\n\nCognee is the open-source AI memory platform that gives AI agents persistent long-term memory across sessions. Ingest data in any format, build a self-hosted knowledge graph, and let every agent recall, connect, and act with full context\n\n
\n\n📄 Read the research paper: [Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning](https://arxiv.org/abs/2505.24478) — Markovic et al., 2025\n\n\n## About Cognee\n\nCognee is an open-source AI memory platform for AI Agents. Ingest data in any format, and Cognee continuously builds a self-hosted knowledge graph that gives your agents persistent long-term memory across sessions. Cognee combines vector embeddings, graph reasoning, and cognitive-science-grounded ontology generation to make documents both searchable by meaning and connected by relationships that evolve as your knowledge does.\n\n:star: _Help us reach more developers and grow the cognee community. Star this repo!_\n\n:books: _Check our detailed [documentation](https://docs.cognee.ai/getting-started/installation#environment-configuration) for setup and configuration._\n\n:crab: _Available as a plugin for your OpenClaw — [cognee-openclaw](https://www.npmjs.com/package/@cognee/cognee-openclaw)_\n\n✴️ _Available as a plugin for your Claude Code — [claude-code-plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code)_\n\n🦀 _Available as a Rust client — [cognee-rs](https://github.com/topoteretes/cognee-rs)_\n\n🟦 _Available as a TypeScript client — [@cognee/cognee-ts](https://www.npmjs.com/package/@cognee/cognee-ts)_\n\n\n\n### Why use Cognee:\n\n- Easily Build Company Brain - unify data from various sources in one place and enable Agents with your domain knowledge\n- Knowledge infrastructure — unified ingestion, graph/vector search, runs locally, ontology grounding, multimodal\n- Persistent and Learning Agents - learn from feedback, context management, cross-agent knowledge sharing\n- Reliable and Trustworthy Agents - agentic user/tenant isolation, traceability, OTEL collector, audit traits\n\n### How it Works\n\n
\n \n
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\n\n## Basic Usage & Feature Guide\n\nTo learn more, [check out this short, end-to-end Colab walkthrough](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing) of Cognee's core features.\n\n[](https://colab.research.google.com/drive/12Vi9zID-M3fpKpKiaqDBvkk98ElkRPWy?usp=sharing)\n\n## Quickstart\n\nLet’s try Cognee in just a few lines of code.\n\n### Prerequisites\n\n- Python 3.10 to 3.14\n\n### Step 1: Install Cognee\n\nYou can install Cognee with **pip**, **poetry**, **uv**, or your preferred Python package manager.\n\n```bash\nuv pip install cognee\n```\n\n### Step 2: Configure the LLM\n```python\nimport os\nos.environ[\"LLM_API_KEY\"] = \"YOUR OPENAI_API_KEY\"\n```\nAlternatively, create a `.env` file using our [template](https://github.com/topoteretes/cognee/blob/main/.env.template).\n\nTo integrate other LLM providers, see our [LLM Provider Documentation](https://docs.cognee.ai/setup-configuration/llm-providers).\n\n### Step 3: Run the Pipeline\n\nCognee's API gives you four operations — `remember`, `recall`, `forget`, and `improve`:\n\n```python\nimport cognee\nimport asyncio\n\n\nasync def main():\n # Store permanently in the knowledge graph (runs add + cognify + improve)\n await cognee.remember(\"Cognee turns documents into AI memory.\")\n\n # Store in session memory (fast cache, syncs to graph in background)\n await cognee.remember(\"User prefers detailed explanations.\", session_id=\"chat_1\")\n\n # Query with auto-routing (picks best search strategy automatically)\n results = await cognee.recall(\"What does Cognee do?\")\n for result in results:\n print(result)\n\n # Query session memory first, fall through to graph if needed\n results = await cognee.recall(\"What does the user prefer?\", session_id=\"chat_1\")\n for result in results:\n print(result)\n\n # Delete when done\n await cognee.forget(dataset=\"main_dataset\")\n\n\nif __name__ == '__main__':\n asyncio.run(main())\n\n```\n\n### Use the Cognee CLI\n\n```bash\ncognee-cli remember \"Cognee turns documents into AI memory.\"\n\ncognee-cli recall \"What does Cognee do?\"\n\ncognee-cli forget --all\n```\n\nTo open the local UI, run:\n```bash\ncognee-cli -ui\n```\n\n> **Note:** The MCP server launched by `cognee-cli -ui` runs inside a Docker container.\n> Docker Desktop, Colima, or any OCI-compatible runtime with a working `docker` CLI is\n> required. See [Docker & Colima Setup](docs/docker-colima-setup.md) for details.\n\n## Run with Docker\n\nPrefer containers? Cognee publishes prebuilt images to Docker Hub on every push to `main`:\n[`cognee/cognee`](https://hub.docker.com/r/cognee/cognee) (the API server) and\n[`cognee/cognee-mcp`](https://hub.docker.com/r/cognee/cognee-mcp) (the MCP server).\n\n### Option A — Docker Compose (build from source)\n\nClone the repo, create a `.env` with at least `LLM_API_KEY`, then:\n\n```bash\ncp .env.template .env # then edit .env and set LLM_API_KEY\n\n# Start the API server (http://localhost:8000)\ndocker compose up\n\n# Optional profiles (combine as needed):\ndocker compose --profile ui up # + frontend on http://localhost:3000\ndocker compose --profile mcp up # + MCP server on http://localhost:8001\ndocker compose --profile postgres up # + Postgres/PGVector\ndocker compose --profile neo4j up # + Neo4j\n```\n\n> The `cognee` and `cognee-mcp` services publish different host ports (`8000` vs `8001`),\n> so you can run both at once.\n\n### Option B — Pull the prebuilt image (no clone required)\n\n```bash\n# Create a minimal .env in the current directory\necho 'LLM_API_KEY=\"YOUR_OPENAI_API_KEY\"' > .env\n\n# API server\ndocker run --env-file ./.env -p 8000:8000 --rm -it cognee/cognee:main\n\n# MCP server (HTTP transport)\ndocker pull cognee/cognee-mcp:main\ndocker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main\n```\n\nSee the [MCP server README](cognee-mcp/README.md) for SSE/stdio transports, optional\nextras, and MCP client configuration.\n\n## Use with AI Agents\n\n### Claude Code\n\nInstall the [Cognee memory plugin](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) to give Claude Code persistent memory across sessions. The plugin captures prompts, tool traces, and assistant responses into session memory, injects relevant context on every prompt, and syncs session memory into the permanent knowledge graph at session end.\n\n**Install** from the Claude Code marketplace. The recommended way is from your shell, *before* launching Claude Code, so the first `claude` launch is a clean session that bootstraps memory automatically:\n\n```bash\n# Add the marketplace and install the plugin (one-time, user-scoped)\nclaude plugin marketplace add topoteretes/cognee-integrations\nclaude plugin install cognee-memory@cognee\n\n# Set env vars for your mode (see below), then launch\nexport LLM_API_KEY=\"sk-...\" # local mode; or COGNEE_BASE_URL + COGNEE_API_KEY for cloud\nclaude\n```\n\n**Local mode** (default) — the plugin bootstraps a local Cognee API at `http://localhost:8011`. Only `LLM_API_KEY` is required; the Cognee API key is auto-minted if absent:\n\n```bash\nexport LLM_API_KEY=\"sk-...\"\n```\n\n**Cognee Cloud or a remote server** — set both:\n\n```bash\nexport COGNEE_BASE_URL=\"https://your-instance.cognee.ai\"\nexport COGNEE_API_KEY=\"ck_...\"\n```\n\nOn startup you should see a \"Cognee Memory Connected\" system message.\n\nThe plugin hooks into Claude Code's lifecycle — `SessionStart` selects mode and sets up identity, `UserPromptSubmit` injects dataset-scoped context, `PostToolUse` captures tool traces, `Stop` writes the assistant's answer, `PreCompact` preserves memory across context resets, and `SessionEnd` triggers the final sync into the permanent graph.\n\nSee the [plugin README](https://github.com/topoteretes/cognee-integrations/tree/main/integrations/claude-code) for sessions, datasets, and full configuration.\n\n### Connect to Cognee Cloud\n\nPoint any Python agent at a managed Cognee instance — all SDK calls route to the cloud:\n\n```python\nimport cognee\n\nawait cognee.serve(url=\"https://your-instance.cognee.ai\", api_key=\"ck_...\")\n\nawait cognee.remember(\"important context\")\nresults = await cognee.recall(\"what happened?\")\n\nawait cognee.disconnect()\n```\n\n## Examples\n\nBrowse more examples in the [`examples/`](examples/) folder — demos, guides, custom pipelines, and database configurations.\n\n**Use Case 1 — Customer Support Agent**\n\n```python\nGoal: Resolve customer issues using their personal data across finance, support, and product history.\n\nUser: \"My invoice looks wrong and the issue is still not resolved.\"\n\nCognee tracks: past interactions, failed actions, resolved cases, product history\n\n# Agent response:\nAgent: \"I found 2 similar billing cases resolved last month.\n The issue was caused by a syn",
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\n\n# CodeGraph\n\n## 🎉 1.0 Released!\n\nAlready installed? Run `codegraph upgrade` to update in place.\n\nFollow [@getcodegraph](https://x.com/getcodegraph) on X for updates.\n\n### Supercharge Claude Code, Cursor, Codex, OpenCode, Hermes Agent, Gemini, Antigravity, and Kiro with Semantic Code Intelligence\n\n**Surgical context · fewer tool calls · faster answers · 100% local**\n\n### [Documentation & Website →](https://colbymchenry.github.io/codegraph/)\n\n[](https://www.npmjs.com/package/@colbymchenry/codegraph)\n[](https://opensource.org/licenses/MIT)\n[](https://nodejs.org/)\n\n[](#supported-platforms)\n[](#supported-platforms)\n[](#supported-platforms)\n\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n[](#supported-agents)\n\n \n\n**The CodeGraph platform is coming** — for every PR, know exactly what to test, what could break, which flows are affected, and whether business logic is compromised.\n\n\n\nGet early beta access to the hosted product · getcodegraph.com\n\n
\n\n## Get Started\n\n### 1. Install the CLI\n\n**No Node.js required** — one command grabs the right build for your OS:\n\n```bash\n# macOS / Linux\ncurl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh\n\n# Windows (PowerShell)\nirm https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.ps1 | iex\n```\n\n\nAlready have Node? Use npm instead (works on any version)\n\n```bash\nnpm i -g @colbymchenry/codegraph\n```\n\nCodeGraph bundles its own runtime — nothing to compile, no native build, works the same everywhere. The installer puts `codegraph` on your PATH but **doesn't change your current shell** — open a new terminal before the next step so the command resolves.\n\n**Upgrade any time** with `codegraph upgrade` — it detects how you installed (bundle, npm, or npx) and updates in place. Add `--check` to see if an update is available, or `codegraph upgrade ` to pin one.\n\n\n\n### 2. Wire up your agent(s)\n\nIn a **new terminal**, run the installer to connect CodeGraph to the agents you use:\n\n```bash\ncodegraph install\n```\n\nDetects and auto-configures Claude Code, Cursor, Codex CLI, opencode, Hermes Agent, Gemini CLI, Antigravity IDE, and Kiro — wiring the CodeGraph MCP server into each. **This is the step that connects CodeGraph to your agent;** installing the CLI in step 1 does not do it on its own. It only wires up your agent — it does **not** index any code; building each project's graph is the separate `codegraph init` in step 3. (Shortcut: `npx @colbymchenry/codegraph` downloads and runs this in one go.)\n\n### 3. Initialize each project\n\n```bash\ncd your-project\ncodegraph init\n```\n\n`codegraph init` creates the local `.codegraph/` directory and builds the full graph in the same step — one command, done.\n\n
