Your AI coding agent forgets everything between sessions. This gives it a memory that doesn't.
Not a vector database. Not a code index. A small, typed graph of the things that only get learned the hard way — the bug that came back six times, the rule nobody wrote down, the decision that looks wrong until you know why. Plain markdown, in your repo, in git.
Works with Antigravity, Claude Code, Cursor, Copilot, Zed, Codex CLI, and anything that takes custom instructions.
You ask your agent to change how sessions are stored. Before it edits anything, it checks the graph:
⚠ Found 1 node(s) (1 HIGH priority):
## Directly affected (1)
**Matched on:** edited file `src/auth/session.ts`
### REG_SESSION_FLAG_RESET
**Type:** Regression | **Priority:** HIGH
**Label:** "Secured" badge reappears for protected users after every deploy
**REGRESSED_N_TIMES:** 6
**RootCause:** "Secured" was inferred from three independently-wipeable
sentinels instead of read from one source of truth. Fixes 1–5 each added
another place that re-stamps the flag; none removed the ambiguity.
**Files:** `src/auth/session.ts`, `src/auth/keystore.ts`
That node is real — multiple recurrences on a production codebase, anonymized here. Without the graph, the agent confidently writes fix number seven, in a seventh location. With this data, the agent knows the shape of the trap before it steps in.
That is the whole thing. It's as simple as possible on purpose. Read below to learn how to use it for your projects.
curl -fsSL https://raw.githubusercontent.com/karstom/simplegraph-agentic/main/install.sh | bashRun it from inside your project. It detects your AI tool, installs the graph, and — if Node 18+ is available — builds and wires the MCP server so your agent can call the graph directly.
Prefer to read before you pipe:
curl -fsSL https://raw.githubusercontent.com/karstom/simplegraph-agentic/main/install.sh -o install.sh
less install.sh && bash install.shOptions: --tool antigravity (or comma-separated --tool antigravity,claude-code), --tool cursor, --dir path/to/project, --no-mcp, --yes. Through a pipe, pass them with bash -s --. Re-run the same command to upgrade. Supported on Linux, WSL, and macOS.
On Windows (PowerShell / CMD): Clone the repository and install the cross-platform Node CLI:
cd mcp
npm install
npm run build
npm link # exposes `simplegraph` globally, or run via: node dist/seed/cli.js <cmd>A new graph doesn't have to start empty — most of what it wants is already in your git history:
simplegraph seed --dry-run # mine reverts, repeat-fixes, ADRs, and TODOs. Write nothing.
simplegraph seed # review the draft, then commit itDeterministic, offline, no API key, full provenance on every node. On a 718-commit repo it finds around 60 nodes to start from. See seeding.
The bundled simplegraph CLI also provides cross-platform graph maintenance without bash dependencies:
simplegraph check— validates edge targets and duplicate node IDs acrosscore/andshared/simplegraph stale— detects outdated nodes (>90 days) and broken file/symbol anchorssimplegraph reindex— rebuildsgraph_index.mdQuick Index deterministically to resolve merge conflictssimplegraph index— prints the index with the Quick Index rendered from the nodes (for hooks and non-MCP readers; see derived mode in docs/multi-agent.md)
A flat memory file gets read in full on every single request. A graph gets routed — the agent reads a ~50-line index, then loads only the two or three files that matter for the task in front of it.
Measured on a production graph of 36 files and 251 nodes, built over 718 commits of real use:
| Session start | Per task | |
|---|---|---|
| simplegraph (tiered) | ~974 tokens | ~6,343 tokens |
| One flat file | ~52,300 tokens | ~52,300 tokens |
53× less at session start, 8× less for a typical task.
The gap widens as you learn more. Between two measurements this graph grew from 31 files to 36, and the flat-file cost went from ~30,700 to ~52,300 tokens while session start stayed under 1,000. A monolith gets more expensive every time you record something; a routed graph doesn't.
(Estimated at 1.3 tokens/word — read them as ratios. bash scripts/token_benchmark.sh measures your own.)
A flat file loaded in full doesn't miss anything, exactly. It's all in there. But one invariant is competing with thousands of tokens of other content and the task itself. Chunk that file and search it instead, and recall becomes probabilistic — embedding similarity doesn't signal causality, and a missed reference is invisible until the agent makes the same error again.
simplegraph works in part because the trigger isn't a query. check_files is
an exact join on the path and symbol the agent is already editing.
For example: Your agent adds a logging call to IdentityService.ts. Semantic
search on "add logging to identity service" will not surface a node about the reason for the logging - a
user badge that reappeared after every deploy when it should not have. Searching instead for a path key surfaces the issue directly
— sixth recurrence, root cause attached.
Semantic search needs the agent to remember to ask. This fires because the agent opened a file.
| Approach | Where it breaks |
|---|---|
Flat AGENT.md |
Nothing is absent, but one line competes with everything else — and you burn tokens for all of it on every request |
| Semantic search / vector DB | Retrieval is probabilistic and missed retrieval is a silent failure; needs infrastructure to run |
| Code index (LSP, code graph) | Knows what the code is, not what it has done to you — compose with it instead |
| Fine-tuning | Expensive, opaque, but worse - stale the moment the code changes |
simplegraph is intentionally limited: recall is bounded by what was recorded — a vector DB over your docs at least contains everything anyone ever typed. And for open-ended "what's our approach to X?" questions, semantic search over prose beats simplegraph's keyword search outright. simplegraph was built for answering this question: what do I need to know before touching this file?
