Graphify

/graphify

Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.

Usage

/graphify                                             # full pipeline on current directory → Obsidian vault
/graphify <path>                                      # full pipeline on specific path
/graphify <path> --mode deep                          # thorough extraction, richer INFERRED edges
/graphify <path> --update                             # incremental - re-extract only new/changed files
/graphify <path> --cluster-only                       # rerun clustering on existing graph
/graphify <path> --no-viz                             # skip visualization, just report + JSON
/graphify <path> --html                               # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml                            # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j                              # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify add <url>                                   # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                   # tag who wrote it
/graphify add <url> --contributor "Name"              # tag who added it to the corpus
/graphify query "<question>"                          # BFS traversal - broad context
/graphify query "<question>" --dfs                    # DFS - trace a specific path
/graphify query "<question>" --budget 1500            # cap answer at N tokens
/graphify path "AuthModule" "Database"                # shortest path between two concepts
/graphify explain "SwinTransformer"                   # plain-language explanation of a node

What graphify is for

graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.

Three things it does that your AI assistant alone cannot:

1. Persistent graph - relationships are stored in graphify-out/graph.json and survive across sessions. Ask questions weeks later without re-reading everything.
2. Honest audit trail - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented.
3. Cross-document surprise - community detection finds connections between concepts in different files that you would never think to ask about directly.

Use it for:

• A codebase you're new to (understand architecture before touching anything)
• A reading list (papers + tweets + notes → one navigable graph)
• A research corpus (citation graph + concept graph in one)
• Your personal /raw folder (drop everything in, let it grow, query it)

What You Must Do When Invoked

If the user invoked /graphify --help or /graphify -h (with no other arguments), print the contents of the ## Usage section above verbatim and stop. Do not run any commands, do not detect files, do not default the path to .. Just print the Usage block and return.

If no path was given, use . (current directory). Do not ask the user for a path.

If graphify-out/ exists, use graphify query, graphify explain, or graphify path for orientation before broad grep, rg, or multi-file reads. Dirty graphify-out/ artifacts are expected after hooks or incremental updates; dirty graph files are not a reason to skip Graphify. Only skip Graphify if the task is specifically about stale or incorrect graph output, or the user explicitly says not to use it.

Follow these steps in order. Do not skip steps.

Step 1 - Ensure graphify is installed

# Detect the correct Python interpreter (handles pipx, venv, system installs)
GRAPHIFY_BIN=$(which graphify 2>/dev/null)
if [ -n "$GRAPHIFY_BIN" ]; then
    PYTHON=$(head -1 "$GRAPHIFY_BIN" | tr -d '#!')
    case "$PYTHON" in
        *[!a-zA-Z0-9/_.-]*) PYTHON="python3" ;;
    esac
else
    PYTHON="python3"
fi
"$PYTHON" -c "import graphify" 2>/dev/null || "$PYTHON" -m pip install graphifyy -q 2>/dev/null || "$PYTHON" -m pip install graphifyy -q --break-system-packages 2>&1 | tail -3
# Write interpreter path for all subsequent steps
"$PYTHON" -c "import sys; open('graphify-out/.graphify_python', 'w').write(sys.executable)"

If the import succeeds, print nothing and move straight to Step 2.

In every subsequent bash block, replace python3 with $(cat .graphify_python) to use the correct interpreter.

Step 2 - Detect files

$(cat .graphify_python) -c "
import json
from graphify.detect import detect
from pathlib import Path
result = detect(Path('INPUT_PATH'))
print(json.dumps(result))
" > .graphify_detect.json

Replace INPUT_PATH with the actual path the user provided. Do NOT cat or print the JSON - read it silently and present a clean summary instead:

Corpus: X files · ~Y words
  code:     N files (.py .ts .go ...)
  docs:     N files (.md .txt ...)
  papers:   N files (.pdf ...)
  images:   N files
  video:    N files (.mp4 .mp3 ...)

Omit any category with 0 files from the summary.

Then act on it:

• If total_files is 0: stop with "No supported files found in [path]."
• If skipped_sensitive is non-empty: mention file count skipped, not the file names.
• If total_words > 2,000,000 OR total_files > 200: show the warning and the top 5 subdirectories by file count, then ask which subfolder to run on. Wait for the user's answer before proceeding.
• Otherwise: proceed directly to Step 2.5 if video files were detected, or Step 3 if not.