\n\n### 4. No more syncing!\n\nAuto-sync is enabled by default. CodeGraph watches the project and updates the graph on every file change — while your agent edits code, or you add, modify, or delete files. **The index is never stale, and there is nothing to re-run.**\n\n### Uninstall\n\nChanged your mind? One command removes CodeGraph from every agent it configured:\n\n```bash\ncodegraph uninstall\n```\n\nReverses the installer — strips CodeGraph's MCP server config, instructions, and permissions from each configured agent. Your project indexes (`.codegraph/`) are left untouched; remove those per-project with `codegraph uninit`. Use `--target` to remove from specific agents, or `--yes` to run non-interactively.\n\n---\n\n## Why CodeGraph?\n\nWhen an AI agent needs to understand code — to answer a question or make a change — it discovers structure the slow way: grep, glob, and Read, one file at a time, rebuilding call paths and dependencies by hand. That's a pile of tool calls and round-trips before it even starts the real work.\n\n**CodeGraph hands the agent the exact code it needs in one call.** It's a pre-built knowledge graph of every symbol, call edge, and dependency in your codebase — so instead of crawling files, the agent asks one question and gets back the relevant source, the call paths between those symbols (including dynamic-dispatch hops grep can't follow), and the blast radius of a change. **Surgical context, not a file-by-file search** — which means fewer tool calls and faster answers on every codebase, large or small.\n\n\n\n> **A note on cost:** CodeGraph's win on *every* codebase is precision and speed — fewer tool calls, faster answers. It cuts token and dollar cost too, but those savings are **scale-dependent**: small and noisy on a modest codebase, and material only once a repo is large and tangled — at the scale of a Google or Microsoft monorepo, multiplied by a whole team's daily agent usage — for them to compound into a real line item. On a 500-file project, adopt CodeGraph for the speed; the cost savings show up when the codebase (and the team) gets big.\n\n### Benchmark Results\n\nTested across **7 real-world open-source codebases** spanning 7 languages, comparing an agent (Claude Code, headless) answering one architecture question **with** and **without** CodeGraph, at the **median of 4 runs per arm**. _Re-validated on Opus 4.8 (2026-06-02), on the current build (`codegraph_explore` as the primary tool)._\n\n> **The universal win — every repo, every size: 58% fewer tool calls · 22% faster · file reads cut to ~zero.**\n\nThe reliable, universal payoff is **surgical context and speed**: CodeGraph collapses the agent's grep/find/Read crawl into a few direct queries — returning the exact methods you asked about even when they're buried in a multi-thousand-line file — so it answers with **near-zero file reads** while the no-CodeGraph agent spends its budget on discovery. The **Tokens** and **Cost** columns are real too, but — as noted above — they're **scale-dependent**: small and noisy per query, compounding into real money only at large-codebase, high-volume scale.\n\n| Codebase | Language | Tool calls | Time | File reads | Tokens | Cost |\n|----------|----------|------------|------|------------|--------|------|\n| **VS Code** | TypeScript · ~10k files | 81% fewer | 11% faster | 0 vs 9 | 64% fewer | 18% cheaper |\n| **Excalidraw** | TypeScript · ~640 | 40% fewer | 27% faster | 0 vs 7 | 25% fewer | even |\n| **Django** | Python · ~3k | 77% fewer | 13% faster | 0 vs 9 | 60% fewer | 8% cheaper |\n| **Tokio** | Rust · ~790 | 57% fewer | 18% faster | 0 vs 8 | 38% fewer | even |\n| **OkHttp** | Java · ~645 | 50% fewer | 31% faster | 0 vs 4 | 54% fewer | 25% cheaper |\n| **Gin** | Go · ~110 | 44% fewer | 24% faster | 1 vs 6 | 23% fewer | 19% cheaper |\n| **Alamofire** | Swift · ~110 | 58% fewer | 33% faster | 0 vs 9 | 64% fewer | 40% cheaper |\n\n**File reads** = median files the agent opened **with** vs **without** CodeGraph — the surgical-context win in one column. **Tokens** and **Cost** are the same with-vs-without deltas; they're directional (they move run-to-run) and, per query, small in absolute terms — which is why they only become a line item at scale. `codegraph_explore` also collapses redundant interchangeable implementations to signatures, so a response is sized to the *answer* rather than the file count.\n\n\nPer-repo breakdown — WITH vs WITHOUT (median of 4)\n\n**VS Code** · ~10k files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 59s | 2m 13s | 11% faster |\n| File Reads | 0 | 9 | −9 |\n| Grep/Bash | 0 | 11 | −11 |\n| Tool calls | 4 | 21 | 81% fewer |\n| Total tokens | 640k | 1.79M | 64% fewer |\n| Cost | $0.68 | $0.83 | 18% cheaper |\n\n**Excalidraw** · ~640 files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 32s | 2m 6s | 27% faster |\n| File Reads | 0 | 7 | −7 |\n| Grep/Bash | 1 | 8 | −7 |\n| Tool calls | 9 | 15 | 40% fewer |\n| Total tokens | 1.27M | 1.69M | 25% fewer |\n| Cost | $0.78 | $0.78 | even |\n\n**Django** · ~3k files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 43s | 1m 58s | 13% faster |\n| File Reads | 0 | 9 | −9 |\n| Grep/Bash | 0 | 5 | −5 |\n| Tool calls | 3 | 13 | 77% fewer |\n| Total tokens | 559k | 1.41M | 60% fewer |\n| Cost | $0.57 | $0.62 | 8% cheaper |\n\n**Tokio** · ~790 files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 55s | 2m 20s | 18% faster |\n| File Reads | 0 | 8 | −8 |\n| Grep/Bash | 0 | 6 | −6 |\n| Tool calls | 6 | 14 | 57% fewer |\n| Total tokens | 1.08M | 1.73M | 38% fewer |\n| Cost | $0.82 | $0.82 | even |\n\n**OkHttp** · ~645 files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 1s | 1m 29s | 31% faster |\n| File Reads | 0 | 4 | −4 |\n| Grep/Bash | 2 | 6 | −4 |\n| Tool calls | 5 | 10 | 50% fewer |\n| Total tokens | 502k | 1.10M | 54% fewer |\n| Cost | $0.41 | $0.55 | 25% cheaper |\n\n**Gin** · ~110 files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 14s | 1m 37s | 24% faster |\n| File Reads | 1 | 6 | −5 |\n| Grep/Bash | 1 | 2 | −1 |\n| Tool calls | 5 | 9 | 44% fewer |\n| Total tokens | 651k | 847k | 23% fewer |\n| Cost | $0.46 | $0.57 | 19% cheaper |\n\n**Alamofire** · ~110 files\n| Metric | WITH cg | WITHOUT cg | Δ |\n|---|---|---|---|\n| Time | 1m 35s | 2m 21s | 33% faster |\n| File Reads | 0 | 9 | −9 |\n| Grep/Bash | 0 | 4 | −4 |\n| Tool calls | 5 | 12 | 58% fewer |\n| Total tokens | 766k | 2.10M | 64% fewer |\n| Cost | $0.57 | $0.95 | 40% cheaper |\n\n\n\n\nFull benchmark details\n\n**Methodology.** Each arm is `claude -p` (Claude Opus 4.8) run headlessly against the repo with `--strict-mcp-config`: **WITH** = CodeGraph's MCP server enabled, **WITHOUT** = an empty MCP config. Built-in Read/Grep/Bash stay available to both. Same question per repo, **4 runs per arm, median reported**. Cost = the run's `total_cost_usd`; Tokens = total tokens processed (input incl. cached + output); Time = wall-clock; Tool calls = every tool invocation, including those inside any sub-agents the model spawns. Repos cloned at `--depth 1` and indexed by the same CodeGraph build that served them. Re-validated 2026-06-02 on the current build. These numbers are lower than the prior Opus 4.7 validation — not a CodeGraph regression but a stronger native baseline: Opus 4.8 greps/reads efficiently on the main thread instead of fanning out into large Expl",
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"description": "Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More",
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\n Claude-Mem seamlessly preserves context across sessions by automatically capturing tool usage observations, generating semantic summaries, and making them available to future sessions. This enables Claude to maintain continuity of knowledge about projects even after sessions end or reconnect.\n
\n\n---\n\n## Quick Start\n\nInstall with a single command:\n\n```bash\nnpx claude-mem install\n```\n\nOr install for Gemini CLI (auto-detects `~/.gemini`):\n\n```bash\nnpx claude-mem install --ide gemini-cli\n```\nOr install for OpenCode:\n\n```bash\nnpx claude-mem install --ide opencode\n```\n\nOr install from the plugin marketplace inside Claude Code:\n\n```bash\n/plugin marketplace add thedotmack/claude-mem\n\n/plugin install claude-mem\n```\n\nRestart Claude Code or Gemini CLI. Context from previous sessions will automatically appear in new sessions.\n\n> **Note:** Claude-Mem is also published on npm, but `npm install -g claude-mem` installs the **SDK/library only** — it does not register the plugin hooks or set up the worker service. Always install via `npx claude-mem install` or the `/plugin` commands above.\n\n### 🦞 OpenClaw Gateway\n\nInstall claude-mem as a persistent memory plugin on [OpenClaw](https://openclaw.ai) gateways with a single command:\n\n```bash\ncurl -fsSL https://install.cmem.ai/openclaw.sh | bash\n```\n\nThe installer handles dependencies, plugin setup, AI provider configuration, worker startup, and optional real-time observation feeds to Telegram, Discord, Slack, and more. See the [OpenClaw Integration Guide](https://docs.claude-mem.ai/openclaw-integration) for details.\n\n**Key Features:**\n\n- 🧠 **Persistent Memory** - Context survives across sessions\n- 📊 **Progressive Disclosure** - Layered memory retrieval with token cost visibility\n- 🔍 **Skill-Based Search** - Query your project history with mem-search skill\n- 🖥️ **Web Viewer UI** - Real-time memory stream at http://localhost:37777\n- 💻 **Claude Desktop Skill** - Search memory from Claude Desktop conversations\n- 🔒 **Privacy Control** - Use `` tags to exclude sensitive content from storage\n- ⚙️ **Context Configuration** - Fine-grained control over what context gets injected\n- 🤖 **Automatic Operation** - No manual intervention required\n- 🔗 **Citations** - Reference past observations with IDs (access via http://localhost:37777/api/observation/{id} or view all in the web viewer at http://localhost:37777)\n- 🧪 **Beta Channel** - Try experimental features like Endless Mode via version switching\n\n---\n\n## Documentation\n\n📚 **[View Full Documentation](https://docs.claude-mem.ai/)** - Browse on official website\n\n### Getting Started\n\n- **[Installation Guide](https://docs.claude-mem.ai/installation)** - Quick start & advanced installation\n- **[Gemini CLI Setup](https://docs.claude-mem.ai/gemini-cli/setup)** - Dedicated guide for Google's Gemini CLI integration\n- **[Usage Guide](https://docs.claude-mem.ai/usage/getting-started)** - How Claude-Mem works automatically\n- **[Search Tools](https://docs.claude-mem.ai/usage/search-tools)** - Query your project history with natural language\n- **[Beta Features](https://docs.claude-mem.ai/beta-features)** - Try experimental features like Endless Mode\n\n### Best Practices\n\n- **[Context Engineering](https://docs.claude-mem.ai/context-engineering)** - AI agent context optimization principles\n- **[Progressive Disclosure](https://docs.claude-mem.ai/progressive-disclosure)** - Philosophy behind Claude-Mem's context priming strategy\n\n### Architecture\n\n- **[Overview](https://docs.claude-mem.ai/architecture/overview)** - System components & data flow\n- **[Architecture Evolution](https://docs.claude-mem.ai/architecture-evolution)** - The journey from v3 to v5\n- **[Hooks Architecture](https://docs.claude-mem.ai/hooks-architecture)** - How Claude-Mem uses lifecycle hooks\n- **[Hooks Reference](https://docs.claude-mem.ai/architecture/hooks)** - 7 hook scripts explained\n- **[Worker Service](https://docs.claude-mem.ai/architecture/worker-service)** - HTTP API & Bun management\n- **[Database](https://docs.claude-mem.ai/architecture/database)** - SQLite schema & FTS5 search\n- **[Search Architecture](https://docs.claude-mem.ai/architecture/search-architecture)** - Hybrid search with Chroma vector database\n\n### Configuration & Development\n\n- **[Configuration](https://docs.claude-mem.ai/configuration)** - Environment variables & settings\n- **[Development](https://docs.claude-mem.ai/development)** - Building, testing, contributing\n- **[Troubleshooting](https://docs.claude-mem.ai/troubleshooting)** - Common issues & solutions\n\n---\n\n## How It Works\n\n**Core Components:**\n\n1. **5 Lifecycle Hooks** - SessionStart, UserPromptSubmit, PostToolUse, Stop, SessionEnd (6 hook scripts)\n2. **Smart Install** - Cached dependency checker (pre-hook script, not a lifecycle hook)\n3. **Worker Service** - HTTP API on port 37777 with web viewer UI and 10 search endpoints, managed by Bun\n4. **SQLite Database** - Stores sessions, observations, summaries\n5. **mem-search Skill** - Natural language queries with progressive disclosure\n6. **Chroma Vector Database** - Hybrid semantic + keyword search for intelligent context retrieval\n\nSee [Architecture Overview](https://docs.claude-mem.ai/architecture/overview) for details.\n\n---\n\n## MCP Search Tools\n\nClaude-Mem provides intelligent memory search through **4 MCP tools** following a token-efficient **3-layer workflow pattern**:\n\n**The 3-Layer Workflow:**\n\n1. **`search`** - Get compact index with IDs (~50-100 tokens/result)\n2. **`timeline`** - Get chronological context around interesting results\n3. **`get_observations`** - Fetch full details ONLY for filtered IDs (~500-1,000 tokens/result)\n\n**How It Works:**\n- Claude uses MCP tools to search your memory\n- Start with `search` to get an index of results\n- Use `timeline` to see what was happening around specific observations\n- Use `get_observations` to fetch full details for relevant IDs\n- **~10x token savings** by filtering before fetching details\n\n**Available MCP Tools:**\n\n1. **`search`** - Search memory index with full-text queries, filters by type/date/project\n2. **`timeline`** - Get chronological context around a specific observation or query\n3. **`get_observations`** - Fetch full observation details by IDs (always batch multiple IDs)\n\n**Example Usage:**\n\n```typescript\n// Step 1: Search for index\nsearch(query=\"authentication bug\", type=\"bugfix\", limit=10)\n\n// Step 2: Review index, identify relevant IDs (e.g., #123, #456)\n\n// Step 3: Fetch full details\nget_observations(ids=[123, 456])\n```\n\nSee [Search Tools Guide](https://docs.claude-mem.ai/usage/search-tools) for detailed examples.\n\n---\n\n## Beta Features\n\nClaude-Mem offers a **beta channel** with experimental features like **Endless Mode** (biomimetic memory ",
