Backends¶

llenvs supports multiple inference backends with a unified interface.

Lifecycle¶

Backends are closeable and may be used as context managers:

from llenvs.inference.backends import OpenAIBackend

with OpenAIBackend(model="gpt-4o") as backend:
    result = backend.generate_chat(...)

Callers own backend lifecycle. Local backends release heavyweight model resources best-effort on close(), and API backends close reusable client sessions. Repeated close() calls are safe.

Available Backends¶

Backend	Package	Features
vLLM	`vllm`	Logprobs, batching, prefix continuation, streaming, vision (auto-detected VLMs)
HuggingFace	`transformers`	Logprobs, batching, prefix continuation, chat templates, vision (auto-detected VLMs)
OpenAI	`openai`	Logprobs, batching (concurrent), streaming, function calling, vision
Anthropic	`anthropic`	Batching (concurrent), prefix continuation (prefill), streaming, vision
OpenRouter	`openai`	Batching (concurrent), access to multiple models, vision
LiteLLM	`litellm`	Batching (concurrent), 100+ providers via one SDK, function calling, vision, logprobs (provider-dependent), thinking budgets
Codex CLI	`codex`	Chat, batching (subprocess concurrency), isolated temp workspace, read-only sandbox
Claude Code CLI	`claude` (Claude Code v2.1+)	Chat, batching (subprocess concurrency), isolated temp workspace, tools disabled, reasoning effort

vLLM (Local Inference)¶

vLLM runs in-process — no external server needed. Just point it at a model and a GPU.

YAML Config¶

model:
  backend: vllm
  model: meta-llama/Llama-3.1-8B-Instruct
  params:
    tensor_parallel_size: 2
    dtype: bfloat16
    max_model_len: 4096
    gpu_memory_utilization: 0.9

Python API¶

from llenvs.inference.backends import VLLMBackend

backend = VLLMBackend(
    model_path="meta-llama/Llama-3.1-8B-Instruct",
    tensor_parallel_size=2,  # Use 2 GPUs
    dtype="bfloat16",
    max_model_len=4096,
    gpu_memory_utilization=0.9,
)

# Batch generation
results = backend.generate(
    ["Question 1?", "Question 2?", "Question 3?"],
    SamplingParams(temperature=0.0),
)

# With logprobs
results = backend.generate_with_logprobs(
    ["What is 2+2?"],
    SamplingParams(temperature=0.0),
    num_logprobs=5,
)
for token_lp in results[0].token_logprobs:
    print(f"{token_lp.token}: {token_lp.logprob:.3f}")

Capabilities¶

Feature	Supported	Notes
logprobs	✓	Native support
prefix_continuation	✓	Native support
batching	✓	Single batched GPU call
streaming	✓	Via async iterator
chat	✓	Via `tokenizer.apply_chat_template()`
function_calling	✗	Not supported
vision	Auto	VLMs detected automatically

Tip: If you already have a remote vllm serve instance running, use OpenAIBackend(model="your-model", base_url="http://host:port/v1") instead — vLLM's server exposes an OpenAI-compatible API.

HuggingFace Transformers (Local Inference)¶

A lightweight alternative to vLLM for running HuggingFace models directly with the transformers library.

from llenvs.inference import SamplingParams
from llenvs.inference.backends import HuggingFaceBackend

backend = HuggingFaceBackend(
    model_path="gpt2",  # or any HuggingFace model
    device="auto",  # "cuda", "mps", "cpu", or "auto"
    dtype="auto",  # "float16", "bfloat16", "float32", or "auto"
)

# Basic generation
results = backend.generate(
    ["Hello, world!"],
    SamplingParams(max_tokens=50, temperature=0.7),
)
print(results[0].text)

# With logprobs
results = backend.generate_with_logprobs(
    ["The capital of France is"],
    SamplingParams(temperature=0.0),
    num_logprobs=5,
)
for token_lp in results[0].token_logprobs:
    print(f"{token_lp.token}: {token_lp.logprob:.3f}")

Advanced Options¶

# Multi-GPU with device_map
backend = HuggingFaceBackend(
    model_path="meta-llama/Llama-3.1-8B-Instruct",
    device_map="auto",  # Automatically distribute across GPUs
    dtype="bfloat16",
)

