AbstractCore Capabilities

What AbstractCore IS

AbstractCore is production-ready LLM infrastructure. It provides a unified, reliable interface to language models with essential features built-in.

Core Philosophy

• Infrastructure, not application logic
• Reliability over features
• Simplicity over complexity
• Provider agnostic

Optional capability plugins (voice/audio/vision)

AbstractCore stays dependency-light by default. Deterministic modality APIs (STT/TTS, generative vision) live in optional packages and are exposed through the capability plugin layer:

• Install abstractvoice → llm.voice / llm.audio (TTS/STT)
• Install abstractvision → llm.vision (text→image, image→image, …)

pip install abstractvoice
pip install abstractvision

from abstractcore import create_llm

llm = create_llm("openai", model="gpt-4o-mini")  # example; pick a provider/model you have access to
print(llm.capabilities.status())  # availability + selected backend ids + install hints

# Voice/audio
wav_bytes = llm.voice.tts("Hello", format="wav")
text = llm.audio.transcribe("speech.wav")

# Vision (requires vision_base_url / ABSTRACTVISION_BASE_URL)
# png_bytes = llm.vision.t2i("a red square")

What AbstractCore Does Well

1. Universal LLM Provider Interface

What it does: Provides identical APIs across all major LLM providers.

# Same code works with any provider
def ask_llm(provider_name, question):
    llm = create_llm(provider_name, model="default")
    return llm.generate(question)

# All of these use the same API surface
ask_llm("openai", "What is Python?")
ask_llm("anthropic", "What is Python?")
ask_llm("ollama", "What is Python?")

Why this helps: Provides consistent tool calling, streaming, and structured output across supported providers.

2. Production-Grade Reliability

What it does: Handles failures gracefully with retry logic, circuit breakers, and comprehensive error handling.

• Automatic retries with exponential backoff for rate limits and network errors
• Circuit breakers prevent cascade failures when providers go down
• Smart error classification - retries recoverable errors, fails fast on auth errors
• Event system for monitoring and alerting

Why this helps: Includes production reliability features like retry logic and error handling.

3. Universal Tool Calling

What it does: Tools work consistently across supported providers, even those without native tool support.

tools = [{"name": "get_weather", "description": "Get weather", ...}]

# Works with providers that have native tool support
openai_response = openai_llm.generate("Weather in Paris?", tools=tools)

# Also works with providers that don't (via intelligent prompting)
ollama_response = ollama_llm.generate("Weather in Paris?", tools=tools)

Why this helps: Tools work with any provider, including those without native tool support.

4. Tool Call Tag Rewriting for Agentic CLI Compatibility

What it does: Automatically rewrites tool call tags to match different agentic CLI requirements in real-time.

# Rewrite tool calls for different CLIs
# Use a prompted-tools provider (tool-call markup lives in assistant content)
llm = create_llm("ollama", model="qwen3:4b-instruct")

# For Codex CLI (Qwen3 format)
response = llm.generate("Weather in Paris?", tools=tools, tool_call_tags="qwen3")
# Output: <|tool_call|>{"name": "get_weather", "arguments": {"location": "Paris"}}</|tool_call|>

# For Crush CLI (LLaMA3 format)  
response = llm.generate("Weather in Paris?", tools=tools, tool_call_tags="llama3")
# Output: <function_call>{"name": "get_weather", "arguments": {"location": "Paris"}}</function_call>

# For Gemini CLI (XML format)
response = llm.generate("Weather in Paris?", tools=tools, tool_call_tags="xml")
# Output: <tool_call>{"name": "get_weather", "arguments": {"location": "Paris"}}</tool_call>

Why this helps: Works with different agentic CLIs without code changes.

5. Tool Execution Control

What it does: Control whether AbstractCore executes tools automatically or lets the agent handle execution.

# Default (recommended): passthrough mode (tools are *not* executed in AbstractCore)
llm = create_llm("openai", model="gpt-4o-mini")
response = llm.generate("Weather in Paris?", tools=tools)
# response.tool_calls contains structured tool call requests; host/runtime executes them

# Optional (deprecated): direct execution in AbstractCore for simple scripts only
# llm = create_llm("openai", model="gpt-4o-mini", execute_tools=True)

Why this helps: Allows flexible tool execution control for different deployment scenarios.

