Graphicle-AI/graphicle

Graphicle maps your repository into a code graph, remembers your project's docs, decisions and past agent sessions, and gives your coding agent cited excerpts from both: the exact files and lines. It runs on your machine: no API key needed, no account, no telemetry.
_{Works with Claude Code · Cursor · Codex · VS Code · Claude Desktop · any MCP client}

Quick start · How it works · Benchmarks · Docs · Coming from Graphify? · Discussions

brew install graphicle-ai/tap/graphicle
cd your-repo
graphicle setup

_{No Homebrew? curl -fsSL https://github.com/graphicle-ai/graphicle/releases/latest/download/install.sh | sh · Release archives · Other ways to install
Windows: a preview build for x86-64, not yet code-signed (Windows (preview)).}

graphicle setup indexes the repository, builds its code graph and connects the coding agents it finds. It also turns on session capture, adds a two-line section to AGENTS.md (unless Claude Code is the only agent) and installs Git hooks that keep the project current. It lists every file it changes and backs each one up first; graphicle disconnect undoes it all.

Then ask your agent to "use graphicle to find where failed requests are retried", or ask from the terminal:

$ graphicle ask "where is tax added to prices?"
Evidence for "where is tax added to prices?"
4 excerpts · 598 of 3,000 tokens

[1] shop/pricing.py:1-7  price_with_tax()
    """Prices and tax."""
    TAX_RATE = 0.2


    def price_with_tax(price):
        """A price with the sales tax added."""
        return round(price * (1 + TAX_RATE), 2)

_{Real output, cut after the first excerpt. With no model configured, ask shows the evidence; graphicle config set model anthropic (or openai, ollama) adds written answers.}

Install Graphicle with `brew install graphicle-ai/tap/graphicle` (or, without Homebrew,
`curl -fsSL https://github.com/graphicle-ai/graphicle/releases/latest/download/install.sh | sh`),
then run `graphicle setup --yes` in this repository and list every file it changed.

• Native and fast. One Rust program. On 13 codebases, Graphicle's 0.1.1 memory engine (a pre-release build) took 3.0 to 22.7 times less time than Graphify 0.9.91 in all 39 measurements (code-only extract, priming update and update with no change, on the same machine with 3 CPUs and 8 workers each). The graphs differ slightly: node counts within 4.7%, edge counts within 2.0% (speed table). A later pre-release build with 0.1.1's memory changes, whose graphs also differ from Graphify's, used less peak memory than Graphify 0.9.91 in all 39 measurements, from 0.29 to 0.97 times Graphify's figure (memory table).
• Cited context, within a budget you set. Your agent asks Graphicle and gets the relevant code, docs and decisions as cited excerpts, within a token budget you set. In our tests it tied grep on finding code, and we do not claim it saves tokens (what we measured).
• Answers you can check. Every excerpt names its file and lines. Memories an agent proposes wait for your approval.
• One program, not a stack. The code graph, the memory, one MCP server, the web app and the command line come in one install, with one SQLite file. No Python, Docker or database server.
• Local and private. No account and no telemetry. Your code and data leave your machine only when you ask. The only automatic request is a daily check for a new release, which GRAPHICLE_NO_UPDATE_NOTIFIER=1 turns off.

• Code graph. Files, functions, classes, calls and imports, parsed with tree-sitter and no model. graphicle graph searches it, explains a symbol, traces a path and exports an interactive HTML graph. After graphicle graph extract . --code-only, graphicle graph affected "retry()" lists what depends on a symbol.
• Project memory. Docs (Markdown, PDF, Office, HTML), reviewed decisions and procedures, and past Claude Code and Codex sessions, searchable alongside the code.
• Cited context on a budget. graphicle context and the MCP tools return excerpts with their sources, within a token budget, without calling a model.
• One MCP server. graphicle mcp serves the code-graph tools, under Graphify's tool names such as query_graph, get_neighbors and shortest_path, next to the memory tools. With --pull-requests it also serves the pull-request tools list_prs, get_pr_impact and triage_prs, which use the GitHub CLI (MCP).
• Memory tied to your code. Reviewed memories are tied to the functions they are about: a memory goes stale when that code changes, and edits elsewhere in the file usually leave it current (the exact rule). Mentions of code in your docs are linked to the definitions they name (graphicle call graph.doc_links lists them).
• Across repositories. Recall can span the repositories of a workspace: graphicle context --workspace, or the workspace MCP endpoint.
• Always current. Before each answer Graphicle syncs the few files you changed; Git hooks sync after each commit, merge and checkout.
• Sessions carry over. Session capture keeps each finished turn, and once you have reviewed memories, each new session starts with them and the latest turns.
• An app. graphicle open shows the graph, the sources and the memory in your browser. It is where you approve memories.

graphicle setup connects every client it finds over MCP. Each file is backed up once as <file>.graphicle-bak, and graphicle disconnect removes what it added.

1. Sync. Graphicle reads the folder (it respects .gitignore and skips secrets), keeps a versioned copy of each source and builds the code graph.
2. Recall. When your agent calls a tool, Graphicle syncs what changed, ranks excerpts from code, documents, sessions and reviewed memory, and returns them with citations. The tools call no model; your agent's own model reads the excerpts.
3. Remember. Captured sessions become sources. Memories that agents propose wait for your approval in the app.

The left column is a code-graph tool, such as Graphify's open-source engine, plus a separate memory server. It is not a comparison with Graphify Cloud, a hosted service that includes memory.

Every figure is labelled, and docs/benchmarks.md has the full tables, methods and limits. In the agent tests, a difference counts only when its 95% interval excludes 0; otherwise it is a tie. Long sessions and memory built up across coding sessions are not tested yet.

