Athenaeum

Production-grade agentic memory for teams deploying multiple AI agents.
Athenaeum follows trunk-style development with develop as the active branch
and main as the released-revision pointer.
Append-only intake, a tiered librarian that compiles raw observations into a
trustworthy wiki, and a sidecar that makes recall happen passively on every
turn.

Is this for me? If you're running more than one agent on shared
knowledge — or if you want agents and humans reading and writing the same
institutional memory — yes. If you're building a single-user chatbot,
mem0 or
Letta may be a better fit.

Why Athenaeum

Four design choices separate a production memory system from a single-user
markdown file. Each one fixes something that quietly breaks when a team scales
past one agent:

1. Sources as first-class objects — every claim carries provenance, the way Wikipedia does. An unfootnoted fact is an assertion.
2. The librarian — a tiered compilation pipeline — agents can only append to raw intake. A separate compiler is the only writer to the wiki. Safety from structure, not trust.
3. Passive recall — a hybrid FTS5+vector search fires on every turn and injects breadcrumbs into context. The agent doesn't have to remember to look.
4. An editable observation filter — what the agent saves is governed by a prompt you can read, edit, and audit. Not a black box.

Full rationale, comparison with alternatives (Claude memory, Anthropic's
memory tool, RAG, Karpathy's gist, mem0/Letta/Zep/Cognee), and the lessons
from running it on our own operations live in
docs/why-athenaeum.md. For the companion blog
post: What We Learned Running Our Own Operations on Agentic
Memory.

Installation

pip install athenaeum

Quick start

# Initialize a knowledge directory
athenaeum init                  # default: ~/knowledge
athenaeum init --path ~/my-knowledge

# Run the librarian (compile raw intake → wiki entities)
athenaeum run
athenaeum run --dry-run         # inspect without writing

# Check status
athenaeum status

Full run with custom paths and budgets:

athenaeum run \
  --raw-root ~/knowledge/raw \
  --wiki-root ~/knowledge/wiki \
  --knowledge-root ~/knowledge \
  --max-files 50 \
  --max-api-calls 200 \
  --verbose

MCP memory server

Athenaeum ships an MCP server exposing remember and recall tools so AI
agents can write to raw intake and search the compiled wiki:

pip install athenaeum[mcp]
athenaeum serve --path ~/knowledge

# Smoke test the round-trip without a live session
athenaeum test-mcp

Claude Code integration. Add to your MCP config and it auto-starts with
every session:

claude mcp add --scope user athenaeum -- athenaeum serve --path ~/knowledge

Example round-trip:

User: Tristan's partner is Amanda; they met at Stanford GSB.

(Claude calls remember(content="Tristan's partner is Amanda; they met at Stanford GSB.", source="claude-session"))

A raw observation lands in raw/claude-session/20260417T…-…md. On the
next athenaeum run, the pipeline compiles it into Tristan's wiki
entity (under "Key Contacts") and Amanda's own entity if she doesn't
exist yet. Later sessions can ask "who is Amanda?" and recall
returns the compiled page.

Answering pending questions

When Tier 3 can't resolve an ambiguity or a principled contradiction, the
librarian escalates to wiki/_pending_questions.md. Each escalation lands
as a block like:

## [2026-04-20] Entity: "Acme Corp" (from sessions/20240406T120000Z-aabb0011.md)
- [ ] Is Acme still Series A after the 2026 recapitalisation?
**Conflict type**: principled
**Description**: Prior wiki says Series A; the 2026-04 raw file implies Series B.

You resolve a question one of two ways — pick whichever fits your workflow:

Option 1 — Edit the file directly

Flip [ ] to [x] on the checkbox line and type your answer below the
checkbox (above or below the conflict-type / description lines — either
works; the parser strips those metadata lines when extracting the answer):

## [2026-04-20] Entity: "Acme Corp" (from sessions/20240406T120000Z-aabb0011.md)
- [x] Is Acme still Series A after the 2026 recapitalisation?

They closed Series B on 2026-03-12, led by Acme Growth Partners.
The 2026-04 raw file is correct; the prior wiki entry is stale.

**Conflict type**: principled
**Description**: Prior wiki says Series A; the 2026-04 raw file implies Series B.

Option 2 — Use the MCP tool

For containerized agents that can't touch the filesystem, athenaeum serve
exposes two tools:

• list_pending_questions() returns unanswered blocks as JSON — each item
  carries a stable id derived from the header + question text.
• resolve_question(id, answer) flips the checkbox and writes the answer
  body under it. It does not archive on its own — archival runs on the
  next ingest-answers pass.

Step 2 — ingest the answers

Either way, run:

athenaeum ingest-answers --path ~/knowledge

Each [x] block is rewritten as a raw intake file under
raw/answers/{timestamp}-{entity-slug}.md with frontmatter linking back
to the original source, then moved into
wiki/_pending_questions_archive.md (newest-first, append-only — answered
blocks are never deleted, only moved). The next athenaeum run picks the
raw file up like any other intake and folds the answer into the wiki
entity.

Re-running with no new [x] blocks is a no-op. Malformed blocks are
preserved in place and logged to stderr, so a corrupt single entry cannot
poison the rest of the file.

Transparent sidecar (Claude Code hooks)

For a fully passive experience where Claude auto-recalls relevant context on
every prompt and saves observations without explicit commands, configure
Claude Code hooks:

1. Copy the example hooks from examples/claude-code/ to your scripts directory.
2. Add hook entries to ~/.claude/settings.json (see examples/claude-code/settings-snippet.json).
3. Add CLAUDE.md instructions for proactive memory (see examples/claude-code/CLAUDE.md.example).

