docs: remove feature showcase and update memory and Python SDK documentation for clarity and completeness

This commit is contained in:
Xubin Ren
2026-04-19 19:25:05 +08:00
committed by Xubin Ren
parent ed150a4228
commit 508e247c82
3 changed files with 197 additions and 33 deletions
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# Feature Showcase
<table align="center">
<tr align="center">
<th><p align="center">📈 24/7 Real-Time Market Analysis</p></th>
<th><p align="center">🚀 Full-Stack Software Engineer</p></th>
<th><p align="center">📅 Smart Daily Routine Manager</p></th>
<th><p align="center">📚 Personal Knowledge Assistant</p></th>
</tr>
<tr>
<td align="center"><p align="center"><img src="../case/search.gif" width="180" height="400"></p></td>
<td align="center"><p align="center"><img src="../case/code.gif" width="180" height="400"></p></td>
<td align="center"><p align="center"><img src="../case/schedule.gif" width="180" height="400"></p></td>
<td align="center"><p align="center"><img src="../case/memory.gif" width="180" height="400"></p></td>
</tr>
<tr>
<td align="center">Discovery • Insights • Trends</td>
<td align="center">Develop • Deploy • Scale</td>
<td align="center">Schedule • Automate • Organize</td>
<td align="center">Learn • Memory • Reasoning</td>
</tr>
</table>
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# Memory in nanobot
> **Note:** This design is currently an experiment in the latest source code version and is planned to officially ship in `v0.1.5`.
nanobot's memory is built on a simple belief: memory should feel alive, but it should not feel chaotic.
Good memory is not a pile of notes. It is a quiet system of attention. It notices what is worth keeping, lets go of what no longer needs the spotlight, and turns lived experience into something calm, durable, and useful.
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# Python SDK
Use nanobot as a library — no CLI, no gateway, just Python:
Use nanobot as a library — no CLI, no gateway, just Python.
## Quick Start
```python
import asyncio
from nanobot import Nanobot
async def main() -> None:
bot = Nanobot.from_config()
result = await bot.run("What time is it in Tokyo?")
print(result.content)
asyncio.run(main())
```
`Nanobot.from_config()` reuses your normal `~/.nanobot/config.json`, so the SDK follows the same provider, model, tools, and workspace defaults as the CLI unless you override them.
## Common Patterns
### Use a specific config or workspace
```python
from nanobot import Nanobot
bot = Nanobot.from_config()
result = await bot.run("Summarize the README")
print(result.content)
bot = Nanobot.from_config(
config_path="~/.nanobot/config.json",
workspace="/my/project",
)
```
Each call carries a `session_key` for conversation isolation — different keys get independent history:
### Isolate conversations with `session_key`
Different session keys keep independent conversation history:
```python
await bot.run("hi", session_key="user-alice")
await bot.run("hi", session_key="task-42")
```
Add lifecycle hooks to observe or customize the agent:
### Attach hooks for observability
Hooks let you inspect tool calls, streaming, and iteration state without modifying nanobot internals:
```python
from nanobot.agent import AgentHook, AgentHookContext
class AuditHook(AgentHook):
async def before_execute_tools(self, ctx: AgentHookContext) -> None:
for tc in ctx.tool_calls:
async def before_execute_tools(self, context: AgentHookContext) -> None:
for tc in context.tool_calls:
print(f"[tool] {tc.name}")
result = await bot.run("Hello", hooks=[AuditHook()])
result = await bot.run("Review this change", hooks=[AuditHook()])
```
## API Reference
### `Nanobot.from_config(config_path=None, *, workspace=None)`
Create a `Nanobot` instance from a config file.
| Param | Type | Default | Description |
|-------|------|---------|-------------|
| `config_path` | `str \| Path \| None` | `None` | Path to `config.json`. Defaults to `~/.nanobot/config.json`. |
| `workspace` | `str \| Path \| None` | `None` | Override the workspace directory from config. |
Raises `FileNotFoundError` if an explicit config path does not exist.