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"readme_content": "# herdr\n\n\n
\n\n---\n\nhttps://github.com/user-attachments/assets/043ec09f-4bdd-41d5-aee0-8fda6b83e267\n\n**agent multiplexer that lives in your terminal.**\n\nworkspaces, tabs, panes. mouse-native: click, drag, split. every agent at a glance: blocked, working, done. detach and reattach, agents keep running. no gui app, no electron, no mac-only native wrapper. you see the agent's own terminal, not someone's interpretation of it.\n\n---\n\n## install\n\n```bash\ncurl -fsSL https://herdr.dev/install.sh | sh\n```\n\non windows preview beta:\n\n```powershell\npowershell -ExecutionPolicy Bypass -c \"irm https://herdr.dev/install.ps1 | iex\"\n```\n\nor install with homebrew:\n\n```bash\nbrew install herdr\n```\n\nor install with mise:\n\n```bash\nmise use -g herdr\n```\n\nif mise reports `herdr not found in mise tool registry`, update mise and retry. older mise versions predate the herdr registry entry; `mise use -g github:ogulcancelik/herdr` works as a temporary fallback.\n\nor download the stable Linux/macOS binary from [releases](https://github.com/ogulcancelik/herdr/releases). Native Windows binaries are preview-only beta builds.\n\n## quick start\n\nStart Herdr in the directory where the work lives:\n\n```bash\nherdr\n```\n\nHerdr starts or attaches to one background session server. When a session has no workspaces, Herdr opens one automatically. Run an agent in the root pane. Press `ctrl+b`, then `shift+n` to create another workspace, `ctrl+b`, then `v` or `minus` to split panes, `ctrl+b`, then `c` to create a tab, and `ctrl+b`, then `w` to switch workspaces.\n\nPress `ctrl+b q` to detach the client. The server and pane processes keep running. Open another terminal and run `herdr` again to reattach.\n\n## core concepts\n\n**Server and client.** By default, `herdr` attaches to a background server. Detaching closes only the client. `herdr server stop` stops the default server and kills its panes. Named sessions are separate server namespaces: use `herdr session attach work`, `herdr session stop work`, and `herdr session list` when you want fully separate runtime state.\n\n**Workspaces, tabs, panes.** A workspace is the project-level container. Tabs group panes inside a workspace. Panes are real terminal processes, not rewritten agent views.\n\n**Copy.** Herdr copies pane text, not the sidebar. Drag-select inside a pane, double-click a word or token, or press `prefix+[` for keyboard copy mode. In copy mode, move with `h/j/k/l`, `w/b/e`, and `{`/`}`, start selection with `v` or Space, copy with `y` or Enter, and leave with `q` or Esc. In PuTTY and some SSH terminals, hold `Shift` while dragging to use the terminal's own selection, and `Shift` + right click to paste.\n\n**Update and restore.** `herdr update` installs a new binary, but a running server keeps using the old process until it is stopped or handed off. Stop the old server to use the new version. Stopping exits pane processes. Run `herdr server stop`, then run `herdr` again for the default session. For a named session, run `herdr session stop `, then run `herdr session attach ` again. `herdr update --handoff` is experimental and tries to move live panes, including foreground processes such as dev servers, from the old server to the new one. With current official integrations installed, supported agent panes can restart from their native agent sessions after a server restart or update.\n\n**Keybindings.** Herdr uses explicit keybinding strings. `prefix+n` means press the configured prefix, then `n`. `ctrl+alt+n`, `cmd+k`, `alt+1`, and function-key chords are direct terminal-mode shortcuts and do not need the prefix. Plain direct printable keys such as `n` steal normal typing, so use `prefix+n` unless you intentionally want a modifier-gated direct binding.\n\n**Agent awareness.** The sidebar shows blocked, working, done, and idle states. Detection works with process names and terminal output by default. Official integrations can add native session identity for restore, semantic state reports, or both.\n\n## update\n\nHerdr notifies you when a new version is available. Run manually:\n\n```bash\nherdr update\n```\n\n`herdr update` is for installs managed by Herdr's own installer. Homebrew, mise, and Nix installs update through `brew upgrade herdr`, `mise upgrade herdr`, or your Nix workflow, then use the same stop-and-run-again flow if a session is still running the old server. Linux and macOS direct installs can opt into development preview builds with `herdr channel set preview` and return to stable with `herdr channel set stable`. Windows beta installs are preview-only for now. See [install docs](https://herdr.dev/docs/install/) and [session state docs](https://herdr.dev/docs/session-state/) for the full update, restart, restore, and handoff matrix.\n\nLinux and macOS direct installs use the stable update channel by default. Windows beta installs default to preview. To test preview builds from `master` before the next stable release:\n\n```bash\nherdr channel set preview\n```\n\nTo return Linux and macOS direct installs to stable:\n\n```bash\nherdr channel set stable\n```\n\nFor direct installs, changing channels also checks that channel and installs its latest binary. If that update fails, run `herdr update` to retry from the configured channel.\n\nPreview is only for direct installs managed by Herdr's updater. Homebrew, mise, and Nix stay on stable and update through their package managers.\n\n## how it compares\n\n| | tmux | gui managers | herdr |\n|--------------------------|------|--------------|-------|\n| persistent sessions | ✓ | — | ✓ |\n| detach / reattach | ✓ | — | ✓ |\n| panes, tabs, workspaces | ✓ | ✓ | ✓ |\n| agent awareness | — | ✓ | ✓ |\n| lives in your terminal | ✓ | — | ✓ |\n| real terminal views | ✓ | — | ✓ |\n| mouse-native | — | ✓ | ✓ |\n| lightweight binary | ✓ | — | ✓ |\n| agents can orchestrate | ? | ? | ✓ |\n\ntmux gives you persistence and panes, but it was built before agents existed. gui managers show agent state, but they make you leave your terminal and use their wrapped view. herdr is persistence and awareness in one tool that stays out of your way.\n\n## remote and attach\n\nHerdr works over normal SSH. Run it on the remote host, detach, and reattach later:\n\n```\nssh you@yourserver\nherdr\n```\n\nYou can also attach from your local terminal without opening a shell first:\n\n```bash\nherdr --remote workbox\nherdr --remote ssh://you@yourserver:2222\n```\n\nRemote attach adds fallback SSH keepalives by default while preserving your own SSH config. Set `[remote].manage_ssh_config = false` to use plain `ssh`.\n\nDirect attach connects your current terminal to one server-owned terminal:\n\n```bash\nherdr agent attach \nherdr terminal attach \n```\n\nSee [persistence and remote docs](https://herdr.dev/docs/persistence-remote/) for remote keybinding, named-session, and handoff details.\n\n## agent awareness\n\nthe sidebar shows which agents are blocked, working, or done. workspaces roll up to their most urgent state so you can scan the full list at a glance.\n\nstates:\n\n- 🔴 **blocked** — agent needs input or approval\n- 🟡 **working** — agent is actively running\n- 🔵 **done** — work finished, you have not looked at it yet\n- 🟢 **idle** — done and seen\n\ndetection works by reading foreground process and terminal output. zero config, no hooks required. official claude code, codex, github copilot cli, devin, droid, kimi code cli, qodercli, and cursor agent cli integrations provide session restore identity; pi, omp, kimi code cli, opencode, kilo code cli, hermes, and custom socket integrations can report their own state.\n\n## lives in your terminal\n\nnot a gui window, not a web dashboard, not electron. herdr runs inside whatever terminal you already use. single rust binary, no dependencies. works inside tmux as the outer terminal environment.\n\n## what you get\n\n- **workspaces** — organized around git repos or folder names, each with its own tabs and panes\n- **tabs** — first-class in the socket api and cli\n- **copy-friendly** — drag-select pane text, double-click tokens, or use keyboard copy mode with `prefix+[`, `h/j/k/l`, `{`/`}`, `v`, and `y`\n- **notifications** — sounds and toasts for background events; tab-aware suppression\n- **18 built-in themes** — catppuccin, terminal, tokyo night, gruvbox, one, solarized, kanagawa, rosé pine, vesper, and light variants for the main palettes\n- **session persistence** — pane processes survive client detach; sessions restore panes after full restart, with opt-in recent screen history\n\n## agents can use herdr too\n\nThe local Unix socket lets agents create workspaces, split or zoom panes, spawn helpers, read output, and wait for state changes. Start with the [socket API docs](https://herdr.dev/docs/socket-api/) and [`SKILL.md`](./SKILL.md).\n\n## supported agents\n\nautomatic detection works out of the box. process name matching plus terminal output heuristics.\n\n| agent | idle / done | working | blocked |\n|-------|-------------|---------|---------|\n| [pi](https://pi.dev) | ✓ | ✓ | partial |\n| [claude code](https://docs.anthropic.com/en/docs/claude-code) | ✓ | ✓ | ✓ |\n| [codex](https://github.com/openai/codex) | ✓ | ✓ | ✓ |\n| [droid](https://factory.ai) | ✓ | ✓ | ✓ |\n| [amp](https://ampcode.com) | ✓ | ✓ | ✓ |\n| [opencode](https://github.com/anomalyco/opencode) | ✓ | ✓ | ✓ |\n| [grok cli](https://x.ai/grok) | ✓ | ✓ | ✓ |\n| [hermes agent](https://github.com/NousResearch/hermes-agent) | ✓ | ✓ | ✓ |\n| [kilo code cli](https://kilo.ai/) | ✓ | ✓ | ✓ |\n| [devin cli](https://docs.devin.ai/cli) | ✓ | ✓ | ✓ |\n| cursor agent | ✓ | ✓ | ✓ |\n| antigravity cli | ✓ | ✓ | ✓ |\n| kimi code cli | ✓ | ✓ | ✓ |\n| [github copilot cli](https://github.com/features/copilot) | ✓ | ✓ | ✓ |\n| [qodercli](https://qoder.com/cli) | ✓ | ✓ | ✓ |\n| [kiro cli](https://kiro.dev/docs/cli/) | ✓ | ✓ | — |\n\ndetected but not fully tested: gemini cli, cline.\n\nfor agents outside the built-in list, herdr still works as a terminal multiplexer with workspaces, panes, and tiling. custom integrations can report agent labels over the socket api. see the [socket api docs](https://herdr.dev/docs/socket-api/).\n\n### direct integrations\n\nofficial integrations have two roles. claude code, codex, github copilot cli, devin, droid, qodercli, and cursor agent cli report session identity for native restore, while their state still comes from screen detection. pi, omp, kimi code cli, opencode, kilo code cli, and hermes report both semantic state and session identity. install with:\n\n```bash\nherdr integration install pi\nherdr integration install omp\nherdr integration install claude\nherdr integration install codex\nherdr integration install copilot\nherdr integration install devin\nherdr integration install droid\nherdr integration install kimi\nherdr integration install opencode\nherdr integration install kilo\nherdr integration install hermes\nherdr integration install qodercli\nherdr integration install cursor\n```\n\nsee the [integrations docs](https://herdr.dev/docs/integrations/) for setup details.\n\n## keybindings\n\nPress `ctrl+b` to enter prefix mode. Default actions are prefix-first and tmux-like:\n\n| key | action |\n|-----|--------|\n| `prefix+c` | new tab |\n| `prefix+n` / `prefix+p` | next / previous tab |\n| `prefix+1..9` | switch tab |\n| `prefix+w` | workspace navigation |\n| `prefix+g` | session navigator |\n| `prefix+shift+n` |",