6. Structured Output with Automatic Retry

What it does: Gets typed Python objects from LLMs with automatic validation and retry on failures.

class Product(BaseModel):
    name: str
    price: float

# Automatically retries with error feedback if validation fails
product = llm.generate(
    "Extract: Gaming laptop for $1200",
    response_model=Product
)

Why this helps: Built-in validation retry reduces manual error handling.

See: Structured Output Guide for native vs prompted strategies, schema design, and production deployment

5. Streaming with Tool Support

What it does: Real-time response streaming that properly handles tool calls.

# Streams content in real-time, executes tools at the end
for chunk in llm.generate("Tell me about Paris weather", tools=tools, stream=True):
    print(chunk.content, end="", flush=True)

Why this helps: Streaming works correctly with tool calls.

6. Event-Driven Observability

What it does: Comprehensive events for monitoring, debugging, and control.

from abstractcore.events import EventType, on_global

def cost_monitor(event):
    if event.type != EventType.GENERATION_COMPLETED:
        return
    cost = event.data.get("cost_usd")
    if isinstance(cost, (int, float)) and cost > 0.10:
        # NOTE: `cost_usd` is a best-effort estimate based on token usage.
        alert(f"High estimated cost: ${cost:.2f}")

on_global(EventType.GENERATION_COMPLETED, cost_monitor)

Why this helps: Provides built-in observability for monitoring and debugging.

7. Built-in Production Applications

What it does: Provides ready-to-use command-line applications for common LLM tasks without any programming.

# Document summarization with multiple strategies
summarizer document.pdf --style executive --length brief
summarizer report.txt --focus "technical details" --output summary.txt

# Entity and relationship extraction
extractor research_paper.pdf --format json-ld --focus technology
extractor article.txt --entity-types person,organization,location

# Text evaluation and scoring
judge essay.txt --criteria clarity,accuracy,coherence --context "academic writing"
judge code.py --context "code review" --format plain

# Intent analysis and deception detection
intent conversation.txt --focus-participant user --depth comprehensive
intent email.txt --format plain --context document --verbose

Available Applications: - Summarizer: Document summarization with customizable styles and focus areas - Extractor: Entity and relationship extraction with multiple output formats - Judge: Text evaluation with custom criteria and scoring rubrics - Intent Analyzer: Psychological intent analysis with deception detection

Why this helps: Provides ready-to-use CLI tools that work with any LLM provider.

What AbstractCore Does NOT Do

Understanding limitations is crucial for choosing the right tool.

1. RAG Pipelines (Use Specialized Tools)

What AbstractCore provides: Vector embeddings via EmbeddingManager What it doesn't provide: Document chunking, vector databases, retrieval strategies

# AbstractCore gives you this
from abstractcore.embeddings import EmbeddingManager
embedder = EmbeddingManager()
similarity = embedder.compute_similarity("query", "document")

# You need to build this yourself
def rag_pipeline(query, documents):
    # 1. Chunk documents - YOU implement
    # 2. Store in vector DB - YOU implement
    # 3. Retrieve relevant chunks - YOU implement
    # 4. Construct prompt - YOU implement
    return llm.generate(prompt)

Better alternatives: - LlamaIndex - Full RAG framework - LangChain - RAG components and chains

2. Complex Agent Workflows (Use Agent Frameworks)

What AbstractCore provides: Single LLM calls with tool execution What it doesn't provide: Multi-step agent reasoning, planning, memory persistence

# AbstractCore is great for this
response = llm.generate("What's 2+2?", tools=[calculator_tool])

# AbstractCore is NOT for this
def complex_agent():
    # 1. Plan multi-step solution - NOT provided
    # 2. Execute steps with memory - NOT provided
    # 3. Reflect and re-plan - NOT provided
    # 4. Persist agent state - NOT provided
    pass

Better alternatives: - AbstractAgent - Built on AbstractCore - LangGraph - Agent orchestration - AutoGPT - Autonomous agents