# With torch.compile optimization
backend = HuggingFaceBackend(
    model_path="gpt2",
    torch_compile=True,  # Enable torch.compile for faster inference
)

# Pass additional kwargs to from_pretrained
backend = HuggingFaceBackend(
    model_path="gpt2",
    trust_remote_code=True,
    revision="main",
)

Chat Generation¶

from llenvs.inference import ChatMessage

# Uses tokenizer.apply_chat_template() internally
result = backend.generate_chat(
    [
        ChatMessage(role="system", content="You are helpful."),
        ChatMessage(role="user", content="Hello!"),
    ],
    SamplingParams(temperature=0.7),
)

Prefix Continuation¶

# Generate multiple continuations from a prefix
continuations = backend.continue_from_prefix(
    prefix="Once upon a time",
    params=SamplingParams(temperature=0.8, max_tokens=100),
    num_continuations=3,
)
for i, cont in enumerate(continuations):
    print(f"Continuation {i + 1}: {cont.text}")

Capabilities¶

Feature	Supported	Notes
logprobs	✓	Via `output_scores=True`
prefix_continuation	✓	Native support
batching	✓	With attention masks
streaming	✗	Would require TextIteratorStreamer
chat	✓	Via `tokenizer.apply_chat_template()`
function_calling	✗	Not directly supported
vision	Auto	VLMs detected and loaded with `AutoProcessor` + `AutoModelForVision2Seq`

OpenAI¶

from llenvs.inference.backends import OpenAIBackend

# Uses OPENAI_API_KEY env var by default
backend = OpenAIBackend(model="gpt-4o")

# Or explicit key with concurrency control
backend = OpenAIBackend(
    model="gpt-4o",
    api_key="sk-...",
    organization="org-...",
    max_concurrency=32,  # Max concurrent API requests (default: 64)
)

# Chat generation
from llenvs.inference import ChatMessage

result = backend.generate_chat(
    [
        ChatMessage(role="system", content="You are helpful."),
        ChatMessage(role="user", content="Hello!"),
    ],
    SamplingParams(temperature=0.7),
)

Anthropic¶

from llenvs.inference.backends import AnthropicBackend

backend = AnthropicBackend(
    model="claude-sonnet-4-20250514",
    max_concurrency=32,  # Max concurrent API requests (default: 64)
)

# Supports prefix continuation via assistant prefill
continuations = backend.continue_from_prefix(
    prefix="Let me solve this step by step:\n1.",
    params=SamplingParams(temperature=0.5),
    num_continuations=3,
)

OpenRouter¶

from llenvs.inference.backends import OpenRouterBackend

backend = OpenRouterBackend(
    model="anthropic/claude-sonnet-4-20250514",
    site_url="https://mysite.com",
    app_name="MyApp",
    max_concurrency=32,  # Max concurrent API requests (default: 64)
)

Reasoning / Thinking Budget¶

SamplingParams.thinking_budget is forwarded as extra_body.reasoning.max_tokens on the chat-completion request, which OpenRouter routes to the upstream provider's native budget knob (Anthropic thinking.budget_tokens, Gemini thinking budget, Qwen thinking_budget); for effort-only models (OpenAI o-series, Grok) OpenRouter derives an effort bucket from the token count. Non-reasoning models silently ignore the field. The other thinking-budget knobs (thinking_budget_per_block, thinking_budget_suffix, thinking_budget_soft_ratio) require token-level logits intervention and are inert on this backend — they only take effect for vllm and huggingface.

To override the derived reasoning block (e.g. send effort instead of max_tokens, or add exclude: true), pass extra={"extra_body": {"reasoning": {...}}} on SamplingParams. The caller-supplied dict shallow-merges over the derived one and wins on key collisions, so it is also the escape hatch for reasoning.effort/reasoning.exclude/reasoning.enabled.

OpenRouter returns hidden reasoning separately from visible assistant content. GenerationResult.text contains only message.content, while diagnostics are preserved in GenerationResult.metadata: normalized finish_reason, raw native_finish_reason when present, completion_tokens_details, top-level reasoning_tokens, and lightweight presence/count/length fields for message.reasoning and message.reasoning_details. The full reasoning payload is not copied into metadata.