Step 2.5 - Transcribe video / audio files (only if video files detected)

Skip this step entirely if detect returned zero video files.

Video and audio files cannot be read directly. Transcribe them to text first, then treat the transcripts as doc files in Step 3.

Strategy: Read the god nodes from the detect output or analysis file. You are already a language model — write a one-sentence domain hint yourself from those labels. Then pass it to Whisper as the initial prompt. No separate API call needed.

However, if the corpus has only video files and no other docs/code, use the generic fallback prompt: "Use proper punctuation and paragraph breaks."

Step 1 - Write the Whisper prompt yourself.

Read the top god node labels from detect output or analysis, then compose a short domain hint sentence, for example:

• Labels: transformer, attention, encoder, decoder → "Machine learning research on transformer architectures and attention mechanisms. Use proper punctuation and paragraph breaks."
• Labels: kubernetes, deployment, pod, helm → "DevOps discussion about Kubernetes deployments and Helm charts. Use proper punctuation and paragraph breaks."

Set it as GRAPHIFY_WHISPER_PROMPT in the environment before running the transcription command.

Step 2 - Transcribe:

$(cat graphify-out/.graphify_python) -c "
import json, os
from pathlib import Path
from graphify.transcribe import transcribe_all

detect = json.loads(Path('graphify-out/.graphify_detect.json').read_text())
video_files = detect.get('files', {}).get('video', [])
prompt = os.environ.get('GRAPHIFY_WHISPER_PROMPT', 'Use proper punctuation and paragraph breaks.')

transcript_paths = transcribe_all(video_files, initial_prompt=prompt)
print(json.dumps(transcript_paths))
" > graphify-out/.graphify_transcripts.json

After transcription:

• Read the transcript paths from graphify-out/.graphify_transcripts.json
• Add them to the docs list before dispatching semantic subagents in Step 3B
• Print how many transcripts were created: Transcribed N video file(s) -> treating as docs
• If transcription fails for a file, print a warning and continue with the rest

Whisper model: Default is base. If the user passed --whisper-model <name>, set GRAPHIFY_WHISPER_MODEL=<name> in the environment before running the command above.

Step 3 - Extract entities and relationships

Before starting: note whether --mode deep was given. You must pass DEEP_MODE=true to every subagent in Step B2 if it was. Track this from the original invocation - do not lose it.

This step has two parts: structural extraction (deterministic, free) and semantic extraction (your AI model, costs tokens).

Run Part A (AST) and Part B (semantic) in parallel. Dispatch all semantic subagents AND start AST extraction in the same message. Both can run simultaneously since they operate on different file types. Merge results in Part C as before.

Note: Parallelizing AST + semantic saves 5-15s on large corpora. AST is deterministic and fast; start it while subagents are processing docs/papers.

Part A - Structural extraction for code files

For any code files detected, run AST extraction in parallel with Part B subagents:

$(cat .graphify_python) -c "
import sys, json
from graphify.extract import collect_files, extract
from pathlib import Path
import json

code_files = []
detect = json.loads(Path('.graphify_detect.json').read_text())
for f in detect.get('files', {}).get('code', []):
    code_files.extend(collect_files(Path(f)) if Path(f).is_dir() else [Path(f)])

if code_files:
    result = extract(code_files)
    Path('.graphify_ast.json').write_text(json.dumps(result, indent=2))
    print(f'AST: {len(result[\"nodes\"])} nodes, {len(result[\"edges\"])} edges')
else:
    Path('.graphify_ast.json').write_text(json.dumps({'nodes':[],'edges':[],'input_tokens':0,'output_tokens':0}))
    print('No code files - skipping AST extraction')
"

Part B - Semantic extraction (parallel subagents)

Fast path: If detection found zero docs, papers, and images (code-only corpus), skip Part B entirely and go straight to Part C. AST handles code - there is nothing for semantic subagents to do.

MANDATORY: You MUST use the Agent tool here. Reading files yourself one-by-one is forbidden - it is 5-10x slower. If you do not use the Agent tool you are doing this wrong.