This gives you:

• Auto-recall — an FTS5 index is built at session start (~300ms); each user message triggers a <50ms search that injects relevant wiki pages into context.
• Auto-remember — Claude proactively saves important facts without being asked.
• Context checkpointing — observations are saved before context-window compaction.

Full setup guide, smoke test, and environment-variable reference:
examples/claude-code/README.md.

Integrations

• Claude Code auto-memory — bridge ~/.claude/projects/<scope>/memory/ into Athenaeum's raw/ intake so the librarian can cluster, merge, and contradiction-check Claude Code's durable memory alongside other sources. See docs/integrations/claude-code.md.

Vector search (optional)

Athenaeum supports a vector search backend (chromadb + all-MiniLM-L6-v2)
for semantic recall alongside the default FTS5 keyword backend. The recall
hook runs a hybrid FTS5 + vector merge when vector is configured —
each backend rescues a failure class the other has (short-query proper-noun
collisions for vector; no-lexical-overlap semantic queries for FTS5).

pip install athenaeum[vector]

Enable it in athenaeum.yaml:

search_backend: vector

Full walkthrough and the four invariants a future simplification must not
remove: docs/recall-architecture.md.

Query-topic extraction (optional)

athenaeum query-topics "your prompt" runs a Haiku classifier that returns
substantive topics and ignores meta-instructions:

$ athenaeum query-topics "Without calling any tools, quote the block about Return Path verbatim"
Return Path

The naive regex+stopword fallback returns
block,calling,quote,return,tools,verbatim,without — burying "Return Path"
behind meta-instruction tokens. The example recall hook uses query-topics
to rescue named-entity recall on instruction-heavy prompts and falls back
silently to the regex extractor if the API key or CLI is unavailable.

Environment variables

Shell-env overrides. AUTO_RECALL and SEARCH_BACKEND are read from the
shell environment after the hook sources ~/.cache/athenaeum/config.env, so
exports in your shell profile beat the cached config. Intentional (lets you
A/B-test a backend without editing athenaeum.yaml), but it's the first
thing to check when the hook "ignores" a config change.

Claude Code auth caveat. Claude Code's own CLAUDE_CODE_OAUTH_TOKEN is
scoped to its inference endpoint, and the Anthropic Messages API rejects it
with 401 OAuth authentication is currently not supported. The pipeline and
example hooks need a separate console API key — see
docs/recall-architecture.md
for the 1Password bootstrap pattern.

Data formats

Raw intake lives in raw/{source}/*.md with the naming convention
{timestamp}-{uuid8}.md (e.g., 20240406T120000Z-aabb0011.md). Each file is
a plain markdown document containing observations, notes, or session
transcripts. The {source} directory identifies the origin (e.g.,
sessions, imports).

Wiki entity pages live in wiki/ with YAML frontmatter:

---
uid: a1b2c3d4
type: person
name: Alice Zhang
aliases: [Alice]
access: internal
tags: [active]
created: '2024-04-06'
updated: '2024-04-06'
---

Entities are indexed in wiki/_index.md grouped by type. Conflicts requiring
human review are appended to wiki/_pending_questions.md. Each run logs
token usage and estimated costs at the end.

Known limitations (v0.2.x)

Athenaeum is pre-1.0. These trade-offs are intentional for this release and
slated for revisit in v0.3:

• No retrieval benchmarks yet. The hybrid-search claim rests on concrete
  failure modes (proper-noun collision, no-overlap semantic queries) and
  production use — not a published eval against mem0 / Letta / Zep /
  Cognee. If you need benchmarked recall@k on a closed corpus, pick a tool
  that publishes numbers. If you want a knowledge base that survives your
  tool choices, this is for you. PRs adding an eval harness are very
  welcome.
• FTS5 index rebuilds are non-atomic and unlocked. A shell hook and the
  librarian run rebuilding simultaneously can race; the window is small and
  single-user wikis do not hit it in practice, but multi-writer safety is
  v0.3 work. Workaround: don't invoke athenaeum rebuild-index and
  athenaeum run concurrently on the same $KNOWLEDGE_ROOT.
• The keyword search backend is a scan-on-query fallback. It reads
  every wiki page on every query; fine under ~1,000 entities, painful past
  that. Use search_backend: fts5 (default in the CLI and hooks) for any
  non-trivial wiki. The keyword backend exists as a zero-dependency baseline
  for tests and bootstrap.
• Tier 4 (human escalation) is a file, not a workflow. Conflicts land in
  wiki/_pending_questions.md; you read it and decide. No PR-opening, no
  Slack integration, no UI — on purpose, for now.

Development

git clone https://github.com/Kromatic-Innovation/athenaeum.git
cd athenaeum
pip install -e ".[dev]"

pytest tests/ -v
ruff check src/ tests/

Branch flow

Athenaeum uses a trunk-style branch model:

• develop is the active development branch and the GitHub default. All
  pull requests target develop.
• main carries the most recent released revision. Release tags
  (vX.Y.Z) live on main and trigger the PyPI release workflow.

Most users should install via pip install athenaeum (above). To work from
source against the latest released revision instead of the active branch,
clone and check out the latest tag:

git clone https://github.com/Kromatic-Innovation/athenaeum.git
cd athenaeum
git checkout "$(git describe --tags --abbrev=0)"

See CONTRIBUTING.md for the full promotion flow.

Getting help

Rolling this out on a team? Open an
issue or reach out
via kromatic.com. We talk to teams working through
agent-memory rollouts often and are happy to point at whatever's useful.

License

Apache 2.0 — see LICENSE.