LiteLLM¶

Routes requests through the litellm SDK, giving unified access to 100+ providers through one interface. Models use litellm's provider/model format:

from llenvs.inference.backends import LiteLLMBackend

# Direct provider access — API key read from the provider's native
# env var (GEMINI_API_KEY, ANTHROPIC_API_KEY, AZURE_API_KEY, ...)
backend = LiteLLMBackend(model="gemini/gemini-2.5-flash")

# Through a LiteLLM proxy/gateway with a virtual key
backend = LiteLLMBackend(
    model="litellm_proxy/Qwen/Qwen3.6-35B-A3B",
    api_base="https://your-gateway.example.com",
    api_key="sk-your-virtual-key",
)

For the litellm_proxy/ prefix, litellm also reads LITELLM_PROXY_API_KEY and LITELLM_PROXY_API_BASE natively, so both constructor params can be omitted.

YAML config:

model:
  backend: litellm
  model: litellm_proxy/Qwen/Qwen3.6-35B-A3B
  max_concurrency: 32
  params:
    api_base: https://your-gateway.example.com
    rate_limit_wait: 30.0

Parameter Dropping¶

drop_params=True (the default) makes litellm silently drop request parameters the target provider does not support (e.g. presence_penalty on Anthropic) instead of erroring. Set drop_params=False on the constructor to fail loudly instead. Logprobs keep a hard guarantee either way: if logprobs=True was requested and the provider returns none, the backend raises LogprobsNotReturnedError.

Reasoning / Thinking Budget¶

SamplingParams.thinking_budget maps to litellm's cross-provider thinking={"type": "enabled", "budget_tokens": N} parameter (Anthropic thinking.budget_tokens, Gemini thinking budget, Bedrock/Vertex equivalents); providers without a budget concept drop it via drop_params. disable_thinking=True sends reasoning_effort="none" and wins over any other reasoning knob. For effort-style control, pass extra={"reasoning_effort": "high"} on SamplingParams — params.extra keys flow as top-level kwargs into litellm.completion() and override derived values. The logits-level thinking knobs (thinking_budget_per_block, thinking_budget_suffix, thinking_budget_soft_ratio) are inert on this backend — they only take effect for vllm and huggingface.

Reasoning diagnostics land in GenerationResult.metadata: completion_tokens_details, reasoning_tokens, and presence/length fields for reasoning_content. When litellm computes a request cost it is surfaced as metadata["response_cost"].

Rate-Limit Retries¶

Same knobs as OpenRouter: rate_limit_wait (seconds to wait before retrying a rate-limited call; 0 disables backend-level retries) and rate_limit_max_retries. The retry path also covers providers that surface their own 429 as an HTTP-200 body with choices=null and a rate-limit message. litellm's in-SDK retries are available via num_retries but default to off — the evaluation runner already retries transient failures per slot.

Codex CLI¶

from llenvs.inference.backends import CodexCLIBackend

backend = CodexCLIBackend(
    model="codex-mini-latest",
    max_concurrency=4,  # Recommended: keep this small; each request spawns a CLI process
    timeout=600.0,
    profile="default",
)

Notes¶

Runs codex exec in a fresh temporary directory for every request.
Locks Codex to --sandbox read-only and disables session persistence with --ephemeral.
Supports chat generation only. Native tool calling, vision, logprobs, prefix continuation, and full scoring are not available.
SamplingParams.max_tokens is accepted but ignored because codex exec does not expose a direct equivalent.
Use the backend constructor's config_overrides argument to pass Codex-specific -c key=value overrides when your local Codex CLI/config supports them.

