memwalk

Ask AI about any codebase — local, cached, SSM-state-backed.

memwalk reads an entire codebase into a Mamba-based LLM via persistent state, so subsequent questions answer in <1 s without re-reading anything. The state is byte-portable (via memba) and cached per-directory by file manifest hash, so re-asking is free until the source changes.

Built on memba for state persistence and NVIDIA Nemotron-3-Nano-4B (hybrid Mamba-2 + Transformer, 1M training context) for inference.

What makes this different from Cursor / Cody / Aider

Tool	Approach	Whole-repo question
Cursor	Embed chunks, retrieve at Q	Fragmented context
Cody	BM25 + dense embeddings (RAG)	Pre-indexed, retrieved
Aider	Symbol-level repo map	Signatures only
memwalk	Read everything once, cache the SSM state	Holistic answer; <1s re-asks

SSM state is fixed-size (Mamba's defining property), so even a 1M-token codebase compresses into a constant-size file (~85 MB at our settings). Reload is millisecond-scale — re-asking a freshly-cached repo costs no model inference until you ask the next question.

Status

v0.2 — alpha. Works for the maintainer end-to-end; APIs and on-disk format may shift.

Install

memba is not on PyPI yet, so install via git:

pip install git+https://github.com/emil28092005/memwalk.git
# (pulls memba @ main as a transitive git dep)

Or from a local clone:

pip install -e ~/Desktop/Coding/memwalk

Make sure you have a GGUF Mamba-2 / hybrid model. Recommended:

hf download nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF \
    NVIDIA-Nemotron3-Nano-4B-Q4_K_M.gguf \
    --local-dir ~/.memwalk/models

Quickstart

memwalk init                                  # one-time: set model path
memwalk digest ~/Desktop/Coding/myrepo        # first time: read everything (~10s)
memwalk ask    ~/Desktop/Coding/myrepo "How does auth work?"   # <1s
memwalk ask    ~/Desktop/Coding/myrepo "Which file owns the migration logic?"
memwalk list                                  # show all cached codebases
memwalk drop   ~/Desktop/Coding/myrepo        # invalidate cache
memwalk status                                # config + cache summary

memwalk ask auto-digests on first use, so the explicit digest step is optional. The cache is invalidated automatically when any source file changes (mtime / size).

Use from an AI agent (MCP)

memwalk mcp     # starts a stdio MCP server

Tools: digest(path), ask(path, question), list_caches(), drop_cache(path), status().

Claude Code

claude mcp add memwalk -- memwalk mcp

Or by hand in your MCP config:

{
  "mcpServers": {
    "memwalk": { "command": "memwalk", "args": ["mcp"] }
  }
}

opencode / Hermes / other MCP clients

Same shape — they all consume {"command": "memwalk", "args": ["mcp"]}.

After the agent connects it sees mcp__memwalk__digest, mcp__memwalk__ask, etc. Typical flow:

User: "What changed in the migrations folder of my CU_Points repo this month?"

Agent: calls mcp__memwalk__ask(path="~/Desktop/Coding/AI/CU_Points", question="..."). memwalk auto-digests if needed, returns answer.

What does it actually do well?

Validated on memba's own codebase (13 files, ~63 K chars):

• Listed every header field of the state file format in order
• Explained the architectural reason for the eval+sample rewrite
• Identified which side of the C/Python boundary writes the MEMB trailer
• Listed all CLI subcommands accurately
• Suggested correct file path + approach for adding a new command

Recall is descriptive-strong — facts that are in the source. It is not a substitute for a real debugger or a code generator. For complex reasoning over small snippets, a bigger code-tuned model is still better.

Limits

• Single-shot context, not chunked retrieval. Whole corpus must fit in n_ctx (default 32 K tokens ≈ ~120 K chars). Bigger repos: bump n_ctx, use a beefier GPU, or filter INCLUDE_SUFFIXES in corpus.py.
• No code-aware filtering yet — every text file under the root is read. Use .gitignore-style filtering in v0.3.
• No GPU-less mode tested — should work on CPU but slow.

License

MIT.