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https://github.com/xpressai/xpressclaw

XpressAI on your machine. Build continuous AI agents that work autonomously, remember what they've learned and stay on budget while you sleep.
https://github.com/xpressai/xpressclaw

Last synced: about 2 months ago
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XpressAI on your machine. Build continuous AI agents that work autonomously, remember what they've learned and stay on budget while you sleep.

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README 

          


  xpressclaw




Collaborative Local Agent Workspace





A single binary that gives you a complete AI agent runtime — chat with your agents, give them tasks, and let them work autonomously. Built-in memory, budget controls, scheduling, and a polished web UI.






  XpressClaw-screenshot






Website •

Hub •

Contribute •

Discord






License

Release

Rust




---



```bash

xpressclaw init

xpressclaw up

```



That's it. Open `http://localhost:8935` and start chatting with your agents.



## Why xpressclaw?



Most agent frameworks give you a library. xpressclaw gives you a **running system** — a ~12MB binary with everything included: server, web UI, LLM router, and agent management. No Python environment to configure, no Docker Compose sprawl, no YAML templating engines.



- **Chat-first interface** — Talk to your agents in a messaging UI, not a terminal. `@mention` agents in conversations, just like Slack.

- **Single binary, zero dependencies** — Download one file, run it. The server, API, and web frontend are all embedded.

- **Native desktop app** — Tauri-based `.app` / `.dmg` with system tray. Runs in the background, always available.

- **Production-tested architecture** — Built on the same agent orchestration patterns that power Xpress AI's enterprise platform, deployed at regulated financial institutions.

- **Local-first, cloud-optional** — Works with Ollama out of the box. Add OpenAI or Anthropic keys when you need them.

- **Secure by default** — Agents run in Docker containers. Budget controls prevent runaway costs. No exceptions.



## Features



### Chat with Your Agents



The primary interface is a **messaging UI**. Create conversations, add agents, and talk to them — individually or in groups. Agents respond via the configured LLM (local or cloud).



### Autonomous Task Execution



Agents pick up tasks from a queue and work through them. Schedule recurring work with cron expressions. Define SOPs (Standard Operating Procedures) so agents perform consistently.



### Persistent Memory



Zettelkasten-style knowledge base with vector search (sqlite-vec). Agents remember context across sessions and retrieve relevant information automatically.



### Multiple LLM Backends



- **Local:** Qwen 3.5, Llama 3, Mistral, and more via Ollama

- **Cloud:** Claude (Anthropic), GPT-4o (OpenAI), and 100+ models via OpenRouter

- **Framework agnostic:** Agent harnesses for Claude SDK, LangChain, Xaibo, and generic



### Privacy & Safety



- **Container isolation** — each agent runs in its own Docker container

- **Budget controls** — daily/monthly spending limits per agent and globally

- **Tool permissions** — explicit allow-list; agents only access what you grant

- **Everything local** — your data never leaves your machine unless you choose a cloud LLM



### Full Observability



Activity logs, budget dashboards, agent status monitoring. Know what your agents did at 3am.



## Quick Start



### Option 1: Download Binary



Grab the latest release from [GitHub Releases](https://github.com/XpressAI/xpressclaw/releases).



```bash

xpressclaw init

xpressclaw up

# Open http://localhost:8935

```



### Option 2: Native App (macOS)



Download `xpressclaw.dmg` from [Releases](https://github.com/XpressAI/xpressclaw/releases) — double-click to install. The app runs in the system tray.



### Option 3: Build from Source



See [Building](#building) below.



### Requirements



- Docker or Podman (required for agent container isolation)

- Ollama (optional, for local LLM — `ollama pull qwen3.5:latest`)

- Or an API key for Claude / OpenAI / OpenRouter



## What Can It Do?



**Chat with agents from the web UI:**



Create a conversation, add an agent, and start talking. Use `@atlas` to mention a specific agent in a multi-agent conversation.



**Schedule recurring tasks:**

```bash

xpressclaw tasks create "Summarize top 10 HN stories" --agent atlas

```



**Review what happened while you were away:**

```bash

xpressclaw logs

xpressclaw status

xpressclaw budget

```



**Define SOPs for consistent behavior:**

```yaml

name: weekly-report

steps:

  - Check JIRA for completed tickets this week

  - Summarize key accomplishments

  - Identify blockers and risks

  - Draft report and send to team channel

```



**Interactive CLI chat:**

```bash

xpressclaw chat atlas

```



## Configuration



`xpressclaw init` creates a `xpressclaw.yaml` in your project:



```yaml

system:

  budget:

    daily: $20.00

    on_exceeded: pause

  isolation: docker



agents:

  - name: atlas

    backend: generic

    role: |

      You are a helpful assistant.



memory:

  near_term_slots: 8

  eviction: least-recently-relevant



llm:

  default_provider: local

  # local_model: qwen3.5:latest

  # Set OPENAI_API_KEY or ANTHROPIC_API_KEY env vars for cloud providers

```



## Building



### Prerequisites



- [Bazel](https://bazel.build/) 8.2+ (via [Bazelisk](https://github.com/bazelbuild/bazelisk))

- [Rust](https://rustup.rs/) (stable toolchain, used by Bazel and for fmt/clippy)

- [LLVM](https://releases.llvm.org/) (provides `libclang`, required by llama.cpp bindings)

- [CMake](https://cmake.org/) (required by llama.cpp build)

- [Node.js](https://nodejs.org/) 18+ (for the frontend)

- Docker (for running agents)