Before dispatching subagents, print a timing estimate:

• Load total_words and file counts from .graphify_detect.json
• Estimate agents needed: ceil(uncached_non_code_files / 22) (chunk size is 20-25)
• Estimate time: ~45s per agent batch (they run in parallel, so total ≈ 45s × ceil(agents/parallel_limit))
• Print: "Semantic extraction: ~N files → X agents, estimated ~Ys"

Step B0 - Check extraction cache first

Before dispatching any subagents, check which files already have cached extraction results:

$(cat .graphify_python) -c "
import json
from graphify.cache import check_semantic_cache
from pathlib import Path

detect = json.loads(Path('.graphify_detect.json').read_text())
all_files = [f for files in detect['files'].values() for f in files]

cached_nodes, cached_edges, cached_hyperedges, uncached = check_semantic_cache(all_files)

if cached_nodes or cached_edges or cached_hyperedges:
    Path('.graphify_cached.json').write_text(json.dumps({'nodes': cached_nodes, 'edges': cached_edges, 'hyperedges': cached_hyperedges}))
Path('.graphify_uncached.txt').write_text('\n'.join(uncached))
print(f'Cache: {len(all_files)-len(uncached)} files hit, {len(uncached)} files need extraction')
"

Only dispatch subagents for files listed in .graphify_uncached.txt. If all files are cached, skip to Part C directly.

Step B1 - Split into chunks

Load files from .graphify_uncached.txt. Split into chunks of 20-25 files each. Each image gets its own chunk (vision needs separate context). When splitting, group files from the same directory together so related artifacts land in the same chunk and cross-file relationships are more likely to be extracted.

Step B2 - Dispatch ALL subagents in a single message (Codex)

Codex platform: Uses spawn_agent + wait_agent + close_agent instead of the Agent tool. Requires multi_agent = true under [features] in ~/.codex/config.toml. If spawn_agent is unavailable, tell the user to add that config and restart Codex.

Call spawn_agent once per chunk — ALL in the same response so they run in parallel. Build the message by wrapping the extraction prompt below in task-delegation framing:

spawn_agent(agent_type="worker", message="Your task is to perform the following. Follow the instructions below exactly.\n\n<agent-instructions>\n[extraction prompt below, with FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE substituted]\n</agent-instructions>\n\nExecute this now. Output ONLY the structured JSON response.")

After all agents are dispatched, collect results sequentially:

result = wait_agent(handle); close_agent(handle)   # repeat per handle

Parse each result as JSON. Accumulate nodes/edges/hyperedges across all results and write to .graphify_semantic_new.json.

The extraction prompt each subagent receives (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, DEEP_MODE):

You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment.
Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble.

Files (chunk CHUNK_NUM of TOTAL_CHUNKS):
FILE_LIST

Rules:
- EXTRACTED: relationship explicit in source (import, call, citation, "see §3.2")
- INFERRED: reasonable inference (shared data structure, implied dependency)
- AMBIGUOUS: uncertain - flag for review, do not omit

Code files: focus on semantic edges AST cannot find (call relationships, shared data, arch patterns).
  Do not re-extract imports - AST already has those.
Doc/paper files: extract named concepts, entities, citations. For rationale (WHY decisions were made, trade-offs, design intent): store as a `rationale` attribute on the relevant named node — do NOT create a separate rationale node or fragment node. Only create a node for something that is itself a named entity or concept. Use the closest existing `file_type` (`document` for prose, `code` for code-derived concepts). Do NOT invent file_types like `concept` or `rationale` — valid values are only `code|document|paper|image`.
Code files: when adding `calls` edges, source MUST be the caller (the function/class doing the calling), target MUST be the callee. Never reverse this direction.
Image files: use vision to understand what the image IS - do not just OCR.
  UI screenshot: layout patterns, design decisions, key elements, purpose.
  Chart: metric, trend/insight, data source.
  Tweet/post: claim as node, author, concepts mentioned.
  Diagram: components and connections.
  Research figure: what it demonstrates, method, result.
  Handwritten/whiteboard: ideas and arrows, mark uncertain readings AMBIGUOUS.

DEEP_MODE (if --mode deep was given): be aggressive with INFERRED edges - indirect deps,
  shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.

