langcore-litellm 1.0.5

LangCore provider plugin for LiteLLM

LangCore LiteLLM

Provider plugin for LangCore — access 100+ language models through a single, unified interface via LiteLLM.

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Overview

langcore-litellm is a provider plugin for LangCore that adds support for 100+ language models through LiteLLM's unified API. Install it, prefix your model ID with litellm/, and every LangCore extraction call routes through LiteLLM transparently — auto-discovered via Python entry points.

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Features

• 100+ model support — OpenAI, Anthropic, Google, Azure, Mistral, Groq, Cohere, HuggingFace, Ollama, vLLM, and more through a single provider
• Native async — uses litellm.acompletion() with asyncio.Semaphore for true non-blocking concurrent I/O (no thread pool overhead)
• Multi-pass cache bypass — automatic per-pass cache control ensures fresh LLM responses on repeat extraction passes while keeping the first pass cacheable
• Token usage tracking — captures prompt, completion, and total token counts (UsageStats) from every inference call
• Concurrency control — configurable max_workers semaphore limits parallel async requests to prevent rate-limit errors
• Zero-config plugin — auto-registered via Python entry points; no manual wiring required
• Full parameter passthrough — forward any LiteLLM-supported parameter (temperature, top_p, timeout, etc.) through provider_kwargs

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Installation

pip install langcore-litellm

Or install from source:

git clone https://github.com/JustStas/langcore-litellm
cd langcore-litellm
pip install -e .

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Quick Start

Integration with LangCore

langcore-litellm integrates with LangCore through the provider plugin system. Create a model configuration, and LangCore handles the rest:

import langcore as lx

# Configure the LiteLLM provider
config = lx.factory.ModelConfig(
    model_id="litellm/gpt-4o",
    provider="LiteLLMLanguageModel",
)
model = lx.factory.create_model(config)

# Use with any LangCore extraction
result = lx.extract(
    text_or_documents="Acme Corp agrees to pay $50,000 to Beta LLC by March 2025.",
    model=model,
    prompt_description="Extract parties, monetary amounts, and dates.",
    examples=[
        lx.data.ExampleData(
            text="Alpha Inc will pay $10,000 to Omega Ltd by January 2024.",
            extractions=[
                lx.data.Extraction("party", "Alpha Inc", attributes={"role": "payer"}),
                lx.data.Extraction("party", "Omega Ltd", attributes={"role": "payee"}),
                lx.data.Extraction("monetary_amount", "$10,000"),
                lx.data.Extraction("date", "January 2024", attributes={"type": "deadline"}),
            ],
        )
    ],
)

print(result)

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Usage

Supported Models

Model IDs must be prefixed with litellm/ (or litellm-) to route through this provider:

Provider	Example Model IDs
OpenAI	litellm/gpt-4o, litellm/gpt-4o-mini, litellm/gpt-4-turbo
Anthropic	litellm/claude-3-opus, litellm/claude-3.5-sonnet, litellm/claude-3-haiku
Google	litellm/gemini-2.5-pro, litellm/gemini-2.0-flash
Azure OpenAI	litellm/azure/your-deployment-name
Mistral	litellm/mistral-large-latest
Groq	litellm/groq/llama-3.1-70b
Ollama	litellm/ollama/llama3.1
And 100+ more	See LiteLLM providers

Environment Variables

Set the appropriate API key for your provider:

# OpenAI
export OPENAI_API_KEY="sk-..."

# Anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

# Google (Gemini)
export GEMINI_API_KEY="..."

# Azure OpenAI
export AZURE_API_KEY="..."
export AZURE_API_BASE="https://your-resource.openai.azure.com/"
export AZURE_API_VERSION="2024-02-01"

# Ollama (local)
export OLLAMA_API_BASE="http://localhost:11434"

See the LiteLLM documentation for the full list of provider-specific variables.

