GRAASF

Graph Retrieval and Awareness Session Framework

Every new AI coding session starts cold. GRAASF fixes that. It attaches a persistent knowledge graph to your project, extracts structured knowledge from Claude Code sessions, and injects the right context back at the start of the next one — automatically, in the background, without changing how you work.

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Quickstart

cd my-project
npx graasf init

That's it. Open Claude Code normally. GRAASF runs in the background from here.

graasf init scans your codebase, builds an initial knowledge graph, installs Claude Code hooks, and writes a CLAUDE.md to your repo root. Claude Code reads CLAUDE.md automatically at the start of every session.

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Recording decisions — the annotation system

Layer 0 (free, no API key) tracks code structure but not intent. It knows what functions exist; it doesn't know why you made the choices you made. Annotations are how you bridge that gap.

Type any of these in a Claude Code prompt and GRAASF records them immediately, before the AI even sees your message:

Annotation	What it records
graasf:decision Use RS256 for JWT — needed for gateway verification	A pinned architectural decision. Never decays.
graasf:goal Refactor the auth module to be stateless	The intent for this session. Appears in the next session's context.
graasf:note Token TTL is 15 minutes per security policy	A plain note node in the graph.
graasf:dead-end Redis session store — adds infra dependency, abandoned	A path you ruled out. Shows in Remnant traces so you don't revisit it.

Annotations work on all three layers. On Layer 0 they are the only way to capture intent. On Layers 1 and 2, the LLM extracts decisions and goals automatically from conversation — but annotations override and extend that.

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The three layers

GRAASF auto-detects which layer to use based on what's available. You can force a layer with the GRASF_LAYER environment variable.

Layer	Requires	Cost	What it extracts
0 — Structural	Nothing	Free	Code structure: functions, classes, imports, file relationships. Annotations you type explicitly.
1 — API	ANTHROPIC_API_KEY	~$0.01–0.03/session	Everything in Layer 0, plus: decisions, goals, dead-ends, outcomes — extracted from the conversation transcript.
2 — Local	Ollama + a model	Free	Same as Layer 1. Quality depends on the local model. Needs ~4 GB RAM for a 7B model.

Layer 0 is honest about what it gives you. It tracks your codebase and whatever you annotate. It does not read conversations. If you want GRAASF to understand why you made a change — not just what changed — use Layer 1 or Layer 2.

Detection priority:

GRASF_LAYER env var (override) → ANTHROPIC_API_KEY set → Ollama running → Layer 0

Setting up Layer 1

export ANTHROPIC_API_KEY=sk-ant-...

Uses claude-haiku-4-5 — the cheapest, fastest Anthropic model. GRAASF sends the session transcript once at session end, not per-message. A typical session costs $0.01–0.03.

Setting up Layer 2

# Install Ollama: https://ollama.com
ollama pull mistral

GRAASF checks http://localhost:11434 at session end and selects the best available model in this order: mistral:7b, llama3.2:3b, llama3:8b, phi3:mini. No API key needed.

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How it works

After every Claude Code session ends, the Stop hook fires automatically:

1. Rescans files modified during the session (Tree-sitter AST)
2. If Layer 1 or 2: reads the JSONL session transcript and sends it to the LLM for extraction
3. Merges extracted knowledge into the SQLite graph
4. Runs a decay pass — nodes not recently accessed fade toward archival; decisions and goals never decay below a floor
5. Regenerates CLAUDE.md (and AGENTS.md if present) from the updated graph
6. Records the session

The next time Claude Code starts, it reads the freshly generated CLAUDE.md and already knows what was done, what was decided, and what was ruled out.

Nothing in this pipeline is visible to you during the session. The hooks run silently before and after your prompts.

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CLI reference

graasf init                     # Scan repo, build initial graph, install hooks
graasf status                   # Show graph stats and active layer
graasf query <text>             # FTS5 search across the graph
graasf inject                   # Regenerate CLAUDE.md and AGENTS.md from current graph
graasf inject --stdout          # Print context slice to stdout
graasf inject --format claude   # Regenerate only CLAUDE.md
graasf inject --format agents   # Regenerate only AGENTS.md
graasf hooks install            # (Re)install Claude Code hooks
graasf hooks uninstall          # Remove GRAASF hooks
graasf session list             # List sessions with goal summaries
graasf session show <id>        # Show full session record
graasf decay run                # Manually trigger a decay pass
graasf gc                       # Archive remnants older than 30 days
graasf config set <key> <val>   # Set a config value
graasf config show              # Show current config
graasf watch                    # Start file-change watcher (daemon, Layer 0)

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Config reference

GRAASF stores per-project config at .grasf/config.json. The defaults work for most projects. Change them with graasf config set <key> <value>.

