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Memory

Skill harshanandak/forge/skills/memory

Capture and retrieve durable project memory the right way -- and, when the graph backend is enabled, use temporal knowledge-graph memory. Reach for this whenever you are about to write down a lasting fact, decision, convention, or gotcha ("remember that...", "note for later", "save this"), or when you need to recall what was learned before ("what did we decide about X", "have we seen this"). It explains WHEN to use `forge remember` / `forge recall` versus a per-issue `forge issue comment`, how the two public backends work (local JSONL default, opt-in Graphiti; kernel is internal-only), and -- when Graphiti is enabled -- how to add episodes and search facts via the graphiti-memory MCP tools (add_memory, search_memory_facts, search_nodes) with group_id scoping and provenance. The local backend is always the offline floor. Not for: issue create/list/close ops (issue-basics), workflow status (status), or transient scratch notes.From its SKILL.md

Install
npx -y skills add harshanandak/forge --skill memory

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SKILL.md

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Memory

Durable project memory for agents. This skill teaches you where a fact belongs, which backend is active, and how to use graph memory when it is enabled.

Applies to: Forge-managed memory

This skill describes memory managed by Forge's own store — forge remember / forge recall, whose backend the Backends section (below) explains. If the repo you are working in ships its own memory system (a database, an API, a "log a decision" surface, an in-app memory service), that system is authoritative here and this skill does not apply — follow the repo's own memory instructions instead. When unsure, prefer the host repo's memory over forge remember; a note written to the wrong store is a note nobody finds.

The one rule

Persistent, project-level knowledge goes to forge remember — never to a MEMORY.md file, never to a scratch note that dies with the session. Retrieve it with forge recall. Both verbs route through one backend router; the default is a local file store, so they always work offline with zero setup.

When to use what

  • forge remember "<note>" — a lasting fact that outlives this issue: a convention, a decision and its rationale, a non-obvious gotcha, an environment quirk, a "we tried X, it failed because Y". Add --tag <label> for retrieval.
  • forge recall "<query>" — before assuming, check what is already known. Search first; do not re-derive knowledge the project already recorded. Note that memory relevant to your current prompt may already be injected automatically (a per-turn hook surfaces the best-matching notes; silent when nothing clears the relevance bar). Treat auto-surfaced memory as a head start, and still recall explicitly when you need to search for something specific it did not surface.
  • forge issue comment <id> "<note>" — progress or context that belongs to ONE issue's lifecycle (status, a blocker, a hand-off). Issue-scoped, not global.
  • Rule of thumb: would a future session on a different issue want this? → remember. Is it only meaningful inside this issue? → issue comment.

Backends (config: memory.backend in .forge/config.yaml)

The router picks a backend; remember/recall behave the same from your side. The public backends are exactly two:

  • local (DEFAULT) — flat JSONL at .forge/memory/notes.jsonl. Instant, offline, no dependencies, travels with the repo in git. This is the floor and is always available.
  • graphiti — opt-in temporal knowledge graph served to agents over MCP. Richer recall (relational, temporal, provenance-tracked) but needs a graph DB and an LLM. See "Graph memory" below. When selected, the CLI still writes to the local store as a safety floor; the graph is consumed by agents via MCP.

Check the active backend with forge doctor — it reports the memory backend and, for graphiti, whether the configured MCP server path is present (read-only, non-fatal).

Graph memory (when memory.backend: graphiti)

Graphiti turns notes into a bi-temporal knowledge graph: you add episodes (text/JSON), an LLM extracts entities (nodes) and facts (edges), and every fact keeps two timelines — when it was true in the world and when the system learned it. Superseded facts are invalidated, not deleted, so you can ask "what is true now" or "what was true at time T", with a pointer back to the source episode (provenance).

When enabled and the graphiti-memory MCP server is wired into your agent, use its tools directly:

  • add_memory — record a durable fact/decision as an episode. Pass a clear name, the content, and the project group_id (namespaces one project's memory). Ingestion is LLM-backed, so treat it as async — confirm it queued, don't block on it. Prefer this for facts that evolve (status, ownership, versions, preferences).
  • search_memory_facts — retrieve relationships/facts (edges) for a query, with validity windows. Use this before assuming a fact; it returns current truth plus history.
  • search_nodes — find entities (people, services, components) and their summaries.
  • Scope every call with the project's group_id so memory stays per-project.
  • Respect provenance: facts trace back to episodes — cite them when it matters.

If graph memory is unreachable or unconfigured, fall back to forge remember (local) — never lose the note. forge doctor surfaces a clear error when the graphiti backend is selected but not configured.

Good memory hygiene

  • Write atomic facts, not essays — one decision or gotcha per note.
  • Include the why, not just the what (rationale ages better than commands).
  • Tag consistently so recall is precise.
  • Recall before you act on an assumption; record after you learn something.

Setup pointers

  • Local is the default — nothing to install.
  • To opt into graph memory: set memory.backend: graphiti (plus a memory.graphiti block) in .forge/config.yaml, then run FalkorDB + the Graphiti MCP server. Wiring the server into each agent's MCP config is a follow-up step. Full how-to (graph DB, LLM/embedder, privacy/cost): docs/guides/memory-backends.md. Design rationale: docs/work/2026-07-06-graphiti-memory/research.md.

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