Record useful findings

Work memory records what you learned while using the graph. It is an optional layer over graph exploration, not a replacement for current source evidence.

Save a result

graphify save-result --question "Where is email normalized?" \
  --answer "normalize_email handles normalization before lookup." \
  --nodes normalize_email --outcome useful

Use useful, dead_end, or corrected for the outcome. Replace the example with findings you actually verified in your project.

Reflect on prior results

graphify reflect
graphify reflect --if-stale

The default lessons file is under graphify-out/reflections/LESSONS.md. To relate lessons to the current graph:

graphify reflect --graph graphify-out/graph.json

This can produce a learning overlay that marks nodes as preferred, tentative, or contested. Query and explain output can show lesson hints, including a prompt to re-verify when source has changed.

Keep lessons grounded

A previously useful answer can become stale. Record the nodes involved, keep outcomes specific, and check the current source before using a lesson to justify a change. Share memory files only with people who can access their underlying source context.

Run it on a real repository

These examples come from a code-only graph of the open-source httpx repository, built with Graphify Local 0.9.97. Your counts will differ.

$ graphify save-result --question "Where are redirects followed?" \
    --answer "Client._send_handling_redirects" \
    --nodes "_send_handling_redirects" --outcome useful
Saved to graphify-out/memory/query_20261028_133846_eb497924_where_are_redirects_followed.md

$ graphify reflect --graph graphify-out/graph.json
Reflected 1 memories (1 useful, 0 dead ends, 0 corrected) -> graphify-out/reflections/LESSONS.md

The first lines of the generated LESSONS.md:

# Lessons

_Auto-generated by `graphify reflect` from 1 session memory in graphify-out/memory/. Deterministic; no LLM. Use for orientation — verify before relying, and revisit dead ends if the code has changed since._

## Summary

- 1 useful · 0 dead ends · 0 corrected · 0 unmarked

With a single memory, the Lessons section reads “No marked outcomes yet”. A node becomes preferred only once enough distinct useful results agree; see --min-corroboration below.

Flags

Command	Flag	What it does
`save-result`	`--question Q` / `--answer A`	The question and the answer you’re saving.
`save-result`	`--type T`	`query`, `path_query` or `explain`. The default is `query`.
`save-result`	`--nodes N1 N2 ...`	The node labels the answer cites.
`save-result`	`--outcome O`	`useful`, `dead_end` or `corrected`.
`save-result`	`--correction TEXT`	The right answer. Use it with `--outcome corrected`.
both	`--memory-dir DIR`	Where memories are stored. The default is `graphify-out/memory`.
`reflect`	`--out FILE`	The output path. The default is `graphify-out/reflections/LESSONS.md`.
`reflect`	`--graph PATH`	Groups memories by community and drops nodes that no longer exist.
`reflect`	`--half-life-days N`	A signal’s weight halves every N days. The default is 30.
`reflect`	`--min-corroboration N`	How many distinct useful results a node needs before it’s preferred. The default is 2.

What a saved result looks like

Each save-result writes a Markdown file with YAML frontmatter, so it’s easy to read, diff and grep:

---
type: "query"
date: "2026-10-28T13:38:46.613425+00:00"
question: "Where are redirects followed?"
contributor: "graphify"
outcome: "useful"
source_nodes: ["_send_handling_redirects"]
---

# Q: Where are redirects followed?

## Answer

Client._send_handling_redirects

## Outcome

- Signal: useful

To record a wrong lead, save it with --outcome dead_end. To record a fix, use --outcome corrected --correction "...". The next reflect run counts both.

FAQ

Does reflect use an LLM? No. The output is deterministic: the same memories give the same file. Where do memories live? As Markdown files under graphify-out/memory/. You can read them, diff them or delete them like any other file. Should I commit them? That’s up to you. They’re plain files, so they can go through review like code.

Next steps

Try it in Graphify

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Agent memory

Memory your agents share in a Cloud workspace.

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Features

Configuration

Support

Record useful findings

Save a result

Reflect on prior results

Keep lessons grounded

Run it on a real repository

Flags

What a saved result looks like

FAQ

Next steps

Try it in Graphify

Agent memory

Export and reuse your graph

​Save a result

​Reflect on prior results

​Keep lessons grounded

​Run it on a real repository

​Flags

​What a saved result looks like

​FAQ

​Next steps

Try it in Graphify

Agent memory

Export and reuse your graph

Save a result

Reflect on prior results

Keep lessons grounded

Run it on a real repository

Flags

What a saved result looks like

FAQ

Next steps