This is more important than it sounds. Higher tier models do a better job choosing the "right" answer more frequently than lower tier models. With simplegraph every mistake or invariant gets recorded, and the intelligence required to make the right call goes down. In testing, a project with a well-maintained graph was able to move many functions off of frontier models to cheaper cloud or even locally-hosted models. This tool doesn't prevent a first mistake, but it does a good job of preventing a reoccurrence of a bad pattern once identified. This can make smaller models more cost-effective to run since they spend less time re-learning lessons already recorded in the graph.
Typed nodes and edges. Nodes are Components, Invariants, Regressions, Decisions, or Watchlists, connected by typed edges. The agent can follow a chain:
AUTH_SERVICE --VIOLATED_BY--> REG_TOKEN_LEAK (×3) --FIXED_BY--> DEC_ROTATE_ON_REFRESH
Three hops tell your agent what is fragile here and why.
A counter that means something. Every recurrence increments REGRESSED_N_TIMES. At 2, the MCP server refuses to record another recurrence until the agent answers three questions: what is the source of truth, which invariant is being violated, and why every previous fix was symptomatic. A bug that keeps coming back is often a design problem, and the tool makes the agent work through that before it patches again.
Anchors that survive refactors. Nodes attach to files, to symbols (AuthService.refreshToken), and to owned directories. A symbol anchor still fires after the file is renamed — and fires when a caller is edited, which is usually where the bug actually returns.
It composes with your code index. simplegraph doesn't parse your source, and won't try. If you run a structural code graph — codebase-memory-mcp, code-review-graph, Graphify, or an LSP — hand its blast radius to simplegraph_check_files and get back which of those files have a history. They know what your code is; the graph knows what it has done to you. See code graphs.
Zero infrastructure. No database, no server, no embeddings, no API key. Markdown and git. It reviews in the PR diff like everything else, because a memory node is agent-written text that other agents will later trust.
| Tool | Installed to |
|---|---|
| Antigravity | .agents/plugins/simplegraph/ + AGENTS.md |
| Claude Code | CLAUDE.md + .mcp.json + .claude/settings.json (Stop hook) |
| Cursor | .cursor/rules/memory.mdc + .cursor/mcp.json |
| GitHub Copilot | .github/copilot-instructions.md + .vscode/mcp.json |
| Zed | .zed/rules/memory.md + .zed/settings.json context server |
| Codex CLI | AGENTS.md + .codex/config.toml |
| Anything else | Generic adapter for custom instructions |
The installer picks the right one automatically (or pass multiple comma-separated tools, e.g. --tool antigravity,claude-code or interactive choices 1,3). With the MCP server, the agent gets sixteen tools — the three that matter day to day being simplegraph_check_files before an edit, simplegraph_anti_patterns before generating code, and simplegraph_add_node after a fix. See mcp/README.md.
Different coding agents provide different levels of enforcement. simplegraph supports three tiers depending on the host platform:
| Tool | Session-start context | Mid-task safety (MCP) | Turn-end gate (Stop hook) | Enforcement level |
|---|---|---|---|---|
| Antigravity | .agents/plugins/simplegraph/rules/ + AGENTS.md |
.agents/plugins/simplegraph/mcp_config.json |
hooks.json (simplegraph hook require-doc) |
Deterministic |
| Claude Code | CLAUDE.md |
.mcp.json |
.claude/settings.json (simplegraph hook require-doc) |
Deterministic |
| Cursor | .cursor/rules/memory.mdc (alwaysApply: true) |
.cursor/mcp.json |
(Not supported by tool) | Advisory (MCP-backed) |
| Zed | .zed/rules/memory.md |
.zed/settings.json context server |
(Not supported by tool) | Advisory (MCP-backed) |
| Codex CLI | AGENTS.md |
.codex/config.toml |
(Not supported by tool) | Advisory (MCP-backed) |
| GitHub Copilot | .github/copilot-instructions.md |
.vscode/mcp.json |
(Not supported by tool) | Advisory (MCP-backed) |
| Generic | Custom instructions | Manual config | (Not supported by tool) | Passive |
- Deterministic: The agent is gated by active lifecycle hooks (
Stop). If a HIGH-priority danger zone was touched without updating the memory graph, the agent is blocked and reminded to document before finishing. - Advisory (MCP-backed): The agent is equipped with MCP tools for checking regressions and updating nodes, but enforcement is prompt-driven (the model decides when to query).
- Passive: Relies on custom prompt instructions and manual file reading.
| Seeding | Bootstrap a graph from git history |
| Graph format | Node types, edge types, anchors, layout |
| Maintenance | The CI gate, staleness checks, scaling |
| Code graphs | Composing with a structural index |
| Multi-agent | Parallel agents, branches, shared org graphs |
| MCP server | Tools, configuration, environment variables |
| Updating a graph | When to add a node, and the root-cause gate |
- Near Zero infrastructure. Markdown and git. Nothing to run, nothing to host unless you want MCP.
- Stay small. Five high-signal nodes beat fifty shallow ones.
- The agent writes the graph alongside the code. Graph updates ship in the same commit as the fix.
- Tiered loading. Read fifty lines at session start, not five thousand.
- Git-native. Committed, versioned, branched, and reviewed like code.
Issues and PRs welcome — see CONTRIBUTING.md. If you use this, an issue describing what your graph looks like after a month is genuinely useful.
MIT