Turn any conversation into a portable knowledge graph.
Captures what you said, what the AI produced, how you felt, and who you psychologically are —
compressed into a single .md file any LLM can read cold.
Every message in a long conversation re-sends the entire history as input tokens.
That cost compounds fast. graphifychat compresses your full conversation into a tiny memory file — paste it into a fresh chat and resume instantly at a fraction of the cost.
Real numbers — Claude Sonnet 4.6 · $3 / MTok input
| Conversation length | Full context tokens | graphifychat tokens | Tokens saved | Cost/msg (full) | Cost/msg (GC) | Savings |
|---|---|---|---|---|---|---|
| 5 turns | 4,150 | 500 | 3,650 | $0.0125 | $0.0015 | 88% |
| 10 turns | 8,300 | 1,000 | 7,300 | $0.0249 | $0.0030 | 88% |
| 20 turns | 16,600 | 2,000 | 14,600 | $0.0498 | $0.0060 | 88% |
| 50 turns | 41,500 | 5,000 | 36,500 | $0.1245 | $0.0150 | 88% |
| 100 turns | 83,000 | 10,000 | 73,000 | $0.2490 | $0.0300 | 88% |
| 200 turns | 166,000 | 20,000 | 146,000 | $0.4980 | $0.0600 | 88% |
88% input token reduction on every resumed session, at any length.
A 200-turn project costs $0.498/message to resume with full context vs $0.060 with graphifychat.
Over 20 resumed messages: ~$8.76 saved — on a single project.
Verified on a real 18-turn session — building this very skill:
| Category | Captured |
|---|---|
| Turns tracked | 18 full turns (user input + AI output, all 17 fields) |
| Sparse6 layers | 8 layers — turns · concepts · emotions · files · entities · characters · communities · hyperedges |
| Emotion data points | 54 (top 3 per turn × 18, with intensity scores 1–5) |
| Character archetypes | 3 detected: visionary · perfectionistic · opportunistic |
| Archetype shift events | 4 CHARSHIFT arcs tracked |
| God nodes | 3: tron_block · sparse6_block · graphifychat |
| Topic communities | 5 Leiden-style clusters |
| Hyperedges | 4 multi-turn patterns beyond pairwise edges |
| Decision anchors | 5 [DEC] turns where direction was locked |
| Open threads | 0 — RES score: 10 / 10 |
| Files tracked | 13 files with full provenance chains |
| Memory file size | 13.2 KB · ~3,400 tokens |
| Full chat size (est.) | ~14,900 tokens |
| Compression ratio | 4.4× smaller than the original conversation |
Every long conversation is a knowledge graph you lose when you close the tab.
Switching AI tools means starting over.
No standard way to carry memory, context, decisions, or emotional state across sessions.
graphifychat solves all of this — in one .md file, paste-portable into any AI.
Three passes. One file. Updated on every call.
┌──────────────────────────────────────────────────────────────────┐
│ TRON — structural pass, every turn │
│ user asked · AI produced · files · emotions · │
│ character archetypes · intent · thread · confidence flags │
├──────────────────────────────────────────────────────────────────┤
│ Sparse6 — relational graph pass, every turn │
│ 8 layers: turns · concepts · emotions · files · │
│ entities · characters · communities · hyperedges │
│ every edge tagged EXTRACTED/INFERRED/AMBIGUOUS + score 0–1 │
├──────────────────────────────────────────────────────────────────┤
│ GRAPH_REPORT — on-demand summary │
│ god nodes · emotional arc · character profile · 4-5 bullets │
│ auto turns 1–3, then only when you ask for it │
└──────────────────────────────────────────────────────────────────┘
| Section | Updated | Token cost |
|---|---|---|
| TRON | Every call | ~50 tokens / turn |
| Sparse6 | Every call | ~40 tokens / turn |
| GRAPH_REPORT | Auto T1–T3, then on-demand | 0 unless requested |
Add SKILL.md to your Claude skill library, or paste its contents as a system prompt.
At the start — always-on (Mode A):
/graphifychat
TRON + Sparse6 update every turn automatically. GRAPH_REPORT auto turns 1–3, then on demand.