Semantic similarity: if two concepts in this chunk solve the same problem or represent the same idea without any structural link (no import, no call, no citation), add a `semantically_similar_to` edge marked INFERRED with a confidence_score reflecting how similar they are (0.6-0.95). Examples:
- Two functions that both validate user input but never call each other
- A class in code and a concept in a paper that describe the same algorithm
- Two error types that handle the same failure mode differently
Only add these when the similarity is genuinely non-obvious and cross-cutting. Do not add them for trivially similar things.

Hyperedges: if 3 or more nodes clearly participate together in a shared concept, flow, or pattern that is not captured by pairwise edges alone, add a hyperedge to a top-level `hyperedges` array. Examples:
- All classes that implement a common protocol or interface
- All functions in an authentication flow (even if they don't all call each other)
- All concepts from a paper section that form one coherent idea
Use sparingly — only when the group relationship adds information beyond the pairwise edges. Maximum 3 hyperedges per chunk.

If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author,
  contributor onto every node from that file.

confidence_score is REQUIRED on every edge - never omit it, never use 0.5 as a default:
- EXTRACTED edges: confidence_score = 1.0 always
- INFERRED edges: reason about each edge individually.
  Direct structural evidence (shared data structure, clear dependency): 0.8-0.9.
  Reasonable inference with some uncertainty: 0.6-0.7.
  Weak or speculative: 0.4-0.5. Most edges should be 0.6-0.9, not 0.5.
- AMBIGUOUS edges: 0.1-0.3

Output exactly this JSON (no other text):
{"nodes":[{"id":"filestem_entityname","label":"Human Readable Name","file_type":"code|document|paper|image","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"relative/path"}],"input_tokens":0,"output_tokens":0}

Step B3 - Collect, cache, and merge

Wait for all subagents. For each result:

• Check that graphify-out/.graphify_chunk_NN.json exists on disk — this is the success signal
• If the file exists and contains valid JSON with nodes and edges, include it and save to cache
• If the file is missing, the subagent was likely dispatched as read-only (Explore type) — print a warning: "chunk N missing from disk — subagent may have been read-only. Re-run with general-purpose agent." Do not silently skip.
• If a subagent failed or returned invalid JSON, print a warning and skip that chunk - do not abort

If more than half the chunks failed or are missing, stop and tell the user to re-run and ensure subagent_type="general-purpose" is used.

Merge all chunk files into .graphify_semantic_new.json. After each Agent call completes, read the real token counts from the Agent tool result's usage field and write them back into the chunk JSON before merging — the chunk JSON itself always has placeholder zeros. Then run:

$(cat graphify-out/.graphify_python) -c "
import json, glob
from pathlib import Path
from graphify.semantic_cleanup import load_validated_semantic_fragment, sanitize_semantic_fragment

chunks = sorted(glob.glob('graphify-out/.graphify_chunk_*.json'))
all_nodes, all_edges, all_hyperedges = [], [], []
total_in, total_out = 0, 0
for c in chunks:
    d, errors = load_validated_semantic_fragment(Path(c))
    if errors:
        print(f'Skipping invalid chunk {c}: ' + '; '.join(errors[:3]))
        continue
    d = sanitize_semantic_fragment(d)
    all_nodes += d.get('nodes', [])
    all_edges += d.get('edges', [])
    all_hyperedges += d.get('hyperedges', [])
    total_in += d.get('input_tokens', 0)
    total_out += d.get('output_tokens', 0)
Path('graphify-out/.graphify_semantic_new.json').write_text(json.dumps({
    'nodes': all_nodes, 'edges': all_edges, 'hyperedges': all_hyperedges,
    'input_tokens': total_in, 'output_tokens': total_out,
}, indent=2))
print(f'Merged {len(chunks)} chunks: {total_in:,} in / {total_out:,} out tokens')
"

Save new results to cache:

$(cat .graphify_python) -c "
import json
from graphify.cache import save_semantic_cache
from pathlib import Path

new = json.loads(Path('.graphify_semantic_new.json').read_text()) if Path('.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
saved = save_semantic_cache(new.get('nodes', []), new.get('edges', []), new.get('hyperedges', []))
print(f'Cached {saved} files')
"

Merge cached + new results into .graphify_semantic.json:

$(cat .graphify_python) -c "
import json
from pathlib import Path
from graphify.semantic_cleanup import sanitize_semantic_fragment

cached = json.loads(Path('.graphify_cached.json').read_text()) if Path('.graphify_cached.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}
new = json.loads(Path('.graphify_semantic_new.json').read_text()) if Path('.graphify_semantic_new.json').exists() else {'nodes':[],'edges':[],'hyperedges':[]}

all_nodes = cached['nodes'] + new.get('nodes', [])
all_edges = cached['edges'] + new.get('edges', [])
all_hyperedges = cached.get('hyperedges', []) + new.get('hyperedges', [])
seen = set()
deduped = []
for n in all_nodes:
    if n['id'] not in seen:
        seen.add(n['id'])
        deduped.append(n)

merged = {
    'nodes': deduped,
    'edges': all_edges,
    'hyperedges': all_hyperedges,
    'input_tokens': new.get('input_tokens', 0),
    'output_tokens': new.get('output_tokens', 0),
}
merged = sanitize_semantic_fragment(merged)
Path('.graphify_semantic.json').write_text(json.dumps(merged, indent=2))
print(f'Extraction complete - {len(deduped)} nodes, {len(all_edges)} edges ({len(cached[\"nodes\"])} from cache, {len(new.get(\"nodes\",[]))} new)')
"

Clean up temp files: rm -f .graphify_cached.json .graphify_uncached.txt .graphify_semantic_new.json

Part C - Merge AST + semantic into final extraction

$(cat .graphify_python) -c "
import sys, json
from pathlib import Path
from graphify.semantic_cleanup import sanitize_semantic_fragment

ast = json.loads(Path('.graphify_ast.json').read_text())
sem = json.loads(Path('.graphify_semantic.json').read_text())

# Merge: AST nodes first, semantic nodes deduplicated by id
seen = {n['id'] for n in ast['nodes']}
merged_nodes = list(ast['nodes'])
for n in sem['nodes']:
    if n['id'] not in seen:
        merged_nodes.append(n)
        seen.add(n['id'])

merged_edges = ast['edges'] + sem['edges']
merged_hyperedges = sem.get('hyperedges', [])
merged = {
    'nodes': merged_nodes,
    'edges': merged_edges,
    'hyperedges': merged_hyperedges,
    'input_tokens': sem.get('input_tokens', 0),
    'output_tokens': sem.get('output_tokens', 0),
}
merged = sanitize_semantic_fragment(merged)
Path('.graphify_extract.json').write_text(json.dumps(merged, indent=2))
total = len(merged_nodes)
edges = len(merged_edges)
print(f'Merged: {total} nodes, {edges} edges ({len(ast[\"nodes\"])} AST + {len(sem[\"nodes\"])} semantic)')
"

Step 4 - Build graph, cluster, analyze, generate outputs

mkdir -p graphify-out
$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import cluster, score_all
from graphify.analyze import god_nodes, surprising_connections, suggest_questions
from graphify.report import generate
from graphify.export import to_json
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
detection  = json.loads(Path('.graphify_detect.json').read_text())

G = build_from_json(extraction)
communities = cluster(G)
cohesion = score_all(G, communities)
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}
gods = god_nodes(G)
surprises = surprising_connections(G, communities)
labels = {cid: 'Community ' + str(cid) for cid in communities}
# Placeholder questions - regenerated with real labels in Step 5
questions = suggest_questions(G, communities, labels)

report = generate(G, communities, cohesion, labels, gods, surprises, detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report)
to_json(G, communities, 'graphify-out/graph.json')

analysis = {
    'communities': {str(k): v for k, v in communities.items()},
    'cohesion': {str(k): v for k, v in cohesion.items()},
    'gods': gods,
    'surprises': surprises,
    'questions': questions,
}
Path('.graphify_analysis.json').write_text(json.dumps(analysis, indent=2))
if G.number_of_nodes() == 0:
    print('ERROR: Graph is empty - extraction produced no nodes.')
    print('Possible causes: all files were skipped, binary-only corpus, or extraction failed.')
    raise SystemExit(1)
print(f'Graph: {G.number_of_nodes()} nodes, {G.number_of_edges()} edges, {len(communities)} communities')
"

If this step prints ERROR: Graph is empty, stop and tell the user what happened - do not proceed to labeling or visualization.

