Bloom

Persistent cross-session memory for Claude Code and any MCP-compatible client. Built to survive Opus 4.7's small context window.

Bloom is a single-file SQLite memory that survives between sessions. Ask Claude Code "what did we decide about X last week" and it can actually answer — by searching every prior turn it has saved.

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Why Bloom — the small-context problem

If you've used Claude Opus 4.7 in long sessions, you already know the pain:

• Auto-compaction kicks in early. 4.7's working context is tighter than 4.6's, so the model starts losing detail mid-session.
• Cross-session memory is zero. Open a new session tomorrow and Claude has no idea what you decided today.
• --resume is one session. It doesn't help when you're switching between projects, days, or machines.

Bloom is the direct counter to that:

1. Recall on demand, not always-on. recall("the postgres decision") pulls the exact original turn back into context only when needed — ~200 tokens to retrieve, vs. 20K to keep it loaded the whole session.
2. Session-end checkpointing. remember the conclusion of a working session, throw away the verbose path. The next session resumes with the result, not the journey.
3. SessionStart hook = automatic warm-up. Every new session opens with a short "recent memory" block injected at startup, so 4.7 doesn't begin empty.
4. Forever continuity. 4.7's compaction is per-session and ephemeral; Bloom is persistent. Day 1's "we picked X" is queryable on day 30.

The trick is keeping recall out of the model's context until it's needed. Loading 200 turns at session start would defeat the purpose. Loading the 5 most relevant on demand is the architecture.

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$ pipx install bloom-mcp
$ bloom-mcp init
$ # Bloom is now wired into Claude Code. Open a new session and ask it to recall.

• Works offline. Default scoring is keyword + recency, backed by SQLite FTS5 — no API key, no network.
• Optional semantic recall. Install bloom-mcp[openai] (or [anthropic] for Voyage AI, or [local] for offline sentence-transformers), set the API key, and recall re-ranks the top FTS5 candidates by cosine similarity. Falls back cleanly to keyword-only on any embedder failure.
• Six MCP tools. recall, remember, recent, sessions, forget, stats.
• One file, one process. SQLite at ~/.bloom/loom.db. No daemon, no Docker, no cloud.
• MIT-licensed. Use it however you want.

Note — Bloom is an independent open-source project, not affiliated with Anthropic. "Claude" and "Claude Code" are trademarks of Anthropic PBC.

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Quickstart

1. Install

The recommended way is pipx (isolates Bloom from your system Python):

pipx install bloom-mcp

Or if you prefer plain pip / uv:

pip install bloom-mcp
# or
uv tool install bloom-mcp

2. Set up

bloom-mcp init

The wizard runs five steps:

1. Pick a database location (default: ~/.bloom/loom.db).
2. Choose an embedder (none is recommended — works offline, no API key). If you pick openai, anthropic (Voyage), or local, it can also capture the API key into ~/.bloom/.env (chmod 600).
3. Tune recall settings (top_k, max snippet chars).
4. Optionally register Bloom with Claude Code (claude mcp add ...). Defaults to no — re-run bloom-mcp register later if you change your mind.
5. Optionally install the SessionStart auto-recall hook so every Claude Code session opens with a recent-memory block.

3. Use it

In Claude Code, the assistant now has six new tools available. Try:

"Search Bloom for what we said about the postgres migration."

Claude will call recall("postgres migration") and surface relevant past turns.

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What Bloom does

Bloom solves one problem: Claude Code forgets every session. Even with --resume, you lose context across days/weeks/projects. Bloom adds a tiny memory layer:

• remember — store a turn (decision, learning, summary) so future sessions can find it.
• recall — search by query; get the top-k most relevant past turns ranked by keyword overlap (SQLite FTS5), recency, and (when an embedder is configured) semantic similarity.
• recent — pull the last N turns of a specific session.
• sessions — list known sessions and their turn counts.
• forget — soft-delete a single turn by id (recoverable: the row is marked with deleted_at and hidden from recall, but the content is still in the SQLite file until you run bloom-mcp purge --hard).
• stats — DB size, schema version, embedder.

You can use it as a Python library too:

from bloom.config import Config
from bloom.db import Database
from bloom.tools import tool_remember, tool_recall

cfg = Config.load()
db = Database(cfg.db_path)

tool_remember(db, cfg, content="we picked Postgres SKIP LOCKED for the queue", session="proj-x", tags="decision")
print(tool_recall(db, cfg, query="queue choice"))

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Embedders (optional)

Bloom's default none embedder uses keyword + recency scoring backed by SQLite FTS5. It's fast, offline, and good enough for most use cases — turn it on only if you want semantic recall (queries that don't share keywords with the stored content).