### `await bot.run(message, *, session_key="sdk:default", hooks=None)`
Run the agent once and return a `RunResult`.
| Param | Type | Default | Description |
|-------|------|---------|-------------|
| `message` | `str` | *(required)* | The user message to process. |
| `session_key` | `str` | `"sdk:default"` | Session identifier for conversation isolation. Different keys get independent history. |
| `hooks` | `list[AgentHook] \| None` | `None` | Lifecycle hooks for this run only. |
### `RunResult`
| Field | Type | Description |
|-------|------|-------------|
| `content` | `str` | The agent's final text response. |
| `tools_used` | `list[str]` | Reserved for richer SDK introspection; may be empty in current versions. |
| `messages` | `list[dict]` | Reserved for richer SDK introspection; may be empty in current versions. |
## Hooks
Hooks let you observe or customize the agent loop. Subclass `AgentHook` and override the methods you need.
### Hook lifecycle
| Method | When |
|--------|------|
| `wants_streaming()` | Return `True` if you want token-by-token `on_stream()` callbacks |
| `before_iteration(context)` | Before each LLM call |
| `on_stream(context, delta)` | On each streamed token when streaming is enabled |
| `on_stream_end(context, *, resuming)` | When streaming finishes |
| `before_execute_tools(context)` | Before tool execution |
| `after_iteration(context)` | After each iteration |
| `finalize_content(context, content)` | Transform final output text |
Useful fields on `AgentHookContext` include:
- `iteration`
- `messages`
- `response`
- `usage`
- `tool_calls`
- `tool_results`
- `tool_events`
- `final_content`
- `stop_reason`
- `error`
### Example: audit tool calls
```python
from nanobot.agent import AgentHook, AgentHookContext
class AuditHook(AgentHook):
def __init__(self) -> None:
super().__init__()
self.calls: list[str] = []
async def before_execute_tools(self, context: AgentHookContext) -> None:
for tc in context.tool_calls:
self.calls.append(tc.name)
print(f"[audit] {tc.name}({tc.arguments})")
```
```python
hook = AuditHook()
result = await bot.run("List files in /tmp", hooks=[hook])
print(result.content)
print(f"Tools observed: {hook.calls}")
```
### Example: receive streaming tokens
```python
from nanobot.agent import AgentHook, AgentHookContext
class StreamingHook(AgentHook):
def wants_streaming(self) -> bool:
return True
async def on_stream(self, context: AgentHookContext, delta: str) -> None:
print(delta, end="", flush=True)
async def on_stream_end(self, context: AgentHookContext, *, resuming: bool) -> None:
print()
```
### Compose multiple hooks
Pass multiple hooks when you want to combine behaviors:
```python
result = await bot.run("hi", hooks=[AuditHook(), MetricsHook()])
```
Async hook methods are fan-out with error isolation. `finalize_content` is a pipeline: each hook receives the previous hook's output.
### Example: post-process final content
```python
from nanobot.agent import AgentHook
class Censor(AgentHook):
def finalize_content(self, context, content):
return content.replace("secret", "***") if content else content
```
## Full Example
```python
import asyncio
import time
from nanobot import Nanobot
from nanobot.agent import AgentHook, AgentHookContext
class TimingHook(AgentHook):
def __init__(self) -> None:
super().__init__()
self._started_at = 0.0
async def before_iteration(self, context: AgentHookContext) -> None:
self._started_at = time.perf_counter()
async def after_iteration(self, context: AgentHookContext) -> None:
elapsed_ms = (time.perf_counter() - self._started_at) * 1000
print(f"[timing] iteration {context.iteration} took {elapsed_ms:.1f}ms")
async def main() -> None:
bot = Nanobot.from_config(workspace="/my/project")
result = await bot.run(
"Explain the main function",
session_key="sdk:demo",
hooks=[TimingHook()],
)
print(result.content)
asyncio.run(main())
```