8a53cb59f1
Restructure the OpenClaw workspace so all SRS project directories are accessible via symlinks from `.openclaw/`, eliminating the need for parent traversal or absolute paths. All AI tools (OpenClaw, Claude Code, Codex, Kiro) now see the same relative paths from the workspace root. **Workspace restructuring** - Add symlinks in `.openclaw/` for `trunk/`, `cmd/`, `internal/`, `cmake/`, `docs/`, `objs/`, and a self-referential `.openclaw` link - Add root-level `memory` symlink pointing to `.openclaw/memory` - Simplify `TOOLS.md` working directory rules: everything is relative from CWD - Update `.gitignore` patterns for `personal*`, `support*`, `srs-consults*` directories **New codebase map (`memory/srs-codebase-map.md`)** - Comprehensive map of the entire SRS codebase: C++ media server modules (`core/`, `kernel/`, `protocol/`, `app/`), State Threads, Go next-gen server (`cmd/` + `internal/`), documentation, and testing structure - Enables AI to reason about which files are relevant to a question instead of blind grepping - Added "Codebase map first" rule to `MEMORY.md`: always load the map before searching code **Skill updates** The `srs-support` has been reorganized into a three-phase workflow consisting of Setup, Load Knowledge, and Answer by Topic. It now features a tiered approach to knowledge integration, with the codebase map being incorporated as the third layer. - `st-develop`: Simplified setup, added codebase map reference For both skills, the dynamic resolution logic for `SRS_ROOT` has been eliminated. Now, all paths are relative. **Documentation rewrite (`getting-started-ai.md`)** - Replaced Augment Code / GitHub Copilot / PR review content with current AI tooling: SRS Robot (Telegram/Discord), Claude Code, Codex, Kiro, and OpenClaw - Added sections on skills and the knowledge base philosophy **Cleanup** - Removed `docs/ideas.md`, `docs/youtube/` transcripts, and `proxy/README.md` - Removed "Ideas Capture" and "YouTube Channel Content" sections from `MEMORY.md` - Fixed origin cluster doc build command (`cd srs && make`) --------- Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
116 lines
5.5 KiB
Markdown
116 lines
5.5 KiB
Markdown
---
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title: AI Agent
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sidebar_label: AI Agent
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hide_title: false
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hide_table_of_contents: false
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---
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# AI Agent
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SRS provides several ways to use AI: you can chat with the SRS Robot in Telegram or Discord for quick answers, or run AI locally with OpenClaw, Claude Code, Codex, or Kiro using the pre-configured SRS knowledge base.
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## SRS Robot
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SRS provides an AI-powered support robot built on OpenClaw, available in both the Telegram group and Discord channel. The robot has a deep knowledge base covering SRS code, documentation, and common usage scenarios, and is powered by the latest AI model.
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You can ask the SRS Robot anything about SRS — how to use it, how to match your use case, how to configure it, how to debug issues, or any other questions. The robot will give you accurate, up-to-date answers based on the latest SRS knowledge base.
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Join the SRS Telegram group: [https://t.me/+RiynvKOxpQ42MGJl](https://t.me/+RiynvKOxpQ42MGJl)
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Join the SRS Discord channel: [https://discord.gg/yZ4BnPmHAd](https://discord.gg/yZ4BnPmHAd)
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Once you're in the group, @ the SRS Robot and ask your question directly.
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## Claude Code
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You can use Claude Code locally with the SRS codebase. SRS ships with a pre-configured `.claude` directory so Claude Code works out of the box with full context of the project.
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Clone the SRS code and start Claude Code:
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```bash
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git clone https://github.com/ossrs/srs.git
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cd srs
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claude
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```
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Claude Code will automatically load the configuration from `srs/.claude`, giving it deep knowledge of the SRS codebase so you can ask questions, debug issues, write code, and more.
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## Codex
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You can also use Codex locally with the SRS codebase. SRS ships with a pre-configured `.codex` directory so Codex works out of the box.
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Clone the SRS code and start Codex:
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```bash
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git clone https://github.com/ossrs/srs.git
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cd srs
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codex
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```
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Codex will automatically load the configuration from `srs/.codex`.
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## Kiro
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You can also use Kiro locally with the SRS codebase. SRS ships with a pre-configured `.kiro` directory so Kiro works out of the box.
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Clone the SRS code and start Kiro:
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```bash
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git clone https://github.com/ossrs/srs.git
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cd srs
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kiro-cli
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```
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Kiro will automatically load the configuration from `srs/.kiro`.
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## OpenClaw
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You can create an OpenClaw agent with the SRS knowledge base for local use. First clone the SRS code:
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```bash
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git clone https://github.com/ossrs/srs.git
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```
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There are two ways to point the agent at the SRS knowledge base:
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- **Set the workspace directly** — when creating the agent, set the workspace path to `srs/.openclaw`.
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- **Soft link** — create the agent with its default workspace, then remove the default workspace directory and replace it with a soft link to `srs/.openclaw`:
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```bash
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ln -sf ~/git/srs/.openclaw ~/.openclaw/workspace
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```
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Once the agent is running, list the available skills and trigger them to load the knowledge base. The skills tell the AI which files to load and how to work more effectively with the SRS codebase.
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## Skills
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After starting OpenClaw, Claude Code, Codex, or Kiro with the SRS codebase, you should use skills to get the best results. Skills load the right knowledge base for your task and guide the AI through the correct workflow.
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The `srs-support` skill is provided for answering questions about the SRS project. It automatically loads the relevant knowledge base based on your question, so the AI can give you accurate and context-aware answers.
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More skills will be added over time. To see what skills are currently available, simply ask the AI:
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```
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What skills can I use for SRS?
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```
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## Knowledge Base
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Code and documentation alone are not enough for AI to truly understand and maintain a project. There is background knowledge, design thinking, accumulated experience, use cases, community communication, and debugging workflows that live only in people's heads — not in any file. The SRS knowledge base is an effort to make all of that explicit.
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The knowledge base is the OpenClaw memory — files that encode the background, experience, and context behind SRS. It is built by having AI read the code and documents, then talking with AI to surface the implicit knowledge and write it down. Over time, the knowledge base will cover everything: not just what the code does, but why it was designed that way, how to think about problems, and how to operate and maintain the project.
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The knowledge base and the code together are the single source of truth for SRS. The knowledge base captures what the code cannot — background, design decisions, use cases, debugging experience, and community knowledge. In the future, traditional documentation will be generated from the knowledge base and maintained entirely by AI.
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On top of the knowledge base, there are skills. Skills are workflows that tell AI how to handle specific tasks, such as:
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- **Support** — answering user questions and matching use cases
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- **Issue triage and fix** — understanding, reproducing, and resolving issues
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- **Feature development** — designing and implementing new features
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- **Maintenance** — reviewing pull requests, managing releases, keeping the project healthy
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- **Debugging** — diagnosing and tracing problems in the codebase
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Each skill loads the relevant parts of the knowledge base and guides the AI through the right workflow for that task. This is what makes AI effective at maintaining SRS — not just raw intelligence, but structured knowledge and workflows built up over time.
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