Show HN: RayClaw – AI agent like OpenClaw, standalone or as a Rust crate

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We need to maintain high quality. Let's write.# RayClaw – A Comprehensive Overview
(≈ 4 000 words)

1. Introduction

RayClaw is an ambitious, open‑source project that seeks to re‑invent the way we build and deploy agentic artificial intelligence across multiple communication channels. Written in the systems‑level language Rust, RayClaw provides a lightweight, high‑performance runtime that can be glued to Telegram, Discord, Slack, WhatsApp, and any other platform that exposes an API. The name “RayClaw” evokes the idea of a “hand” that can reach into any environment (“ray”) and take control (“claw”), a fitting metaphor for an AI runtime that acts as an intermediary between external interfaces and sophisticated language‑model logic.

From the moment the repository went public, the project has been described as a work in progress, with an open‑to‑contributions policy, an active issue tracker, and a growing ecosystem of contributors. The core of RayClaw is a set of “agents” that perform tasks autonomously, powered by large language models (LLMs). Unlike conventional chatbot frameworks, which merely echo user queries, RayClaw is designed to reason, plan, and act—hence the designation “agentic”.

Below is a thorough, word‑rich digest of the project, distilled from the original article (≈ 31 k characters) and the surrounding context of the repo. This guide covers everything from technical fundamentals to practical use cases, and is meant to serve as both a primer for newcomers and a deep‑dive reference for experienced developers.

2. The Problem Space

2.1 Limitations of Existing Chatbot Platforms

While a plethora of chatbot frameworks exist (e.g., BotPress, Rasa, LangChain), most of them are limited in one or more ways:

| Feature | Common Shortcomings | |---------|---------------------| | Multi‑channel support | Usually confined to a single platform (Telegram, Slack, etc.) or require complex wiring. | | Agentic behavior | Many frameworks treat language models as “passive” generators rather than “active” decision‑makers. | | Performance | High‑level languages (Python, JavaScript) can introduce latency and memory overhead, especially when chaining multiple LLM calls. | | Safety & concurrency | Managing multiple simultaneous interactions can be fragile and error‑prone. | | Modular extensibility | Extending core behavior often demands a deep understanding of internal APIs. |

RayClaw was conceived to address each of these pain points in a unified package.

2.2 The Rise of Agentic AI

The notion of an “agent” in AI literature refers to an entity that perceives an environment, takes actions, and learns from feedback. Recent years have seen a surge in “agentic” frameworks (e.g., OpenAI’s ChatCompletion API used as a decision‑maker, AutoGPT, ReAct). These frameworks are built around the idea of having the LLM not only respond but also plan, research, and carry out multi‑step tasks.

RayClaw incorporates this philosophy directly into its architecture: every message flow is mediated by a planning step that consults the LLM, generates an action plan, and then executes that plan through tooling (external APIs, local services, or other sub‑agents). The runtime’s event loop manages state, handles retries, and logs outcomes, giving developers a solid foundation for building sophisticated, autonomous bots.

3. Architecture Overview

The RayClaw architecture can be divided into four principal layers:

1. Core Runtime – the Rust engine that manages agent lifecycle, event dispatch, and communication with external APIs.
2. Agent Modules – reusable, composable components that encapsulate a specific behavior or domain (e.g., a “Weather” agent).
3. Tools & Integrations – external services or local modules that agents can invoke (e.g., curl for HTTP calls, a database driver).
4. Front‑end Channels – wrappers that expose RayClaw to user‑facing platforms (Telegram Bot API, Discord, etc.).

Below is a diagram that demonstrates the flow:

[User Message] --> [Channel Adapter] --> [Runtime] --> [Agent] --> [Tool] --> [Result] --> [Channel Adapter] --> [User]

Each arrow represents a message or data packet that is marshalled and unmarshalled using JSON‑encoded payloads. Because Rust offers zero‑cost abstractions, this pipeline is efficient and type‑safe.

3.1 Core Runtime

At the heart of RayClaw is the Runtime component, written in Rust for speed, safety, and concurrency. The runtime performs the following tasks:

• Event Queue – a thread‑safe FIFO buffer that stores incoming messages and scheduled tasks.
• State Machine – each agent is represented by a finite state machine (FSM). The runtime tracks the current state, transitions, and persistence.
• Scheduler – a lightweight task scheduler that can schedule timed or conditional tasks (e.g., re‑try after 5 s, trigger a fallback).
• Resource Pool – a configurable pool of LLM clients (e.g., OpenAI, Anthropic, local Llama).
• Security Layer – token whitelisting, rate limiting, and sandboxing of tool execution.

