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/aleph - External memory workflow for large local data.
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仓库 README 来源：README.md
# Aleph [](LICENSE) [](https://www.python.org/downloads/) [](https://pypi.org/project/aleph-rlm/) Aleph is an [MCP server](https://modelcontextprotocol.io/) 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 large codebases, long-lived projects, logs, documents, and data without burning context on raw content. ```text +-----------------+ 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. For JS/TS repos, `exec_javascript` and `exec_typescript` provide a persistent Node.js runtime over the same `ctx`. - **Recurse.** Sub-queries and recipes split complex work across multiple reasoning passes. - **Keep workspaces warm.** Bind contexts back to files or generated workspace manifests, refresh them, and resume long investigations later. ## Quick Start ```bash 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: ```text get_status() list_contexts() ``` The optional `/aleph` (Claude Code) or `$aleph` (Codex) skill shortcut starts a structured RLM workflow. Install [`docs/prompts/aleph.md`](docs/prompts/aleph.md) into your client's command/skill folder — see [MCP_SETUP.md](MCP_SETUP.md) for exact paths. If you are using action tools on a real repo, the safest default is: ```bash aleph --enable-actions --action-policy read-only ``` ### Cursor Use **global** MCP (`aleph-rlm install cursor`) for `--workspace-mode any`, or **project** MCP (`aleph-rlm install cursor-project` from the repo) for `${workspaceFolder}` + `--workspace-mode fixed`. Chat, Composer, and the Cursor CLI share that MCP config; a Cursor extension is optional and not required for Aleph — see [MCP_SETUP.md](MCP_SETUP.md#cursor). ## 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` | ```bash aleph-rlm install claude-code --profile claude aleph-rlm configure --profile codex # overwrite existing config ``` See [docs/CONFIGURATION.md](docs/CONFIGURATION.md) for all env vars, CLI flags, and runtime `configure(...)` options. ## Large Codebase Workflow If your main use case is a repo or multi-folder project, start by loading a compact workspace manifest instead of throwing raw source files into the model window. That gives the model a map of the project, lets it search aggressively, and keeps the session refreshable as the repo changes. ```python load_workspace_manifest(paths=["src", "tests"], context_id="repo") rg_search(pattern="FastAPI|APIRouter|router\\.", paths=["src", "tests"], load_context_id="routes") load_file(path="pyproject.toml", context_id="pyproject") exec_python(code=""" files = [line for line in ctx.splitlines() if line.startswith("- ")] summary = { "indexed_entries": len(files), "top_python_files": [line for line in files if "| python |" in line][:10], } """, context_id="repo") get_variable(name="summary", context_id="repo") refresh_context(context_id="repo") ``` Use `load_workspace_manifest` as the default front door for large codebases and projects. Then pull in specific files with `load_file`, search the repo with `rg_search`, and refresh the bound context when the workspace changes. Refreshes preserve the session's reasoning state, evidence log, and tracked tasks. ### Single File Workflow Aleph is also strong when you load one large file once, do the heavy work inside Aleph, and only pull back compact answers. ```python 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: ```bash 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](https://github.com/ggml-org/llama.cpp) and a GGUF model file. ```bash # 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: ```bash 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: ```bash 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](docs/CONFIGURATION.md) for all `ALEPH_LLAMACPP_*` variables. ## Common Workloads | Scenario | What Aleph Is Good At | |---|---| | Large codebase / project analysis | Build a workspace map, search quickly, load only the files that matter, and keep the session refreshable | | 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`, `load_workspace_manifest`, `refresh_context`, `list_contexts`, `diff_contexts` | Put data into Aleph memory, bind it back to workspace assets, 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`, `exec_javascript`, `exec_typescript`, `get_variable` | Run Python or JS/TS 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 | ## Python vs JS/TS REPL Aleph's **primary control layer is still Python**. `exec_python` remains the default REPL for general-purpose analysis, recipes, and orchestration. - Use `exec_python` when you need the full Aleph surface area: Python-first prompts, Python's numeric / symbolic stack (`cmath`, `mpmath`, `decimal`, `fractions`, `statistics`, `numpy`, `scipy`, `sympy`, `networkx`), or recipe execution via `run_recipe_code`. - Use `exec_javascript` / `exec_typescript` when the target repo or analysis is naturally JS/TS-shaped and you want persistent Node state, JS-native array / object manipulation, or async recursion with `await`. - `exec_python` Full Aleph helper surface, including recipe DSL helpers, synchronous `sub_query(...)