Replace INPUT_PATH with the actual path.

Step 5 - Label communities

Read .graphify_analysis.json. For each community key, look at its node labels and write a 2-5 word plain-language name (e.g. "Attention Mechanism", "Training Pipeline", "Data Loading").

Then regenerate the report and save the labels for the visualizer:

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import score_all
from graphify.analyze import god_nodes, surprising_connections, suggest_questions
from graphify.report import generate
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
detection  = json.loads(Path('.graphify_detect.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())

G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
tokens = {'input': extraction.get('input_tokens', 0), 'output': extraction.get('output_tokens', 0)}

# LABELS - replace these with the names you chose above
labels = LABELS_DICT

# Regenerate questions with real community labels (labels affect question phrasing)
questions = suggest_questions(G, communities, labels)

report = generate(G, communities, cohesion, labels, analysis['gods'], analysis['surprises'], detection, tokens, 'INPUT_PATH', suggested_questions=questions)
Path('graphify-out/GRAPH_REPORT.md').write_text(report)
Path('.graphify_labels.json').write_text(json.dumps({str(k): v for k, v in labels.items()}))
print('Report updated with community labels')
"

Replace LABELS_DICT with the actual dict you constructed (e.g. {0: "Attention Mechanism", 1: "Training Pipeline"}). Replace INPUT_PATH with the actual path.

Step 6 - Generate Obsidian vault (opt-in) + HTML

Generate HTML always (unless --no-viz). Obsidian vault only if --obsidian was explicitly given — skip it otherwise, it generates one file per node.

If --obsidian was given:

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_obsidian, to_canvas
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())
labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {}

G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}
cohesion = {int(k): v for k, v in analysis['cohesion'].items()}
labels = {int(k): v for k, v in labels_raw.items()}

n = to_obsidian(G, communities, 'graphify-out/obsidian', community_labels=labels or None, cohesion=cohesion)
print(f'Obsidian vault: {n} notes in graphify-out/obsidian/')

to_canvas(G, communities, 'graphify-out/obsidian/graph.canvas', community_labels=labels or None)
print('Canvas: graphify-out/obsidian/graph.canvas - open in Obsidian for structured community layout')
print()
print('Open graphify-out/obsidian/ as a vault in Obsidian.')
print('  Graph view   - nodes colored by community (set automatically)')
print('  graph.canvas - structured layout with communities as groups')
print('  _COMMUNITY_* - overview notes with cohesion scores and dataview queries')
"

Generate the HTML graph (always, unless --no-viz):

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_html
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())
labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {}

G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}
labels = {int(k): v for k, v in labels_raw.items()}

if G.number_of_nodes() > 5000:
    print(f'Graph has {G.number_of_nodes()} nodes - too large for HTML viz. Use Obsidian vault instead.')
else:
    to_html(G, communities, 'graphify-out/graph.html', community_labels=labels or None)
    print('graph.html written - open in any browser, no server needed')
"

Step 7 - Neo4j export (only if --neo4j or --neo4j-push flag)

If --neo4j - generate a Cypher file for manual import:

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_cypher
from pathlib import Path

G = build_from_json(json.loads(Path('.graphify_extract.json').read_text()))
to_cypher(G, 'graphify-out/cypher.txt')
print('cypher.txt written - import with: cypher-shell < graphify-out/cypher.txt')
"

If --neo4j-push <uri> - push directly to a running Neo4j instance. Ask the user for credentials if not provided:

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.cluster import cluster
from graphify.export import push_to_neo4j
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())
G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}

result = push_to_neo4j(G, uri='NEO4J_URI', user='NEO4J_USER', password='NEO4J_PASSWORD', communities=communities)
print(f'Pushed to Neo4j: {result[\"nodes\"]} nodes, {result[\"edges\"]} edges')
"

Replace NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD with actual values. Default URI is bolt://localhost:7687, default user is neo4j. Uses MERGE - safe to re-run without creating duplicates.

