abstractcore 2.11.6

Unified interface to all LLM providers with essential infrastructure for tool calling, streaming, and model management

AbstractCore

Unified LLM Interface

Write once, run everywhere

AbstractCore is a Python library that provides a unified create_llm(...) API across cloud + local LLM providers (OpenAI, Anthropic, Ollama, LMStudio, and more). The default install is intentionally lightweight; add providers and optional subsystems via explicit install extras.

First-class support for:

• sync + async
• streaming + non-streaming
• universal tool calling (native + prompted tool syntax)
• structured output (Pydantic)
• media input (images/audio/video + documents) with explicit, policy-driven fallbacks (*)
• optional capability plugins (core.voice/core.audio/core.vision) for deterministic TTS/STT and generative vision (via abstractvoice / abstractvision)
• glyph visual-text compression for long documents (**)
• unified openai-compatible endpoint for all providers and models

(*) Media input is policy-driven (no silent semantic changes). If a model doesn’t support images, AbstractCore can use a configured vision model to generate short visual observations and inject them into your text-only request (vision fallback). Audio/video attachments are also policy-driven (audio_policy, video_policy) and may require capability plugins for fallbacks. See Media Handling and Centralized Config. (**) Optional visual-text compression: render long text/PDFs into images and process them with a vision model to reduce token usage. See Glyph Visual-Text Compression (install pip install "abstractcore[compression]"; for PDFs also install pip install "abstractcore[media]").

Docs: Getting Started · FAQ · Docs Index · https://lpalbou.github.io/AbstractCore

Install

# Core (small, lightweight default)
pip install abstractcore

# Providers
pip install "abstractcore[openai]"       # OpenAI SDK
pip install "abstractcore[anthropic]"    # Anthropic SDK
pip install "abstractcore[huggingface]"  # Transformers / torch (heavy)
pip install "abstractcore[mlx]"          # Apple Silicon local inference (heavy)
pip install "abstractcore[vllm]"         # NVIDIA CUDA / ROCm (heavy)

# Optional features
pip install "abstractcore[tools]"       # built-in web tools (web_search, skim_websearch, skim_url, fetch_url)
pip install "abstractcore[media]"       # images, PDFs, Office docs
pip install "abstractcore[compression]" # glyph visual-text compression (Pillow-only)
pip install "abstractcore[embeddings]"  # EmbeddingManager + local embedding models
pip install "abstractcore[tokens]"      # precise token counting (tiktoken)
pip install "abstractcore[server]"      # OpenAI-compatible HTTP gateway

# Combine extras (zsh: keep quotes)
pip install "abstractcore[openai,media,tools]"

# Turnkey "everything" installs (pick one)
pip install "abstractcore[all-apple]"    # macOS/Apple Silicon (includes MLX, excludes vLLM)
pip install "abstractcore[all-non-mlx]"  # Linux/Windows/Intel Mac (excludes MLX and vLLM)
pip install "abstractcore[all-gpu]"      # Linux NVIDIA GPU (includes vLLM, excludes MLX)

Quickstart

OpenAI example (requires pip install "abstractcore[openai]"):

from abstractcore import create_llm

llm = create_llm("openai", model="gpt-4o-mini")
response = llm.generate("What is the capital of France?")
print(response.content)

Conversation state (BasicSession)

from abstractcore import create_llm, BasicSession

session = BasicSession(create_llm("anthropic", model="claude-haiku-4-5"))
print(session.generate("Give me 3 bakery name ideas.").content)
print(session.generate("Pick the best one and explain why.").content)

Streaming

from abstractcore import create_llm

llm = create_llm("ollama", model="qwen3:4b-instruct")
for chunk in llm.generate("Write a short poem about distributed systems.", stream=True):
    print(chunk.content or "", end="", flush=True)

Async

import asyncio
from abstractcore import create_llm

async def main():
    llm = create_llm("openai", model="gpt-4o-mini")
    resp = await llm.agenerate("Give me 5 bullet points about HTTP caching.")
    print(resp.content)

asyncio.run(main())

Token budgets (unified)

from abstractcore import create_llm

llm = create_llm(
    "openai",
    model="gpt-4o-mini",
    max_tokens=8000,        # total budget (input + output)
    max_output_tokens=1200, # output cap
)

Providers (common)

• openai: OPENAI_API_KEY, optional OPENAI_BASE_URL
• anthropic: ANTHROPIC_API_KEY, optional ANTHROPIC_BASE_URL
• openrouter: OPENROUTER_API_KEY, optional OPENROUTER_BASE_URL (default: https://openrouter.ai/api/v1)
• ollama: local server at OLLAMA_BASE_URL (or legacy OLLAMA_HOST)
• lmstudio: OpenAI-compatible local server at LMSTUDIO_BASE_URL (default: http://localhost:1234/v1)
• vllm: OpenAI-compatible server at VLLM_BASE_URL (default: http://localhost:8000/v1)
• openai-compatible: generic OpenAI-compatible endpoints via OPENAI_COMPATIBLE_BASE_URL (default: http://localhost:1234/v1)

You can also persist settings (including API keys) via the config CLI:

• abstractcore --status
• abstractcore --configure (alias: --config)
• abstractcore --set-api-key openai sk-...