` / `sub_aleph(...)`, and the widest compatibility with existing prompts and workflows. - `exec_javascript` / `exec_typescript` Persistent Node.js runtime per context for JS/TS-heavy repos. Shares the same `ctx`, supports top-level `await`, and can recurse with async `await sub_query(...)`, `await sub_query_batch(...)`, `await sub_query_map(...)`, `await sub_query_strict(...)`, and `await sub_aleph(...)`. Also includes the recipe DSL (`Recipe`, `Search`, `Take`, etc.) for building recipe payloads in JS/TS. The JS/TS runtime also ships with a broader local helper set than the first handoff slice: search/peek/lines/chunk, extraction helpers (`extract_emails`, `extract_todos`, `extract_routes`, etc.), text utilities (`number_lines`, `grep_v`, `sort_lines`, `normalize_whitespace`, etc.), text comparison helpers (`diff`, `similarity`, `common_lines`, `diff_lines`), collection helpers (`flatten`, `group_by`, `frequency`, `sample_items`, `shuffle_items`, etc.), validation helpers (`is_json`, `is_email`, `is_uuid`, etc.), CSV / JSON converters, and `semantic_search`. The JS/TS runtime now also includes the Recipe DSL: `RecipeStep`, `RecipeBuilder`, and all step constructors (`Recipe`, `Search`, `Peek`, `Lines`, `Take`, `Chunk`, `Filter`, `MapSubQuery`, `SubQuery`, `Aggregate`, `Assign`, `Load`, `Finalize`, `as_recipe`). You can build recipes with fluent chaining or pipe-style: ```javascript // Fluent style Recipe("doc").search("ERROR").take(5).finalize().compile() // Pipe style Recipe("doc").pipe(Search("ERROR")).pipe(Take(5)).pipe(Finalize()).compile() ``` The `compile_recipe` and `run_recipe_code` MCP tools accept a `language` parameter (`"python"`, `"javascript"`, `"typescript"`) to compile recipe DSL code in the corresponding runtime. What still differs from Python: - Python is still the default and best-supported Aleph REPL. - JS/TS recursion helpers are async and require `await`. - Recipe *execution* (`run_recipe`) always uses the Python runtime. The JS/TS path covers recipe *building and compilation* only. - JS uses `RecipeBuilder.pipe()` / fluent methods instead of Python's `|` operator (JS `|` is bitwise OR, not overloadable for this purpose). - Python's import ecosystem remains Python-only. The Node runtime is helper-led: no `require`, no `process`, no `module`, and no npm package loading inside the sandbox. - `exec_typescript` strips type syntax for execution; it is not a full TS compiler, typechecker, or `ts-node` environment. - Regex flag behavior follows each runtime: Python helpers use Python `re` flags, while JS/TS helpers use JavaScript regex flag strings. Example JS/TS workflow: ```python exec_typescript(code=` const routes: string[] = extract_routes('javascript').map((item) => item.value); const routeKinds = frequency( routes.map((route) => (route.includes('.post(') ? 'write' : 'read')), 2, ); const notes = await sub_query_map( routes.map((route) => `Explain ${route}`), routes, ); ({ routeCount: routes.length, routeKinds, notes }) `, context_id="repo") ``` ## Safety Model Aleph is built to keep raw context out of the model window unless you explicitly pull it back: - Tool responses are capped and truncated. - `get_variable("ctx")` is policy-aware and should not be your default path. - `exec_python` stdout, stderr, and return values are bounded independently. - `ALEPH_CONTEXT_POLICY=isolated` adds stricter session export/import rules and more defensive defaults. - `ALEPH_ACTION_POLICY=read-only` (or `--action-policy read-only`) keeps action tools in read-only mode: search and file loading still work, but writes and subprocess execution are blocked. The safest pattern is always: 1. Load the large context into Aleph memory. 2. Search or compute inside Aleph. 3. Retrieve only the small result you need. ## Docs Map - [MCP_SETUP.md](MCP_SETUP.md): client-by-client MCP and skill installation. - [docs/prompts/aleph.md](docs/prompts/aleph.md): the `/aleph` and `$aleph` workflow plus tool patterns. - [docs/CONFIGURATION.md](docs/CONFIGURATION.md): flags, env vars, limits, and safety settings. - [docs/langgraph-rlm-default.md](docs/langgraph-rlm-default.md): LangGraph integration with Aleph-style tool usage. - [examples/langgraph_rlm_repo_improver.py](examples/langgraph_rlm_repo_improver.py): repo improvement example with optional LangSmith tracing. - [CHANGELOG.md](CHANGELOG.md): release history. - [DEVELOPMENT.md](DEVELOPMENT.md): contributor guide. ## Development ```bash git clone https://github.com/Hmbown/aleph.git cd aleph pip install -e ".[dev,mcp]" # Optional extras: # .[docs] -> MarkItDown-backed document conversion # .[observability] -> OpenTelemetry spans pytest tests/ -v ruff check aleph/ tests/ ``` ## References - Zhang, A. L., Kraska, T., Khattab, O. (2025) [Recursive Language Models (arXiv:2512.24601)](https://arxiv.org/abs/2512.24601) ## License MIT