ai-memory/docs/cookbook.md

ai-memory cookbook

A task-oriented cheat sheet: "I want to do X" → how. For the full reference see ARCHITECTURE.md; for install see install.md (including the macOS menu bar app); for the tool-routing table see usage.md.

What ai-memory is, in one paragraph

ai-memory gives your AI coding agents long-term, cross-session memory. It runs as one server; your agent talks to it over MCP (tools like memory_query, memory_write_page) and over lifecycle hooks that automatically capture what you do. Memory is a markdown wiki in git (the source of truth, hand-editable) plus a derived SQLite index for search. Everything is scoped per project (workspace, project), resolved from your working directory. It works with no LLM at all (capture + full-text search + rule-based summaries); adding a provider enables consolidation and auto-improvement.

Everyday tasks (through your agent, over MCP)

You mostly just talk to your agent; it calls the right tool. Common ones:

You want	Say to your agent	Tool it uses
Recall prior work	"Have we discussed X?" / "what did we decide about Y?"	memory_query
Catch up after time away	"Where did we leave off?" / "catch me up"	handoff block / memory_explore
Remember a durable fact	"Remember that we always use pnpm here"	memory_write_page
Remember until a date	"Remember this until the migration ships"	memory_write_page + expires_at
A standing rule for every project	"Always: never force-push"	memory_write_page with scope: "global"
Read a specific saved page in full	"Read the _rules/deploy page"	memory_read_page
Save context for the next session	"Wrap up — save context for next time"	memory_handoff_begin
See project stats	"How much memory do we have?"	memory_status / memory_briefing

You rarely write pages by hand: lifecycle hooks capture prompts and tool calls automatically, and (with an LLM) consolidation compiles them into pages.

Recipe: keep a rule a project must follow

"Remember: before touching the payment code, read the PCI notes."

Your agent calls memory_write_page and it lands as a durable wiki page (routed under _rules/ when it's a rule). Next session, memory_query surfaces it, and if the project's .ai-memory.toml opts into the on-start brief, rules are prepended to the agent's context automatically. To make it apply to all your projects, ask for it as a global rule (scope: "global").

Recipe: have a project read a specific document before implementing

Two ways, depending on where the document lives:

1. It's already a wiki page (you saved it, or imported it — below): tell the agent "read the norms/gdpr-retention page before implementing" — it calls memory_read_page with that path and works from the full body.
2. It's an external file (a norm/spec on disk): save it as a page first ("remember this spec as norms/gdpr-retention", paste or point at it), then reference it as in (1). Large references are best split into a page per document so retrieval can pull just the relevant one.

Recipe: import an existing knowledge base (e.g. OKF norms/laws/specs)

ai-memory's wiki is OKF (Obsidian-compatible markdown + frontmatter). To bring an existing body of documents in as project memory:

• A few documents: save each as a durable page via your agent (memory_write_page) or the CLI (ai-memory write-page), one page per document, under a stable prefix like norms/…. Then any project can read a specific one before implementing (recipe above).
• A whole OKF/Obsidian vault or an export from another tool: use the companion importer (see companion-crates.md) — it ingests OMC and external-conversation exports into the store. ai-memory export-okf is the inverse (export your wiki as an OKF bundle), useful to see the exact on-disk shape your documents should take.
• The wiki lives at <data_dir>/wiki/<workspace>/<project>/… and is plain markdown in git, so you can also drop files in and let the file watcher + reindex pick them up. Keep them under a clear prefix and commit.

Once the material is saved as pages in the right scope, any project can search it (memory_query) and read a specific document in full (memory_read_page) before implementing against it.

Recipe: control what gets kept, aged, or consolidated

By default nothing you have to think about: memory decays on a single gentle curve, and an upgrade to 2.4 changes no scores and evicts nothing. When you do want to tune aging, it is all opt-in and reversible — the original of anything compacted or merged stays in git and the supersession chain, recoverable with ai-memory restore-page.

• Keep something forever: pin it. A pinned page is exempt from the forget-sweep regardless of tier or age. Semantic and procedural pages never decay either — only working/episodic memory ages.
• Make a note expire on a deadline: ask your agent to remember it "until " (an expires_at); the forget-sweep deletes it when the time passes, no matter how often it was read. A TTL outranks pinning.
• Tune how long each tier lasts: set per-tier half-lives in the project's .ai-memory.toml [decay.half_life_days] (e.g. keep episodic history longer, working-tier scratch shorter). Omit it for the default single curve.
• Compact instead of evict, and de-duplicate: [decay] compact_cold_episodic keeps a cold page's durable facts (paths, error codes, decisions) and drops the prose; [decay] dedup_cold_clusters collapses near-duplicate cold pages into one survivor. Both are zero-LLM, off by default, and supersede rather than delete.
• Keep used memory longer: nothing to configure — a page you open, search, or reach through a related-pages walk is reinforced automatically and resists decay.
• Surface likely contradictions: run memory_lint (through your agent or the CLI); with embeddings configured it flags pairs of same-topic pages that look like they conflict, advisory only. Similarity reads shared vocabulary as much as disagreement, so a single-domain or single-language store yields mostly candidate pairs: read each finding as a pair to check, not as a defect. If that noise is too high, raise contradiction_band_min (config.toml or AI_MEMORY_CONTRADICTION_BAND_MIN, default 0.4) — on such a store the band measures domain proximity more than conflict, so a higher floor trims same-domain-but-unrelated pairs.
• Let an LLM consolidate on idle ("dream"): with a provider and an embedder configured, [dream] enabled turns on a background pass that rewrites clusters of cold notes into single coherent pages while you're idle and cancels the moment you return. It is off by default, never deletes a source, and is gated on an internal recall eval before it could ever become default behavior.

