Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/agent-one-lab/agentfly/llm_backendsgit clone --depth 1 https://github.com/Agent-One-Lab/AgentFlyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00709 |
| Opus 5 | $0.00000 | $0.00354 |
| Sonnet 5 | $0.00000 | $0.00142 |
| Haiku 4.5 | $0.00000 | $0.00071 |
Grade A, and why
llm_backends scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Backends
Overview
AgentFly supports multiple LLM backends for text generation, each with their own configuration options. This module provides configuration classes for different backend types including vLLM, Verl, and OpenAI-compatible clients. Among them, Verl backend is designed for internal training usage. The Verl backend is the core design that decouples agent system and rl training.
Configuration Classes
Async VLLM Backend
Configuration for asynchronous vLLM backend with engine arguments:
::: agentfly.utils.llm_backends.backend_configs.AsyncVLLMConfig options: show_inheritance: true
Async Verl Backend
Configuration for asynchronous Verl backend:
::: agentfly.utils.llm_backends.backend_configs.AsyncVerlConfig options: show_inheritance: true
Client Backend
Configuration for OpenAI-compatible client backends:
::: agentfly.utils.llm_backends.backend_configs.ClientConfig options: show_inheritance: true
Backend Implementations
Base Backend
Abstract base class for all LLM backends:
::: agentfly.utils.llm_backends.llm_backends.LLMBackend options: show_inheritance: true
Async VLLM Backend
Asynchronous vLLM implementation for high-performance model inference:
::: agentfly.utils.llm_backends.llm_backends.AsyncVLLMBackend options: show_inheritance: true
Async Verl Backend
Asynchronous Verl implementation for distributed model inference:
::: agentfly.utils.llm_backends.llm_backends.AsyncVerlBackend options: show_inheritance: true
Client Backend
OpenAI-compatible client backend for remote API inference:
::: agentfly.utils.llm_backends.llm_backends.ClientBackend options: show_inheritance: true
Usage Examples
Backends are designed to work together with agents. Here are examples showing how to configure different backends when creating agents:
Async VLLM Backend
from agentfly.agents import HFAgent
from agentfly.tools import calculator
from agentfly.rewards import math_equal_reward_tool
from agentfly.utils.llm_backends import AsyncVLLMConfig
agent = HFAgent(
model_name_or_path="Qwen/Qwen2.5-3B-Instruct",
tools=[calculator],
reward_fn=math_equal_reward_tool,
template="qwen2.5",
backend_config=AsyncVLLMConfig(
pipeline_parallel_size=2,
data_parallel_size=1,
tensor_parallel_size=1,
gpu_memory_utilization=0.8
)
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 118 lines · 0 tokens per session scan A 66db9813154f
llm_backends is an agent published in the GitHub repository Agent-One-Lab/AgentFly (122 stars, last pushed 7d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 709 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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