Expand front-page docs with runtime flow and improvement areas

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Bernardo 2026-03-14 13:41:26 +01:00
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@ -112,6 +112,53 @@ The stack is provider-agnostic, but a practical low-cost deployment can split wo
- Voice can use STT/TTS providers that are better than a general-purpose chat model. - Voice can use STT/TTS providers that are better than a general-purpose chat model.
- Workspace actions like Gmail or Drive are usually better handled as tool/API calls than as LLM-only reasoning. - Workspace actions like Gmail or Drive are usually better handled as tool/API calls than as LLM-only reasoning.
## ⚙️ How It Works
At a high level, PicoClaw is a small Go runtime that sits between chat channels, tools, and model providers:
1. A message arrives from Telegram, WhatsApp Native, CLI, or another enabled channel.
2. PicoClaw resolves the active agent, workspace, conversation scope, and available tools.
3. The agent picks the right provider path for the turn:
- text -> main chat model chain
- image -> image model chain
- voice -> STT / TTS chain
- actions -> local tools or external APIs
4. Tool calls run locally in the workspace or through configured APIs such as Google Workspace.
5. Results are fed back into the agent loop until the task is done, then a final reply is sent back through the original channel.
This means PicoClaw is not only a chat wrapper around one LLM. It is closer to a small execution runtime:
- channels handle transport
- agent loop handles routing, memory, retries, and tool orchestration
- providers handle model calls
- tools handle real work such as shell, file I/O, web fetch, previews, scheduling, and integrations
- workspace state keeps per-user or per-agent context on disk
## 🧭 Practical Operating Model
In a real deployment, PicoClaw works best when each capability is given the cheapest reliable path instead of forcing everything through one provider:
- conversation stays on a low-cost text model
- image understanding is routed directly to a vision-capable model
- voice uses dedicated speech providers for transcription and synthesis
- Gmail, Drive, and Calendar are handled through explicit tool/API calls
- previews are served locally from the built-in preview server instead of an external platform
That design keeps the runtime portable enough for low-end hardware, including RISC-V boards, while still allowing richer multi-modal behavior.
## 🔧 What Still Needs Improving
PicoClaw is useful today, but the front page should be honest about where the system still needs work:
- Tool-call robustness: malformed or partially emitted tool arguments still happen and recovery is not perfect.
- Long-running task continuity: multi-minute jobs need better checkpointing and recovery across restarts.
- Preview workflow polish: stable preview URLs now exist, but site build flows still need better automatic follow-up and status reporting.
- Safer default behavior: command execution, git operations, and external fetches need tighter default guardrails for shared or production environments.
- Provider ergonomics: model aliasing, fallback transparency, and capability-specific routing should be easier to understand from config alone.
- Event-driven integrations: inbox watching, reminders, and notification workflows still rely too much on polling or inferred tool usage.
- Docs consistency: feature docs, migration docs, and live deployment docs need to stay closer to the actual shipped behavior.
- Worktree hygiene: active development branches can accumulate large dirty states; release-ready branches and cleaner change isolation need improvement.
## 🦾 Demonstration ## 🦾 Demonstration
### 🛠️ Standard Assistant Workflows ### 🛠️ Standard Assistant Workflows