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Aleph

"What my eyes beheld was simultaneous, but what I shall now write down will be successive, because language is successive." — Jorge Luis Borges, "The Aleph" (1945)

MCP server for recursive LLM reasoning over documents. Instead of cramming context into one prompt, the model iteratively explores with search, code execution, and structured thinking—converging on answers with citations.

License: MIT Python 3.10+ PyPI version

Quick Start

pip install aleph-rlm[mcp]
aleph-rlm install        # auto-detects Claude Desktop, Cursor, Windsurf, VS Code
aleph-rlm doctor         # verify installation
Manual configuration

Add to your MCP client config:

{
  "mcpServers": {
    "aleph": {
      "command": "aleph-mcp-local"
    }
  }
}

How It Works

┌──────────────────────────────────────────────────────────────────┐
│  CONTEXT  →  stored once as `ctx`                                │
└──────────────────────────────────────────────────────────────────┘
                                 │
                                 ▼
┌──────────────────────────────────────────────────────────────────┐
│  🔧 80+ TOOLS                                                    │
├──────────────────────────────────────────────────────────────────┤
│  extract_*  │ emails, IPs, money, dates, URLs, functions, TODOs │
│  grep/head  │ filter lines, sort, uniq, columns                 │
│  search     │ regex with context, contains, find_all            │
│  stats      │ word_count, frequency, ngrams, diff               │
│  transform  │ replace, split, before/after, normalize           │
│  validate   │ is_email, is_url, is_json, is_ip                  │
│  convert    │ to_json, to_snake_case, slugify                   │
└──────────────────────────────────────────────────────────────────┘
                                 │
                                 ▼
┌──────────────────────────────────────────────────────────────────┐
│  📋 EVIDENCE  →  cite() accumulates provenance with line numbers │
└──────────────────────────────────────────────────────────────────┘
                                 │
                        ┌────────┴────────┐
                        ▼                 ▼
                    Continue          Finalize
                  (loop back)    (answer + citations)

The model sees metadata, not full text. It writes Python to explore iteratively. Evidence auto-accumulates.

Example

You: Load this contract and find all liability exclusions

[AI calls load_context, search_context, cite(), evaluate_progress, finalize]

AI: Found 3 liability exclusions:
    1. Section 4.2: Consequential damages excluded (lines 142-158)
    2. Section 7.1: Force majeure carve-out (lines 289-301)
    3. Section 9.3: Cap at contract value (lines 445-452)

    Evidence: [4 citations with line ranges]

When to Use

Use Aleph Skip Aleph
Long documents (>10 pages) Short docs (<30k tokens)
Need regex search Simple lookups
Need computation on extracted data Latency-critical apps
Want citations with line numbers
Iterative analysis across turns
MCP Tools Reference
Tool Purpose
load_context Store document in sandboxed REPL as ctx
peek_context View character or line ranges
search_context Regex search with evidence logging
exec_python Run code against context (includes cite() helper)
chunk_context Split into navigable chunks with metadata
think Structure reasoning sub-steps
evaluate_progress Check confidence and convergence
get_evidence Retrieve citation trail with filtering
get_status Session state and metrics
summarize_so_far Compress history to manage context
finalize Complete with answer and citations
REPL Helpers (80+ functions available in exec_python)

Core: peek, lines, search, chunk, cite

Extraction (auto-detect from context): extract_numbers, extract_money, extract_percentages, extract_dates, extract_times, extract_timestamps, extract_emails, extract_urls, extract_ips, extract_phones, extract_paths, extract_env_vars, extract_versions, extract_uuids, extract_hashes, extract_hex

Code analysis: extract_functions, extract_classes, extract_imports, extract_comments, extract_strings, extract_todos

Log analysis: extract_log_levels, extract_exceptions, extract_json_objects

Statistics: word_count, char_count, line_count, sentence_count, paragraph_count, unique_words, word_frequency, ngrams