Capabilities¶

Feature	Supported	Notes
logprobs	✗	Not exposed by the CLI
prefix_continuation	✗	Stateless transcript replay only
batching	✓	Parallel subprocess calls
streaming	✗	Returns one final assistant message
chat	✓	Transcript rendered into each request
function_calling	✗	Native tool calls unsupported
vision	✗	Text-only

Claude Code CLI¶

from llenvs.inference.backends import ClaudeCodeBackend

backend = ClaudeCodeBackend(
    model="sonnet",       # alias, or a full name like "claude-sonnet-4-6"
    max_concurrency=4,    # Recommended: keep this small; each request spawns a CLI process
    timeout=600.0,
    effort="low",         # "low" | "medium" | "high" | "xhigh" | "max"
    max_budget_usd=0.10,  # per-call USD safety cap
)

Notes¶

Runs claude --print --output-format json in a fresh temporary directory for every request.
Always passes --no-session-persistence and --strict-mcp-config. By default --tools "" is sent so the spawned CLI cannot run Bash/Edit/Read tools — the backend behaves as a pure text-completion wrapper.
--bare is off by default so the CLI can use the same OAuth/keychain login as the interactive TUI (e.g. a Claude Pro/Max subscription). Pass bare=True to force ANTHROPIC_API_KEY / apiKeyHelper auth and skip auto-discovery — recommended for reproducible CI.
Supports chat generation only. Native tool calling, vision, logprobs, prefix continuation, and full scoring are not available.
SamplingParams.max_tokens is accepted but ignored because the Claude Code CLI does not expose a direct equivalent. Use effort to control reasoning level and max_budget_usd as a per-call USD cap.
Errors from the underlying Anthropic API are classified into PromptTooLongError (context-limit messages), RuntimeError (auth failures — not retryable), or QuotaExhaustedError (everything else, including is_error: true payloads, rate limits, server overload, timeouts, and unparseable output). The CLI itself retries 10× internally before surfacing errors (configurable via CLAUDE_CODE_MAX_RETRIES).
Use the backend constructor's extra_args argument (a list of strings) to pass any Claude Code flag this backend does not model directly.

Capabilities¶

Feature	Supported	Notes
logprobs	✗	Not exposed by the CLI
prefix_continuation	✗	Stateless transcript replay only
batching	✓	Parallel subprocess calls
streaming	✗	Returns one final assistant message
chat	✓	Transcript rendered into each request
function_calling	✗	Native tool calls unsupported
vision	✗	Text-only

Sampling Parameters¶

from llenvs.inference import SamplingParams

params = SamplingParams(
    max_tokens=2048,
    temperature=0.0,
    top_p=1.0,
    top_k=0,
    stop_sequences=("</answer>",),
    presence_penalty=0.0,
    frequency_penalty=0.0,
    n=1,
    logprobs=False,
)

Backend-Specific Parameters¶

Use the extra parameter to pass backend-specific options that aren't part of the common interface. These are forwarded directly to the underlying library.

# HuggingFace-specific (transformers)
params = SamplingParams(
    temperature=0.7,
    extra={
        "repetition_penalty": 1.2,
        "num_beams": 4,
        "do_sample": True,
        "length_penalty": 1.0,
    },
)

# vLLM-specific
params = SamplingParams(
    temperature=0.7,
    extra={
        "best_of": 3,
        "use_beam_search": True,
        "length_penalty": 1.0,
    },
)

# OpenAI-specific
params = SamplingParams(
    temperature=0.7,
    extra={
        "seed": 42,
        "response_format": {"type": "json_object"},
        "logit_bias": {123: -100},
    },
)

# Anthropic-specific
params = SamplingParams(
    temperature=0.7,
    extra={
        "metadata": {"user_id": "user-123"},
    },
)

Note: Parameters in extra take precedence over the common parameters if there's a conflict. Unknown parameters may cause errors from the underlying API.

Chat Template Parameters¶

Local backends (vLLM and HuggingFace) support passing extra keyword arguments to tokenizer.apply_chat_template() via chat_template_kwargs. This is useful for models like Qwen3 that use special template parameters (e.g., enable_thinking=True).

model:
  backend: vllm
  model: Qwen/Qwen3-8B
  params:
    chat_template_kwargs:
      enable_thinking: true

from llenvs.inference.backends import VLLMBackend

backend = VLLMBackend(
    model_path="Qwen/Qwen3-8B",
    chat_template_kwargs={"enable_thinking": True},
)

The kwargs are spread into every apply_chat_template() call — both generate_chat() and generate_chat_batch().