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\n\nType `/graphify` in your AI coding assistant and it maps your entire project — code, docs, PDFs, images, videos — into a knowledge graph you can query instead of grepping through files.\n\nWorks in Claude Code, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Amp, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, Devin CLI, and Google Antigravity.\n\n```\n/graphify .\n```\n\nThat's it. You get three files:\n\n```\ngraphify-out/\n├── graph.html open in any browser — click nodes, filter, search\n├── GRAPH_REPORT.md the highlights: key concepts, surprising connections, suggested questions\n└── graph.json the full graph — query it anytime without re-reading your files\n```\n\nFor a readable architecture page with Mermaid call-flow diagrams, run:\n\n```bash\ngraphify export callflow-html\n```\n\n---\n\n## Prerequisites\n\n| Requirement | Minimum | Check | Install |\n|---|---|---|---|\n| Python | 3.10+ | `python --version` | [python.org](https://www.python.org/downloads/) |\n| uv *(recommended)* | any | `uv --version` | `curl -LsSf https://astral.sh/uv/install.sh \\| sh` |\n| pipx *(alternative)* | any | `pipx --version` | `pip install pipx` |\n\n**macOS quick install (Homebrew):**\n```bash\nbrew install python@3.12 uv\n```\n\n**Windows quick install:**\n```powershell\nwinget install astral-sh.uv\n```\n\n**Ubuntu/Debian:**\n```bash\nsudo apt install python3.12 python3-pip pipx\n# or install uv:\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n---\n\n## Install\n\n> **Official package:** The PyPI package is `graphifyy` (double-y). Other `graphify*` packages on PyPI are not affiliated. The CLI command is still `graphify`.\n\n**Step 1 — install the package:**\n\n```bash\n# Recommended (uv puts graphify on PATH automatically):\nuv tool install graphifyy\n\n# Alternatives:\npipx install graphifyy\npip install graphifyy # may need PATH setup — see note below\n```\n\n**Step 2 — register the skill with your AI assistant:**\n\n```bash\ngraphify install\n```\n\nThat's it. Open your AI assistant and type `/graphify .`\n\nTo install the assistant skill into the current repository instead of your user\nprofile, add `--project`:\n\n```bash\ngraphify install --project\ngraphify install --project --platform codex\n```\n\nProject-scoped installs write under the current directory, for example\n`.claude/skills/graphify/SKILL.md` or `.agents/skills/graphify/SKILL.md` (plus a\n`references/` sidecar the skill loads on demand), and\nprint a `git add` hint for files that can be committed.\nPer-platform commands that support project-scoped installs accept the same flag,\nfor example `graphify claude install --project` or `graphify codex install --project`.\n\n> **PowerShell note:** Use `graphify .` not `/graphify .` — the leading slash is a path separator in PowerShell.\n\n> **`graphify: command not found`?** Use `uv tool install graphifyy` or `pipx install graphifyy` — both put the CLI on PATH automatically. With plain `pip`, add `~/.local/bin` (Linux) or `~/Library/Python/3.x/bin` (Mac) to your PATH, or run `python -m graphify`.\n\n> **Avoid `pip install` on Mac/Windows** if possible. The skill resolves Python at runtime from `graphify-out/.graphify_python`; if that points to a different environment than where `pip` installed the package, you'll get `ModuleNotFoundError: No module named 'graphify'`. `uv tool install` and `pipx install` isolate the package in their own env and avoid this entirely.\n\n> **Git hooks and uv tool / pipx:** `graphify hook install` embeds the current interpreter path directly into the hook scripts at install time, so the post-commit hook fires correctly even in GUI git clients and CI runners where `~/.local/bin` is not on PATH. If you reinstall or upgrade graphify, re-run `graphify hook install` to refresh the embedded path.\n\n### Pick your platform\n\n| Platform | Install command |\n|----------|----------------|\n| Claude Code (Linux/Mac) | `graphify install` |\n| Claude Code (Windows) | `graphify install` (auto-detected) or `graphify install --platform windows` |\n| CodeBuddy | `graphify install --platform codebuddy` |\n| Codex | `graphify install --platform codex` |\n| OpenCode | `graphify install --platform opencode` |\n| Kilo Code | `graphify install --platform kilo` |\n| GitHub Copilot CLI | `graphify install --platform copilot` |\n| VS Code Copilot Chat | `graphify vscode install` |\n| Aider | `graphify install --platform aider` |\n| OpenClaw | `graphify install --platform claw` |\n| Factory Droid | `graphify install --platform droid` |\n| Trae | `graphify install --platform trae` |\n| Trae CN | `graphify install --platform trae-cn` |\n| Gemini CLI | `graphify install --platform gemini` |\n| Hermes | `graphify install --platform hermes` |\n| Kimi Code | `graphify install --platform kimi` |\n| Amp | `graphify amp install` |\n| Agent Skills (cross-framework) | `graphify install --platform agents` (alias `--platform skills`) |\n| Kiro IDE/CLI | `graphify kiro install` |\n| Pi coding agent | `graphify install --platform pi` |\n| Cursor | `graphify cursor install` |\n| Devin CLI | `graphify devin install` |\n| Google Antigravity | `graphify antigravity install` |\n\nCodex users also need `multi_agent = true` under `[features]` in `~/.codex/config.toml` for parallel extraction. CodeBuddy uses the same Agent tool and PreToolUse hook mechanism as Claude Code. Factory Droid uses the `Task` tool for parallel subagent dispatch. OpenClaw and Aider use sequential extraction (parallel agent support is still early on those platforms). Trae uses the Agent tool for parallel subagent dispatch and does **not** support PreToolUse hooks — AGENTS.md is the always-on mechanism.\n\n`--platform agents` (alias `--platform skills`) targets the generic cross-framework [Agent-Skills](https://github.com/anthropics/skills) locations: the spec's user-global `~/.agents/skills/` (read by `npx skills` and spec-compliant frameworks) for a global install, and `./.agents/skills/` for a project (`--project`) install. The bare `graphify install` stays single-platform (Claude Code) by design — use the named `agents` platform when you want the skill discoverable by any framework that reads `.agents/skills`.\n\n> Codex uses `$graphify` instead of `/graphify`.\n\n### Optional extras\n\nInstall only what you need:\n\n| Extra | What it adds | Install |\n|---|---|---|\n| `pdf` | PDF extraction | `uv tool install \"graphifyy[pdf]\"` |\n| `office` | `.docx` and `.xlsx` support | `uv tool install \"graphifyy[office]\"` |\n| `google` | Google Sheets rendering | `uv tool install \"graphifyy[google]\"` |\n| `video` | Video/audio transcription (faster-whisper + yt-dlp) | `uv tool install \"graphifyy[video]\"` |\n| `mcp` | MCP stdio server | `uv tool install \"graphifyy[mcp]\"` |\n| `neo4j` | Neo4j push support | `uv tool install \"graphifyy[neo4j]\"` |\n| `falkordb` | FalkorDB push support | `uv tool install \"graphifyy[falkordb]\"` |\n| `svg` | SVG graph export | `uv tool install \"graphifyy[svg]\"` |\n| `leiden` | Leiden community detection (Python < 3.13 only) | `uv tool install \"graphifyy[leiden]\"` |\n| `ollama` | Ollama local inference | `uv tool install \"graphifyy[ollama]\"` |\n| `openai` | OpenAI / OpenAI-compatible APIs | `uv tool install \"graphifyy[openai]\"` |\n| `gemini` | Google Gemini API | `uv tool install \"graphifyy[gemini]\"` |\n| `anthropic` | Anthropic Claude API (`--backend claude`, uses `ANTHROPIC_API_KEY`) | `uv tool install \"graphifyy[anthropic]\"` |\n| `bedrock` | AWS Bedrock (uses IAM, no API key) | `uv tool install \"graphifyy[bedrock]\"` |\n| `azure` | Azure OpenAI Service (`--backend azure`, uses `AZURE_OPENAI_API_KEY` + `AZURE_OPENAI_ENDPOINT`) | `uv tool install \"graphifyy[openai]\"` |\n| `sql` | SQL schema extraction | `uv tool install \"graphifyy[sql]\"` |\n| `postgres` | Live PostgreSQL introspection (`--postgres DSN`) | `uv tool install \"graphifyy[postgres]\"` |\n| `dm` | BYOND DreamMaker `.dm`/`.dme` AST extraction (may need a C compiler + `python3-dev` if no wheel matches your platform) | `uv tool install \"graphifyy[dm]\"` |\n| `terraform` | Terraform / HCL `.tf`/`.tfvars`/`.hcl` AST extraction | `uv tool install \"graphifyy[terraform]\"` |\n| `chinese` | Chinese query segmentation (jieba) | `uv tool install \"graphifyy[chinese]\"` |\n| `all` | Everything above | `uv tool install \"graphifyy[all]\"` |\n\n---\n\n## Make your assistant always use the graph\n\nRun this once in your project after building a graph:\n\n| Platform | Command |\n|----------|---------|\n| C",
"manifest_file": "pyproject.toml",
"manifest_content": "[build-system]\nrequires = [\"setuptools>=68\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"graphifyy\"\nversion = \"0.8.50\"\ndescription = \"AI coding assistant skill (Claude Code, CodeBuddy, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, Aider, OpenClaw, Factory Droid, Trae, Hermes, Kiro, Pi, Devin CLI, Google Antigravity) - turn any folder of code, docs, papers, images, or videos into a queryable knowledge graph\"\nreadme = \"README.md\"\nlicense = { file = \"LICENSE\" }\nkeywords = [\"claude\", \"claude-code\", \"codex\", \"opencode\", \"kilo\", \"cursor\", \"gemini\", \"aider\", \"kiro\", \"pi\", \"devin\", \"knowledge-graph\", \"rag\", \"graphrag\", \"obsidian\", \"community-detection\", \"tree-sitter\", \"leiden\", \"llm\"]\nrequires-python = \">=3.10\"\ndependencies = [\n \"networkx>=3.4\",\n \"numpy>=1.21\",\n \"rapidfuzz>=3.0\",\n \"tree-sitter>=0.23.0,<0.26\",\n \"tree-sitter-python>=0.23,<0.26\",\n \"tree-sitter-javascript>=0.23,<0.26\",\n \"tree-sitter-typescript>=0.23,<0.25\",\n \"tree-sitter-go>=0.23,<0.26\",\n \"tree-sitter-rust>=0.23,<0.25\",\n \"tree-sitter-java>=0.23,<0.25\",\n \"tree-sitter-groovy>=0.1,<0.3\",\n \"tree-sitter-c>=0.23,<0.25\",\n \"tree-sitter-cpp>=0.23,<0.25\",\n \"tree-sitter-ruby>=0.23,<0.25\",\n \"tree-sitter-c-sharp>=0.23,<0.25\",\n \"tree-sitter-kotlin>=1.0,<2.0\",\n \"tree-sitter-scala>=0.23,<0.27\",\n \"tree-sitter-php>=0.23,<0.25\",\n \"tree-sitter-swift>=0.7,<0.9\",\n \"tree-sitter-lua>=0.2,<0.6\",\n \"tree-sitter-zig>=1.0,<2.0\",\n \"tree-sitter-powershell>=0.26,<0.28\",\n \"tree-sitter-elixir>=0.3,<0.5\",\n \"tree-sitter-objc>=3.0,<4.0\",\n \"tree-sitter-julia>=0.23,<0.25\",\n \"tree-sitter-verilog>=1.0,<2.0\",\n \"tree-sitter-fortran>=0.6,<0.8\",\n \"tree-sitter-bash>=0.23,<0.27\",\n \"tree-sitter-json>=0.23,<0.26\",\n]\n\n[project.urls]\nHomepage = \"https://github.com/safishamsi/graphify\"\nRepository = \"https://github.com/safishamsi/graphify\"\nIssues = \"https://github.com/safishamsi/graphify/issues\"\n\n[project.optional-dependencies]\n# starlette is pulled in transitively by mcp, but graphify/serve.py imports it\n# directly for the HTTP transport, so declare it here and floor it above the\n# CVE-2026-48818 / CVE-2026-54283 fixes (both resolved by 1.3.1) (#1391, #1396).\nmcp = [\"mcp\", \"starlette>=1.3.1\"]\nneo4j = [\"neo4j\"]\nfalkordb = [\"falkordb\"]\npdf = [\"pypdf>=6.12.0\", \"markdownify\"]\nwatch = [\"watchdog\"]\nsvg = [\"matplotlib\", \"numpy>=2.0; python_version >= '3.13'\"]\nleiden = [\"graspologic; python_version < '3.13'\"]\noffice = [\"python-docx\", \"openpyxl\"]\ngoogle = [\"openpyxl\"]\npostgres = [\"psycopg[binary]\"]\nvideo = [\"faster-whisper; python_version >= '3.11'\", \"yt-dlp>=2026.6.9\"]\nkimi = [\"openai\", \"tiktoken\"]\nollama = [\"openai\"]\nbedrock = [\"boto3\"]\nanthropic = [\"anthropic\"]\ngemini = [\"openai\", \"tiktoken\"]\nopenai = [\"openai\", \"tiktoken\"]\nchinese = [\"jieba\"]\nsql = [\"tree-sitter-sql\"]\n# tree-sitter-dm (BYOND DreamMaker) ships only a Windows wheel, so on Linux/Mac it\n# must compile from source (needs a C toolchain + python3-dev). Keeping it optional\n# avoids breaking the default `uv tool install graphifyy` for everyone (#1104).\ndm = [\"tree-sitter-dm\"]\nterraform = [\"tree-sitter-hcl\"]\nall = [\"mcp\", \"starlette>=1.3.1\", \"neo4j\", \"falkordb\", \"pypdf>=6.12.0\", \"markdownify\", \"watchdog\", \"graspologic; python_version < '3.13'\", \"python-docx\", \"openpyxl\", \"faster-whisper; python_version >= '3.11'\", \"yt-dlp>=2026.6.9\", \"matplotlib\", \"numpy>=2.0; python_version >= '3.13'\", \"openai\", \"tiktoken\", \"boto3\", \"anthropic\", \"tree-sitter-sql\", \"jieba\", \"tree-sitter-dm\", \"tree-sitter-hcl\"]\n\n[project.scripts]\ngraphify = \"graphify.__main__:main\"\ngraphify-mcp = \"graphify.serve:_main\"\n\n[dependency-groups]\ndev = [\n \"bandit>=1.9.4\",\n \"build>=1.5.0\",\n \"hypothesis>=6.152.7\",\n \"nuitka>=4.1\",\n \"patchelf>=0.17.2.4 ; sys_platform != 'win32'\",\n \"pip-audit>=2.10.0\",\n \"pre-commit>=4.6.0\",\n \"pyright>=1.1.409\",\n \"pytest>=9.0.3\",\n \"pytest-cov>=7.1.0\",\n \"ruff>=0.15.13\",\n \"safety>=3.7.0\",\n \"setuptools>=82.0.1\",\n \"wheel>=0.47.0\",\n \"tomli>=2.0 ; python_version < '3.11'\",\n \"tree-sitter-hcl>=1.2.0\",\n]\n\n[tool.uv]\n# Install via: uv tool install graphifyy\n# Run without installing: uvx graphifyy install\npackage = true\n\n[tool.setuptools]\npackages = [\"graphify\", \"graphify.extractors\"]\ninclude-package-data = false\n\n[tool.setuptools.package-data]\n# The skill bodies ship as graphify/skill*.md (the SKILL.md a host installs is\n# copied from one of these). The progressive-disclosure references sidecar ships\n# under graphify/skills//references/, and the always-on injection blocks\n# under graphify/always_on/. There is no graphify/skills//SKILL.md in the\n# repo, so no SKILL.md glob is needed here.\ngraphify = [\"skill.md\", \"skill-codex.md\", \"skill-opencode.md\", \"skill-kilo.md\", \"command-kilo.md\", \"skill-aider.md\", \"skill-amp.md\", \"skill-agents.md\", \"skill-copilot.md\", \"skill-claw.md\", \"skill-windows.md\", \"skill-droid.md\", \"skill-trae.md\", \"skill-kiro.md\", \"skill-vscode.md\", \"skill-pi.md\", \"skill-devin.md\", \"skills/*/references",