Line operations (grep-like): head, tail, grep, grep_v, grep_c, uniq, sort_lines, number_lines, strip_lines, blank_lines, non_blank_lines, columns

Text manipulation: replace_all, split_by, between, before, after, truncate, wrap_text, indent_text, dedent_text, normalize_whitespace, remove_punctuation, to_lower, to_upper, to_title

Pattern matching: contains, contains_any, contains_all, count_matches, find_all, first_match

Comparison: diff, similarity, common_lines, diff_lines

Collections: dedupe, flatten, first, last, take, drop, partition, group_by, frequency, sample_items, shuffle_items

Validation: is_numeric, is_email, is_url, is_ip, is_uuid, is_json, is_blank

Conversion: to_json, from_json, to_csv_row, from_csv_row, to_int, to_float, to_snake_case, to_camel_case, to_pascal_case, to_kebab_case, slugify

# Examples
emails = extract_emails()  # Auto-extracts from ctx
money = extract_money()    # Finds $1,234.56 patterns
errors = grep("ERROR")     # Filter lines
word_frequency(top_n=10)   # Most common words
Sandbox Builtins

Types: bool, int, float, str, dict, list, set, tuple, type, frozenset, bytes, bytearray, complex, slice, object

Functions: len, range, enumerate, zip, map, filter, iter, next, callable, min, max, sum, sorted, reversed, any, all, abs, round, pow, divmod, repr, ascii, chr, ord, format, hex, oct, bin, print, isinstance, issubclass, hash, id

Exceptions: Exception, ValueError, TypeError, RuntimeError, KeyError, IndexError, ZeroDivisionError, NameError, AttributeError, StopIteration, AssertionError, LookupError, ArithmeticError, UnicodeError

Imports: re, json, csv, math, statistics, collections, itertools, functools, datetime, textwrap, difflib, random, string, hashlib, base64, urllib.parse, html

Configuration

Environment Variables:

Variable Purpose
ALEPH_MAX_ITERATIONS Iteration limit
ALEPH_MAX_COST Cost limit in USD

CLI Commands:

aleph-rlm install              # Interactive installer
aleph-rlm install <client>     # Install to specific client
aleph-rlm uninstall <client>   # Remove from client
aleph-rlm doctor               # Verify installation

Supported clients: claude-desktop, cursor, windsurf, vscode, claude-code

Security

The sandbox is best-effort, not hardened.

Blocked: open, os, subprocess, socket, eval, exec, dunder access, imports outside allowlist

For production: Run in a container with resource limits. Do not expose to untrusted users without additional isolation.

Development

git clone https://github.com/Hmbown/aleph.git
cd aleph
pip install -e '.[dev,mcp]'
pytest  # 190 tests

Recent Changes

v0.2.0 (December 2025)

80+ new REPL helpers for document analysis:

  • 16 extraction functions (emails, IPs, money, dates, phones, URLs, paths, versions, UUIDs, functions, classes, TODOs, log levels)
  • 8 statistics (word/line/char count, word frequency, n-grams)
  • 12 grep-like line operations (head, tail, grep, sort, uniq, columns)
  • 15 text manipulation (replace, split, before/after, truncate, normalize)
  • 6 pattern matching (contains, count_matches, find_all)
  • 4 comparison (diff, similarity)
  • 11 collection utilities (dedupe, flatten, group_by, frequency)
  • 7 validators (is_email, is_url, is_ip, is_uuid, is_json)
  • 11 converters (to_json, to_snake_case, slugify)

30+ new builtins: map, filter, iter, next, repr, chr, ord, pow, divmod, hash, id, callable, frozenset, bytes, slice...

6 new allowed imports: random, string, hashlib, base64, urllib.parse, html

v0.1.3 (December 2025)

  • Added type builtin to sandbox
  • Added NameError and AttributeError exceptions

Research

Inspired by Recursive Language Models by Alex Zhang and Omar Khattab.

License

MIT

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