bellamem

RAG retrieves documents. Agents need to retrieve beliefs.

Bella is the visual brand; the Python package and CLI remain
bellamem. pipx install bellamem, then bellamem save.

The problem

Your AI agent is like a brilliant intern with amnesia. Every morning
you re-explain the project. Every afternoon it re-introduces the bug
you fixed yesterday. It smiles and nods and produces confidently wrong
output. Fluent, agreeable, and fundamentally untrustworthy.

Loses continuity. Today's session suggests the exact bandaid you
rejected yesterday. Yesterday doesn't exist.

Hits the wall. /compact and /clear evaporate the specifics —
rejected approaches, causal chains, small invariants that took ten
messages to earn.

Confabulates with conviction. When it forgets, it doesn't ask to
be reminded. It re-asserts the wrong approach with full confidence,
and you're the one who has to catch it every time.

One root cause: agents only have working memory. The context
window is the memory. When a turn falls out, it's gone — and the
agent carries on unaware, filling the gap with plausible defaults.
You can't guardrail a system that doesn't own its own beliefs.

Bella is the long-term memory layer. It extracts the structure
of every conversation — decisions, rejected approaches, causes,
self-observations — into a belief hypergraph that survives /clear,
new sessions, and new days. When tomorrow's session asks about the
flaky test, it loads what yesterday decided, what yesterday rejected,
and why.

Before and after

A real debugging session — twenty turns of dead-ends, side-questions,
acknowledgments, and the actual fix at the end. Left column is what
the context window holds. Right column is what Bella extracts
alongside it — and what survives after /clear.

user:      test flaked again, 3rd time
assistant: which one — auth or sync?
user:      sync_external_api, like last time
assistant: ok, looking at retry.py
assistant: timeout hardcoded to 2s. bump to 5s?
user:      bandaid, don't paper over it
user:      we need to know WHY it times out
assistant: fair. tracing retry logic
assistant: sync retries with 200ms backoff
assistant: huh, only one retry — that's tight
user:      CI load? Mondays spike hard
assistant: checking metrics dashboard...
assistant: yes — CI hits the rate-limiter
assistant: first retry hits same limit, fails
assistant: 2s window exceeded before retry
user:      real fix: retry jitter, not timeout
assistant: agreed. patching: backoff ± jitter
user:      good
assistant: add latency metrics too?
user:      not now, focus on the fix

[retry-jitter is the fix]
   m=0.74  v=2  (user + assistant)

  ⇒ cause chain
     CI load → rate-limiter → first retry
     → 2s window exceeded before retry

  ⊥ rejected
     "bump timeout from 2s to 5s"
     (user: "bandaid, not a fix")

  __self__  observation
     "I reach for timeout bumps when retry
      semantics are the real problem"

Same information content, different geometry. The left column lets an
agent reconstruct what was said. The right column lets it
reconstruct what was decided, what was rejected, and what caused
what — in far fewer tokens, and across the session boundaries where
the left column can't go.

And the four items on the right are exactly the ones the agent would
otherwise forget, re-suggest, or confabulate about tomorrow: a
ratified decision (mass earned from two voices), a causal chain (the
why), a dispute (the rejected bandaid, which Bella's edit guard
will block if the agent tries it again), and a self-observation about
its own reasoning pattern.

Install

pipx is the recommended path — a single global bellamem command,
no .venv to remember, no PATH surgery:

pipx install bellamem
# or, from a local clone:
git clone https://github.com/immartian/bellamem
pipx install -e ./bellamem                  # editable install, still global

Per-project venv also works:

cd your-project
python3 -m venv .venv
.venv/bin/pip install bellamem

Optional extras:

pipx inject bellamem 'sentence-transformers>=2.2'   # local embeddings
pipx inject bellamem 'openai>=1.0'                  # OpenAI embeddings + LLM EW
# or with pip:
pip install 'bellamem[st]'      # sentence-transformers
pip install 'bellamem[openai]'  # OpenAI
pip install 'bellamem[all]'     # both

Copy .env.example → .env in your project and fill in the backends
you enabled. .env is gitignored.

Requirements: Python 3.10+. Git (Bella scopes per-project state via
the git repo root). No other system dependencies.