Async Extraction

The provider uses litellm.acompletion() for native async, avoiding thread overhead:

import asyncio
import langcore as lx

config = lx.factory.ModelConfig(
    model_id="litellm/gpt-4o",
    provider="LiteLLMLanguageModel",
)
model = lx.factory.create_model(config)

async def main():
    result = await lx.async_extract(
        text_or_documents="Agreement between Acme Corp and Beta LLC...",
        model=model,
        prompt_description="Extract parties and obligations.",
        examples=[...],
    )
    print(result)

asyncio.run(main())

Multi-Pass Extraction

When running multiple extraction passes (extraction_passes > 1), the provider automatically manages cache behaviour:

Pass	Cache Behaviour
1st pass	Normal — may be served from cache
2nd pass	Bypass — forces a fresh LLM response
3rd+ passes	Bypass — forces a fresh LLM response

This is fully automatic. LangCore threads a pass_num argument to the provider, which injects cache={"no-cache": True} for passes ≥ 1:

result = lx.extract(
    text_or_documents="Contract text...",
    model=model,
    prompt_description="Extract all entities.",
    examples=[...],
    extraction_passes=3,  # 3 passes, only the first is cacheable
)

Advanced Configuration

Forward any LiteLLM parameter through provider_kwargs:

config = lx.factory.ModelConfig(
    model_id="litellm/gpt-4o",
    provider="LiteLLMLanguageModel",
    provider_kwargs={
        "temperature": 0.2,
        "max_tokens": 2000,
        "top_p": 0.9,
        "timeout": 60,
        "api_key": "sk-...",  # override env var
    },
)
model = lx.factory.create_model(config)

Reserved Parameters

These parameters are consumed internally and are not forwarded to LiteLLM:

Parameter	Type	Default	Description
max_workers	int	10	Maximum concurrent async requests (semaphore size)
pass_num	int	0	Current extraction pass index — set automatically by LangCore during multi-pass extraction

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Composing with Other Plugins

langcore-litellm serves as the base provider that other LangCore plugins wrap. Stack decorators to add audit logging, output validation, or hybrid rule-based extraction:

import langcore as lx
from langcore_audit import AuditLanguageModel, LoggingSink
from langcore_guardrails import GuardrailLanguageModel, SchemaValidator, OnFailAction

# Base LLM provider
config = lx.factory.ModelConfig(
    model_id="litellm/gpt-4o",
    provider="LiteLLMLanguageModel",
)
llm = lx.factory.create_model(config)

# Add output validation
guarded = GuardrailLanguageModel(
    model_id="guardrails/gpt-4o",
    inner=llm,
    validators=[SchemaValidator(MySchema, on_fail=OnFailAction.REASK)],
    max_retries=3,
)

# Add audit logging
audited = AuditLanguageModel(
    model_id="audit/gpt-4o",
    inner=guarded,
    sinks=[LoggingSink()],
)

# Use the full stack with LangCore
result = lx.extract(
    text_or_documents="Contract text...",
    model=audited,
    prompt_description="Extract entities.",
    examples=[...],
)

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Output Format

Extractions return LangCore's standard AnnotatedDocument with precise character intervals:

AnnotatedDocument(
    extractions=[
        Extraction(
            extraction_class='party',
            extraction_text='Acme Corp',
            char_interval=CharInterval(start_pos=0, end_pos=9),
            alignment_status=<AlignmentStatus.MATCH_EXACT: 'match_exact'>,
            attributes={'role': 'payer'}
        ),
        Extraction(
            extraction_class='monetary_amount',
            extraction_text='$50,000',
            char_interval=CharInterval(start_pos=24, end_pos=31),
            alignment_status=<AlignmentStatus.MATCH_EXACT: 'match_exact'>,
            attributes={}
        ),
    ],
    text='Acme Corp agrees to pay $50,000 to Beta LLC by March 2025.'
)

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Error Handling

API failures are captured gracefully — no unhandled exceptions:

ScoredOutput(score=0.0, output="LiteLLM API error: [error details]")

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Development

pip install -e .            # Install in development mode
python test_plugin.py       # Run tests
python -m build             # Build package
twine upload dist/*         # Publish to PyPI

Requirements

• Python ≥ 3.12
• langcore
• litellm ≥ 1.81.13

License

Apache License 2.0 — see LICENSE for details.