Thinking Budget¶

This section describes the in-process logits-processor implementation used by vllm and huggingface — those backends own token generation and can intervene per-step. For OpenRouter the same thinking_budget field is accepted but flows through the provider's reasoning API (reasoning.max_tokens); see the OpenRouter section above for the behavior and limitations there. Pure HTTP backends without a reasoning API (OpenAI, Anthropic direct, Codex CLI, Claude Code CLI, vllm_singularity) ignore these fields entirely.

For models that produce <think>...</think> reasoning blocks (e.g., Qwen3 with enable_thinking=True), you can cap the number of tokens generated inside each thinking block using thinking_budget on InferenceConfig:

from llenvs.core.config import InferenceConfig

config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
)

inference:
  max_tokens: 4096
  thinking_budget: 512

When the budget is reached, the processor forces the model to close the thinking block and continue with its answer. By default, the budget is shared across all <think> blocks — if the model opens multiple thinking blocks, their token counts accumulate toward the same limit.

Per-Block Budget¶

To give each <think> block its own independent budget (counter resets on each new block), set thinking_budget_per_block:

config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
    thinking_budget_per_block=True,
)

inference:
  max_tokens: 4096
  thinking_budget: 512
  thinking_budget_per_block: true

Early Stopping Text¶

When the budget is hit, the processor forces a natural-language suffix before </think> to help the model transition cleanly from reasoning to answering. This is controlled via thinking_budget_suffix. By default it is None (bare </think>). Pass DEFAULT_EARLY_STOPPING_SUFFIX to enable the built-in natural-language transition text.

from llenvs.core.config import InferenceConfig
from llenvs.inference import DEFAULT_EARLY_STOPPING_SUFFIX

# Default: bare </think> (no suffix)
config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
)

# Enable natural-language transition suffix
config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
    thinking_budget_suffix=DEFAULT_EARLY_STOPPING_SUFFIX,
)

# Custom suffix
config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
    thinking_budget_suffix="\n\nLet me answer directly.\n</think>\n\n",
)

# Default: bare </think> (no suffix)
inference:
  max_tokens: 4096
  thinking_budget: 512

# Custom suffix
inference:
  max_tokens: 4096
  thinking_budget: 512
  thinking_budget_suffix: "\n\nLet me answer directly.\n</think>\n\n"

The suffix should end with </think> so the model naturally transitions out of the thinking block.

Soft Budget Transition¶

The hard cutoff can cause reasoning to leak into the response when the model is mid-thought. To mitigate this, set thinking_budget_soft_ratio to begin boosting the </think> logit before the hard cutoff:

config = InferenceConfig(
    max_tokens=4096,
    thinking_budget=512,
    thinking_budget_soft_ratio=0.9,
)

inference:
  max_tokens: 4096
  thinking_budget: 512
  thinking_budget_soft_ratio: 0.9

With soft_ratio=0.9 and budget=512, the </think> logit boost begins at token 460 (90% of 512) and ramps linearly up to a max boost of ~5.0 at token 511. At token 512 the hard cutoff forces </think> as before. This gives the model a chance to close its thinking block naturally before being forced.

The feature is opt-in — omitting thinking_budget_soft_ratio preserves the hard-cutoff-only behavior. The ratio must be between 0 and 1 (exclusive).

Requirements: - The tokenizer must have <think> and </think> as single dedicated tokens - Works with both vLLM (logits_processors) and HuggingFace (logits_processor) backends - The vLLM processor is stateful (O(1) per token); the HuggingFace processor derives state from the full token history for batch safety - vLLM V1 support: On vLLM's V1 engine (default since 0.8), the thinking budget processor is registered as an AdapterLogitsProcessor at engine init, and per-request budgets are passed via SamplingParams.extra_args. On V0, per-request logits_processors callables are used instead. Both paths are transparent — just set the thinking_budget* fields on InferenceConfig

Second Elicitation¶

When a generation hits the max_tokens limit, the output is abruptly truncated — often mid-thought for reasoning models, never producing an answer. Second elicitation performs a follow-up generation with the truncated output in context, using a smaller token budget, to coax a final answer.

Disabled by default. When enabled, it only triggers for generations with finish_reason == MAX_TOKENS.

