negmas-llm 0.2.1

LLM-based negotiators for the negmas negotiation framework

negmas-llm

LLM-based negotiators for the negmas negotiation framework.

Overview

negmas-llm integrates Large Language Models with the negmas negotiation framework, allowing you to create negotiators that use LLMs for decision-making. The library supports multiple LLM providers through litellm, including:

• Cloud providers: OpenAI, Anthropic, Google Gemini, Cohere, Mistral, Groq, Together AI, Azure OpenAI, AWS Bedrock, OpenRouter, DeepSeek, Hugging Face
• Local models: Ollama, vLLM, LM Studio, text-generation-webui

Installation

pip install negmas-llm

Or with uv:

uv add negmas-llm

Quick Start

from negmas import SAOMechanism
from negmas.preferences import LinearAdditiveUtilityFunction
from negmas.outcomes import make_issue
from negmas_llm import OllamaNegotiator, OpenAINegotiator

# Define negotiation issues
issues = [
    make_issue("price", (0, 100)),
    make_issue("quantity", (1, 10)),
]

# Create utility functions for each party
seller_ufun = LinearAdditiveUtilityFunction(
    weights=[0.6, 0.4],
    issues=issues,
)
buyer_ufun = LinearAdditiveUtilityFunction(
    weights=[-0.6, 0.4],  # Buyer prefers lower prices
    issues=issues,
)

# Create LLM-based negotiators
seller = OpenAINegotiator(
    model="gpt-4o",
    name="seller",
    ufun=seller_ufun,
)
buyer = OllamaNegotiator(
    model="llama3.2",
    name="buyer",
    ufun=buyer_ufun,
)

# Run the negotiation
mechanism = SAOMechanism(outcome_space=issues, n_steps=20)
mechanism.add(seller)
mechanism.add(buyer)
result = mechanism.run()

print(f"Agreement: {result.agreement}")

Supported Negotiators

Class	Provider	Default Model
OpenAINegotiator	OpenAI	gpt-4o
AnthropicNegotiator	Anthropic	claude-sonnet-4-20250514
GeminiNegotiator	Google	gemini-2.0-flash
CohereNegotiator	Cohere	command-r-plus
MistralNegotiator	Mistral AI	mistral-large-latest
GroqNegotiator	Groq	llama-3.3-70b-versatile
TogetherAINegotiator	Together AI	Meta-Llama-3.1-70B-Instruct-Turbo
AzureOpenAINegotiator	Azure OpenAI	(deployment name)
AWSBedrockNegotiator	AWS Bedrock	claude-3-sonnet
OpenRouterNegotiator	OpenRouter	openai/gpt-4o
DeepSeekNegotiator	DeepSeek	deepseek-chat
HuggingFaceNegotiator	Hugging Face	Llama-3.2-3B-Instruct
OllamaNegotiator	Ollama (local)	llama3.2
VLLMNegotiator	vLLM (local)	(model name)
LMStudioNegotiator	LM Studio (local)	local-model
TextGenWebUINegotiator	text-generation-webui	local-model

Customization

You can customize how information is presented to the LLM by subclassing LLMNegotiator and overriding the formatting methods:

from negmas_llm import LLMNegotiator

class CustomNegotiator(LLMNegotiator):
    def format_outcome_space(self, state):
        # Customize how the outcome space is described
        return "Custom outcome space description..."

    def format_own_ufun(self, state):
        # Customize how your utility function is described
        return "Custom utility function description..."

    def format_state(self, state, offer=None):
        # Customize how the negotiation state is presented
        return "Custom state description..."

For complete control over the prompts, override build_system_prompt or build_user_message:

from negmas_llm import LLMNegotiator

class CustomPromptNegotiator(LLMNegotiator):
    def build_system_prompt(self, state):
        # Complete control over the system prompt
        # By default, this combines: format_outcome_space(), format_nmi_info(),
        # format_own_ufun(), format_partner_ufun(), format_response_instructions()
        return f"""You are an aggressive negotiator who never accepts the first offer.

{self.format_outcome_space(state)}
{self.format_own_ufun(state)}
{self.format_response_instructions()}

Always aim for at least 80% of your maximum utility."""

    def build_user_message(self, state, offer, source):
        # Complete control over the per-round user message
        if offer is not None:
            return f"Round {state.step}: Opponent offered {offer}. Counter aggressively."
        return f"Round {state.step}: Make an ambitious opening offer."

Documentation

Full documentation is available at negmas-llm.readthedocs.io.

License

GNU Affero General Public License v3.0 or later - see LICENSE for details.

Related Projects

• negmas - NEGotiation MAnagement System
• litellm - Call all LLM APIs using the OpenAI format