3. Advanced Memory Systems (Use Memory Frameworks)

What AbstractCore provides: Basic conversation history via BasicSession What it doesn't provide: Semantic memory, long-term memory, knowledge graphs

# AbstractCore provides basic sessions
session = BasicSession(provider=llm)
session.generate("My name is Alice")
session.generate("What's my name?")  # Remembers within session

# For advanced memory, use specialized tools
temporal_graph = AbstractMemory()  # Persistent, semantic memory
temporal_graph.add_memory("Alice likes Python programming", context="conversation")

Better alternatives: - AbstractMemory - Temporal knowledge graphs - Mem0 - Personalized memory layer

4. Prompt Template Management (Use Template Libraries)

What AbstractCore provides: Direct prompt strings What it doesn't provide: Template engines, prompt optimization, A/B testing

# AbstractCore expects you to handle prompts
prompt = f"Translate '{text}' to {language}"
response = llm.generate(prompt)

# For advanced templating, use other tools
template = PromptTemplate("Translate '{text}' to {language}")  # Not provided

Better alternatives: - Jinja2 - Template engine - LangChain Prompts - Prompt management - Guidance - Prompt programming

5. Training and Fine-tuning (Use ML Frameworks)

What AbstractCore provides: Interface to existing models What it doesn't provide: Model training, fine-tuning, or optimization

Better alternatives: - Transformers - Model training - Axolotl - Fine-tuning framework - Unsloth - Fast fine-tuning

6. Multi-Agent Orchestration (Use Orchestration Frameworks)

What AbstractCore provides: Single agent with tools What it doesn't provide: Agent-to-agent communication, hierarchical agents

Better alternatives: - CrewAI - Multi-agent teams - AutoGen - Agent conversations - LangGraph - Agent networks

When to Choose AbstractCore

Choose AbstractCore When You Need:

1. Reliable LLM Infrastructure
2. Production-ready error handling and retry logic
3. Consistent interface across different providers

4. Built-in monitoring and observability

5. Provider Flexibility

6. Easy switching between OpenAI, Anthropic, Ollama, etc.
7. Provider-agnostic code that runs anywhere

8. Local and cloud provider support

9. Universal Tool Calling

10. Tools that work across supported providers
11. Consistent tool execution regardless of native support

12. Event-driven tool control and monitoring

13. Structured Output Reliability

14. Type-safe responses with automatic validation
15. Built-in retry logic for validation failures

16. Production-grade error handling

17. Streaming with Tools

18. Real-time responses that handle tools correctly
19. Proper streaming implementation across providers

Don't Choose AbstractCore When You Need:

1. Full RAG Frameworks → Use LlamaIndex or LangChain
2. Complex Agent Workflows → Use AbstractAgent or LangGraph
3. Advanced Memory Systems → Use AbstractMemory or Mem0
4. Prompt Template Management → Use Jinja2 or LangChain Prompts
5. Model Training/Fine-tuning → Use Transformers or Axolotl
6. Multi-Agent Systems → Use CrewAI or AutoGen

AbstractCore in the Ecosystem

AbstractCore is designed to be the foundation that other tools build on:

AbstractCore = The reliable foundation AbstractAgent = Agent workflows and planning AbstractMemory = Advanced memory and knowledge graphs Your Application = Business logic and user interface

Decision Tree

Need LLM functionality?
├── Simple LLM calls with reliability? → AbstractCore ✅
├── Complex agents with planning? → AbstractAgent (built on AbstractCore)
├── Advanced memory/knowledge graphs? → AbstractMemory (with AbstractCore)
├── Full RAG with document management? → LlamaIndex or LangChain
├── Multi-agent conversations? → CrewAI or AutoGen
└── Just API compatibility? → LiteLLM

Capabilities Summary

Next Steps

Based on your needs:

• Start with AbstractCore: Getting Started Guide
• Need agents: Check out AbstractAgent
• Need advanced memory: Check out AbstractMemory
• Compare frameworks: Read Framework Comparison
• See real examples: Browse Examples

Remember: AbstractCore is infrastructure, not a full framework. It focuses on LLM provider abstraction and integrates with specialized tools for other needs.