Configuration¶

from llenvs.core.config import InferenceConfig
from llenvs.inference import DEFAULT_EARLY_STOPPING_SUFFIX

config = InferenceConfig(
    max_tokens=2048,
    second_elicitation_suffix=DEFAULT_EARLY_STOPPING_SUFFIX,  # enables the feature
    second_elicitation_max_tokens=256,                         # budget for the follow-up call
)

inference:
  max_tokens: 2048
  second_elicitation_suffix: "\n\nPlease answer now.\n"  # any non-null value enables the feature
  second_elicitation_max_tokens: 256

When a generation is truncated (MAX_TOKENS), the runner:

Appends the truncated output + suffix as an assistant message
Appends "Please provide the final answer now. Follow the formatting instructions specified above exactly." as a user message
Calls the backend with second_elicitation_max_tokens as the token budget
Merges the two results: concatenated text, summed token counts, second call's finish reason

The merged result's metadata includes "second_elicitation": True.

Custom Suffix¶

The suffix you set on second_elicitation_suffix is appended to the truncated output in the follow-up call. DEFAULT_EARLY_STOPPING_SUFFIX provides a natural-language transition that helps the model shift from reasoning to answering. To customize:

config = InferenceConfig(
    second_elicitation_suffix="\n\nI need to give my final answer now.\n",
)

`max_model_len` Sizing¶

For vLLM, your max_model_len must accommodate the full sequence: prompt + max_tokens + suffix + second_elicitation_max_tokens. The runner does not auto-adjust max_model_len — set it via ModelConfig.params:

model:
  backend: vllm
  model: Qwen/Qwen3-8B
  params:
    max_model_len: 8192  # Enough for prompt + first gen + elicitation

Interaction with Thinking Budget¶

Second elicitation and thinking budget are complementary for the first generation, but the follow-up call is forced into no-thinking mode. The runner clears local thinking-budget fields, sets SamplingParams.disable_thinking=True, and backends that support per-call thinking control honor that request:

vLLM and HuggingFace call the chat template with enable_thinking=False and skip thinking-budget logits processors.
The Singularity-hosted vLLM server backend injects chat_template_kwargs={"enable_thinking": false} into the request body (the server renders the chat template, so the toggle must travel with the request).
OpenRouter sends extra_body.reasoning = {"effort": "none"} while preserving unrelated request body fields such as provider routing.

This keeps the rescue call focused on producing the final answer in the required format instead of spending its smaller token budget on another reasoning trace.

Batch Generation¶

All backends support generate_chat_batch() for processing multiple independent conversations efficiently. This is the foundation for parallel evaluation.

from llenvs.inference import ChatMessage, SamplingParams

messages_batch = [
    [ChatMessage(role="user", content="What is 2+2?")],
    [ChatMessage(role="user", content="What is 3*3?")],
    [ChatMessage(role="user", content="What is 10/2?")],
]

results = backend.generate_chat_batch(messages_batch, SamplingParams())
for result in results:
    print(result.text)

How each backend implements batching:

vLLM / HuggingFace: Converts all conversations to prompt strings and calls the model in a single batched GPU call. Maximum throughput.
API backends (OpenAI, Anthropic, OpenRouter): Fires concurrent async HTTP requests with a semaphore limiting parallelism to max_concurrency. No API provider supports sending multiple prompts in a single call, so concurrency is the only way to parallelize.

Tool-calling has an equivalent generate_with_tools_batch().

Capabilities¶

Check what a backend supports:

caps = backend.capabilities
print(f"Logprobs: {caps.supports_logprobs}")
print(f"Batching: {caps.supports_batching}")
print(f"Prefix continuation: {caps.supports_prefix_continuation}")
print(f"Function calling: {caps.supports_function_calling}")

Prompt Engineering¶

For pre-built system prompts, question templates, and model profiles, see the Prompts guide. The pipeline system below is the low-level API that those higher-level tools build on.