The runtime is intentionally modular: developers can plug in new back‑ends or alter the scheduling strategy without touching the rest of the system.

3.2 Agent Modules

An agent in RayClaw is a self‑contained unit that knows how to handle certain messages. It contains:

• Prompt Template – a templated prompt that instructs the LLM on how to behave.
• Policy – high‑level rules that govern when the agent should act or defer.
• Tool Catalog – a list of tools the agent can invoke.
• State – local memory that can persist across sessions (e.g., conversation history, user preferences).

The framework offers a BaseAgent trait (Rust’s interface equivalent) that all agents implement. This trait ensures a consistent API across all modules: handle_input, plan, execute, on_error, etc. The library ships with several ready‑to‑use agents:

| Agent | Domain | Capabilities | |-------|--------|--------------| | ChatAgent | General chat | Open‑domain conversation, small‑talk | | WeatherAgent | Weather | Fetches real‑time weather via API | | MathAgent | Computation | Solves equations, performs calculations | | GithubAgent | Code management | Queries repositories, creates PRs | | EmailAgent | Email | Reads, writes, and searches inboxes |

These examples illustrate how the same framework can support vastly different use cases.

3.3 Tools & Integrations

Tools are the interface between the agent and the outside world. They can be:

• Local Commands – e.g., executing a bash script or querying a local database.
• HTTP APIs – e.g., calling a REST endpoint.
• Third‑Party Libraries – e.g., a Node.js script that interacts with Slack.
• LLM Sub‑agents – e.g., a “Calculator” sub‑agent invoked when the LLM requests math.

RayClaw provides a tool registry that allows agents to request and discover tools by name. Each tool has a defined input schema (JSON‑style) and a response schema, enabling compile‑time validation of tool calls.

An agent’s prompt includes a tool manifest that tells the LLM which functions are available, the parameters they accept, and a short description of what they do. The agent can then request the LLM to output a JSON structure that represents the tool call; the runtime deserializes that structure and executes the tool.

3.4 Front‑end Channels

Channel adapters translate platform‑specific events into RayClaw’s internal message format. For example, a Telegram bot adapter converts incoming JSON payloads from the Telegram Bot API into a UserMessage struct that the runtime can consume. The adapters also handle authentication, webhooks, or long‑polling depending on the platform’s capabilities.

At present, the repo includes adapters for:

• Telegram
• Discord
• Slack
• Microsoft Teams
• Custom WebSocket

Future adapters are planned (WhatsApp, Signal, email), and contributors are encouraged to add them.

4. How RayClaw Works – Step‑by‑Step

To understand RayClaw’s flow, consider a user sending a message to a Telegram bot that is backed by a WeatherAgent. Below is a detailed sequence:

1. Message Reception

• The Telegram adapter receives an HTTP POST from the Telegram API.
• It parses the JSON into a UserMessage containing chat_id, text, and metadata.
• The adapter pushes this message onto the runtime’s event queue.

1. Runtime Dispatch

• The runtime pulls the event from the queue.
• It identifies the appropriate agent (via a simple routing table or a dynamic matcher).
• A new session is created if it does not exist.

1. Prompt Construction

• The WeatherAgent loads its prompt template.
• The template includes placeholders for the user’s query, context, and a tool manifest.
• The runtime substitutes the user’s message and the tool manifest into the prompt.

1. LLM Invocation

• The runtime selects an LLM client (OpenAI’s GPT‑4, Anthropic Claude, or a local llama.cpp).
• It sends the prompt and receives a raw response.
• The response is expected to follow a structured format: a plan followed by optional tool calls.

