vibemem 0.2.1

Memory management tool for vibe-coding agents using Weaviate and optional Chroma cache

VibeMem (vibemem)

VibeMem is a pip-installable CLI tool to manage reusable “memories” (findings / recipes / gotchas / preferences) for vibe-coding agents.

• Canonical storage: Weaviate (single collection: VibeMemMemory)
• Optional local cache: Chroma persisted under ~/.vibemem/chroma/ (per-repo subdirectory)
• Works from any directory: derives repo root + scope from your current working directory (override via flags)
• Default output: JSON (agent-friendly). Use --human for pretty output.

Install

From PyPI:

python -m pip install -U vibemem

Editable install from this repo:

python -m pip install -e .

Optional cache support (Chroma):

python -m pip install -e ".[cache]"

Dev tools/tests:

python -m pip install -e ".[dev]"
pytest

Install troubleshooting

If pip install -U vibemem fails with an OSError: [Errno 2] No such file or directory, it usually means pip fell back to building a dependency from source and a build tool is missing on your machine.

Try:

python -m pip install -U pip setuptools wheel
python -m pip install -U vibemem -v

If it still fails, paste the full -v output (it should include the missing executable/file), and try a clean reinstall:

python -m pip uninstall -y vibemem
python -m pip install -U --no-cache-dir vibemem

Configuration

VibeMem reads connection settings from environment variables first, then falls back to a JSON config file at ~/.vibemem/config.

Env vars

• VIBEMEM_WEAVIATE_URL (required for Weaviate operations)
  • Examples: http://localhost:8080 or https://YOUR_CLUSTER.weaviate.cloud
• VIBEMEM_WEAVIATE_API_KEY (optional; required for Weaviate Cloud URLs)
• VIBEMEM_WEAVIATE_GRPC_URL (optional)
  • Example: http://localhost:50051
• VIBEMEM_WEAVIATE_COLLECTION (default: VibeMemMemory)
• VIBEMEM_CACHE_MODE (default: auto) — auto|on|off

Show effective config

vibemem config show

Scope model

VibeMem derives scope from your current directory:

• Repo root is detected by searching upwards for .git/ first; fallback markers: pyproject.toml, package.json, go.mod
• repo_slug = basename(repo_root)
• rel_path = path from repo_root to cwd (empty at repo root)

Scope ID derivation is controlled by --granularity:

• repo (default): scope_id = repo_slug
• cwd: scope_id = repo_slug::<rel_path>
• path:N: scope_id = repo_slug::<first N path parts>

Show current derived scope:

vibemem scope

Commands

Search

Search returns ranked memories with scope-aware bubbling (current scope → parent scopes → repo-level; and optionally global).

vibemem search "TypeError: ..." --top 8

Options:

• --include-global/--no-include-global
• --include-parents/--no-include-parents
• --cache auto|on|off

When Weaviate is unreachable and cache is ON (and built), search will fall back to Chroma.

Add a memory

vibemem add --type recipe --text "Use X to fix Y" --tags "python,typing" --confidence high --verification "ran pytest"

Add structured metadata:

vibemem add --type gotcha --text "Chroma where filters differ by version" --error "ModuleNotFoundError: chromadb" --file "vibemem/store/chroma_cache.py" --cmd "pip install -e '.[cache]'"

Edit / remove

vibemem edit <uuid> --text "updated text" --tags "a,b"
vibemem rm <uuid>

List

vibemem list --scope project --limit 20
vibemem list --scope global --type gotcha
vibemem list --scope all --tag python

Sync (pull)

Rebuild local cache from Weaviate:

vibemem sync --pull --limit 200

Notes:

• “Push/offline queue” is a TODO stub (not implemented).

Output modes

Default output is JSON:

vibemem scope

Pretty output:

vibemem --human scope

Example agent prompt

Use something like this with your AI vibe coder to propose memories for your review before writing anything:

You have access to this repository’s files. I want you to propose a list of “memories” for me to authorize before creating them.

1) Scan the repo for important environment information and setup details that would help in other projects (env vars, required services, ports, build tools, OS-specific steps, CI quirks, etc.).
2) Scan the repo for anything unusual, surprising, or easy to forget (non-obvious defaults, tricky edge cases, sharp corners, gotchas).
3) Output a list of candidate memories for approval. For each item, include:
   - type (recipe|gotcha|preference|note)
   - text (1–3 sentences)
   - suggested tags
   - scope suggestion (global vs project) and why
4) Do not create or write anything until I approve each memory.

Notes

• The Weaviate collection will be auto-created on first use if it doesn’t exist.
• If the Weaviate instance doesn’t have a text vectorizer module configured, VibeMem will fall back to a non-vectorized collection; search will still work via keyword matching (hybrid query behavior depends on server capabilities).