Key	Default	Description
extraction_layer	"auto"	Force a layer: "structural", "api", or "local". Default auto-detects.
token_budget	2000	Max tokens for context injection per prompt.
decay_threshold	0.15	Nodes with score below this become remnants.
decision_floor	0.3	Minimum decay score for decision and goal nodes — they never fully fade.
ollama_url	"http://localhost:11434"	Ollama endpoint. Override if running on a different host.
ollama_model_preference	["mistral:7b", "llama3.2:3b", "llama3:8b", "phi3:mini"]	Model priority order. First found in /api/tags is used.
anthropic_model	"claude-haiku-4-5-20251001"	Anthropic model for semantic extraction.
skip_dirs	["node_modules", ".git", "dist", ...]	Directories excluded from structural scan.
max_inject_lines	150	Max lines in a generated context file.

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What GRAASF writes to your project

your-project/
├── CLAUDE.md              ← Auto-generated context file. Read by Claude Code on startup.
├── AGENTS.md              ← Same content, formatted for Codex / other agents.
└── .grasf/
    ├── graph.db           ← SQLite knowledge graph. All data lives here.
    ├── config.json        ← Project config.
    ├── error.log          ← Hook errors (never surfaced to Claude Code — silent).
    └── generated/
        ├── CLAUDE.md      ← Source for the repo-root copy.
        └── AGENTS.md

Add .grasf/graph.db to .gitignore if you do not want to commit the graph. The CLAUDE.md at the repo root is safe to commit and useful for team members.

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Requirements

• Node.js ≥ 20.0.0
• Claude Code — hooks require Claude Code to be installed
• Git — optional, but structural extraction uses git metadata when available

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Debugging

# See what layer is active and graph stats
graasf status

# Check for hook errors (stop.js always exits 0 — errors go here, not to Claude Code)
cat .grasf/error.log

# Query the graph directly
graasf query "auth token"

# Force context regeneration without a session
graasf inject

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Contributing

git clone https://github.com/fatimaazfar/grasf
cd grasf
npm install
node --test test/**/*.test.js        # Unit + mocked integration tests (44 tests, no credentials)
node test/integration-layer0.mjs    # Layer 0 full pipeline — no credentials needed
node test/integration-layer1.mjs    # Layer 1 graceful fallback — uses invalid key, no real key needed
node test/integration-layer2.mjs    # Layer 2 graceful fallback — no Ollama needed
node test/integration.test.js       # Mocked Layer 1 + 2 end-to-end with path normalisation

Before opening a PR:

• All unit tests must pass: node --test test/**/*.test.js
• The mocked integration test must pass: node test/integration.test.js
• No new dependencies without discussion — the existing stack (better-sqlite3, tree-sitter, commander, @anthropic-ai/sdk) was chosen deliberately

Project layout:

src/
├── cli.js                 # Commander CLI — all 13 commands
├── config.js              # Config loading + repo root detection
├── logger.js              # Console wrapper
├── extract/
│   ├── mode.js            # Layer detection (single source of truth)
│   ├── structural.js      # Tree-sitter AST extraction
│   ├── llm.js             # Anthropic + Ollama extraction (same prompt, both paths)
│   ├── ollama.js          # Ollama HTTP client (Node.js fetch, no extra dep)
│   ├── transcript.js      # JSONL transcript parser
│   ├── merge.js           # Merge LLM results into graph
│   └── annotations.js     # graasf:* annotation parser
├── graph/
│   ├── db.js              # SQLite open + schema init (WAL mode, FTS5)
│   ├── nodes.js           # Node CRUD + deterministic IDs
│   ├── edges.js           # Edge CRUD
│   └── decay.js           # Decay scoring + remnant threshold
├── retrieval/
│   ├── rank.js            # Context slice builder (scoped, budget-limited)
│   └── query.js           # FTS5 search
├── adapters/
│   ├── claude-md.js       # CLAUDE.md renderer + writer
│   └── agents-md.js       # AGENTS.md renderer + writer
├── hooks/
│   ├── stop.js            # Stop hook — full extraction pipeline
│   ├── prompt-submit.js   # UserPromptSubmit hook — < 200ms, FTS injection
│   ├── session-start.js   # SessionStart hook — session log
│   └── install.js         # Hook installer (merges into .claude/settings.json)
└── init/
    ├── scanner.js         # Initial codebase scan
    └── git.js             # Git metadata helpers

The GRASF_BUILD.md in the repo root is the single source of truth for all design decisions. Read it before making any architectural change.

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License

MIT