Mid-conversation — snapshot (Mode B):
/graphifychat
Generates the full file for all turns so far. Only updates again when you call it again.
Here is my session memory. Please resume from this context:
[paste .md file]
Any LLM picks up exactly where you left off — with full context, emotions, and decision history.
One line per turn. 17 fields. User input AND AI output, both compressed.
T<N>|U:<keywords>|O:<keywords>|F:<files>|A:<attachments>|IMG:<images>
|EMO:<e>:<i>,<e>:<i>,<e>:<i>|BEH:<pattern>|INT:<type>|THR:<thread>
|CHG:<+/~/!>|CONF:<tag>|OPEN:<y/n>|SHIFT:<y/n>|GOD:<concept>
|CHAR:<arch>:<i>,<arch>:<i>|CHARSHIFT:<y/n>
| Field | What it captures |
|---|---|
U: |
User prompt → intent keywords, emphasis, corrections |
O: |
AI output → result type, decisions made, artifacts produced |
EMO: |
Top 3 user emotions + intensity 1–5 (171-emotion model) |
BEH: |
Behavioral pattern: deep_dive · co_designing · pressure_testing… |
INT: |
Intent: create · debug · explain · refine · decide · explore |
THR: |
Topic thread a/b/c — groups related turns |
CHG: |
Delta: + new · ~ refined · ! corrected/reversed |
CONF: |
Confidence: EXTRACTED (1.0) · INFERRED (0.6–0.9) · AMBIGUOUS (<0.3) |
OPEN: |
Unresolved question or task left this turn |
SHIFT: |
Auto — dominant emotion changed by ≥2 intensity |
GOD: |
Concept introduced here that becomes central (4+ Sparse6 edges) |
CHAR: |
Top 2 user archetypes + intensity — persists across entire session |
CHARSHIFT: |
Auto — dominant archetype shifted this turn |
T6|U:add sparse6 network graph,visible to hidden,emotional+behavioral,layered nodes,no info loss|O:sparse6 design questions,4 layers proposed|INT:explore|THR:a|CHG:+|CONF:EXTRACTED|OPEN:yes|SHIFT:yes|EMO:visionary:5,ambitious:4,excited:3|BEH:abstracting|CHAR:visionary:5,perfectionistic:3|CHARSHIFT:yes
T15|U:summary too long 4-5 bullets,push context to tron+sparse6,rename graphifychat,research graphify repo|O:graphify researched,redesign diagram shown,6 concepts mapped|INT:refine|THR:a|CHG:~|CONF:EXTRACTED|OPEN:yes|EMO:strategic:5,analytical:4,ambitious:4|BEH:course_correcting|CHAR:visionary:5,perfectionistic:4
8 named layers. Confidence-tagged. Append-only.
## LAYER:TURNS — T2[DEC] -> two_tier_design : decided [EXTRACTED:1.0]
## LAYER:CONCEPTS — tron_block -> sparse6_block : complements [EXTRACTED:1.0]
## LAYER:EMOTIONS — T5.EMO:analytical -> T6.EMO:visionary : resolves_to [INFERRED:0.85]
## LAYER:FILES — SKILL.md -> T16 : created_at [EXTRACTED:1.0]
## LAYER:ENTITIES — graphify[repo] -> graphifychat : inspired [INFERRED:0.9]
## LAYER:CHARACTERS — visionary+ambitious -> scope_escalation : triggers [INFERRED:0.9]
## LAYER:COMMUNITIES — COMMUNITY:psychology_layer -> [T11,T12,T13,char_archetypes] : comprises
## LAYER:HYPEREDGES — HYPEREDGE:skill_rewrites -> [T3,T8,T13,T16] : iterate_on [EXTRACTED:1.0]
Persistent psychological character detection. Persists until contradicted. Top 2 per turn.