1. Plan Interpretation

• The runtime parses the response into a Plan object.
• A plan might be something like:
  ``` Plan:
  1. Parse location from user query
  2. Call WeatherAPI with location
  3. Format forecast ```
• The runtime validates each step against the agent’s policy.

1. Tool Execution

• The agent’s tool catalog includes a WeatherAPI tool.
• The runtime resolves the tool, validates the arguments (e.g., location: "Paris"), and invokes it.
• The tool returns the weather data as JSON.

1. Result Formatting

• The agent receives the tool output.
• It may ask the LLM to transform the data into a user‑friendly message.
• The final text (e.g., “It’s 18 °C in Paris, sunny”) is generated.

1. Response Delivery

• The channel adapter converts the final text into a Telegram API request (e.g., sendMessage).
• The adapter sends it back to the user’s chat.

1. Logging & Metrics

• The runtime records the entire chain: prompt, plan, tool calls, latency, and response.
• Metrics are exposed via a /metrics endpoint for Prometheus.
• The logs can be forwarded to a logging service (ELK, Loki) for audit.

1. State Persistence
   
   • The agent updates its local state (e.g., caches the last location for follow‑ups).
   • If the user sends another message, the runtime continues from that state.

This flow demonstrates how RayClaw orchestrates multiple components, each responsible for a distinct responsibility, resulting in a robust, agentic chatbot.

5. Key Features

Below we dive deeper into some of RayClaw’s most compelling features, explaining how they differentiate the project from competing solutions.

5.1 Rust‑Powered Performance

• Zero‑Cost Abstractions – Rust’s ownership model eliminates runtime overhead, allowing RayClaw to process hundreds of messages per second on modest hardware.
• Memory Safety – No garbage collector, no null pointers; the runtime is resilient to crashes.
• Cross‑Compilation – Deployable on Linux, macOS, Windows, and even embedded devices (e.g., Raspberry Pi) without re‑implementation.

5.2 Multi‑Channel Out‑of‑The‑Box

RayClaw’s channel adapters expose a unified API across platforms, meaning you can spin up a bot that talks to users on Telegram and Discord with minimal code changes. The adapters handle authentication (e.g., Telegram bot tokens), webhook registration, and retry logic automatically.

5.3 Agentic Workflow

Unlike typical chat frameworks that treat the LLM as a black box, RayClaw treats the LLM as a planner:

• The LLM receives the prompt and decides which steps to take.
• It can call tools to retrieve information or perform actions.
• The agent can ask for clarifications or request retries.

This architecture brings a level of autonomy that is increasingly demanded by modern business and consumer use cases (e.g., self‑service customer support, smart assistants).

5.4 Tool Registry & Typed Calls

Tools are described using JSON Schema, which ensures that:

• Input validation – The runtime verifies that all required fields are present.
• Output validation – Responses are parsed into typed Rust structs.
• Extensibility – Adding a new tool is simply a matter of writing a Rust function and registering it.

Because the LLM is instructed to return structured JSON, there is almost zero risk of parsing errors.

5.5 State Persistence & Conversation Memory

The runtime provides an optional persistence layer (PostgreSQL, SQLite, or in‑memory). Each agent’s conversation history, user preferences, and cached API responses are stored, enabling:

• Contextual follow‑ups (e.g., “Do you need anything else?”).
• Personalization (e.g., remembering a user’s favorite coffee size).
• Fault tolerance (resume after a crash).

The persistence interface is generic, allowing developers to plug in any database that implements the trait.

5.6 Safety & Sandbox

RayClaw includes a sandbox layer that prevents arbitrary code execution:

• Only registered tools can be called.
• A tool’s code runs inside a separate process or thread with limited privileges.
• A timeout mechanism ensures tools cannot stall the bot indefinitely.

Additionally, the runtime enforces rate limits per user and per channel to comply with API restrictions (Telegram’s 30 messages per second per bot, Slack’s 50 messages per minute, etc.).

5.7 Extensible Prompt Templates

The prompt template system uses the askama crate (Rust’s templating engine) and can include:

• Jinja‑style variables ({{ variable }}).
• Conditional blocks ({% if condition %} ... {% endif %}).
• Loops ({% for item in list %}).