Building Pipelines¶

from llenvs.inference.prompting import (
    AnswerFormatInjector,
    ChainOfThoughtWrapper,
    FewShotInjector,
    SystemPromptInjector,
)

# Compose with >> operator
pipeline = (
    SystemPromptInjector("You are an expert mathematician.")
    >> FewShotInjector(
        [
            ("What is 2+3?", "2 + 3 = 5\n<answer>5</answer>"),
        ]
    )
    >> ChainOfThoughtWrapper("think_step_by_step")
    >> AnswerFormatInjector("xml_answer", tag_name="answer")
)

# Apply to messages
messages = [ChatMessage(role="user", content="What is 7*8?")]
transformed = pipeline.transform(messages)

Standard Pipeline Helper¶

from llenvs.inference.prompting import build_standard_pipeline

pipeline = build_standard_pipeline(
    system_prompt="You are a helpful assistant.",
    examples=[("Q1", "A1"), ("Q2", "A2")],
    use_cot=True,
    answer_format="xml_answer",
    tag_name="answer",
)

Chain of Thought Styles¶

from llenvs.inference.prompting import ChainOfThoughtWrapper

ChainOfThoughtWrapper("think_step_by_step")
# → "Think through this step by step..."

ChainOfThoughtWrapper("show_work")
# → "Show your work and reasoning..."

ChainOfThoughtWrapper("explain")
# → "Explain your thought process..."

Answer Format Options¶

from llenvs.inference.prompting import AnswerFormatInjector

AnswerFormatInjector("xml_answer", tag_name="answer")
# → "Put your final answer in <answer>...</answer> tags."

AnswerFormatInjector("json")
# → "Provide your answer as a JSON object..."

AnswerFormatInjector("boxed")
# → "Put your final answer in \\boxed{}."

AnswerFormatInjector("gsm8k")
# → "End your response with '#### ' followed by..."

Answer Extraction¶

Built-in Extractors¶

from llenvs.core.extraction import (
    CompositeExtractor,
    GSM8KExtractor,
    MultipleChoiceExtractor,
    RegexExtractor,
    TagBasedExtractor,
)

# XML tags
extractor = TagBasedExtractor(tag_name="answer")
answer, _ = extractor.extract("Result: <answer>42</answer>")
# answer = "42"

# GSM8K format
extractor = GSM8KExtractor()
answer, _ = extractor.extract("So the answer is #### 42")
# answer = "42"

# Multiple choice
extractor = MultipleChoiceExtractor(choices="ABCD")
answer, _ = extractor.extract("The answer is (B)")
# answer = "B"

# Try multiple extractors
extractor = CompositeExtractor(
    extractors=[
        TagBasedExtractor(tag_name="answer"),
        GSM8KExtractor(),
        RegexExtractor(pattern=r"(\d+)$"),
    ]
)

Custom Extractor¶

class JsonExtractor:
    def extract(self, response: str) -> tuple[str | None, dict]:
        import json

        try:
            data = json.loads(response)
            return str(data.get("answer")), {"parsed": True}
        except json.JSONDecodeError:
            return None, {"parsed": False}

Backends¶

Lifecycle¶

Available Backends¶

vLLM (Local Inference)¶

YAML Config¶

Python API¶

Capabilities¶

HuggingFace Transformers (Local Inference)¶

Advanced Options¶

Chat Generation¶

Prefix Continuation¶

Capabilities¶

OpenAI¶

Anthropic¶

OpenRouter¶

Reasoning / Thinking Budget¶

LiteLLM¶

Parameter Dropping¶

Reasoning / Thinking Budget¶

Rate-Limit Retries¶

Codex CLI¶

Notes¶

Capabilities¶

Claude Code CLI¶

Notes¶

Capabilities¶

Sampling Parameters¶

Backend-Specific Parameters¶

Chat Template Parameters¶

Thinking Budget¶

Per-Block Budget¶

Early Stopping Text¶

Soft Budget Transition¶

Second Elicitation¶

Configuration¶

Custom Suffix¶

max_model_len Sizing¶

Interaction with Thinking Budget¶

Batch Generation¶

Capabilities¶

Prompt Engineering¶

Building Pipelines¶

Standard Pipeline Helper¶

Chain of Thought Styles¶

Answer Format Options¶

Answer Extraction¶

Built-in Extractors¶

Custom Extractor¶

`max_model_len` Sizing¶