| Archetype | Signal |
|---|---|
perfectionistic |
"not quite right" — one more tweak, hesitates before committing |
visionary |
Rapid scope expansion, ambitious framing, big-picture focus |
dogmatic |
"that's not how it works" — resists alternatives |
skeptical |
Slow to commit; repeated requests for sources |
opportunistic |
Many parallel threads; FOMO language |
egotistic |
Rejects corrections; rephrases as AI's error |
optimistic |
Skips validation; minimal risk language |
realistic |
Accepts tradeoffs; updates beliefs on evidence (goal state) |
pessimistic |
Worst-case first; "but what if it fails" |
idealistic |
Frustrated by pragmatic constraints |
cynical |
Distrusts outputs; seeks hidden flaws |
masochistic |
Repeats patterns that previously failed |
hedonistic |
Celebrates small wins; avoids discussing blockers |
fatalistic |
Passive; "whatever happens" |
narcissistic |
Dismisses alternatives without engagement |
legalistic |
Applies rules mechanically even when absurd |
academicistic |
Over-relies on models; ignores real signals |
egocentric |
"It's great because I own it" |
10 interaction patterns fire automatically when archetype + emotion co-occur:
scope_escalation · analysis_paralysis · deep_refinement · doubles_down · blame_displacement · calibrated_decision · decision_freeze · overcommitment · rapid_lock_in · context_switching
## GRAPH_REPORT
**God nodes:** `tron_block` · `sparse6_block` · `graphifychat`
**Open threads:** none
**Emotional arc:** visionary → decisive → analytical → ambitious → strategic → satisfied
**Character profile:** visionary:5 + perfectionistic:4 — scope_escalation + deep_refinement active
- T1–T5: Established two-tier TRON+Sparse6 design; locked update protocol
- T6–T13: Sparse6 8 layers; 6 new TRON fields; 18 archetypes; github v1–v4
- T14–T16: graphify concepts integrated; renamed graphifychat; SKILL.md final
- T17–T18: Final GitHub repo packaged; README rewritten with cost table
- Files: 📁 graphifychat/SKILL.md · 📁 graphifychat-github.zip
| File | What it shows |
|---|---|
coding-session.md |
React dashboard, 8 turns, bug fix (CHG:!), frustrated→satisfied arc |
research-session.md |
Literature review, 2 attachments, open thread, archetype shift |
creative-session.md |
Story writing, god node tracking, ambiguous ending decision |
| Model | Status |
|---|---|
| Claude (all versions) | ✅ Native — best with SKILL.md installed |
| ChatGPT (GPT-4o, o1) | ✅ Full — paste memory file in first message |
| Gemini (1.5, 2.0) | ✅ Full — long context handles large files well |
| Mistral / Llama / local | ✅ Full — any instruction-following model |
| Custom system prompt | ✅ Full — paste SKILL.md as system prompt |
| RAG / Embeddings | Memory plugins | graphifychat | |
|---|---|---|---|
| Setup required | Server + DB + API | Platform-specific | None |
| Works offline | ❌ | ❌ | ✅ |
| Portable across AI tools | ❌ | ❌ | ✅ |
| Human-readable | ❌ | Partial | ✅ |
| Captures emotions | ❌ | ❌ | ✅ |
| Captures psychology | ❌ | ❌ | ✅ |
| Version-controllable | ❌ | ❌ | ✅ |
| Token cost | High (retrieval) | Hidden | ~90 tokens/turn |
- Share examples — anonymised
.mdmemory files inexamples/ - Suggest TRON fields — use the issue template
- Build parsers — Python/JS/Rust TRON line parsers
- Build validators — lint field completeness + Sparse6 edge consistency
- Integrations — Claude Projects, VS Code, Obsidian
See CONTRIBUTING.md.
See CHANGELOG.md.
v4 (graphifychat) — 18 archetypes · CONF/GOD fields · 8 Sparse6 layers · edge confidence scores · Mode A/B · GRAPH_REPORT 4-5 bullets
v3 — INT/THR/CHG/OPEN/SHIFT · LAYER:ENTITIES · RES score
v2 — EMO/BEH · Sparse6 · 4 layers
v1 — TRON format · portable export
Keywords: llm-memory · context-window · prompt-engineering · ai-memory · token-efficiency · claude-skill · chatgpt-memory · knowledge-graph · conversation-state · llm-context · ai-portability · sparse6 · tron-format · emotion-tracking · character-archetypes · psychology · graphify · llm-tools · context-compression · ai-productivity