Developers can create domain‑specific prompts that embed context, tool manifests, and policy directives in a single file.

5.8 Open‑Source Ecosystem & Contribution Model

RayClaw is open source under the Apache‑2.0 license. The repo includes:

• Comprehensive documentation (API reference, developer guide, examples).
• Automated tests (unit, integration, and end‑to‑end).
• CI/CD pipelines (GitHub Actions).
• Community forums (Discord, GitHub Discussions).

The maintainers welcome pull requests and have a clear contribution guide: tests, documentation updates, or new agents/tools.

6. Real‑World Use Cases

Below are some concrete scenarios where RayClaw shines, accompanied by snippets of how you might implement them.

6.1 Customer Support Bot

Scenario: A SaaS company wants an on‑site support bot that can answer FAQs, open support tickets, and guide users through troubleshooting steps.

Implementation:

• Agents:
• FAQAgent – uses a static knowledge base (CSV or SQLite).
• TicketAgent – invokes the company’s ticketing API (e.g., Zendesk).
• TutorialAgent – calls a knowledge graph to fetch step‑by‑step guides.
• Tools:
• SearchDB – SQL query tool.
• CreateTicket – HTTP POST to ticket API.
• SendEmail – SMTP client for follow‑ups.
• Channels:
• Telegram (company’s support line).
• Slack (internal helpdesk).

With RayClaw, the bot can understand a user’s vague description, plan the best action (search FAQ first, then open a ticket if no answer), and provide a structured, friendly response.

6.2 Personal Assistant

Scenario: A busy professional wants a bot that can manage meetings, send reminders, and schedule travel.

Implementation:

• Agents:
• CalendarAgent – interacts with Google Calendar.
• TravelAgent – fetches flight and hotel availability.
• ReminderAgent – pushes notifications to Telegram.
• Tools:
• GoogleAPI – OAuth‑protected calendar access.
• FlightSearch – API calls to Skyscanner.
• EmailSender – for calendar invites.

The bot can parse natural language commands (“Book me a flight to Berlin next Monday”) and plan accordingly.

6.3 E‑Commerce Assistant

Scenario: An online store wants a bot that can recommend products, handle orders, and answer product questions.

Implementation:

• Agents:
• ProductAgent – queries the inventory database.
• CartAgent – manages shopping cart state.
• CheckoutAgent – initiates payment via Stripe.
• Tools:
• DatabaseQuery – SQL tool.
• StripeCharge – HTTPS call.
• SendSMS – Twilio API for order confirmations.

The bot can answer “Do you have a size 10 in blue?” and update the cart in real time.

6.4 Educational Tutor

Scenario: A language learning platform wants an interactive tutor that can give lessons, quizzes, and correct user responses.

Implementation:

• Agents:
• LessonAgent – pulls lesson modules from a CMS.
• QuizAgent – generates adaptive quizzes.
• FeedbackAgent – uses a language model to grade answers.
• Tools:
• FetchLesson – API call to the CMS.
• GradeAnswer – passes text to LLM for evaluation.
• SendProgress – updates student dashboard.

With RayClaw, the tutor can handle multi‑step dialogues (lesson → quiz → feedback) while preserving context across sessions.

7. Performance Benchmarks

The developers ran a series of benchmarks to compare RayClaw against a Python‑based framework (LangChain) on a typical LLM backend (OpenAI GPT‑3.5). The tests involved:

• Message throughput – number of messages processed per second.
• Latency – time from user message to bot response.
• CPU & memory usage – sustained resource consumption under load.

| Metric | RayClaw (Rust) | LangChain (Python) | |--------|----------------|--------------------| | Throughput (msg/s) | 450 | 120 | | Average latency | 1.2 s | 2.5 s | | CPU usage | 25 % (single core) | 55 % (single core) | | Memory footprint | 120 MiB | 550 MiB |

The results demonstrate a 3–4× performance advantage for RayClaw, especially at high load. The memory advantage is also significant for deployments on resource‑constrained devices.

Note: These benchmarks used the same LLM backend and identical prompts. The only variable was the runtime implementation.

8. Comparison to Competitors

| Feature | RayClaw | LangChain | ReAct | AutoGPT | |---------|---------|-----------|-------|---------| | Language | Rust | Python | Python | Python | | Agentic Flow | Built‑in planning + tools | Requires custom wrapper | Planning + tool calls | Planning + tool calls | | Multi‑channel | Out‑of‑the‑box (Telegram, Discord, Slack) | Channel‑specific wrappers | Not included | Not included | | Typed Tool Calls | JSON Schema + Rust types | Free‑form | JSON schema (manual) | JSON schema (manual) | | Performance | High (low latency, low memory) | Moderate | Moderate | Moderate | | Extensibility | Rust traits + plugin system | Python dynamic | Python dynamic | Python dynamic | | Sandbox | Process isolation + timeouts | None (by default) | None | None | | Licensing | Apache‑2.0 | MIT | MIT | MIT |

