Use codemap find "SymbolName" to locate any symbol before reading — it gives exact file + line range and is faster than grep across the whole repo.

Inspecting LLM Streams

When to use

You added a "use blank lines between paragraphs" directive to your agent's system prompt and want proof the model now emits \n\n between sentences. You have a chat UI that renders messages as run-on text and you suspect the model itself is the source rather than the renderer. You want to compare how two models (Claude 3.5 Sonnet vs. a free open-source model) handle the same prompt structurally.

In all these cases you want the raw token deltas from the transport, not an aggregated Completion object — chunk boundaries reveal what the model actually emitted versus what your wire format / parser preserved.

What it does

inspect_stream.py calls a transport's complete(..., stream=True) directly, prints each chunk.delta with repr() (so \n shows explicitly), then prints the full accumulated text and a count of newlines. No agent loop, no tool calls, no SSE framing — just LLM input → LLM output.

--- STREAMED CHUNK DELTAS (repr'd, \n preserved) ---
'Here'
' are'
' the'
' details'
':'
'\n\n'
'-'
' **'
'Recipient'
'**:'
' Bob'
...

--- FULL ACCUMULATED TEXT ---
Here are the details:

- **Recipient**: Bob
...

--- BACKSLASH-N COUNT: 6  --  TOTAL LEN: 91 ---

Running the script

# Set transport credentials (Anthropic / OpenAI / OpenRouter all work).
export OPENROUTER_API_KEY=sk-or-v1-...
export LLM_MODEL="poolside/laguna-xs.2:free"

uv run python skills/inspecting-streams/inspect_stream.py \
    --system "You are a banking assistant.  Use blank lines between paragraphs and put each list item on its own line." \
    --prompt "Summarise the products SecureBank offers."

Or import the helper into a one-off REPL session:

import asyncio
from skills.inspecting_streams.inspect_stream import trace
from lauren_ai import LLMConfig

cfg = LLMConfig.for_anthropic(model="claude-haiku-4-5")
asyncio.run(trace(cfg, system="You are a poet.", user_prompt="Write three lines about the sea."))

Interpreting the output

• '\\n\\n' between chunks → the model is emitting paragraph breaks. Renderer should produce real paragraphs.
• ' ' (single space) where you expected '\\n' → the model is treating the boundary as a soft break. Either tighten the system prompt's formatting directive or accept the result.
• - followed immediately by content with no preceding '\\n' → the model is jamming list items onto the same line as the heading. System-prompt fix: show a concrete bullet-list example.
• Trailing ' \\n' (two spaces + newline) → CommonMark hard line break. GFM renders this as <br>; harmless but worth noting.

Why bypass the agent loop

The agentic loop adds tool execution, hooks, signal emission, and sometimes a follow-up turn. When you only want to know "what does this model emit for this exact prompt", those layers obscure the picture. This skill reaches past AgentRunner.run_stream straight to Transport.complete(..., stream=True) for the cleanest signal.

Anti-patterns

• Don't use this to test agent behaviour end-to-end. Use AgentTestClient (see testing-agents skill) for that — it covers tools, hooks, multi-turn flows, and assertions.
• Don't add ad-hoc whitespace heuristics on the renderer side because this script shows dense output. Fix the system prompt. Renderer heuristics inevitably misfire on legitimate identifiers (SecureBank, OAuth, GitHub) that look structurally identical to missing-space sentence boundaries.