Provider	Install	Auth	Cost
none	(default)	—	Free
openai	pip install bloom-mcp[openai]	OPENAI_API_KEY	~$0.02 / 1M tokens
anthropic (Voyage AI)	pip install bloom-mcp[anthropic]	VOYAGE_API_KEY	~$0.02 / 1M tokens
local	pip install bloom-mcp[local]	— (downloads model)	Free, ~80 MB

Enable

Pick the provider in bloom-mcp init (Step 2) or edit ~/.bloom/config.toml:

[embedder]
provider = "openai"
model = "text-embedding-3-small"

The wizard saves the API key to ~/.bloom/.env (chmod 600) so the server picks it up without an extra shell-export step.

How it works

• Write path. remember calls the embedder's embed_doc(content), serializes the float32 vector, and stores it in the row's embedding BLOB.
• Read path. When an embedder is configured, recall searches two pools: the FTS5 keyword candidates and a semantic pool (the most recent N rows that have an embedding, default 200) ranked by cosine similarity to the query embedding. The two pools are unioned and scored with 0.4 * bm25 + 0.5 * cosine + 0.1 * recency — so a query that shares no keywords with the stored turn ("queue choice" → "we picked postgres SKIP LOCKED for the worker pipeline") can still surface by meaning alone. The semantic pool size is configurable via [semantic] pool_size = 200 in config.toml or the semantic_pool_size argument to recall().
• Failure modes are loud-stderr, never fatal. If the network is down, the API key is missing, or the optional package isn't installed, recall silently degrades to keyword-only ranking and remember writes the row with no embedding (it can be backfilled later).

Backfill

If you enable an embedder after you've already used Bloom, you'll have rows with no embedding. Backfill them in one go:

bloom-mcp backfill-embeddings --confirm
# Backfilling embeddings for 1500 turns using openai...
#   Processed 100/1500…
#   Processed 200/1500…
#   ...

The command batches API calls (100 per request by default — tune with --batch), prints progress, handles Ctrl-C cleanly, and is safe to re-run.

--confirm is required for cloud embedders (openai, anthropic) because backfill is the one bulk-send operation Bloom performs — without the flag it refuses to upload your full memory store. Local sentence-transformers (local) stays on-device and skips the prompt.

Cost estimate

For OpenAI's text-embedding-3-small at ~$0.02 / 1M tokens, an average turn of ~1000 tokens costs ~$0.00002. Embedding 10,000 turns end-to-end costs roughly $0.20.

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Configuration

~/.bloom/config.toml:

[storage]
db_path = "/home/you/.bloom/loom.db"

[recall]
top_k = 5
max_chars = 4000
snippet_max_chars = 600

[embedder]
provider = "none"

[logging]
level = "INFO"

All values can also be set via env vars: BLOOM_DB_PATH, BLOOM_EMBEDDER, BLOOM_EMBEDDER_MODEL, BLOOM_RETRIEVE_TOP_K, BLOOM_LOG_LEVEL, BLOOM_HOME, BLOOM_DEBUG.

~/.bloom/.env is a secrets-only sidecar — Bloom reads it after config.toml and only injects credential-shaped keys (e.g. OPENAI_API_KEY, VOYAGE_API_KEY) into the environment. Any BLOOM_* line in .env is ignored with a one-line stderr warning so config can't be silently overridden by a stale .env file.

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Sharing Bloom with a team

Bloom is per-user, local-first by design. Every install gets its own SQLite file. There is no cloud component, no telemetry, no shared backend. Your conversations live on your machine.

If you want a shared team memory, the right approach is:

1. Each person installs Bloom locally (they get private memory).
2. Run a second Bloom instance on a shared host with a team-only namespace.
3. Use a small wrapper (not yet shipped — track issue #1) that fans out remember to both.

A first-party "team Bloom" mode is on the roadmap but not in v0.1.

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How it compares

• vs mem0 — Bloom is local-first, single-file, MCP-native. Mem0 is hosted/cloud-first with richer extraction.
• vs raw vector DBs (Qdrant, pgvector) — Bloom is the application, not the storage layer. You get tools, scoring, CLI, and Claude Code integration out of the box.
• vs Claude Code's --resume — Resume is one session. Bloom is every session, searchable.

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Roadmap

• v0.1 — initial SQLite-backed recall, six MCP tools, init wizard
• v0.2 — FTS5 keyword search, soft-delete, SessionStart auto-recall hook
• v0.3 — embedding-augmented recall (hybrid keyword + cosine)
• v0.4 — multi-tenant team mode (shared host, per-user namespaces)
• v0.5 — pruning + compaction (auto-summarize old turns to save space)
• v0.6 — alternative backends (Postgres, DuckDB)

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Documentation

• Onboarding — 2-minute setup guide for new users
• Architecture
• Configuration reference
• Claude Code integration
• Security & data handling
• Contributing

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License

MIT — © 2026 Jonah Tebaa.