Aleph - MCP Memory for Large Context
Description: Load a repo, log, or document once. Aleph keeps context in memory so agents can search, run Python, recurse, and return small answers instead of pasting raw context.

v0.8.8

# Load It
Once.

Aleph keeps big working context in RAM so agents can search, run Python, recurse, and return small answers instead of re-sending the whole file.

Recommended Path

Install the Codex CLI, run `aleph-rlm install`, and let Aleph wire the default shared-session sub-query setup for you.

Get Started View on GitHub
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## The Loop

Load once. Search, compute, recurse, finalize.

Load → Search → Compute → Recurse → Finalize
Open the quick start
Core Workflow

## Work On Data,
Not Prompts.

Aleph stores the full working set in a Python process, then gives the model tools for targeted inspection and computation instead of repeated paste-and-pray prompting.

- + load_file — Put repos, logs, and docs into Aleph memory
- ⌕ search_context — Find the relevant slice first
- <> exec_python — Compute against the whole context
- ◎ save_session — Resume long investigations later

### SESSION TRACE

⌕ load_file("/repo/server.log") -> context_id="prod-log"
⌕ search_context("ERROR|WARN") -> 18 hits
✓ exec_python(...) -> grouped failures by service
✓ get_variable("result") -> compact summary

STATE: READY TO FINALIZE

Small answer out, full context still in memory

## Recursive By
Design.

Aleph is shaped by Recursive Language Models: keep the working set outside the prompt, let the model search and compute over it, and recurse only when one pass is not enough.

> "Each iteration lets the model refine its search based on what it learned, rather than betting everything on one attention pass."

> "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)