"strategic_keywords": [
"memory",
"rag",
"skill",
"llm",
"workflow"
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"relationship_label": "Memory 组件",
"data_confidence": "high",
"evidence_sources": [
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"github_repo_api",
"readme",
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"score_breakdown": {
"heat": 18,
"relevance": 20,
"novelty": 11,
"productize": 14,
"adoption": 10,
"relation": 10,
"risk_signal": 8,
"total": 91
},
"strategic_score": 91
},
{
"owner": "MemPalace",
"name": "mempalace",
"full_name": "MemPalace/mempalace",
"url": "https://github.com/MemPalace/mempalace",
"description": "The best-benchmarked open-source AI memory system. And it's free.",
"language": "Python",
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"metadata": {
"topics": [
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"llm",
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"memory",
"python"
],
"license": "MIT",
"open_issues": 572,
"created_at": "2026-04-05T01:12:07Z",
"pushed_at": "2026-06-26T07:53:47Z",
"homepage": "http://mempalaceofficial.com/",
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"readme_content": "
\n\n\n\n# MemPalace\n\nLocal-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.\n\n[![][version-shield]][release-link]\n[![][python-shield]][python-link]\n[![][license-shield]][license-link]\n[![][discord-shield]][discord-link]\n\n
\n\n> [!CAUTION]\n> **Beware of impostor sites.** MemPalace has no other official websites. The **only** official sources are this **[GitHub repository](https://github.com/MemPalace/mempalace)**, the **[PyPI package](https://pypi.org/project/mempalace/)**, and the docs at **[mempalaceofficial.com](https://mempalaceofficial.com)**. Any other domain (including `.tech`, `.net`, or other `.com` variants) is an impostor and may distribute malware. Details and timeline: [docs/HISTORY.md](docs/HISTORY.md).\n\n> [!IMPORTANT]\n> **Claude Code sessions expire in 30 days without auto-save hooks wired.** [Read this →](https://github.com/MemPalace/mempalace/discussions/1388)\n>\n> Need the shortest recovery/setup path? Use the [Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html).\n\n---\n\n## What it is\n\nMemPalace stores your conversation history as verbatim text and retrieves\nit with semantic search. It does not summarize, extract, or paraphrase.\nThe index is structured — people and projects become *wings*, topics\nbecome *rooms*, and original content lives in *drawers* — so searches\ncan be scoped rather than run against a flat corpus.\n\nThe retrieval layer is pluggable. The current default is ChromaDB; the\ninterface is defined in [`mempalace/backends/base.py`](mempalace/backends/base.py)\nand alternative backends can be dropped in without touching the rest of\nthe system.\n\nNothing leaves your machine unless you opt in.\n\nArchitecture, concepts, and mining flows:\n[mempalaceofficial.com/concepts/the-palace](https://mempalaceofficial.com/concepts/the-palace.html).\n\n---\n\n## Install\n\nMemPalace ships a CLI, so install it in an isolated environment to avoid\nPEP 668 errors on Debian/Ubuntu/Homebrew Pythons and to keep mempalace's\ndeps (`chromadb`, `numpy`, `grpcio`, …) from conflicting with anything\nelse in your global site-packages.\n\nWe recommend [`uv`](https://docs.astral.sh/uv/) — `uv tool install` puts\nthe `mempalace` CLI in an isolated environment on your PATH:\n\n```bash\nuv tool install mempalace\nmempalace init ~/projects/myapp\n```\n\n[`pipx`](https://pipx.pypa.io/) works the same way if you prefer it:\n`pipx install mempalace`.\n\nPrefer plain `pip` only inside an activated virtualenv where you\nexplicitly want `import mempalace` available:\n\n```bash\npython -m venv .venv && source .venv/bin/activate\npip install mempalace\n```\n\n### Docker\n\nA container image is also available for running the MCP server or the CLI\nwithout a local Python toolchain. Everything persists under `/data` (palace,\nconfig, and the cached embedding model), so mount a volume there.\n\n```bash\n# Build the image (CPU; bundles the `extract` + `spellcheck` extras)\ndocker build -t mempalace .\n\n# MCP server over stdio — note the `-i` flag (JSON-RPC needs stdin)\ndocker run -i --rm -v mempalace-data:/data mempalace\n\n# Run any CLI command instead (mount the host directory you want to mine)\ndocker run --rm -v mempalace-data:/data -v /path/to/project:/work mempalace mine /work\ndocker run --rm -v mempalace-data:/data mempalace search \"why GraphQL\"\n```\n\nWire it into an MCP client (e.g. Claude Code) as a stdio server:\n\n```json\n{\n \"mcpServers\": {\n \"mempalace\": {\n \"command\": \"docker\",\n \"args\": [\"run\", \"-i\", \"--rm\", \"-v\", \"mempalace-data:/data\", \"mempalace\"]\n }\n }\n}\n```\n\n`docker compose run --rm mcp` works too (see `docker-compose.yml`). For\nCUDA-accelerated embeddings, build the GPU variant with\n`docker build -f Dockerfile.gpu -t mempalace:gpu .` and run it with\n`--gpus all`. Customise the bundled extras at build time, e.g.\n`docker build --build-arg EXTRAS=\"extract,spellcheck\" -t mempalace .`.\n\n## Storage backends\n\nChromaDB is the default. For the pluggable-backend preview, MemPalace also\nships `sqlite_exact` for local exact-vector correctness checks, and two opt-in\nexternal service backends — `qdrant` (REST) and `pgvector` (Postgres). The two\nexternal backends exercise the storage contract on different substrates (a\nREST/dict store and a SQL/JSONB store), so it is not accidentally shaped around\none vendor.\n\n```bash\n# local no-service backend\nmempalace mine ~/projects/myapp --backend sqlite_exact\n\n# Qdrant backend, defaulting to http://localhost:6333\nMEMPALACE_QDRANT_URL=http://localhost:6333 \\\n mempalace mine ~/projects/myapp --backend qdrant\n\n# Postgres + pgvector backend, defaulting to postgresql://localhost:5432/mempalace\n# needs the optional driver: pip install mempalace[pgvector]\n# and the `vector` extension available on the server\nMEMPALACE_PGVECTOR_DSN=postgresql://localhost:5432/mempalace \\\n mempalace mine ~/projects/myapp --backend pgvector\n```\n\nQdrant can also be configured with `MEMPALACE_QDRANT_API_KEY`,\n`MEMPALACE_QDRANT_NAMESPACE`, and `MEMPALACE_QDRANT_TIMEOUT`; pgvector with\n`MEMPALACE_PGVECTOR_NAMESPACE`. Both external backends isolate tenants by\nnamespace (advertised via the `supports_namespace_isolation` capability) and\nwrite a local marker (`qdrant_backend.json` / `pgvector_backend.json`) to guard\nagainst silently opening a palace against the wrong server.\n\nWhen `MEMPALACE_QDRANT_URL` or `MEMPALACE_PGVECTOR_DSN` points anywhere other\nthan your own local or trusted self-hosted service, MemPalace will send and\nstore verbatim drawer text and metadata there. That is an explicit opt-in\nbackend choice, never the default.\n\n## Quickstart\n\n```bash\n# Mine content into the palace\nmempalace mine ~/projects/myapp # project files\nmempalace mine ~/.claude/projects/ --mode convos # Claude Code sessions (scope with --wing per project)\n\n# Search\nmempalace search \"why did we switch to GraphQL\"\n\n# Load context for a new session\nmempalace wake-up\n```\n\nFor Claude Code, Gemini CLI, [Antigravity](https://mempalaceofficial.com/guide/antigravity.html),\nMCP-compatible tools, and local models, see\n[mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html).\n\n---\n\n## Benchmarks\n\nAll numbers below are reproducible from this repository with the commands\nin [`benchmarks/BENCHMARKS.md`](benchmarks/BENCHMARKS.md). Full\nper-question result files are committed under `benchmarks/results_*`.\n\n**LongMemEval — retrieval recall (R@5, 500 questions):**\n\n| Mode | R@5 | LLM required |\n|---|---|---|\n| Raw (semantic search, no heuristics, no LLM) | **96.6%** | None |\n| Hybrid v4, held-out 450q (tuned on 50 dev, not seen during training) | **98.4%** | None |\n| Hybrid v4 + LLM rerank (full 500) | ≥99% | Any capable model |\n\nThe raw 96.6% requires no API key, no cloud, and no LLM at any stage. The\nhybrid pipeline adds keyword boosting, temporal-proximity boosting, and\npreference-pattern extraction; the held-out 98.4% is the honest\ngeneralisable figure.\n\nThe rerank pipeline promotes the best candidate out of the top-20\nretrieved sessions using an LLM reader. It works with any reasonably\ncapable model — we have reproduced it with Claude Haiku, Claude Sonnet,\nand minimax-m2.7 via Ollama Cloud (no Anthropic dependency). The gap\nbetween raw and reranked is model-agnostic; we do not headline a \"100%\"\nnumber because the last 0.6% was reached by inspecting specific wrong\nanswers, which `benchmarks/BENCHMARKS.md` flags as teaching to the test.\n\n**Other benchmarks (full results in [`benchmarks/BENCHMARKS.md`](benchmarks/BENCHMARKS.md)):**\n\n| Benchmark | Metric | Score | Notes |\n|---|---|---|---|\n| LoCoMo (session, top-10, no rerank) | R@10 | 60.3% | 1,986 questions |\n| LoCoMo (hybrid v5, top-10, no rerank) | R@10 | 88.9% | Same set |\n| ConvoMem (all categories, 250 items) | Avg recall | 92.9% | 50 per category |\n| MemBench (ACL 2025, 8,500 items) | R@5 | 80.3% | All categories |\n\nWe deliberately do not include a side-by-side comparison against Mem0,\nMastra, Hindsight, Supermemory, or Zep. Those projects publish different\nmetrics on different splits, and placing retrieval recall next to\nend-to-end QA accuracy is not an honest comparison. See each project's\nown research page for their published numbers.\n\n**Reproducing every result:**\n\n```bash\ngit clone https://github.com/MemPalace/mempalace.git\ncd mempalace\nuv sync --extra dev # or: pip install -e \".[dev]\"\n# see benchmarks/README.md for dataset download commands\nuv run python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json\n```\n\n---\n\n## Knowledge graph\n\nMemPalace includes a temporal entity-relationship graph with validity\nwindows — add, query, invalidate, timeline — backed by local SQLite.\nUsage and tool reference:\n[mempalaceofficial.com/concepts/knowledge-graph](https://mempalaceofficial.com/concepts/knowledge-graph.html).\n\n## MCP server\n\n35 MCP tools cover palace reads/writes, knowledge-graph operations,\ncross-wing navigation, drawer management, and agent diaries. Installation\nand the full tool list:\n[mempalaceofficial.com/reference/mcp-tools](https://mempalaceofficial.com/reference/mcp-tools.html).\n\n## Agents\n\nEach specialist agent gets its own wing and diary in the palace.\nDiscoverable at runtime via `mempalace_list_agents` — no bloat in your\nsystem prompt:\n[mempalaceofficial.com/concepts/agents](https://mempalaceofficial.com/concepts/agents.html).\n\n## Auto-save hooks\n\nAuto-save hooks for **Claude Code, Codex CLI, and Cursor IDE** save\nperiodically and before context compression:\n\n- Claude Code + Codex →\n [mempalaceofficial.com/guide/hooks](https://mempalaceofficial.com/guide/hooks.html)\n- Cursor IDE (adds session-start recall and a transcript snapshot before\n compaction) →\n [mempalaceofficial.com/guide/cursor-hooks](https://mempalaceofficial.com/guide/cursor-hooks.html)\n\nIf you are installing under time pressure, start with the\n[Claude Code retention setup checklist](https://mempalaceofficial.com/guide/claude-code-retention.html):\nwire the hooks, back up existing JSONL transcripts, and backfill them with\n`mempalace mine ~/.claude/projects/ --mode convos`.\n\nFor per-message recall on top of the file-level chunks the hooks produce,\nrun `mempalace sweep ` periodically — it stores one\nverbatim drawer per user/assistant message, idempotent and resume-safe.\n\n---\n\n## Requirements\n\n- Python 3.9+\n- A vector-store backend (ChromaDB by default)\n- ~300 MB disk for the embedding model. Onboarding (`python -m mempalace.onboarding`) offers `embeddinggemma-300m` (multilingual, 100+ languages, recommended) or `all-MiniLM-L6-v2` (English-only, ~30 MB). See the docstring at [`mempalace/embedding.py`](mempalace/embedding.py) for details and migration notes.\n\nNo API key is required for the core benchmark path.\n\n## Docs\n\n- Getting started → [mempalaceofficial.com/guide/getting-started](https://mempalaceofficial.com/guide/getting-started.html)\n- CLI reference → [mempalaceofficial.com/reference/cli](https://mempalaceofficial.com/reference/cli.html)\n- Python API → [mempalaceofficial.com/reference/python-api](https://mempalaceofficial.com/reference/python-api.html)\n- Full benchmark methodology → [benchmarks/BENCHMARKS.md](benchmarks/BENCHMARKS.md)\n- Release notes → [CHANGELOG.md](CHANGELOG.md)\n- Corrections and public notices → [docs/HISTORY.md](docs/HISTORY.md)\n\n## Contributing\n\nPRs welcome. See [CONTRIBUTING.md](CONTRIBUTING.md).\n\n## License\n\nMIT — see [LICENSE](LICENSE).\n\n\n[version-shield]: https://img.shields.io/badge/version-3.5.0-4dc9f6?style=flat-square&labelColor=0a0e14\n[release-link]: https://github.com/MemPalace/mempalace/releases\n[python-shield]: https://img.shields.io/badge/python-3.9+-7dd8f8?style=flat-square&labelColor=0a0e14&logo=python&logoColor=7dd8f8\n[python-link]: https://www.python.org/\n[license-shield]: https://img.shields.io/badge/license-MIT-b0e8ff?style=flat-square&labelColor=0a0e14\n[licens",