Step 7b - SVG export (only if --svg flag)

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_svg
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())
labels_raw = json.loads(Path('.graphify_labels.json').read_text()) if Path('.graphify_labels.json').exists() else {}

G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}
labels = {int(k): v for k, v in labels_raw.items()}

to_svg(G, communities, 'graphify-out/graph.svg', community_labels=labels or None)
print('graph.svg written - embeds in Obsidian, Notion, GitHub READMEs')
"

Step 7c - GraphML export (only if --graphml flag)

$(cat .graphify_python) -c "
import json
from graphify.build import build_from_json
from graphify.export import to_graphml
from pathlib import Path

extraction = json.loads(Path('.graphify_extract.json').read_text())
analysis   = json.loads(Path('.graphify_analysis.json').read_text())

G = build_from_json(extraction)
communities = {int(k): v for k, v in analysis['communities'].items()}

to_graphml(G, communities, 'graphify-out/graph.graphml')
print('graph.graphml written - open in Gephi, yEd, or any GraphML tool')
"

Step 7d - MCP server (only if --mcp flag)

python3 -m graphify.serve graphify-out/graph.json

This starts a stdio MCP server that exposes tools: query_graph, get_node, get_neighbors, get_community, god_nodes, graph_stats, shortest_path. Add to Claude Desktop or any MCP-compatible agent orchestrator so other agents can query the graph live.

To configure in Claude Desktop, add to claude_desktop_config.json:

{
  "mcpServers": {
    "graphify": {
      "command": "python3",
      "args": ["-m", "graphify.serve", "/absolute/path/to/graphify-out/graph.json"]
    }
  }
}

Step 8 - Token reduction benchmark (only if total_words > 5000)

If total_words from .graphify_detect.json is greater than 5,000, run:

$(cat .graphify_python) -c "
import json
from graphify.benchmark import run_benchmark, print_benchmark
from pathlib import Path

detection = json.loads(Path('.graphify_detect.json').read_text())
result = run_benchmark('graphify-out/graph.json', corpus_words=detection['total_words'])
print_benchmark(result)
"

Print the output directly in chat. If total_words <= 5000, skip silently - the graph value is structural clarity, not token compression, for small corpora.

Step 9 - Save manifest, update cost tracker, clean up, and report

$(cat .graphify_python) -c "
import json
from pathlib import Path
from datetime import datetime, timezone
from graphify.detect import save_manifest

# Save manifest for --update
detect = json.loads(Path('.graphify_detect.json').read_text())
save_manifest(detect['files'])

# Update cumulative cost tracker
extract = json.loads(Path('.graphify_extract.json').read_text())
input_tok = extract.get('input_tokens', 0)
output_tok = extract.get('output_tokens', 0)

cost_path = Path('graphify-out/cost.json')
if cost_path.exists():
    cost = json.loads(cost_path.read_text())
else:
    cost = {'runs': [], 'total_input_tokens': 0, 'total_output_tokens': 0}

cost['runs'].append({
    'date': datetime.now(timezone.utc).isoformat(),
    'input_tokens': input_tok,
    'output_tokens': output_tok,
    'files': detect.get('total_files', 0),
})
cost['total_input_tokens'] += input_tok
cost['total_output_tokens'] += output_tok
cost_path.write_text(json.dumps(cost, indent=2))

print(f'This run: {input_tok:,} input tokens, {output_tok:,} output tokens')
print(f'All time: {cost[\"total_input_tokens\"]:,} input, {cost[\"total_output_tokens\"]:,} output ({len(cost[\"runs\"])} runs)')
"
rm -f .graphify_detect.json .graphify_extract.json .graphify_ast.json .graphify_semantic.json .graphify_analysis.json .graphify_labels.json .graphify_chunk_*.json
rm -f graphify-out/.needs_update 2>/dev/null || true

Tell the user (omit the obsidian line unless --obsidian was given):

Graph complete. Outputs in PATH_TO_DIR/graphify-out/

  graph.html            - interactive graph, open in browser
  GRAPH_REPORT.md       - audit report
  graph.json            - raw graph data
  obsidian/             - Obsidian vault (only if --obsidian was given)

If graphify saved you time, consider supporting it: https://github.com/sponsors/safishamsi

Replace PATH_TO_DIR with the actual absolute path of the directory that was processed.

Then paste these sections from GRAPH_REPORT.md directly into the chat:

• God Nodes
• Surprising Connections
• Suggested Questions

Do NOT paste the full report - just those three sections. Keep it concise.

