Aleph

Aleph is an MCP server and skill for
Recursive Language Models (RLMs). It keeps working state — search indexes,
code execution, evidence, recursion — in a Python process outside the prompt
window, so the LLM reasons iteratively over repos, logs, documents, and data
without burning context on raw content.

+-----------------+    tool calls     +-----------------------------+
|   LLM client    | ---------------> |  Aleph (Python process)     |
| (context budget)| <--------------- |  search / peek / exec / sub |
+-----------------+   small results  +-----------------------------+

Why Aleph:

• Load once, reason many times. Data lives in Aleph memory, not the prompt.
• Compute server-side. exec_python runs code over the full context and
  returns only derived results.
• Recurse. Sub-queries and recipes split complex work across multiple
  reasoning passes.
• Persist. Save sessions and resume long investigations later.

Quick Start

pip install "aleph-rlm[mcp]"
aleph-rlm install --profile claude   # or: codex, portable, api
aleph-rlm doctor                     # verify everything is wired up

Then restart your MCP client and confirm Aleph is available:

get_status()
list_contexts()

The optional /aleph (Claude Code) or $aleph (Codex) skill shortcut starts
a structured RLM workflow. Install
docs/prompts/aleph.md into your client's
command/skill folder — see MCP_SETUP.md for exact paths.

Entry Points

Install Profiles

aleph-rlm install asks which sub-query profile to use. Profiles configure
the nested backend that sub_query and sub_query_batch spawn for recursive
reasoning.

aleph-rlm install claude-code --profile claude
aleph-rlm configure --profile codex   # overwrite existing config

See docs/CONFIGURATION.md for all env vars, CLI
flags, and runtime configure(...) options.

First Workflow

Aleph is best when you load data once, do the heavy work inside Aleph, and only
pull back compact answers.

load_file(path="/absolute/path/to/large_file.log", context_id="doc")
search_context(pattern="ERROR|WARN", context_id="doc")
peek_context(start=1, end=60, unit="lines", context_id="doc")
exec_python(code="""
errors = [line for line in ctx.splitlines() if "error" in line.lower()]
result = {
    "error_count": len(errors),
    "first_error": errors[0] if errors else None,
}
""", context_id="doc")
get_variable(name="result", context_id="doc")
save_session(context_id="doc", path=".aleph/doc.json")

The important habit is to compute server-side. Do not treat get_variable("ctx")
as the default path. Search, filter, chunk, or summarize first, then retrieve a
small result.

If you want terminal-only mode instead of MCP, use:

aleph run "Summarize this log" --provider cli --model codex --context-file app.log

Local Models (llama.cpp)

Aleph can use a local model instead of a cloud API. This runs the full RLM
loop — search, code execution, convergence — entirely on your machine with
zero API cost.

Prerequisites: llama.cpp and a
GGUF model file.

# Install llama.cpp
brew install llama.cpp          # Mac
winget install ggml.LlamaCpp    # Windows

# Start the server with your model
llama-server -m /path/to/model.gguf -c 16384 -ngl 99 --port 8080

Point Aleph at the running server:

export ALEPH_PROVIDER=llamacpp
export ALEPH_LLAMACPP_URL=http://127.0.0.1:8080
export ALEPH_MODEL=local
aleph

Or let Aleph start the server automatically:

export ALEPH_PROVIDER=llamacpp
export ALEPH_LLAMACPP_MODEL=/path/to/model.gguf
export ALEPH_LLAMACPP_CTX=16384
export ALEPH_MODEL=local
aleph

Tested with Qwen 3.5 9B (Q8_0, ~9 GB). Any GGUF model works — larger models
give better results in the RLM loop. Models with reasoning/thinking support
(Qwen 3.5, QwQ, etc.) are handled automatically. See
CONFIGURATION.md for all ALEPH_LLAMACPP_*
variables.

Common Workloads

Core Tools

Inside exec_python, Aleph also exposes helpers such as search(...),
chunk(...), lines(...), sub_query(...), sub_query_batch(...), and
sub_aleph(...). Recursive helpers live inside the REPL, not as top-level MCP
tools.

Safety Model

Aleph is built to keep raw context out of the model window unless you
explicitly pull it back:

• Tool responses are capped and truncated.
• get_variable("ctx") is policy-aware and should not be your default path.
• exec_python stdout, stderr, and return values are bounded independently.
• ALEPH_CONTEXT_POLICY=isolated adds stricter session export/import rules and
  more defensive defaults.

The safest pattern is always:

1. Load the large context into Aleph memory.
2. Search or compute inside Aleph.
3. Retrieve only the small result you need.

Docs Map

• MCP_SETUP.md: client-by-client MCP and skill installation.
• docs/prompts/aleph.md: the /aleph and $aleph
  workflow plus tool patterns.
• docs/CONFIGURATION.md: flags, env vars, limits, and
  safety settings.
• docs/langgraph-rlm-default.md: LangGraph
  integration with Aleph-style tool usage.
• examples/langgraph_rlm_repo_improver.py:
  repo improvement example with optional LangSmith tracing.
• CHANGELOG.md: release history.
• DEVELOPMENT.md: contributor guide.

Development

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

References

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

License

MIT