"manifest_file": "pyproject.toml",
"manifest_content": "[project]\nname = \"mempalace\"\nversion = \"3.5.0\"\ndescription = \"Give your AI a memory — mine projects and conversations into a searchable palace. No API key required.\"\nreadme = \"README.md\"\nrequires-python = \">=3.9\"\nlicense = \"MIT\"\nauthors = [\n {name = \"milla-jovovich\"},\n]\nkeywords = [\n \"ai\", \"memory\", \"llm\", \"rag\", \"chromadb\", \"mcp\",\n \"vector-database\", \"claude\", \"chatgpt\", \"embeddings\",\n]\nclassifiers = [\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Intended Audience :: Developers\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Programming Language :: Python :: 3.14\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n \"Topic :: Utilities\",\n]\ndependencies = [\n \"chromadb>=1.5.4,<2\",\n \"pyyaml>=6.0,<7\",\n \"tomli>=2.0.0; python_version < '3.11'\",\n # Multilingual embedding model deps. Onboarding now offers the\n # embeddinggemma-300m ONNX model as the default for new installs, so\n # these are required core deps (not optional extras). The 300 MB ONNX\n # model itself is lazy-downloaded on first use, not at install time.\n # Users who pick minilm during onboarding still get these installed\n # but unused — small wheel-size cost in exchange for one fewer pip\n # command in the multilingual setup path.\n \"huggingface_hub>=0.20\",\n \"tokenizers>=0.15\",\n \"numpy>=1.24\",\n # Tier 6a content-date extraction uses dateutil for natural-language\n # date parsing (filename + content body branches). It happens to install\n # transitively today via chromadb→kubernetes→python-dateutil, but that\n # chain is not a contract — declared explicitly here to ensure\n # _extract_content_date keeps working if upstream kubernetes drops the\n # dependency. Per PR #1584 review (Igor, 2026-05-22).\n \"python-dateutil>=2.8\",\n]\n\n[project.urls]\nHomepage = \"https://github.com/MemPalace/mempalace\"\nRepository = \"https://github.com/MemPalace/mempalace\"\n\"Bug Tracker\" = \"https://github.com/MemPalace/mempalace/issues\"\n\n[project.scripts]\nmempalace = \"mempalace.cli:main\"\nmempalace-mcp = \"mempalace.mcp_server:main\"\n\n[project.entry-points.\"mempalace.backends\"]\nchroma = \"mempalace.backends.chroma:ChromaBackend\"\npgvector = \"mempalace.backends.pgvector:PgVectorBackend\"\nqdrant = \"mempalace.backends.qdrant:QdrantBackend\"\nsqlite_exact = \"mempalace.backends.sqlite_exact:SQLiteExactBackend\"\n\n# RFC 002 source-adapter entry-point group. Core publishes no first-party\n# adapters under this group yet; ``miner.py`` and ``convo_miner.py`` migrate\n# onto ``BaseSourceAdapter`` in a follow-up PR. Third-party adapter packages\n# (``mempalace-source-cursor``, ``mempalace-source-git``, …) register here.\n[project.entry-points.\"mempalace.sources\"]\n\n[project.optional-dependencies]\ndev = [\n \"pytest>=7.0\",\n \"pytest-cov>=4.0\",\n # Retries known-transient ChromaDB-on-Windows HNSW compaction failures in CI\n # (wired only into the test-windows job in ci.yml, scoped to the specific\n # error). Local/Linux/macOS runs never rerun, so real failures stay loud.\n \"pytest-rerunfailures>=12.0\",\n \"ruff==0.15.18\",\n \"psutil>=5.9\",\n # Property-based testing — generates hundreds of random inputs per\n # test to find counterexamples the hand-written positive tests miss.\n # Used opt-in (sprinkle ``@given(...)`` on a test); zero runtime\n # cost on tests that don't use it.\n \"hypothesis>=6.0\",\n # Pre-commit framework — gates every local ``git commit`` on the\n # same ruff checks CI runs. Required activation step in\n # CONTRIBUTING.md: ``pre-commit install`` once per clone. Without\n # this, contributors silently bypass the lint gate locally and only\n # discover failures at CI time.\n \"pre-commit>=3.0\",\n # mypy — static type checker. Catches lazy ``list`` / ``dict``\n # annotations that should be ``list[str]`` / ``dict[str, int]``,\n # missing return types, mismatched signatures across call sites.\n # Run ad-hoc with ``mypy mempalace/``; CI doesn't gate on it yet but\n # contributors should fix mypy errors they see before pushing.\n \"mypy>=1.0\",\n]\nspellcheck = [\"autocorrect>=2.0\"]\n# Opt-in Postgres + pgvector backend. Only the psycopg driver is needed on the\n# client; the server must have the `vector` extension available. Selected via\n# MEMPALACE_BACKEND=pgvector / --backend pgvector; never required for the\n# default (Chroma) install.\npgvector = [\"psycopg[binary]>=3.1\"]\n# Hardware acceleration for the ONNX embedding model. Install exactly one:\n# pip install mempalace[gpu] — NVIDIA CUDA\n# pip install mempalace[dml] — DirectML (Windows AMD/Intel/NVIDIA)\n# pip install mempalace[coreml] — macOS Neural Engine\n# After install, set MEMPALACE_EMBEDDING_DEVICE=cuda|dml|coreml (or \"auto\").\ngpu = [\"onnxruntime-g",
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{
"owner": "HKUDS",
"name": "Vibe-Trading",
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"url": "https://github.com/HKUDS/Vibe-Trading",
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"quantitative-finance",
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\n\n---\n\n## 📰 News\n\n- **2026-06-27** 🧯 **Content-filter resilience + Shadow Account feature contract cleanup**: event-driven and swarm runs now skip individual LLM content-moderation hits, warn in run cards when filter rates are high, and recognize Gemini safety finish reasons instead of aborting an entire analysis ([#308](https://github.com/HKUDS/Vibe-Trading/pull/308), closes [#307](https://github.com/HKUDS/Vibe-Trading/issues/307), thanks @shadowinlife). Shadow Account extraction/codegen now share one `PRICE_FEATURES` contract and keep four-decimal return bounds, preventing rule/codegen drift and precision loss on `prior_5d_return` ([#316](https://github.com/HKUDS/Vibe-Trading/pull/316), thanks @Robin1987China).\n- **2026-06-26** 🎯 **Shadow Account conditional entry + tushare ETF/index/HK routing**: extracted Shadow Account rules now carry RSI / prior-return bounds, so the generated SignalEngine enters on real conditions (RSI in range, prior-return in range) instead of blindly replaying the holding cadence ([#314](https://github.com/HKUDS/Vibe-Trading/pull/314), follows [#302](https://github.com/HKUDS/Vibe-Trading/pull/302), thanks @Robin1987China). The tushare loader also routes ETF/LOF → `fund_daily()`, indices → `index_daily()`, and HK equities → `hk_daily()` instead of always calling `daily()` (which silently returns empty for non-stocks), with per-symbol empty-result + partial-fetch warnings ([#315](https://github.com/HKUDS/Vibe-Trading/pull/315), closes [#310](https://github.com/HKUDS/Vibe-Trading/issues/310), thanks @shadowinlife).\n- **2026-06-25** 🧪 **Strict validation JSON + calmer agent context**: standalone backtest validation now normalizes nested `NaN` / `Infinity` values before writing `artifacts/validation.json` or CLI stdout, so strict JSON parsers no longer choke on validation payloads ([#306](https://github.com/HKUDS/Vibe-Trading/pull/306), thanks @gyx09212214-prog). The agent prompt also derives the current data-source count from the loader registry, and `_microcompact()` now waits for real token pressure instead of clearing older tool results during short runs ([#296](https://github.com/HKUDS/Vibe-Trading/pull/296), closes [#282](https://github.com/HKUDS/Vibe-Trading/issues/282), thanks @MarkfuGod).\n\nEarlier news\n\n- **2026-06-24** 🎯 **Shadow Account price context + reactive Chinese UI + LAN auth fix**: Shadow Account rule extraction now sees PIT-safe entry context — `entry_rsi14` and `prior_5d_return` fetched through the loader registry as of `buy_dt`, with graceful offline/no-data degradation ([#302](https://github.com/HKUDS/Vibe-Trading/pull/302), follows [#295](https://github.com/HKUDS/Vibe-Trading/issues/295), thanks @Robin1987China). The main Web UI panels now use reactive English / zh-CN translations across charts, chat, Alpha Library, Correlation, and Run Detail ([#301](https://github.com/HKUDS/Vibe-Trading/pull/301), thanks @skloxo). Remote same-origin Web UI deployments with `API_AUTH_KEY` can post and upload again after the CSRF hardening, while mismatched cross-site origins remain blocked ([#304](https://github.com/HKUDS/Vibe-Trading/pull/304), thanks @Hinotoi-agent).\n- **2026-06-23** 🛡️ **Local API CSRF hardening**: a malicious web page can no longer drive unsafe cross-site requests (POST/PUT/DELETE) against the loopback API — CORS blocks reading the response but not the side effect, so loopback dev-mode trust now applies the existing cross-site guard to unsafe methods *before* honoring it. Safe methods and local CLI / non-browser uploads are unaffected ([#293](https://github.com/HKUDS/Vibe-Trading/pull/293), thanks @Hinotoi-agent).\n- **2026-06-22** 🔧 **Live-authorize OAuth fix + Alpha Zoo headline fix**: `connector authorize` now holds the OAuth handshake open through a multi-minute broker sign-in (tunable via `VIBE_LIVE_AUTHORIZE_TIMEOUT_SECONDS`) and no longer spawns a competing callback server on retry, so the token actually persists ([#281](https://github.com/HKUDS/Vibe-Trading/pull/281), closes [#259](https://github.com/HKUDS/Vibe-Trading/issues/259), thanks @Robin1987China). The Alpha Zoo page no longer prints its alpha count twice ([#287](https://github.com/HKUDS/Vibe-Trading/pull/287), closes [#286](https://github.com/HKUDS/Vibe-Trading/issues/286), thanks @digger-yu). Scheduled research also picked up end-to-end usage docs ([#288](https://github.com/HKUDS/Vibe-Trading/pull/288)).\n- **2026-06-21** ⏰ **Scheduled-research executor + Reports library + post-backtest attribution**: scheduled research now runs **end to end** — a default-off background executor (`VIBE_TRADING_ENABLE_SCHEDULER`) fires due interval/cron jobs through the session runtime ([#278](https://github.com/HKUDS/Vibe-Trading/pull/278), thanks @mvanhorn, closing [#254](https://github.com/HKUDS/Vibe-Trading/issues/254)). A new **`/reports` Run Library** page lists, searches, and filters report-worthy runs with links into Run Detail + Compare ([#224](https://github.com/HKUDS/Vibe-Trading/pull/224), thanks @LemonCANDY42). And after every backtest the agent now runs **layered attribution** — trade-level winners/losers, beta regression, market-regime analysis, and a Monte Carlo permutation test, gated by data availability and routing ([#280](https://github.com/HKUDS/Vibe-Trading/pull/280), thanks @shadowinlife).