RayClaw’s Rust base and built‑in planning architecture distinguish it as the most robust, type‑safe, and efficient option for agentic bots that must handle multiple channels concurrently.

9. Deployment & DevOps

9.1 Docker & Containerization

The repo ships with Dockerfiles for Alpine Linux and Debian. A minimal Dockerfile looks like:

FROM rust:1.70-alpine AS builder
WORKDIR /app
COPY . .
RUN cargo build --release

FROM alpine:latest
COPY --from=builder /app/target/release/rayclaw /usr/local/bin/rayclaw
EXPOSE 8080
CMD ["rayclaw", "--config", "/etc/rayclaw/config.toml"]

This container can be deployed on Kubernetes, ECS, or directly on a VPS. Because RayClaw is statically linked, the container footprint is small (~25 MiB).

9.2 Configuration

RayClaw uses a TOML configuration file that allows developers to tweak:

• LLM providers and API keys.
• Tool registry.
• Database connection strings.
• Rate limits and retry policies.
• Logging verbosity.

Example config.toml:

[llm]
provider = "openai"
model = "gpt-4"
api_key = "sk-..."

[channels.telegram]
token = "123:ABC"

[tools]
[tools.weather]
url = "https://api.openweathermap.org/data/2.5/weather"
api_key = "..."

[database]
url = "postgres://user:pass@localhost:5432/rayclaw"

The file can be mounted into the container at runtime.

9.3 CI/CD & Testing

The project harnesses GitHub Actions:

• cargo test – runs unit and integration tests.
• cargo clippy – lints code.
• cargo fmt – enforces formatting.
• docker build – builds the Docker image.
• helm – optional chart deployment.

The test suite covers more than 80 % of the codebase, including agent simulation tests that feed synthetic messages and assert on the expected tool calls.

10. Contribution Guidelines

RayClaw’s maintainers actively seek contributions across several axes:

• New Agents – Domain experts can provide ready‑made agents for niche industries.
• Tool Implementations – Any external API that can be represented as a JSON‑structured tool can be added.
• Channel Adapters – Extending support to new messaging platforms is highly encouraged.
• Performance Enhancements – Profiling and optimizing Rust code.
• Documentation & Examples – Clarity is essential for adoption.

The CONTRIBUTING.md file lays out a step‑by‑step workflow:

1. Fork the repo.
2. Create a new feature branch.
3. Add tests and documentation.
4. Submit a pull request.
5. Pass CI checks.

The community values code quality over quantity, so thorough tests and documentation are mandatory.

11. Roadmap – What’s Next?

RayClaw’s roadmap is guided by community feedback and the evolving landscape of LLMs.

| Milestone | Description | ETA | |-----------|-------------|-----| | v0.2 | Add a built‑in debugger that visualizes agent plans and tool calls in real time. | Q3 2026 | | v0.3 | Full support for local LLM hosting (e.g., llama.cpp, Anthropic). | Q4 2026 | | v0.4 | Cross‑platform GUI (Electron) for non‑programmer bot configuration. | Q1 2027 | | v0.5 | Integration with Webhooks for low‑latency messaging (e.g., WhatsApp Business). | Q2 2027 | | v1.0 | Stable release with extensive documentation, a curated list of agents and tools, and an official Docker Compose stack. | Q3 2027 |

Each milestone is accompanied by a beta‑testing phase to ensure stability and gather user feedback.

12. Licensing & Commercial Use

RayClaw is distributed under the Apache‑2.0 license, a permissive open‑source license that permits commercial use, modification, and distribution without a copyleft requirement. This makes RayClaw suitable for:

• Proprietary in‑house bots.
• SaaS products offering bot services.
• Embedded devices that require a lightweight AI runtime.

The license also protects contributors by ensuring any derivative works can be freely distributed, provided the original license terms are maintained.

13. Getting Started – Quick‑Start Guide

Below is a minimal example that spins up a Telegram bot using the default ChatAgent.

13.1 Prerequisites

• Rust 1.70+ (install via rustup).
• Cargo (Rust’s package manager).
• Docker (optional).
• Telegram Bot token (create via BotFather).
• OpenAI API key (optional if you have a local LLM).

13.2 Clone and Build

git clone https://github.com/yourusername/rayclaw.git
cd rayclaw
cargo build --release

13.3 Create Config

# config.toml
[llm]
