bot-brain-basics

Bot Brain Basics — the raw Anthropic SDK

The reference app's Brain is deliberately thin: one call to the raw Anthropic Messages API behind a one-method interface. Simplest thing that's correct, and trivially mockable in tests. This skill teaches that raw SDK so you can extend it with confidence.

The shipped code is the single source of truth: reference-app/app/brain.py defines

class Brain(Protocol):
    async def respond(self, history: list[Message], incoming: str) -> str: ...

and ClaudeBrain implements it with AsyncAnthropic + await self._client.messages.create(...), concatenating the text blocks of the reply. Message (role + text) lives in reference-app/app/models.py. Everything below layers onto that one messages.create call.

The one call the app makes today

from anthropic import AsyncAnthropic

client = AsyncAnthropic()  # reads ANTHROPIC_API_KEY from the environment

response = await client.messages.create(
    model="claude-haiku-4-5",          # current model id; see notes below
    max_tokens=1024,
    system=SYSTEM_PROMPT,
    messages=[{"role": m.role, "content": m.text} for m in history]
             + [{"role": "user", "content": incoming}],
)
reply = "".join(b.text for b in response.content if b.type == "text")

response.content is a list of typed blocks — always check .type before reading .text, because thinking and tool-use blocks can appear in the list too. The API is stateless: you send the full history every turn. That's exactly what SessionStore exists to hold.

Adding tools — the tool-use loop

Give Claude tools, then loop: call the model, run any tools it asks for, feed the results back, repeat until it stops asking. Keep the loop inside respond() so the interface doesn't change.

while True:
    response = await client.messages.create(
        model=self._model, max_tokens=self._max_tokens,
        system=SYSTEM_PROMPT, messages=messages, tools=tools,
    )
    if response.stop_reason == "end_turn":
        break
    messages.append({"role": "assistant", "content": response.content})
    results = [
        {"type": "tool_result", "tool_use_id": b.id, "content": run_tool(b.name, b.input)}
        for b in response.content if b.type == "tool_use"
    ]
    messages.append({"role": "user", "content": results})

Three rules that bite if you skip them:

• Append the whole response.content back as the assistant turn before sending results — that preserves the tool_use blocks the API expects to see.
• Each tool_result must carry the matching tool_use_id. One result per tool call.
• Parse tool.input as the structured object it already is — never string-match the serialized JSON.

The SDK also ships a @beta_tool decorator + client.beta.messages.tool_runner() that runs this loop for you. Use it when you don't need to gate or log each call; hand-roll the loop (above) when you do. See references/tool-use.md.

Streaming

For a chat-style bot, stream so you can forward tokens as they arrive instead of blocking on the full reply:

async with client.messages.stream(
    model=self._model, max_tokens=self._max_tokens,
    system=SYSTEM_PROMPT, messages=messages,
) as stream:
    async for text in stream.text_stream:
        ...                      # forward each chunk
    final = await stream.get_final_message()   # full Message, for usage/history

get_final_message() gives you the complete reply (and usage) after streaming — so you keep timeout protection without hand-handling every event.

Prompt caching — do this

A bot resends a large stable prefix (system prompt, tool defs, conversation history) every turn. Cache it. Caching is a prefix match: mark the end of the stable part with cache_control and reuse pays ~0.1x for the cached tokens.

response = await client.messages.create(
    model=self._model, max_tokens=self._max_tokens,
    system=[{"type": "text", "text": SYSTEM_PROMPT,
             "cache_control": {"type": "ephemeral"}}],
    messages=messages,
)
print(response.usage.cache_read_input_tokens)   # > 0 means a hit

Keep the system prompt frozen — no datetime.now(), no per-request IDs ahead of the breakpoint, or every request misses. Verify hits via usage.cache_read_input_tokens. Full placement patterns and the silent-invalidator checklist: references/prompt-caching.md.

Model ids

Use current Claude 4.x ids — claude-opus-4-8, claude-sonnet-4-6, claude-haiku-4-5. A chat bot defaults well to Haiku (fast, cheap) or Sonnet; the reference app picks the model from config. On Opus 4.8/4.7 the thinking/sampling surface differs — see references/tool-use.md.

Next steps

• Need an agent loop, MCP servers, or built-in tools without writing the plumbing? When you outgrow hand-rolled messages.create, see bot-brain-agent — it layers the Claude Agent SDK behind the same respond() interface.
• Persist history across turns → bot-session-state.
• Screen inputs/outputs → bot-safety.

References

• references/tool-use.md — full tool-use loop, the @beta_tool runner, tool_result error handling, model-id notes.
• references/prompt-caching.md — breakpoint placement, multi-turn caching, the silent-invalidator audit.