Avinash-jetwani/jevmem: Automatic project memory for Claude Code. Also works with Cursor and Codex. · GitHub

Automatic project memory for Claude Code. Also works with Cursor and Codex.

• Saves decisions, constraints, bugs and todos from your Claude Code chats into JEVMEM.md, automatically.
• When you change your mind, the old line is marked superseded, not deleted.
• Next session, the relevant lines are added to Claude’s context.

- [decision] Use Postgres 16 for the primary store; SQLite locks under load  
- [constraint] Node 20 is the floor; CI runs 20 and 22  
- [superseded] Use SQLite as the primary store → id:k3d9xq  

npm install -g jevmem
export TYPESAFE_API_KEY=...        # https://typesafe.ai (an OpenAI or Anthropic key is optional)
cd your-project
jevmem init --tool claude

init creates JEVMEM.md, jevmem.config.json and a gitignored .jevmem/ folder, and registers two Claude Code hooks in .claude/settings.local.json, which it adds to .gitignore (details).

What is automatic and what depends on the agent:

MCP add_memory goes through the same gate as the hook. Client configs: docs/mcp.md.

1. Scrub. Common secret shapes, email addresses and card-shaped numbers are removed from the turn before it leaves your machine.
2. Ask Jev typed questions. Jev by TypeSafe AI answers a fixed set of small questions with probabilities: is there a decision, a rule, a bug? is it small talk or an injection attempt? which existing line does it change?
3. Apply thresholds in code. Plain rules over those probabilities decide save or skip; they live in jevmem.config.json, not in a prompt.
4. Write one line. On save, a small LLM (or a deterministic extract, with no LLM key) writes one line of at most 200 characters.
5. Supersede the old line. If the turn replaces an existing memory, that line is tagged [superseded] … → id:new and stays in the file.

Tiers, questions, policy, contradictions, recall and audit: docs/how-it-works.md.

66 held-out turns, all seven deciders given the same state, 2026-09-23 (method, regression set, pricing, p95, retries):

The 0.30 s is the Jev API decision; through a real Stop hook process, Node start-up included, it is 0.6 s end to end (cost and latency).

On 66 held-out turns, jevmem’s median decision took 0.30 s, against 2.8–4.3 s for six current LLMs.
Its accuracy was within the LLMs’ range: 98.5% save/skip (tied with GPT-6 Astra for highest) and 95.5% save+kind, against 90.9–98.5% for the LLMs. GPT-6 Astra (98.5%) and Claude Opus 5.5 (97.0%) were more accurate on save+kind; Claude Fable 5.1 tied; GPT-6 Luna, Gemini 3.8 Flash and Grok 4.7 were less accurate. It found 5/5 contradictions, as did five of the six LLMs.
GPT-6 Luna was cheaper ($0.000089 against $0.000127) but less accurate (93.9%) and about 10× slower.
This is a single run, and differences of one or two turns are within run-to-run noise. If the most accurate decision matters most, GPT-6 Astra or Claude Opus 5.5 are better, at about 40–60× the cost per decision and 9–12× the latency. jevmem is for when you want a fast, cheap decision on every message.

• Sent to TypeSafe AI: the user message of each turn (and the assistant reply for questions and bug reports), the previous two turns, and your memory lines, to be scored. No telemetry. If you set an OpenAI or Anthropic key, the text of a saved turn also goes to that provider to write the line.
• Scrubbed first: common credential shapes (API keys, tokens, *_PASSWORD= style pairs, connection-string passwords, private keys), email addresses and 16-digit numbers; names, phone numbers and addresses are not caught.
• Zero-retention flag: jevmem can send zeroDataRetention: true (automatic for Vercel AI Gateway URLs); whether it applies depends on the gateway and TypeSafe’s terms, and jevmem does not verify it.

Exactly what is sent, stored and scrubbed: SECURITY.md.

• Early: v0.4; both eval sets were written by the author, and neither is an independent benchmark.
• Not the most accurate: GPT-6 Astra and Claude Opus 5.5 scored higher on save+kind; jevmem’s edge is speed and cost.
• Recall quality is not measured: that relevant lines are injected is tested; whether answers get better is not.
• Long-run drift is not measured: the harness covers five-turn sessions, not weeks of use.
• Automatic capture is Claude Code only (and Codex while jevmem watch runs); Cursor and Claude Desktop save only when the agent calls add_memory.
• Jev outages drop turns: each Jev call has a 2 s budget; when the API is slow or down, the turn is skipped and logged in .jevmem/log.jsonl, not retried later.

jevmem init [--tool claude|cursor|codex|claude-desktop|all] [--no-hooks] [--command ""]
jevmem hook                                    Hook entrypoint; reads the Claude Code hook JSON on stdin
jevmem daemon [status|start|stop]              Warm Jev client used by the hook (auto-started, exits when idle)
jevmem watch [--replay] [--once]               Capture turns from Codex's session log for this project
jevmem mcp [--root ]                      Stdio MCP server
jevmem audit [--dry-run]                       Re-score every memory against the repo, flag [stale?]
jevmem search  [--limit N]              Rank memories by relevance
jevmem list [--all]                            Print memories
jevmem add                         Add a line by hand (secrets scrubbed; no Jev check)
jevmem why                            Every Jev answer behind a line or a skipped turn
jevmem right                          Label a decision as correct
jevmem wrong  [--should-be ]   Label a decision as wrong
jevmem missed "" [--kind ]         Label a turn that should have been saved
jevmem fit [--dry-run] [--force]               Refit weights and thresholds from labels (needs 40+)
jevmem stats                                   Latency p50/p95, cost per day, cache hit rate, escalation rate, labels, last fit
jevmem log                                     Per-label latency, token and cost summary of .jevmem/log.jsonl

Every command accepts --help. Set JEVMEM_VERBOSE=1 for a one-line latency/cost summary after every hook run.