Recipe: two agents / two repos working together

• Continuity across agents in the same project (quit Claude Code, open Codex in the same repo): automatic. A handoff is captured at session end and the next session's on-start hook prepends it. Ask "where did we leave off?".
• Ask an agent in another project to do something without loading that project's context here: cross-project messaging — "send project-b a request to add the export endpoint" (memory_message_send), and over there "check my inbox" (memory_message_pop). See agent-messaging.md.

Recipe: several accounts or an external launcher

Give each account or provider its own config home and pass it with --env, so the harness, its hooks and MCP, and transcript import all use that one home:

ai-memory run --env CLAUDE_CONFIG_DIR="$HOME/.claude-work" claude
ai-memory run --env CODEX_HOME="$HOME/.codex-work" codex   # the dir must exist

• --env and --env-file belong to ai-memory run, so they go before the harness name.
• A launcher or orchestrator that starts harnesses with its own environment (provider keys, an account's config dir) can write it to a file and pass --env-file <path>, one KEY=VALUE per line. Values are taken literally, so use absolute paths in the file.
• Auto-wire runs once per config home, so the second account gets its hooks + MCP on its own first launch. To wire one by hand, export the variable for the installers: CLAUDE_CONFIG_DIR="$HOME/.claude-work" ai-memory install-hooks --agent claude-code --apply, then the same for install-mcp --client claude-code --apply.

See managed-workstreams.md.

Recipe: run the server on a Mac

Use the menu bar app when you want one .app that starts the server and opens the existing tools (web UI, ai-memory status, config, logs). It does not replace those tools with a second dashboard.

./companions/ai-memory-macos/build.sh
open "companions/ai-memory-macos/dist/AI Memory.app"

Drag AI Memory.app to /Applications, then Install & Start Server from the menu extra. Wire an agent with the bundled binary:

BIN="/Applications/AI Memory.app/Contents/Resources/runtime/ai-memory"
"$BIN" install-mcp --client claude-code --apply
"$BIN" install-hooks --agent claude-code --apply

Memory stays in ~/Library/Application Support/ai-memory. Replacing the .app does not rewrite it. Full paths (tarball, source, Docker, launchd): macos.md.

From the terminal (CLI)

Your agent runs most of these for you; run and continue are how you start it:

ai-memory run <harness>              # launch a harness, hooks + MCP auto-wired
ai-memory continue                   # resume the newest managed checkout
ai-memory workstreams                # list this checkout's managed workstreams
ai-memory status                     # counts, paths, health
ai-memory doctor                     # is every harness that ran here captured?
ai-memory backfill                   # import prior local history into an empty store
ai-memory write-page …               # save a durable page
ai-memory handoffs                   # list open handoffs for the project
ai-memory message list               # cross-project inbox
ai-memory export-okf …               # export the wiki as an OKF bundle
ai-memory serve                      # run the server

When it isn't doing what you expect

• Nothing is being remembered: hooks may not be installed. ai-memory run <harness> installs its hooks + MCP on the first launch per harness, ai-memory version and config home, and again after ai-memory uninstall. If that harness already launched through run and its hooks went missing another way (removed by hand, a failed first wire), install them by hand: ai-memory install-hooks --agent <your-agent> --apply and ai-memory install-mcp --client <client> --apply. Then check ai-memory status / ai-memory doctor.
• Only some agents are being remembered: run ai-memory doctor. It lists every harness that has local sessions in this project and whether the server captured them — so a harness you rotated in without installing its hook (a silent gap: it keeps its own local history while capturing nothing) shows up as a warning with the exact install-hooks command to fix it.
• I just installed hooks in a project I've worked in for a while: the first time you open the project after installing, ai-memory imports your existing local session history once (bounded, sanitized on the server, only into an empty store) so it isn't starting from nothing. It runs automatically in the background; you can trigger or preview it yourself with ai-memory backfill (--dry-run to see what it would import), or turn it off with AI_MEMORY_BACKFILL_ON_START=false.
• A search misses something you saved: confirm the scope — memory is per-project; a page saved in project A is not returned in project B unless it was written to the global scope or you query with an explicit scope.
• You want an LLM feature (consolidation, digests): set a provider (AI_MEMORY_LLM_PROVIDER=…); without one, capture + search still work.