What’s inside (quick tour)

• Tools: universal tool calling across providers → Tool Calling
• Built-in tools (optional): web + filesystem helpers (skim_websearch, skim_url, fetch_url, read_file, …) → Tool Calling
• Tool syntax rewriting: tool_call_tags (Python) and agent_format (server) → Tool Syntax Rewriting
• Structured output: Pydantic-first with provider-aware strategies → Structured Output
• Media input: images/audio/video + documents (policies + fallbacks) → Media Handling and Vision Capabilities
• Capability plugins (optional): deterministic llm.voice/llm.audio/llm.vision surfaces → Capabilities
• Glyph visual-text compression: scale long-context document analysis via VLMs → Glyph Visual-Text Compression
• Embeddings and semantic search → Embeddings
• Observability: global event bus + interaction traces → Architecture, API Reference (Events), Interaction Tracing
• MCP (Model Context Protocol): discover tools from MCP servers (HTTP/stdio) → MCP
• OpenAI-compatible server: one /v1 gateway for chat + optional /v1/images/* and /v1/audio/* endpoints → Server

Tool calling (passthrough by default)

By default (execute_tools=False), AbstractCore:

• returns clean assistant text in response.content
• returns structured tool calls in response.tool_calls (host/runtime executes them)

from abstractcore import create_llm, tool

@tool
def get_weather(city: str) -> str:
    return f"{city}: 22°C and sunny"

llm = create_llm("openai", model="gpt-4o-mini")
resp = llm.generate("What's the weather in Paris? Use the tool.", tools=[get_weather])

print(resp.content)
print(resp.tool_calls)

If you need tool-call markup preserved/re-written in content for downstream parsers, pass tool_call_tags=... (e.g. "qwen3", "llama3", "xml"). See Tool Syntax Rewriting.

Structured output

from pydantic import BaseModel
from abstractcore import create_llm

class Answer(BaseModel):
    title: str
    bullets: list[str]

llm = create_llm("openai", model="gpt-4o-mini")
answer = llm.generate("Summarize HTTP/3 in 3 bullets.", response_model=Answer)
print(answer.bullets)

Media input (images/audio/video)

Requires pip install "abstractcore[media]".

from abstractcore import create_llm

llm = create_llm("anthropic", model="claude-haiku-4-5")
resp = llm.generate("Describe the image.", media=["./image.png"])
print(resp.content)

Notes:

• Images: use a vision-capable model, or configure vision fallback for text-only models (abstractcore --config; abstractcore --set-vision-provider PROVIDER MODEL).
• Video: video_policy="auto" (default) uses native video when supported, otherwise samples frames (requires ffmpeg/ffprobe) and routes them through image/vision handling (so you still need a vision-capable model or vision fallback configured).
• Audio: use an audio-capable model, or set audio_policy="auto"/"speech_to_text" and install abstractvoice for speech-to-text.

Configure defaults (optional):

abstractcore --status
abstractcore --set-vision-provider lmstudio qwen/qwen3-vl-4b
abstractcore --set-audio-strategy auto
abstractcore --set-video-strategy auto

See Media Handling and Vision Capabilities.

HTTP server (OpenAI-compatible gateway)

pip install "abstractcore[server]"
python -m abstractcore.server.app

Use any OpenAI-compatible client, and route to any provider/model via model="provider/model":

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")
resp = client.chat.completions.create(
    model="ollama/qwen3:4b-instruct",
    messages=[{"role": "user", "content": "Hello from the gateway!"}],
)
print(resp.choices[0].message.content)

See Server.

CLI (optional)

Interactive chat:

abstractcore-chat --provider openai --model gpt-4o-mini
abstractcore-chat --provider lmstudio --model qwen/qwen3-4b-2507 --base-url http://localhost:1234/v1
abstractcore-chat --provider openrouter --model openai/gpt-4o-mini

Token limits:

• startup: abstractcore-chat --max-tokens 8192 --max-output-tokens 1024 ...
• in-REPL: /max-tokens 8192 and /max-output-tokens 1024

Built-in CLI apps

AbstractCore also ships with ready-to-use CLI apps:

• summarizer, extractor, judge, intent, deepsearch (see docs/apps/)

Documentation map

Start here:

• Docs Index — navigation for all docs
• Prerequisites — provider setup (keys, local servers, hardware notes)
• Getting Started — first call + core concepts
• FAQ — common questions and setup gotchas
• Examples — end-to-end patterns and recipes
• Troubleshooting — common failures and fixes

Core features:

• Tool Calling — universal tools across providers (native + prompted)
• Tool Syntax Rewriting — rewrite tool-call syntax for different runtimes/clients
• Structured Output — schema enforcement + retry strategies
• Media Handling — images/audio/video + documents (policies + fallbacks)
• Vision Capabilities — image/video input, vision fallback, and how this differs from generative vision
• Glyph Visual-Text Compression — compress long documents into images for VLMs
• Generation Parameters — unified parameter vocabulary and provider quirks
• Session Management — conversation history, persistence, and compaction
• Embeddings — embeddings API and RAG building blocks
• Async Guide — async patterns, concurrency, best practices
• Centralized Config — ~/.abstractcore/config/abstractcore.json + CLI config commands
• Capabilities — supported features and current limitations
• Interaction Tracing — inspect prompts/responses/usage for observability
• MCP — consume MCP tool servers (HTTP/stdio) as tool sources

Reference and internals:

• Architecture — system overview + event system
• API (Python) — how to use the public API
• API Reference — Python API (including events)
• Server — OpenAI-compatible gateway with tool/media support
• CLI Guide — interactive abstractcore-chat walkthrough

Project:

• Changelog — version history and upgrade notes
• Contributing — dev setup and contribution guidelines
• Security — responsible vulnerability reporting
• Acknowledgements — upstream projects and communities

License

MIT