• Speed. The chart under Why Graphicle and the first panel above: on 13 codebases, 3 operations each, Graphicle's 0.1.1 memory engine (a pre-release build) took 3.0 to 22.7 times less time than Graphify 0.9.91 in all 39 measurements, on the same machine with 3 CPUs and 8 workers each (one session per program; median of 3 runs, 1 for Kubernetes and VS Code). The graphs differ slightly: node counts within 4.7%, edge counts within 2.0% (every measurement, with both graphs' sizes). docs/benchmarks.md also has the earlier runs in which both programs built the same graph: the 0.1.0 engine against Graphify 0.9.88 on Kubernetes (Kubernetes run), and a pre-release build on Django, which ran a code-only extract in 7.5 s against 72.8 s for Graphify 0.9.77 (medians of five alternating runs, one shared Linux host, 8 workers; the 8 October record).
• Updates. After each of 30 commits on prometheus, tokio and poetry, Graphicle 0.1.0's graph update took 6 to 8 times less time than Graphify 0.9.91's (one Linux node, 4 CPUs per run), and both matched a fresh build apart from three kinds of keying difference that both programs share. In a 30-note sample, the 15 notes whose function changed were flagged and no other; a wider exploratory check found some false flags (details and limits).
• Peak memory. A pre-release build with 0.1.1's changes to how the engine holds its data, later than the one timed above, used less peak memory than Graphify 0.9.91 in all 39 measurements (13 repositories; extract and two kinds of update), for example 2,092 against 2,767 MiB on the Kubernetes extract and 1,426 against 2,800 MiB on the next.js extract. A build of the merged 0.1.1 source stayed below in the 5 measurements rechecked; the second panel above shows its Kubernetes extract, 2,148 against 2,767 MiB (the higher peak of 2 runs). One Linux 8×H100 node, GPUs not used; 8 workers; Graphicle's peak RSS against Graphify's peak PSS (every measurement).
• Finding code (Claude Code 2.1.287 on a Linux node, Opus 5.5, effort medium). Graphicle ties grep. On 100 SWE-bench Verified tasks, plain Claude Code, Graphicle 0.1.0's default setup and Graphify 0.9.91 found every file the fix changed in 95, 95 and 94 of them; Graphicle used 16% and Graphify 29% more tokens than plain Claude Code, and the agent never called Graphicle's tools. On 93 tasks from our own code, which the model has not seen, all three tied again, and a prototype card from Graphicle 0.1.0's index (not shipped) used about 20% fewer tokens than Graphify.
• Answering questions (the same setup). On Graphify's showcase corpus (30 questions), the 0.1.1 agent defaults (a pre-release build) found 99.2% of the facts, against 94.7% for plain Claude Code (a tie) and 87.8% for Graphify 0.9.91: more facts than Graphify, with fewer tokens. On 60 questions about our own project all three tied, and Graphify used about 48k more tokens per question than Graphicle (pre-release 0.1.1 plus a prototype context hook) (every agent test).
• Agent memory. In a comparison with seven memory systems, registered before any answer was scored and run with one open model as every system's reader and judge, a pre-release build of Graphicle trails none on three development sets. Every score, with its method and caveats.

• One data folder. Your sources, memories and the code graph that setup builds live in one SQLite store: ~/Library/Application Support/Graphicle on macOS, ~/.local/share/graphicle on Linux, %LOCALAPPDATA%\Graphicle on Windows. graphicle graph extract writes its graph file to .graphicle/ in the project.
• Loopback by default. The app and its HTTP and MCP endpoints listen on 127.0.0.1.
• No telemetry, no account. Graphicle connects to the network only in these cases: a model or a connector you configure, a full graphicle graph extract (it sends documents and images to the LLM whose API key it finds), the GitHub CLI for graphicle graph add, clone and the graph server's pull-request tools, graphicle update with a feed you name, and a daily check for a new release. That check is on by default: a command you run in a terminal asks GitHub for the latest release, with no code or data, to tell you about a new one (GRAPHICLE_NO_UPDATE_NOTIFIER=1 turns it off). A build from source also downloads the parser sources once. Details
• Careful sync. sync respects .gitignore and skips .env files, keys and credentials. Session capture drops <private>…</private> spans and redacts credentials.
• Forgetting with a receipt. Deleting a memory or a captured session also removes what was derived from it, and you get a hash-chained deletion receipt.

More answers: FAQ · Troubleshooting · or run graphicle doctor.

Quick start · Agents · MCP · Install · Configuration · Team server · Moving from Graphify · Benchmarks · FAQ · Changelog

The code: crates/graphicle/ (the program: command line, app, HTTP and MCP server), crates/graphicle-graph/ (the code-graph engine), crates/graphicle-memory/ (the memory library), SDKs for Python and TypeScript, and a team server recipe in deploy/. Each crate builds on its own (building from source).

Contributions are welcome under the Apache License 2.0, with a Developer Certificate of Origin sign-off (git commit -s) and no CLA. See CONTRIBUTING.md. To report a security problem, follow SECURITY.md rather than opening a public issue.

Graphicle is licensed under the Apache License 2.0. Copyright 2026 KarLex AI, Inc.

Code signing policy: the Windows programs are not code-signed yet; signing through the SignPath Foundation is planned.

The code-graph engine is a Rust port of Graphify by Safi Shamsi and the Graphify contributors, licensed under Apache-2.0, with portions that remain available under the MIT License. Thank you. NOTICE, NOTICE.md and the licenses/ folders hold the attributions and the licences of every adapted project and third-party component.

If Graphicle is useful to you, star the repository so other developers find it.
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Built by KarLex AI}