### Build Everything



```bash

git clone https://github.com/XpressAI/xpressclaw.git

cd xpressclaw



# Build CLI, core, and server (includes frontend)

./build.sh



# Or with a clean build

./build.sh --clean

```



### Build Individual Targets



```bash

# CLI only

bazel build //crates/xpressclaw-cli:xpressclaw



# Core library

bazel build //crates/xpressclaw-core:xpressclaw-core



# Server

bazel build //crates/xpressclaw-server:xpressclaw-server



# The CLI binary is at bazel-bin/crates/xpressclaw-cli/xpressclaw

```



### Build the Desktop App (Tauri)



```bash

# Build everything including the Tauri desktop app

./build.sh



# For signed/notarized macOS builds

./build-signed.sh

```



### Build Agent Harness Images



Agent harnesses are Docker images that run your agents in isolation:



```bash

cd harnesses



# Build all harness images

docker buildx bake



# Or build individually

docker build -t xpressclaw-harness-base ./base

docker build -t xpressclaw-harness-generic ./generic

docker build -t xpressclaw-harness-claude-sdk ./claude-sdk

```



### Run Tests



```bash

# Via Bazel

bazel test //crates/xpressclaw-core:core_test //crates/xpressclaw-server:server_test



# Frontend type check

cd frontend && npm run check



# Formatting and linting (still via Cargo)

cargo fmt -p xpressclaw-core -p xpressclaw-server -p xpressclaw-cli -p xpressclaw-tauri -- --check

cargo clippy -p xpressclaw-core -p xpressclaw-server -p xpressclaw-cli -p xpressclaw-tauri --all-targets -- -D warnings

```



### Development Mode



```bash

# Terminal 1: Run the Rust server with auto-reload

cargo run -- up



# Terminal 2: Run the frontend dev server with hot reload

cd frontend && npm run dev



# The frontend dev server proxies API calls to localhost:8935

```



## Architecture



xpressclaw is a Cargo workspace with four crates:



| Crate | Purpose |

|-------|---------|

| `xpressclaw-core` | Business logic: config, SQLite + sqlite-vec, agents, memory, tasks, budget, LLM router, Docker management, MCP tools |

| `xpressclaw-server` | Axum REST API, SSE streaming, embedded SvelteKit frontend (rust-embed) |

| `xpressclaw-cli` | 10 CLI commands via clap: init, up, down, status, chat, tasks, memory, budget, sop, logs |

| `xpressclaw-tauri` | Native desktop app with system tray (Tauri v2) |



```

xpressclaw (single ~12MB binary)

+-- Axum server (REST API + embedded SvelteKit frontend)

+-- LLM Router (Ollama / OpenAI / Anthropic)

+-- SQLite + sqlite-vec (tasks, memory, conversations, budget)

+-- Docker Manager (agent container lifecycle)

+-- Agent Harnesses (isolated Python containers per backend)

```



**Key design decisions:**

- **Single binary** — server, API, frontend, and CLI in one executable

- **Docker required** — agent isolation is not optional

- **SQLite for everything** — tasks, memory, embeddings, conversations, budget

- **OpenAI-compatible protocol** — harnesses expose `/v1/chat/completions`



## CLI Reference



```

xpressclaw init              Initialize workspace with config + data dir

xpressclaw up [--detach]     Start the server and agents

xpressclaw down              Stop all running agents

xpressclaw status            Show agent status and budget summary

xpressclaw chat       Interactive chat in the terminal

xpressclaw tasks             Task management (list, create, update, delete)

xpressclaw memory            Memory inspection (list, search, add)

xpressclaw budget            Budget report and usage history

xpressclaw sop               SOP management (list, create, run)

xpressclaw logs              Activity log viewer

```



Default port: `8935` (override with `--port`).



## From Open Source to Enterprise



xpressclaw is the open-source foundation. When your team needs collaboration, visual workflows, compliance, and enterprise support — [Xpress AI](https://xpress.ai) has you covered.



| | xpressclaw (Free) | Xpress AI (Enterprise) |

|---|---|---|

| Autonomous AI agents | :white_check_mark: | :white_check_mark: |

| Chat-first web UI | :white_check_mark: | :white_check_mark: |

| SOPs & scheduling | :white_check_mark: | :white_check_mark: |

| Local model support | :white_check_mark: | :white_check_mark: |

| Budget controls | :white_check_mark: | :white_check_mark: |

| Team collaboration | | :white_check_mark: |

| Visual workflow builder (Xircuits) | | :white_check_mark: |

| iOS & Android apps | | :white_check_mark: |

| On-premise deployment | | :white_check_mark: |

| Role-based access control | | :white_check_mark: |

| Audit logging & compliance | | :white_check_mark: |

| Dedicated support & SLA | | :white_check_mark: |



[Request an Enterprise Demo](https://xpress.ai)



## Contributing



We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.



```bash

git clone https://github.com/XpressAI/xpressclaw.git

cd xpressclaw

./build.sh

```



## Community



- **Website:** [xpressclaw.ai](https://xpressclaw.ai)

- **Hub:** [hub.xpressclaw.ai](https://hub.xpressclaw.ai)

- **Discord:** [discord.com/invite/vgEg2ZtxCw](https://discord.com/invite/vgEg2ZtxCw)

- **Twitter/X:** [@xpressclaw](https://twitter.com/xpressclaw)

- **Enterprise:** [xpress.ai](https://xpress.ai)



## License



[GPL-3.0](LICENSE)



---





Built by Xpress AI — the team behind enterprise agent platforms for regulated industries.