Make chat-tuned LLMs continue text instead of answering it.
basemode provides raw text continuation through the terminal, an async Python
API, and an OpenAI-compatible completions server. It selects a prompting method
for each model and repairs common artifacts at the join between the prompt and
the generated text.
Ask a chat model to continue a passage and it may answer with an introduction, quote the continuation, or offer to revise it:
> The ship rounded the headland and
I'd be happy to help continue this passage! Here's one possibility:
"The ship rounded the headland and the harbour opened before them..."
Would you like me to continue in a different style?
With basemode, the same prefix produces continuation text directly:
$ basemode "The ship rounded the headland and"
The ship rounded the headland and the wind dropped all at once, as though
the cliffs had swallowed it. Sails that had been drum-tight for three days
went slack, and in the sudden quiet the crew could hear water moving along
the hull.pip install basemodeRequires Python 3.11 or later. Set the relevant provider key in the environment
or a .env file, or save it with basemode keys set PROVIDER.
# Continue a prefix
basemode "The ship rounded the headland and"
# Parallel continuations
basemode "The ship rounded the headland and" -n 3
# Or just ask a question, like `llm`
basemode ask "why is the sky blue?" -m claude-haiku-4-5
cat notes.md | basemode ask "summarise this"
# Inspect selected strategy and pricing metadata
basemode info claude-sonnet-4-6
# Compare prompting methods and save the best result
basemode bench claude-sonnet-4-6 --save
# Show only key-configured models
basemode models --availableSet a default model with basemode default MODEL. Without one, generation uses
gpt-4o-mini. See the installation guide
for provider-key names and source installation.
basemode --help
basemode run --help
basemode models --help
basemode info --help
basemode strategies --helpbasemode run: stream one or more continuations; this is the default commandbasemode ask: ask a question and stream a normal chat answer (stdin is prepended)basemode models: list known models and filter by provider or availabilitybasemode info: inspect a model's normalized ID, strategy, quirks, price, and local healthbasemode bench: compare strategies using live requests and optionally pin the winnerbasemode verify: collect durable compatibility evidence with bounded live probesbasemode serve: expose an OpenAI-compatible/v1/completionsendpointbasemode keys,default, andrate: manage local preferences
Different models respond to different prompt shapes: native completion,
assistant prefill, a system instruction, few-shot examples, or FIM tokens.
basemode chooses in this order:
- A strategy supplied for the current call.
- A local pin saved by
basemode bench --save. - The strategy recorded in the verified-model registry.
- A model-name heuristic.
basemode info MODEL reports the selected strategy and its source. bench
compares strategies on prose, technical writing, poetry, and dialogue, then
scores assistant preambles, refusals, echoed prefixes, chat turns, and stray
formatting.
basemode bench claude-opus-5 --samples # compare
basemode bench claude-opus-5 --save # pin the winner for this model┏━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ Strategy ┃ Score ┃ Flags / error ┃ Mean s ┃
┡━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ few_shot │ 0.93 │ quoted │ 2.07 │
│ system │ 0.75 │ empty │ 2.24 │
│ prefill │ 0.00 │ BadRequestError: ... │ 0.30 │
└──────────┴───────┴──────────────────────┴────────┘
See Strategies for the prompt shapes, scoring rules, and override behavior.
basemode serve exposes POST /v1/completions for clients that support the
classic OpenAI completions API, including
Tapestry Loom.
pip install 'basemode[server]'
basemode serve --port 8080Set the client endpoint to http://127.0.0.1:8080/v1/completions. Responses
are synchronous JSON; streaming and logprobs are not supported. The
CLI reference
lists accepted fields.
from basemode import continue_text, branch_text
async for token in continue_text(
"The ship rounded the headland and",
model="gpt-4o-mini",
max_tokens=120,
):
print(token, end="", flush=True)
async for idx, token in branch_text(
"The ship rounded the headland and",
model="gpt-4o-mini",
n=3,
max_tokens=80,
):
print(idx, token, end="", flush=True)The documentation covers configuration, strategy behavior, model evidence, and the complete CLI and Python APIs. To build it locally:
make docs-serveFor repository setup and test commands, see the agent quickstart.
The registry currently contains 297 model endpoints with a tested prompt strategy. The full model table lists prices, release dates, strategies, and compatibility quirks. It is generated from the same registry used at runtime.