provider = "openai"
model = "gpt-4"
api_key = "sk-..."

[channels.telegram]
token = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"

[tools]
# No tools for this example

13.4 Run

./target/release/rayclaw --config config.toml

You should see log output:

2026-03-10 12:34:56 [INFO] RayClaw v0.1.0 starting
2026-03-10 12:34:56 [INFO] Telegram channel listening on 0.0.0.0:8080

Send a message to your bot on Telegram, and it should reply using the ChatAgent.

13.5 Adding a Weather Agent

1. Add a new agent file: agents/weather.rs.
2. Implement BaseAgent for WeatherAgent.
3. Register the agent in main.rs.
4. Add the WeatherAPI tool to config.toml.

After restarting, messages like “What’s the weather in Paris?” will trigger the plan → tool call → formatted response flow.

14. Security Considerations

When deploying RayClaw in production, pay attention to the following:

• API Key Management – Store keys in environment variables or secret stores (AWS Secrets Manager, Vault).
• Rate Limiting – Respect the rate limits of both the messaging platform and the LLM provider.
• Input Sanitization – Even though the LLM generates tool calls, validate JSON schema to avoid malformed inputs.
• Tool Isolation – Run tools in separate processes or containers to prevent privilege escalation.
• HTTPS – Ensure all outgoing requests use TLS.
• Audit Logging – Enable the /metrics endpoint and log all interactions for compliance.

The repository already implements basic sandboxing and timeouts, but you may want to tighten constraints for highly regulated environments.

15. Community & Support

• GitHub Issues – Track bugs, request features, or discuss architecture.
• GitHub Discussions – For general questions and tutorials.
• Discord – Real‑time chat with developers and maintainers.
• Contributing Guide – Comprehensive instructions on how to contribute.
• Documentation – Hosted on GitHub Pages (link in repo README).

The community is active, with frequent pull requests and issue triage. Contributors are recognized via GitHub “Contributors” badge.

16. Frequently Asked Questions

| Question | Answer | |----------|--------| | Is RayClaw suitable for low‑latency applications (e.g., voice assistants)? | Yes. Rust’s performance ensures sub‑second response times, but the actual latency depends on the LLM backend and network hops. | | Can I run a local LLM with RayClaw? | Absolutely. RayClaw supports any LLM that implements a simple HTTP or GRPC interface, including llama.cpp, OpenLLM, or proprietary models. | | Does RayClaw support multi‑user contexts? | Each channel user is isolated by default. The runtime can store conversation history per user, so personalized context is possible. | | Can I add my own tool? | Yes. Implement a Rust function, annotate it with the tool! macro, register it in the registry, and add its name to the agent’s tool manifest. | | How does RayClaw handle LLM hallucinations? | Agents can incorporate sanity checks, prompt the LLM to verify outputs, and fall back to external tools or human review. | | Is there a GUI for managing agents? | Not yet. However, the repo includes a minimal web dashboard under /dashboard that displays agent health and logs. A full GUI is planned for v0.4. |

17. Closing Thoughts

RayClaw is more than a library—it is a platform for building intelligent, autonomous systems that can interact across multiple communication channels. Its Rust foundation delivers low‑level efficiency, while its agentic architecture aligns with the modern paradigm of LLM‑powered planning and tool use. By abstracting away boilerplate (prompt engineering, tool registration, state persistence), RayClaw lets developers focus on business logic and domain expertise.

Whether you’re a hobbyist building a personal assistant, a company deploying a support bot, or an academic experimenting with AI autonomy, RayClaw provides a solid, extensible foundation. The project’s active development cycle, generous licensing, and vibrant community signal that it will continue to evolve alongside the rapid advances in large‑language‑model technology.

📌 Next Steps

1. Explore the Docs – Dive into the official documentation on the repo’s GitHub Pages.
2. Try a Demo – Fork the repo, spin up the Docker container, and interact via Telegram or Slack.
3. Contribute – Pick an issue labeled “good first issue” and submit a pull request.
4. Share Feedback – Join the Discord or open a discussion to shape the next milestones.

By engaging with RayClaw, you’re not only building smarter bots—you’re shaping the future of agentic AI runtime design. Happy coding! 🚀