llm-actor 0.2.0

LLM actor: pool, circuit breaker, retries, metrics

LLM Actor: High-Performance Orchestration for Self-Hosted Inference

Documentation: English | Russian

LLM Actor is an efficient actor pool for shared self-hosted inference (vLLM, Ollama) or proxy services. It covers the “last mile”: managing concurrent requests, prioritizing them, and delivering guaranteed structured output without extra boilerplate.

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Why LLM Actor?

While cloud providers have their own rate limits, production self-hosted inference (or shared cloud endpoints) hits other bottlenecks:

• GPU Oversaturation: Too many concurrent requests crash the inference engine.
• Queue Hoarding: Background batch tasks block high-priority user UI requests.
• Unreliable Structuring: Getting guaranteed Structured Output from local models is hard.
• Lack of Resilience: One slow response shouldn't hang your entire orchestration layer.

LLM Actor addresses these via Global Concurrency Control (actor pool) and a Global Circuit Breaker. It prioritizes reactive resilience over proactive token counting, making it a perfect fit for private cloud infrastructure.

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Key Features

• Configurable Actor Pool: Efficiently queue and dispatch requests across a pool of managed workers.
• Global Resilience:
  • Circuit Breaker: Failure state is shared within the process. If one worker detects a provider failure, all workers in the pool "fail fast" immediately to protect your infrastructure.
  • Exponential Backoff: Automatic retries for transient HTTP errors (429, 502, 503).
  • Semantic Validation: Typed response validation with Pydantic; auto-retry on schema mismatch.
• Built-in Tool Calling Loop: Native support for complex agentic flows. Run multiple tools in parallel to slash latency.
• Global Priority Queue: Assign priorities to tasks. Ensure user-facing interactions always jump to the front of the line.
• Multi-Provider Support:
  • Native adapters: OpenAI, Anthropic, Sber GigaChat.
  • Proxy support: vLLM, Ollama, and any OpenAI-compatible endpoint.
• Deep Observability: Full OpenTelemetry integration. Trace every request from the queue through the actor to the final provider response.
• Caller context & extra HTTP headers: Python contextvars set before generate() are restored in the worker when your LLM client runs; LLMRequest.extra_headers is forwarded to the OpenAI and Anthropic SDKs (GigaChat logs a warning and ignores extra headers).

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Limitations

To keep LLM Actor lightweight and universal, we made specific architectural trade-offs:

• In-process state: Circuit Breaker status and Queue state are local to the process. Designed for single-node deployment or vertical scaling.
• No proactive token rate limiting: We don't counting tokens before sending them. Ideal for self-hosted inference (vLLM/Ollama) where TPM quotas aren't the primary bottleneck. For external APIs with strict TPM limits, we rely on Reactive Resilience (Backoff + CB).
• Single-provider per pool: A pool is tied to a single client instance. For multi-provider routing with independent circuit breaking, use separate LLMActorService instances.

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Installation

# Install core package
pip install llm-actor

# Install with your preferred providers
pip install "llm-actor[openai,anthropic]"

# Full installation (all providers + metrics)
pip install "llm-actor[all]"

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Quick Start: 60 Seconds to Scale

Create a service and start processing tasks with priority and auto-recovery:

from llm_actor import LLMActorService, LLMActorSettings, Priority
from pydantic import BaseModel

# 1. Setup Service
service = LLMActorService.from_openai(
    api_key="sk-...", 
    model="gpt-4o",
    settings=LLMActorSettings(LLM_NUM_ACTORS=10) # 10 concurrent workers
)

# 2. Define Output Schema
class UserProfile(BaseModel):
    name: str
    skills: list[str]

# 3. Use via Context Manager (handles Start/Stop automatically)
async with service:
    # 4. Queue a High-Priority Task
    request = service.request(
        "Extract profile from: Alex is a Senior Python Dev with LLM expertise.",
        response_model=UserProfile,
        priority=Priority.HIGH
    )

    # 5. Get Your Results (Blocking or Async)
    result = request.get()
    print(f"Found: {result.name} with skills: {result.skills}")

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Provider Support Matrix

Provider	Generations	Parallel Tools	Tested
OpenAI / compatible	Yes	Yes	Yes Full
Anthropic	Yes	Yes	Yes Full
vLLM / Ollama	Yes	Yes*	Yes Full
Sber GigaChat	Yes	Warning	Experimental

*Tool calling in vLLM requires specific server-side flags.

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Architecture: Reactive Resilience

LLM Actor is built as an In-Process Orchestrator. Instead of complex pre-emptive traffic shaping, we use a reactive chain: Exponential Backoff (managed by the client) -> Circuit Breaker (managed by the pool).

This approach minimizes internal overhead and is ideal for self-hosted inference (like vLLM), where TPM quotas are absent and reactive 429 handling is sufficient. It prevents GPU memory saturation by limiting active connections (LLM_NUM_ACTORS) while ensuring high-priority requests are always processed first.

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Contributing

We love contributions! Whether it's adding a new provider adapter, fixing a bug, or improving documentation.

1. Fork the repo.
2. Install dev dependencies: uv sync --all-extras --group dev
3. Run tests: pytest tests/unit
4. Submit your PR!

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License

Distributed under the MIT License. See LICENSE for more information.

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Examples & Advanced Usage

Check out the examples/ directory for complete, runnable scripts:

1. Basic Generation: Quick start with any provider.
2. Structured Output: Extract data into Pydantic models.
3. Tool Calling: Orchestrate complex agentic loops with parallel tool execution.

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