Quickstart

Three retrieval modes — one for each question you actually ask
about your memory. Most workflows live in these three commands:

Plus utility commands for ingest, audit, render, prune, and bench:

# Ingest Claude Code sessions for the current project.
# Auto-runs R3 consolidation (merges near-duplicates) on new claims.
bellamem save

# Three retrieval modes — same memory, different questions:
bellamem expand "what did we decide about persistence"
bellamem surprises                                      # top jumps, sign flips, disputes
bellamem replay                                         # narrative timeline
bellamem replay "ad-hoc bandaid pattern"                # focused narrative

# The pre-edit pack: no recency, surfaces invariants + disputes + causes
bellamem before-edit "should I wrap this in try/except" --entity embed.py

# Health report: bandaid piles, duplicates, garbage field names, mass limbo
bellamem audit

# Render the graph as a picture (needs the [viz] extra or graphviz CLI)
bellamem render --out graph.svg                           # whole forest
bellamem render --out disputes.svg --disputes-only        # just ⊥ edges
bellamem render --out auth.svg --focus "auth tokens"      # subgraph around a focus

# Forget orphan leaves that never earned their place (dry run by default)
bellamem prune                        # preview candidates
bellamem prune --apply                # actually remove them

# Empirically compare context strategies (flat, compact, RAG, Bella)
bellamem bench

Every command except save, emerge, prune --apply, and scrub is
read-only.

Use with Claude Code

The flow that lets you keep working past the context window without
losing the thread is packaged into four slash commands.

Install the slash command — once, globally

bellamem install-commands           # writes ~/.claude/commands/bellamem.md

/bellamem now works in every Claude Code project on your
machine. Per-project install (--project) is also supported if you
want to commit the slash command into a specific repo.

The commands

The save → clear → resume flow

/bellamem save     ← captures this session into the graph
/clear             ← wipe the context window (Claude Code built-in)
/bellamem resume   ← fresh assistant reconstructs where you were

On a well-tuned project, /bellamem resume comes back in ~30k
tokens and contains enough to pick up the next decision without
re-asking questions already answered. If it's much larger, run
bellamem emerge to consolidate near-duplicates.

The edit guard (v0.0.4)

Install bellamem-guard as a Claude Code PreToolUse hook and an
advisory pack (invariants + disputes + causes for the focus) is
injected automatically before every Edit / Write / MultiEdit
call — no manual invocation needed. The guard exit-2s when the edit
re-suggests a rejected approach (a ⊥ dispute), refusing the tool
call at the boundary.

Hook registration (once per project) in .claude/settings.json:

{
  "hooks": {
    "PreToolUse": [
      { "matcher": "Edit|Write|MultiEdit", "hooks": [{ "type": "command", "command": "bellamem-guard" }] }
    ]
  }
}

Where your data lives

~/.claude/commands/
  bellamem.md            installed once (global slash command)

<your-project>/
  .claude/settings.json  PreToolUse hook registration (optional)
  .graph/
    default.json          belief graph (gitignored by default)
    default.emb.bin       belief embeddings, v3 binary sidecar
    embed_cache.json      embedding cache (pruned to live beliefs on save)
    llm_ew_cache.json     LLM EW cache (if BELLAMEM_EW=hybrid)
  .env                    your API keys + embedder choice (never commit)

.graph/ is gitignored by default.

Bella vs /compact

Both compress a long session. The difference is load-bearing:

On our 13-item bench, compact scored 8% LLM-judge; Bella's
expand scored 92%. Narrative summaries preserve themes;
structured retrieval preserves decisions. The two are complementary:
/compact keeps the feel inside one session; Bella keeps the
decisions across sessions.

See the graph

A preview of the v0.1.1 3D viz rendering a real Bella belief
hypergraph — roughly 1,800 beliefs from a month of Claude Code
sessions on Bella itself, across eight topical fields. Drag to
rotate; the replay bar scrubs history so you can watch the graph
accumulate decision by decision.

Click the image above to play the .webm. (GitHub doesn't reliably embed <video> tags pointing at raw files, so the poster + link is the universal fallback.)

Empirical results

Latest measurement: benchmarks/v0.0.4rc1.md
(2026-04-10, budget = 1200 tokens, LLM judge enabled, 13-item
hand-labeled corpus, 1834-belief forest).

metric               flat_tail      compact     rag_topk       expand  before_edit
----------------------------------------------------------------------------------
exact hit rate            15 %          0 %         15 %         69 %         46 %
embed hit rate            23 %         31 %         31 %         85 %         77 %
llm judge rate             0 %          8 %         31 %         92 %         69 %
avg tokens used           1200          602         1161         1143          964

flat_tail (0%) < compact (8%) < rag_topk (31%) < before_edit (69%) < expand (92%).

Headline story — compare to v0.0.2: as
the forest grew from the v0.0.2 dogfood snapshot to 1834 beliefs,
rag_topk collapsed from 85% → 31% LLM judge (cosine top-k pulls up
more plausible-looking-but-wrong neighbors in a larger forest), while
expand held at 92%. The gap from expand to the next-best contender
widened from 15pp to 61pp. Structured mass-weighted retrieval
scales with forest size; cosine top-k doesn't. The retrieval code
path (core/expand.py, core/bella.py) is unchanged between v0.0.2
and v0.0.4rc1 — every delta is a property of forest growth, not
algorithm changes.

See benchmarks/README.md for the versioning
convention and when to re-run.

