aleph-rlm 0.9.1

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

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

Command	Module	What it does
aleph	aleph.mcp.local_server:main	MCP server. This is what MCP clients launch. Exposes 30+ tools for context management, search, code execution, reasoning, recursion, and action tools.
aleph-rlm	aleph.cli:main	Installer and CLI. install, configure, doctor, uninstall for setting up MCP clients. Also: run (single query), shell (interactive REPL), serve (start MCP server manually).

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.

Profile	What it pins
portable	No nested backend — you choose later or rely on auto-detection
claude	Claude CLI: --model opus, --effort low, shared session enabled
codex	Codex MCP: gpt-5.4, low reasoning effort, shared session enabled
api	OpenAI-compatible API — set ALEPH_SUB_QUERY_API_KEY and ALEPH_SUB_QUERY_MODEL

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

Scenario	What Aleph Is Good At
Large log analysis	Load big files, trace patterns, correlate events
Codebase navigation	Search symbols, inspect routes, trace behavior
Data exploration	Analyze JSON, CSV, and mixed text with Python helpers
Long document review	Load PDFs, Word docs, HTML, and compressed logs
Recursive investigations	Split work into sub-queries instead of one giant prompt
Long-running sessions	Save and resume memory packs across sessions

Core Tools

Category	Primary tools	What they do
Load context	load_context, load_file, list_contexts, diff_contexts	Put data into Aleph memory and inspect what is loaded
Navigate	search_context, semantic_search, peek_context, chunk_context, rg_search	Find the relevant slice before asking for an answer
Compute	exec_python, get_variable	Run code over the full context and retrieve only the derived result
Reason	think, evaluate_progress, get_evidence, finalize	Structure progress and close out with evidence
Orchestrate	configure, validate_recipe, estimate_recipe, run_recipe, run_recipe_code	Switch backends and automate repeated reasoning patterns
Persist	save_session, load_session	Keep long investigations outside the prompt window

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