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Documentation Index

Fetch the complete documentation index at: https://docs.rllm-project.com/llms.txt

Use this file to discover all available pages before exploring further.

A single math task — solve a competition problem with a calculator tool — built four ways in one cookbook. Use this to compare LangGraph, OpenAI Agents SDK, smolagents, and Strands on the same dataset, or as a template for plugging your own framework into rLLM. The point of this cookbook is to make the AgentFlow + model-gateway architecture concrete. Every framework integration collapses to ~6 lines of agent body that points the framework’s LLM client at config.base_url and returns None. The gateway captures every LLM call by URL-routed session, the framework auto-builds an Episode from those captured traces, and the evaluator parses the answer out of the resulting trajectory. No callback handler, no traced chat client, no manual Step / Trajectory construction.

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Pattern

AspectValue
Loop shapeMulti-turn (each framework’s own ReAct loop)
ToolsOne: calculate — AST-based safe arithmetic interpreter, shared across all four flows
TerminationWhatever each framework decides (typically: model emits no more tool calls)
Reward shape1.0 if final answer matches ground truth (mathd + sympy), else 0.0
Return typeNone — the gateway captures everything; the framework auto-builds the Episode
GRPO groupingEach flow’s trajectory name is set on @rllm.rollout and routed via f"{task_id}:{name}"

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Layout

cookbooks/agent_frameworks/
├── README.md
├── pyproject.toml         # one package, four entry-point agents
├── calculator.py          # safe_eval — shared
├── system_prompt.py       # SYSTEM_PROMPT — shared
├── evaluator.py           # math_evaluator — shared
├── agentflow/
│   ├── __init__.py
│   ├── langgraph.py       # langgraph_math
│   ├── openai_agents.py   # openai_agents_math
│   ├── smolagents.py      # smolagents_math
│   └── strands.py         # strands_math
├── train.py               # python train.py +rllm.agent_name=<agent>
├── train_tinker.sh
├── train_verl.sh
└── test.py

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Each flow

# agentflow/langgraph.py
@rllm.rollout(name="langgraph-math")
async def langgraph_math(task, config):
    llm = ChatOpenAI(model=config.model, base_url=config.base_url, api_key="EMPTY", temperature=1.0)
    agent = create_react_agent(llm, tools=[calculate], prompt=SYSTEM_PROMPT)
    await agent.ainvoke({"messages": [("user", task.instruction)]})
    return None

# agentflow/openai_agents.py
@rllm.rollout(name="openai-agents-math")
async def openai_agents_math(task, config):
    client = AsyncOpenAI(base_url=config.base_url, api_key="EMPTY")
    model = OpenAIChatCompletionsModel(model=config.model, openai_client=client)
    agent = Agent(name="solver", instructions=SYSTEM_PROMPT, tools=[calculate], model=model)
    await Runner.run(agent, input=task.instruction)
    return None

# agentflow/smolagents.py
@rllm.rollout(name="smolagents-math")
def smolagents_math(task, config):
    model = OpenAIServerModel(model_id=config.model, api_base=config.base_url, api_key="EMPTY")
    agent = ToolCallingAgent(tools=[calculate], model=model)
    agent.run(SYSTEM_PROMPT + "\n\n" + str(task.instruction))
    return None

# agentflow/strands.py
@rllm.rollout(name="strands-math")
async def strands_math(task, config):
    client = AsyncOpenAI(base_url=config.base_url, api_key="EMPTY")
    model = OpenAIModel(client=client, model_id=config.model)
    agent = Agent(model=model, tools=[calculate], system_prompt=SYSTEM_PROMPT)
    await agent.invoke_async(task.instruction)
    return None

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Install

# rLLM + the backend you want to train on
uv pip install -e ".[tinker]"

# Then pick one framework — or [all] for everything:
uv pip install --no-deps -e "cookbooks/agent_frameworks[langgraph]"
uv pip install --no-deps -e "cookbooks/agent_frameworks[openai-agents]"
uv pip install --no-deps -e "cookbooks/agent_frameworks[smolagents]"
uv pip install --no-deps -e "cookbooks/agent_frameworks[strands]"
uv pip install --no-deps -e "cookbooks/agent_frameworks[all]"

# Verify discovery
rllm agent list

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Datasets

rllm dataset pull deepscaler_math
rllm dataset pull math500

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Eval

rllm eval math500 \
    --agent strands_math \
    --evaluator math_evaluator \
    --model Qwen/Qwen3-4B-Instruct-2507 \
    --base-url http://localhost:8000/v1 \
    --max-examples 20
Substitute --agent with langgraph_math, openai_agents_math, smolagents_math, or strands_math. Same --evaluator math_evaluator for every flow.

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Training

# Tinker (single-machine LoRA) — first arg is the agent name
bash cookbooks/agent_frameworks/train_tinker.sh langgraph_math
bash cookbooks/agent_frameworks/train_tinker.sh strands_math

# Verl (distributed GPU)
bash cookbooks/agent_frameworks/train_verl.sh openai_agents_math
Or directly via train.py: 
python cookbooks/agent_frameworks/train.py \
    +rllm.agent_name=smolagents_math \
    rllm/backend=tinker \
    model.name=Qwen/Qwen3-4B-Instruct-2507

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Adding a new framework

  1. Create agentflow/<framework>.py with one @rllm.rollout(name="<framework>-math") function that wires the framework’s LLM client to config.base_url, runs the agent on task.instruction, and return Nones.
  2. Add it to pyproject.toml’s [project.entry-points."rllm.agents"] and [tool.setuptools] py-modules lists; declare the framework’s package in [project.optional-dependencies].<framework>.
  3. Reinstall the cookbook with uv pip install --no-deps -e "cookbooks/agent_frameworks[<framework>]" and your agent shows up under rllm agent list.
That’s the entire integration surface.

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On GitHub

cookbooks/agent_frameworks

Full source, README, and runnable launch scripts