SUB_QUERY_BACKEND Backend for sub_query(): claude, codex, gemini, or api
ALEPH_SUB_QUERY_SHARE_SESSION Share MCP session with sub-agents (set to 1)
ALEPH_CLI_TIMEOUT Timeout for CLI calls (default: 120s)

Swarm Mode

Aleph enables multi-agent coordination through shared contexts. Multiple agents can read and write to the same context IDs, creating a distributed memory layer for swarm architectures.

How It Works

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Agent A    │     │  Agent B    │     │  Agent C    │
│  (Explorer) │     │  (Analyst)  │     │  (Writer)   │
└──────┬──────┘     └──────┬──────┘     └──────┬──────┘
       │                   │                   │
       └───────────────────┼───────────────────┘
                           │
                    ┌──────▼──────┐
                    │   Aleph     │
                    │  Contexts   │
                    │ (Shared RAM)│
                    └─────────────┘

Agents coordinate by reading/writing to shared context IDs. No message passing needed for data—agents simply load, search, and write to the same contexts.

Context Naming Conventions

Pattern Purpose Example
swarm-{name}-kb Shared knowledge base swarm-docs-kb
task-{id}-spec Task requirements task-42-spec
task-{id}-findings Shared discoveries task-42-findings
{agent}-workspace Private agent workspace explorer-workspace

Basic Swarm Workflow

1. Leader creates shared context:

load_context(content="Project: Analyze auth system", context_id="swarm-auth-kb")

2. Spawn agents with Aleph access:

# Each agent connects to the same Aleph MCP server
# They can all access "swarm-auth-kb"

3. Agents write findings to shared context:

# Agent A finds something
exec_python(code="""
finding = "Auth uses JWT with RS256"
ctx_append(finding)  # Appends to current context
""", context_id="task-42-findings")

4. Agents read each other's work:

search_context(pattern="JWT|token", context_id="task-42-findings")

5. Diff and merge contexts:

diff_contexts(a="agent-a-workspace", b="agent-b-workspace")

Self-Improvement Loop

Swarms can accumulate learnings across sessions:

# After completing a task, log what worked
exec_python(code="""
learning = '''
## Pattern: Parallel Code Search
- Split codebase by directory
- Each agent searches one area
- Merge findings to shared context
- 3x faster than sequential
'''
ctx_append(learning)
""", context_id="swarm-kb")

# Save for next session
save_session(context_id="swarm-kb", path="swarm_learnings.json")

Environment Variables

Variable Description
ALEPH_SUB_QUERY_SHARE_SESSION Set to 1 to let sub-agents access parent's MCP session
ALEPH_SUB_QUERY_BACKEND Backend for sub_query(): claude, codex, gemini, or api

Key Patterns

Parallel Exploration:

# Spawn multiple agents, each with a different context_id
# Agent 1: context_id="explore-frontend"
# Agent 2: context_id="explore-backend"
# All write findings to: context_id="task-findings"

Consensus Building:

# Each agent writes proposal to task-proposals
# Use diff_contexts to compare
# Synthesize with sub_aleph

Knowledge Propagation:

Discovery → Private Workspace → Validate → Shared Context → Knowledge Base

See the /swarm skill for detailed prompts and examples.

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': 'user@example.com', '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)
  • sub_aleph — nested Aleph recursion (RLM -> RLM)
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_aleph(), sub_query_map(), sub_query_batch(), sub_query_strict(), ctx_append(), ctx_set()

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)

Recursion budgets (depth/time/detail):

  • ALEPH_MAX_DEPTH (default: 2) — max sub_aleph nesting depth
  • ALEPH_MAX_ITERATIONS (default: 100) — total RLM loop steps (root + recursion)
  • ALEPH_MAX_WALL_TIME (default: 300s) — wall-time cap per Aleph run
  • ALEPH_MAX_SUB_QUERIES (default: 100) — total sub_query calls allowed
  • ALEPH_MAX_TOKENS (default: unset) — optional per-call output cap

Override these via env vars above or per-call args on sub_aleph. CLI backends run sub_aleph as a single-shot call; use the API backend for full multi-iteration recursion.

See docs/CONFIGURATION.md for all options.

Documentation

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

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Skill + MCP server for recursive LLM reasoning. Load context, iterate with search/code/think tools, converge on answers.

aleph-web.pages.dev/

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mcp repl recursive rlm llm

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