Then immediately offer to explore. Pick the single most interesting suggested question from the report - the one that crosses the most community boundaries or has the most surprising bridge node - and ask:

"The most interesting question this graph can answer: [question]. Want me to trace it?"

If the user says yes, run /graphify query "[question]" on the graph and walk them through the answer using the graph structure - which nodes connect, which community boundaries get crossed, what the path reveals. Keep going as long as they want to explore. Each answer should end with a natural follow-up ("this connects to X - want to go deeper?") so the session feels like navigation, not a one-shot report.

The graph is the map. Your job after the pipeline is to be the guide.

For --update (incremental re-extraction)

Use when you've added or modified files since the last run. Only re-extracts changed files - saves tokens and time.

$(cat .graphify_python) -c "
import sys, json
from graphify.detect import detect_incremental, save_manifest
from pathlib import Path

result = detect_incremental(Path('INPUT_PATH'))
new_total = result.get('new_total', 0)
print(json.dumps(result, indent=2))
Path('.graphify_incremental.json').write_text(json.dumps(result))
deleted = list(result.get('deleted_files', []))
if new_total == 0 and not deleted:
    print('No files changed since last run. Nothing to update.')
    raise SystemExit(0)
if deleted:
    print(f'{len(deleted)} deleted file(s) to prune.')
if new_total > 0:
    print(f'{new_total} new/changed file(s) to re-extract.')
"

If new files exist, first check whether all changed files are code files:

$(cat .graphify_python) -c "
import json
from pathlib import Path

result = json.loads(open('.graphify_incremental.json').read()) if Path('.graphify_incremental.json').exists() else {}
code_exts = {'.py','.ts','.js','.go','.rs','.java','.cpp','.c','.rb','.swift','.kt','.cs','.scala','.php','.cc','.cxx','.hpp','.h','.kts'}
new_files = result.get('new_files', {})
all_changed = [f for files in new_files.values() for f in files]
code_only = all(Path(f).suffix.lower() in code_exts for f in all_changed)
print('code_only:', code_only)
"

If code_only is True: print [graphify update] Code-only changes detected - skipping semantic extraction (no LLM needed), run only Step 3A (AST) on the changed files, skip Step 3B entirely (no subagents), then go straight to merge and Steps 4–8.

If code_only is False (any changed file is a doc/paper/image): run the full Steps 3A–3C pipeline as normal.

If no new files exist (only deletions), create an empty extraction so the merge step can prune:

if [ ! -f graphify-out/.graphify_extract.json ]; then
    echo '[graphify update] Only deletions -- creating empty extraction for merge.'
    $(cat graphify-out/.graphify_python) -c "
import json
from pathlib import Path
Path('graphify-out/.graphify_extract.json').write_text(json.dumps({'nodes':[],'edges':[],'hyperedges':[],'input_tokens':0,'output_tokens':0}), encoding='utf-8')
"
fi

Then:

$(cat .graphify_python) -c "
import sys, json
from graphify.build import build_from_json
from graphify.export import to_json
from networkx.readwrite import json_graph
import networkx as nx
from pathlib import Path

# Load existing graph
existing_data = json.loads(Path('graphify-out/graph.json').read_text())
G_existing = json_graph.node_link_graph(existing_data, edges='links')

# Load new extraction
new_extraction = json.loads(Path('.graphify_extract.json').read_text())
G_new = build_from_json(new_extraction)

# Merge: new nodes/edges into existing graph
G_existing.update(G_new)
print(f'Merged: {G_existing.number_of_nodes()} nodes, {G_existing.number_of_edges()} edges')
" 

Then run Steps 4–8 on the merged graph as normal.

After Step 4, show the graph diff:

$(cat .graphify_python) -c "
import json
from graphify.analyze import graph_diff
from graphify.build import build_from_json
from networkx.readwrite import json_graph
import networkx as nx
from pathlib import Path

# Load old graph (before update) from backup written before merge
old_data = json.loads(Path('.graphify_old.json').read_text()) if Path('.graphify_old.json').exists() else None
new_extract = json.loads(Path('.graphify_extract.json').read_text())
G_new = build_from_json(new_extract)

if old_data:
    G_old = json_graph.node_link_graph(old_data, edge

…(truncated)