Limitations

Bella lives alongside the agent, not inside it. That boundary is
load-bearing and currently unmoved: we can't reach in and rewrite the
context window directly. What we have today is advisory:

• No direct context-window control. Bella can't swap active
  tokens, evict irrelevant context, or replace the window wholesale.
  The agent still controls what it attends to; Bella can only offer
  packs the agent can choose to read.
• /compact stays LLM-driven. Claude Code's native /compact
  writes a narrative summary via an LLM call. A PreCompact hook
  that lets Bella substitute a graph-backed compaction would unlock
  most of the remaining wins — and that hook surface does not exist
  in Claude Code today. We can't intercept it from outside.
• Save/clear/resume is a manual pattern. You run /bellamem save
  → /clear → /bellamem resume yourself. It works, but it's a
  human-in-the-loop ritual, not an autonomous context manager.
• The edit guard is a tool-call boundary, not a semantic gate.
  bellamem-guard injects an advisory pack before every edit and
  exit-2s on a dispute re-suggestion, but it sees tool-call text,
  not model intent. An agent that ignores the advice can still try
  the edit; the block is at the boundary, not deeper in the model.
• One adapter at a time. Claude Code works today. Codex and
  others need their own turn-pair reaction classifier and source
  stamper.

The common thread: every limitation above is about how much of the
agent's context lifecycle we can observe and influence from outside.
With deeper hooks — or a coding agent that exposed its context as a
first-class API — a graph memory like Bella could drive the compaction
cycle itself instead of being handed the leftovers. We expect the
upside of real context-window control to be substantial. For now,
the honest frame is: Bella is the memory layer; the agent is still
the window manager.

Status

v0.1.0 — alpha, dogfooded on its own construction. Bella was
built in Claude Code sessions that were themselves ingested into the
Bella being built. When the assistant drifted into an ad-hoc bandaid
pattern during development, the user's correction landed in the graph
as the highest-surprise belief of the session. That kind of
self-observation is the point.

Since v0.0.2:

• v0.0.3 — per-project .graph/, automatic R3 consolidation on
  ingest, source grounding + narrative replay, structural pruning,
  bellamem save default-to-current-session with incremental ingest,
  and embed-cache prune bounded to live beliefs.
• v0.0.4rc1 — storage split: belief embeddings moved out of
  default.json into a default.emb.bin sidecar (v3 format),
  cutting non-vector operations' load time from ~2s to ~500ms.
  bellamem-guard PreToolUse hook ships: advisory pack before
  every edit, exit-2 block on dispute re-suggestions. Embedder
  batching reduces save latency.
• v0.1.0 — log-odds decay gated on BELLAMEM_DECAY=on: on every
  save, non-exempt beliefs fade exponentially toward the 0.5 prior at
  a 30-day half-life (reserved fields, mass_floor pins, ⊥ disputes,
  and ⇒ causes are exempt). New bellamem decay subcommand for
  dry-run preview + --apply. v3 → v4 snapshot format adds a
  decayed_at header. See the "Decay and reinforcement — the steady
  state" section of THEORY.md for the collision math.
• v0.1.1 (planned) — decay on by default after dogfood validates
  the steady state, Three.js 3D viz with temporal replay, and
  graph-backed compaction when the hook surface allows.

See CHANGELOG.md for details.

Architecture

bellamem.core (Jaynes accumulation, expand, audit, prune, replay)
never imports from bellamem.adapters (Claude Code reader, LLM EW,
turn-pair classifier). Core is domain-agnostic; adapters are where
domain knowledge lives. Seven mutation operations, pluggable
embedders, split snapshot storage.

Full file tree and invariants: ARCHITECTURE.md.

Theory

The formal calculus — six rules, invariants, Bayesian grounding,
domain-agnostic case studies — lives in bella/:

• SPEC.md — the six rules, formal definitions
• VISION.md — theoretical grounding (Jaynes, Gödel, self-reference)
• EXAMPLES.md — case studies (H. pylori, continental drift, …)
• MEMORY.md — how BELLA maps to LLM agent memory

THEORY.md covers the implementation side: how
bellamem maps the BELLA spec to Python — thresholds, data structures,
a worked flaky-test example with before/after diagrams, and the
decay equilibrium math.

Contributing

See CONTRIBUTING.md. Short version:

• The bench is the CI. Run bellamem bench after changes to EW,
  expand, or audit and report the delta in the PR.
• Add new embedders by implementing the Embedder protocol in
  core/embed.py.
• Add new EW logic in adapters/, never in core/.
• Every PR that touches retrieval should include a bench item
  demonstrating the failure mode it fixes.
• Dogfood changes against Bella's own snapshot before shipping.
  Unit tests prove code runs; running Bella against its own graph
  proves the feature is useful.

License

AGPL-3.0-or-later. See LICENSE.