\n- **2026-06-20** 🔬 **Research Autopilot loop closes (Phase 3) + loader OHLC integrity guard + 4 academic alphas**: **Research Autopilot** now runs **hypothesis → signal-engine → backtest** end to end — `scaffold_signal_engine` writes a contract-correct engine and `link_autopilot_backtest` feeds run metrics back to the hypothesis (**68 tools**) ([#267](https://github.com/HKUDS/Vibe-Trading/pull/267)). A structural **OHLC sanity check** drops dirty bars (`high < low`, non-positive prices, bad bracketing) centrally at the loader boundary, guarding every data source ([#274](https://github.com/HKUDS/Vibe-Trading/pull/274), thanks @Shizoqua). And the **academic alpha family grows 6 → 10** — Jegadeesh reversal, George-Hwang 52-week-high, Amihud illiquidity, Harvey-Siddique skew (**456 factors**) ([#277](https://github.com/HKUDS/Vibe-Trading/pull/277), thanks @Robin1987China).\n- **2026-06-19** 🚀 **v0.1.10 — Global data layer**: market-data sources grow 10 → 18 (free **Eastmoney / Sina / Stooq / Yahoo** + key-gated **Finnhub / Alpha Vantage / Tiingo / FMP**, ban-risk fallback) plus **18 read-only data tools** (fund flow, dragon-tiger, northbound, margin, block trades, SEC EDGAR + XBRL, financials, options chains, full-market screening…) across A-share / US / HK, all over MCP. Also bundles everything since 0.1.9 — 10 broker connectors, `alpha compare`, the provider-reliability overhaul, and the opt-in data cache. `pip install -U vibe-trading-ai`\n- **2026-06-18** 🔬 **Research Autopilot Phase 1 + a local Data Bridge loader, + a Discord security notice**: new `run_research_autopilot` + `generate_backtest_config` wire **Hypothesis → Research Goal → backtest** end to end (now **50 tools**), and a **`local`** loader reads OHLCV straight from your own **CSV / Parquet / DuckDB** files ([#260](https://github.com/HKUDS/Vibe-Trading/pull/260), [#252](https://github.com/HKUDS/Vibe-Trading/pull/252), thanks @Robin1987China), alongside DeepSeek `DSML` tool-call parsing and an identifier-containment hardening wave. ⚠️ **Security:** the old community Discord invite now points to a server we don't control running a fake Collab.Land wallet-\"verification\" phishing scam — removed everywhere; the **only** official Discord is the HKUDS server ([discord.gg/6TdQnT5xcF](https://discord.gg/6TdQnT5xcF)), and we'll never ask you to connect a wallet.\n- **2026-06-17** 🧩 **Install compatibility + Opus/Kimi provider fixes**: Baseline `pip install vibe-trading-ai` no longer pulls the optional `pyharmonics` / `ta` dependency chain; harmonic detection now lives behind `vibe-trading-ai[harmonic]` while the bundled detector remains available ([#250](https://github.com/HKUDS/Vibe-Trading/pull/250), closes [#249](https://github.com/HKUDS/Vibe-Trading/issues/249)). The agent loop also avoids assistant-prefill handoff messages rejected by Opus 4.8+, and Kimi/Moonshot can override the client `User-Agent` with `MOONSHOT_USER_AGENT` ([#248](https://github.com/HKUDS/Vibe-Trading/pull/248), closes [#246](https://github.com/HKUDS/Vibe-Trading/issues/246) and [#204](https://github.com/HKUDS/Vibe-Trading/issues/204)); follow-up tests now directly cover background-result and auto-compact handoff paths ([#251](https://github.com/HKUDS/Vibe-Trading/pull/251)).\n- **2026-06-16** 🛡️ **Security/API hardening + GLM/Zhipu alias**: Settings writes require auth when configured ([#245](https://github.com/HKUDS/Vibe-Trading/pull/245)); API shell-capable tools require explicit `VIBE_TRADING_ENABLE_SHELL_TOOLS=1` opt-in ([#243](https://github.com/HKUDS/Vibe-Trading/pull/243)); local shutdown requires auth when an API key is configured ([#241](https://github.com/HKUDS/Vibe-Trading/pull/241)); and untrusted loopback-looking hosts are rejected instead of treated as local ([#242](https://github.com/HKUDS/Vibe-Trading/pull/242)). Runtime edges also got cleaned up: Web chat syncs completed attempts ([#236](https://github.com/HKUDS/Vibe-Trading/pull/236)), r",
"manifest_file": "pyproject.toml",
"manifest_content": "[project]\nname = \"vibe-trading-ai\"\nversion = \"0.1.10\"\ndescription = \"Natural-language finance research AI agent with backtesting\"\nrequires-python = \">=3.11\"\nlicense = \"MIT\"\nreadme = \"README.md\"\nauthors = [\n {name = \"HKUDS\", email = \"hkuds@connect.hku.hk\"},\n]\nkeywords = [\"finance\", \"trading\", \"backtesting\", \"agent\", \"swarm\", \"quantitative\"]\nclassifiers = [\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Financial and Insurance Industry\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Topic :: Office/Business :: Financial :: Investment\",\n]\ndependencies = [\n \"rich>=13.0.0\",\n \"pyyaml>=6.0.0\",\n \"langchain>=1.0.0,<2\",\n \"langchain-core>=1.0.0,<2\",\n \"langchain-openai>=1.0.0,<2\",\n \"langgraph>=1.0.10,<1.1\",\n \"langgraph-checkpoint>=2.1.0,<5\",\n \"python-dotenv>=1.0.0\",\n \"httpx>=0.28.0\",\n \"defusedxml>=0.7.1\",\n \"oauth-cli-kit>=0.1.3\",\n \"pandas>=2.0.0\",\n \"openpyxl>=3.1.0\",\n \"python-docx>=1.1.0\",\n \"python-pptx>=0.6.23\",\n \"pypdfium2>=4.0.0\",\n \"Pillow>=10.0.0\",\n \"numpy>=1.24.0\",\n \"scipy>=1.10.0\",\n \"duckdb>=1.2.0\",\n \"scikit-learn>=1.3.0\",\n \"joblib>=1.3.0\",\n \"smartmoneyconcepts>=0.0.1\",\n \"tushare>=1.2.89\",\n \"requests>=2.31.0\",\n \"yfinance>=0.2.30\",\n \"akshare>=1.12.0\",\n \"ccxt>=4.0.0\",\n \"fastapi>=0.104.0\",\n \"uvicorn[standard]>=0.24.0\",\n \"pydantic>=2.0.0\",\n \"python-multipart>=0.0.18\",\n \"sse-starlette>=1.6.0\",\n \"fastmcp>=2.14.0\",\n \"ddgs>=6.0.0\",\n \"jinja2>=3.1.0\",\n \"matplotlib>=3.7.0\",\n \"weasyprint>=60.0\",\n \"prompt_toolkit>=3.0.0\",\n]\n\n[project.urls]\nHomepage = \"https://vibetrading.wiki/\"\nDocumentation = \"https://vibetrading.wiki/docs/\"\nRepository = \"https://github.com/HKUDS/Vibe-Trading\"\nIssues = \"https://github.com/HKUDS/Vibe-Trading/issues\"\n\n[project.scripts]\nvibe-trading = \"cli:main\"\nvibe-trading-mcp = \"mcp_server:main\"\n\n[tool.setuptools]\npackage-dir = {\"\" = \"agent\"}\npy-modules = [\"api_server\", \"mcp_server\"]\n\n[tool.setuptools.packages.find]\nwhere = [\"agent\"]\ninclude = [\"src*\", \"backtest*\", \"cli*\"]\n\n[tool.setuptools.package-data]\n\"src\" = [\n \"skills/**/*.md\",\n \"skills/**/*.yaml\",\n \"skills/**/*.json\",\n \"skills/**/*.py\",\n \"providers/*.json\",\n \"swarm/presets/*.yaml\",\n \"shadow_account/templates/*.j2\",\n \"shadow_account/templates/*.html\",\n \"shadow_account/templates/*.css\",\n]\n\"backtest\" = [\"*.py\"]\n\"src.factors\" = [\"zoo/**/*.yaml\", \"zoo/**/*.md\", \"zoo/**/NOTICE\"]\n\n[project.optional-dependencies]\nibkr = [\n \"ib_async>=2.0\",\n]\ndeepseek = [\n \"langchain-deepseek>=1.0.0,<2\",\n]\nashare = [\n # Free, no-auth A-share source over BaoStock's TCP protocol; bypasses the\n # HTTP CDN IP blocks that affect eastmoney-backed sources. The Tencent\n # A-share loader needs no extra dependency (stdlib HTTP only).\n \"baostock>=0.8.8\",\n]\nharmonic = [\n \"pyharmonics>=1.5.0\",\n]\ndev = [\n \"pytest>=7.0\",\n \"pytest-cov>=4.0\",\n \"pytest-socket>=0.7.0\",\n]\n\n[tool.pytest.ini_options]\ntestpaths = [\"agent/tests\"]\npythonpath = [\"agent\"]\nmarkers = [\n \"unit: Unit tests (fast, no network)\",\n \"integration: Integration tests (may need network)\",\n]\n\n[tool.ruff]\ntarget-version = \"py311\"\nline-length = 120\nsrc = [\"agent\"]\n\n[tool.coverage.run]\nsource = [\"agent\"]\nomit = [\n \"agent/tests/*\",\n \"agent/tests/**/*\",\n \"agent/**/__init__.py\",\n]\n\n[tool.coverage.report]\nshow_missing = true\nskip_empty = true\nfail_under = 0\nexclude_also = [\n \"if __name__ == .__main__.\",\n \"raise NotImplementedError\",\n \"\\\\.\\\\.\\\\.\",\n]\n\n[tool.ruff.lint]\nselect = [\"E\", \"F\", \"W\"]\nignore = [\"E501\"]\n\n[tool.ruff.lint.per-file-ignores]\n# Alpha zoo files import the full src.factors.base surface verbatim\n# to match research-paper formulas. F401 (unused-import) is noise here;\n# F841 (unused-local-variable) stays active to catch real formula bugs.\n\"agent/src/factors/zoo/**/*.py\" = [\"F401\"]\n",
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{
"owner": "jackwener",
"name": "OpenCLI",
"full_name": "jackwener/OpenCLI",
"url": "https://github.com/jackwener/OpenCLI",
"description": "Make Any Website into CLI & Use your logged-in browser by AI agent.",
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"cli",
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],
"license": "Apache-2.0",
"open_issues": 189,
"created_at": "2026-03-14T22:10:23Z",
"pushed_at": "2026-06-27T18:14:09Z",
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"readme_content": "# OpenCLI\n\n> **Convert any website into a CLI & run Browser Use on your logged-in Chrome.**\n> Turn websites, browser sessions, Electron apps, and local tools into deterministic interfaces for humans and AI agents.\n> Or run Browser Use against any page — navigate, fill forms, click, extract, automate.\n\n[](./README.zh-CN.md)\n[](https://www.npmjs.com/package/@jackwener/opencli)\n[](https://nodejs.org)\n[](./LICENSE)\n\nOpenCLI gives you one surface for three different kinds of automation:\n\n- **Use built-in adapters** for sites like Bilibili, Zhihu, Xiaohongshu, Reddit, HackerNews, Twitter/X, and [many more](#built-in-commands).\n- **Let AI Agents operate any website** — install the `opencli-browser` skill in your AI agent (Claude Code, Cursor, etc.), and it can navigate, click, type/fill, extract, and inspect any page through your logged-in browser via `opencli browser` primitives.\n- **Write new adapters** end-to-end with `opencli browser` + the `opencli-adapter-author` skill, which guides from first recon through field decoding, code, and `opencli browser verify`.\n\nIt also works as a **CLI hub** for local tools such as `gh`, `docker`, `longbridge`, `tg`, `discord`, `wx`, `ntn` (Notion), and other binaries you register yourself, plus **desktop app adapters** for Electron apps like Cursor, Trae CN, Codex, Antigravity, ChatGPT, and Trae SOLO.\n\n## Quick Start\n\n### 1. Install OpenCLI\n\nFor desktop use, start with **OpenCLIApp**. It bundles the OpenCLI runtime,\nkeeps the managed `opencli` command installed, and gives you a system tray UI\nfor setup, diagnostics, updates, browser-login keepalive, and Web → Markdown.\n\n**Option A — OpenCLIApp (recommended for macOS / Windows):**\nDownload the latest app from , install it, then\nopen the app once and use the System page to install or repair the `opencli`\ncommand.\n\n**Option B — npm global install (CLI-only / CI / servers):**\nOpenCLI requires **Node.js >= 20** when installed through npm.\n\n```bash\nnode --version\nnpm install -g @jackwener/opencli\n```\n\n### 2. Install the Browser Bridge Extension\n\nOpenCLI connects to Chrome/Chromium through a lightweight Browser Bridge extension plus a small local daemon. The daemon auto-starts when needed.\n\n**Option A — Chrome Web Store (recommended):**\nInstall **OpenCLI** from the [Chrome Web Store](https://chromewebstore.google.com/detail/opencli/ildkmabpimmkaediidaifkhjpohdnifk).\n\n**Option B — Manual install:**\n1. Download the latest `opencli-extension-v{version}.zip` from the GitHub [Releases page](https://github.com/jackwener/opencli/releases).\n2. Unzip it, open `chrome://extensions`, and enable **Developer mode**.\n3. Click **Load unpacked** and select the unzipped folder.\n\n### 3. Verify the setup\n\n```bash\nopencli doctor\n```\n\n### 4. Optional: name your Chrome profile\n\nEach Chrome profile runs its own OpenCLI extension instance. If you use multiple Chrome profiles, list the connected profiles and assign local aliases:\n\n```bash\nopencli profile list\nopencli profile rename work\nopencli profile use work\nopencli --profile work browser state\n```\n\nWith only one connected profile, OpenCLI uses it automatically. With multiple connected profiles and no default, OpenCLI asks you to choose instead of guessing.\n\n### 5. Run your first commands\n\n```bash\nopencli list\nopencli hackernews top --limit 5\nopencli bilibili hot --limit 5\n```\n\n## For Humans\n\nUse OpenCLI directly when you want a reliable command instead of a live browser session:\n\n- `opencli list` shows every registered command.\n- `opencli ` runs a built-in or generated adapter.\n- `opencli external register mycli` exposes a local CLI through the same discovery surface.\n- `opencli doctor` helps diagnose browser connectivity.\n\n## Extending OpenCLI\n\nIf you want to add your own commands, start with the [Extending OpenCLI guide](./docs/guide/extending-opencli.md). README keeps this short; the guide covers the directory layout, source-control model, and install commands.\n\n| Need | Recommended path |\n|------|------------------|\n| Keep personal website commands in your own Git repo | `opencli plugin create` + `opencli plugin install file://...` |\n| Quickly draft a private local adapter | `opencli browser init /` in `~/.opencli/clis/` |\n| Modify an official adapter locally | `opencli adapter eject ` + `opencli adapter reset ` |\n| Publish or install third-party commands | `opencli plugin install github:user/repo` |\n| Wrap an existing local binary | `opencli external register ` |\n\n## For AI Agents\n\nOpenCLI's browser commands are designed to be used by AI Agents — not run manually. Install skills into your AI agent (Claude Code, Cursor, etc.), and the agent operates websites on your behalf using your logged-in Chrome session.\n\n### Install skills (also refreshes existing installs)\n\n```bash\nnpx skills add jackwener/opencli\n```\n\nOr install only what you need:\n\n```bash\nnpx skills add jackwener/opencli --skill opencli-adapter-author\nnpx skills add jackwener/opencli --skill opencli-autofix\nnpx skills add jackwener/opencli --skill opencli-browser\nnpx skills add jackwener/opencli --skill opencli-browser-sitemap\nnpx skills add jackwener/opencli --skill opencli-sitemap-author\nnpx skills add jackwener/opencli --skill opencli-usage\n```\n\n### Which skill to use\n\n| Skill | When to use | Example prompt to your AI agent |\n|-------|------------|-------------------------------|\n| **opencli-adapter-author** | Write a reusable adapter for a new site or add a command to an existing site | \"Write an adapter for douyin trending\" / \"Make a command that grabs the top posts from this page\" |\n| **opencli-autofix** | Repair a broken adapter when a built-in command fails | \"`opencli zhihu hot` is returning empty — fix it\" |\n| **opencli-browser** | Drive a real Chrome page ad-hoc — navigate, fill forms, click, extract | \"Help me check my Xiaohongshu notifications\" / \"Help me fill out this form\" / \"Use browser commands to scrape this page\" |\n| **opencli-browser-sitemap** | Consume site sitemap context while driving a browser task | \"Use the sitemap to navigate this website without blind clicking\" |\n| **opencli-sitemap-author** | Create or update site sitemap knowledge for browser agents | \"Record the stable workflow you just discovered for this site\" |\n| **opencli-usage** | Quick reference for all OpenCLI commands and sites | \"What commands does OpenCLI have for Twitter?\" |\n\n### How it works\n\nOnce `opencli-browser` is installed, your AI agent can:\n\n1. **Navigate** to any URL using your logged-in browser\n2. **Read** page content via structured DOM snapshots (not screenshots)\n3. **Interact** — click buttons, fill forms, select options, press keys\n4. **Extract** data from the page or intercept network API responses\n5. **Wait** for elements, text, or page transitions\n\nThe agent handles all the `opencli browser` commands internally — you just describe what you want done in natural language.\n\n**Skill references:**\n- [`skills/opencli-browser/SKILL.md`](./skills/opencli-browser/SKILL.md) — drive Chrome ad-hoc (navigate, fill forms, click, extract)\n- [`skills/opencli-browser-sitemap/SKILL.md`](./skills/opencli-browser-sitemap/SKILL.md) — use sitemap context while driving a browser task\n- [`skills/opencli-sitemap-author/SKILL.md`](./skills/opencli-sitemap-author/SKILL.md) — create or update site sitemap knowledge\n- [`skills/opencli-adapter-author/SKILL.md`](./skills/opencli-adapter-author/SKILL.md) — write a new adapter end-to-end\n- [`skills/opencli-autofix/SKILL.md`](./skills/opencli-autofix/SKILL.md) — repair broken adapters\n- [`skills/opencli-usage/SKILL.md`](./skills/opencli-usage/SKILL.md) — command and site reference\n\nAvailable browser commands include `open`, `state`, `click`, `type`, `fill`, `select`, `keys`, `wait`, `get`, `find`, `extract`, `frames`, `screenshot`, `scroll`, `back`, `eval`, `network`, `tab list`, `tab new`, `tab select`, `tab close`, `init`, `verify`, and `close`.\n\n`opencli browser` commands require a `` positional immediately after `browser`. `opencli browser work open ` and `opencli browser work tab new [url]` both return a target ID. Use `opencli browser work tab list` to inspect target IDs, then pass `--tab ` to route a command to a specific tab. `tab new` creates a new tab without changing the default browser target; only `tab select ` promotes that tab to the default target for later untargeted commands in the same session.\n\n## Writing a new adapter\n\nWhen the site you need is not yet covered, use the `opencli-adapter-author` skill end-to-end:\n\n1. **Recon** the site and pick a pattern (SPA / SSR / JSONP / Token / Streaming).\n2. **Discover** the right endpoint — network inspection, initial state, bundle search, token trace, or interceptor fallback.\n3. **Pick auth** — `PUBLIC` / `COOKIE` / `INTERCEPT` / `UI` / `LOCAL`.\n4. **Decode** response fields and design output columns.\n5. `opencli browser recon analyze ` → `opencli browser recon init /` → write adapter → `opencli browser recon verify /`.\n6. Site knowledge persists to `~/.opencli/sites//` so the next adapter for the same site starts from context.\n\n## Configuration\n\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `OPENCLI_PROFILE` | — | Browser Bridge profile alias/contextId to use when multiple Chrome profiles are connected |\n| `OPENCLI_WINDOW` | command default | Set to `foreground` or `background` to override Browser Bridge window placement. Browser-backed commands also accept `--window `. |\n| `OPENCLI_BROWSER_CONNECT_TIMEOUT` | `45` | Seconds to wait for browser connection |\n| `OPENCLI_BROWSER_COMMAND_TIMEOUT` | `60` | Seconds to wait for a single browser command |\n| `OPENCLI_CDP_ENDPOINT` | — | Chrome DevTools Protocol endpoint for remote browser or Electron apps |\n| `OPENCLI_CDP_TARGET` | — | Filter CDP targets by URL substring (e.g. `detail.1688.com`) |\n| `OPENCLI_VERBOSE` | `false` | Enable verbose logging (`-v` flag also works) |\n| `DEBUG_SNAPSHOT` | — | Set to `1` for DOM snapshot debug output |\n\n`opencli browser *` requires an explicit `` positional, uses a foreground browser window by default, and keeps that session's tab lease until `opencli browser close` or idle cleanup. Browser-backed adapters use a background adapter window and release one-shot tab leases by default. Interactive adapters can declare `siteSession: 'persistent'` to keep a stable site tab for continuity; pass `--site-session ephemeral` for a one-shot tab.\n\n## Built-in Commands\n\n| Site | Commands |\n|------|----------|\n| **xiaohongshu** | `search` `ask` `note` `comments` `feed` `user` `download` `publish` `follow` `unfollow` `notifications` `creator-notes` `creator-notes-summary` `creator-note-detail` `creator-profile` `creator-stats` |\n| **bilibili** | `hot` `search` `history` `feed` `ranking` `download` `comments` `dynamic` `favorite` `following` `follow` `unfollow` `me` `subtitle` `summary` `video` `user-videos` |\n| **zhihu** | `hot` `search` `question` `download` `follow` `like` `favorite` `comment` `answer` |\n| **hackernews** | `top` `new` `best` `ask` `show` `jobs` `search` `user` |\n| **geogebra** | `eval` `add-point` `add-line` `add-circle` `add-polygon` `triangle` `hexagon` `list` `info` |\n| **linkedin** | `connect` `inbox` `job-detail` `jobs-preferences` `post-analytics` `posts` `profile-experience` `profile-projects` `profile-read` `profile-analytics` `safe-send` `search` `services-read` `sent-invitations` `thread-snapshot` `timeline` `salesnav-search` `salesnav-inbox` `salesnav-message` `salesnav-thread` ",
"manifest_file": "package.json",
"manifest_content": "{\n \"name\": \"@jackwener/opencli\",\n \"version\": \"1.8.5\",\n \"publishConfig\": {\n \"access\": \"public\"\n },\n \"description\": \"Make any website or Electron App your CLI. AI-powered.\",\n \"engines\": {\n \"node\": \">=20.0.0\"\n },\n \"type\": \"module\",\n \"main\": \"dist/src/main.js\",\n \"bin\": {\n \"opencli\": \"dist/src/main.js\"\n },\n \"exports\": {\n \".\": \"./dist/src/main.js\",\n \"./registry\": \"./dist/src/registry-api.js\",\n \"./errors\": \"./dist/src/errors.js\",\n \"./types\": \"./dist/src/types.js\",\n \"./utils\": \"./dist/src/utils.js\",\n \"./logger\": \"./dist/src/logger.js\",\n \"./launcher\": \"./dist/src/launcher.js\",\n \"./browser/cdp\": \"./dist/src/browser/cdp.js\",\n \"./browser/page\": \"./dist/src/browser/page.js\",\n \"./browser/utils\": \"./dist/src/browser/utils.js\",\n \"./download\": \"./dist/src/download/index.js\",\n \"./download/article-download\": \"./dist/src/download/article-download.js\",\n \"./download/media-download\": \"./dist/src/download/media-download.js\",\n \"./download/progress\": \"./dist/src/download/progress.js\",\n \"./pipeline\": \"./dist/src/pipeline/index.js\"\n },\n \"files\": [\n \"dist/src/\",\n \"clis/\",\n \"skills/opencli-*/**\",\n \"cli-manifest.json\",\n \"scripts/\",\n \"README.md\",\n \"LICENSE\"\n ],\n \"scripts\": {\n \"dev\": \"tsx src/main.ts\",\n \"dev:bun\": \"bun src/main.ts\",\n \"build\": \"npm run clean-dist && npm run copy-yaml && npm run build-manifest\",\n \"prebuild-manifest\": \"tsc --build && node -e \\\"require('fs').chmodSync('dist/src/main.js', 0o755)\\\"\",\n \"build-manifest\": \"tsx src/build-manifest.ts\",\n \"clean-dist\": \"node scripts/clean-dist.cjs\",\n \"copy-yaml\": \"node scripts/copy-yaml.cjs\",\n \"start\": \"node dist/src/main.js\",\n \"start:bun\": \"bun dist/src/main.js\",\n \"preuninstall\": \"node -e \\\"fetch('http://127.0.0.1:19825/shutdown',{method:'POST',headers:{'X-OpenCLI':'1'},signal:AbortSignal.timeout(3000)}).catch(()=>{})\\\" || true\",\n \"postinstall\": \"node scripts/postinstall.js || true; node scripts/fetch-adapters.js || true\",\n \"typecheck\": \"tsc --noEmit\",\n \"prepare\": \"[ -d src ] && npm run build || true\",\n \"prepublishOnly\": \"npm run build\",\n \"test\": \"vitest run --project unit --project extension --project adapter\",\n \"test:bun\": \"bun vitest run --project unit --project extension --project adapter\",\n \"test:adapter\": \"vitest run --project adapter\",\n \"test:all\": \"vitest run\",\n \"test:e2e\": \"vitest run --project e2e-fixed-port --project e2e\",\n \"advise:listing-id-pairing\": \"node scripts/check-listing-id-pairing.mjs\",\n \"check:silent-column-drop\": \"node scripts/check-silent-column-drop.mjs\",\n \"check:typed-error-lint\": \"node scripts/check-typed-error-lint.mjs\",\n \"docs:dev\": \"vitepress dev docs\",\n \"docs:build\": \"vitepress build docs\",\n \"docs:preview\": \"vitepress preview docs\"\n },\n \"keywords\": [\n \"cli\",\n \"browser\",\n \"web\",\n \"ai\"\n ],\n \"author\": \"jackwener\",\n \"license\": \"Apache-2.0\",\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/jackwener/opencli.git\"\n },\n \"dependencies\": {\n \"@mozilla/readability\": \"^0.6.0\",\n \"cli-table3\": \"^0.6.5\",\n \"commander\": \"^14.0.3\",\n \"js-yaml\": \"^4.1.0\",\n \"turndown\": \"^7.2.2\",\n \"turndown-plugin-gfm\": \"^1.0.2\",\n \"undici\": \"^6.27.0\",\n \"ws\": \"^8.18.0\"\n },\n \"devDependencies\": {\n \"@types/jsdom\": \"^27.0.0\",\n \"@types/js-yaml\": \"^4.0.9\",\n \"@types/node\": \"^25.5.2\",\n \"@types/turndown\": \"^5.0.6\",\n \"@types/ws\": \"^8.5.13\",\n \"jsdom\": \"^29.0.2\",\n \"tsx\": \"^4.19.3\",\n \"typescript\": \"^6.0.2\",\n \"vitepress\": \"^1.6.4\",\n \"vitest\": \"^4.1.0\"\n },\n \"overrides\": {\n \"postcss\": \"^8.5.10\",\n \"vitepress\": {\n \"vite\": \"6.4.2\"\n }\n }\n}\n",
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"name": "Agent-Reach",
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"url": "https://github.com/Panniantong/Agent-Reach",
"description": "Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.",
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"claude-code",
"cli",
"cursor",
"free-api",
"llm-tools",
"mcp",
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"reddit-scraper",
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"web-scraper",
"xiaohongshu",
"youtube-transcript"
],
"license": "MIT",
"open_issues": 114,
"created_at": "2026-02-24T02:10:24Z",
"pushed_at": "2026-06-23T13:04:55Z",
"homepage": "",
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\n \n\n ![\"Sponsor\"](\"https://img.shields.io/badge/Sponsor-❤️-ff69b4.svg\"){kind=icon}
\n\n![\"Cognee](\"assets/cognee-demo.gif\")
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\n\n# MemPalace\n\nLocal-first AI memory. Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.\n\n[![][version-shield]][release-link]\n[![][python-shield]][python-link]\n[![][license-shield]][license-link]\n[![][discord-shield]][discord-link]\n\n![\"Vibe-Trading](\"assets/icon.png\"){kind=icon}
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