llm-gent 0.3.0

Agent framework with learning capabilities

llm-gent

Agent framework with trait-based architecture and learning capabilities.

Overview

llm-gent provides a composable framework for building LLM-powered agents. Agents are composed of traits that provide specific capabilities (LLM access, storage, learning, etc.) and can be run standalone or as services via the included HTTP runtime.

Key features:

• Trait-based composition - Mix and match capabilities via traits (LLM, Storage, Rating, Learn)
• Multi-backend LLM support - OpenAI-compatible, Anthropic, and custom backends via llm-infer
• Built-in learning - Collect training data (SFT/DPO) and fine-tune via llm-kelt
• Structured output - Pydantic schema validation with automatic JSON cleanup for small models
• Production ready - HTTP server, PostgreSQL storage, schema migrations

Installation

pip install llm-gent

For HTTP server support:

pip install llm-gent[http]

Quick Start

from appinfra import DotDict
from appinfra.log import LogConfig, LoggerFactory

from llm_gent import Agent, Config, Identity, LLMTrait, DirectiveTrait

# Setup logging
log_config = LogConfig.from_params(level="info", handlers={"console": {"type": "console"}})
lg = LoggerFactory.create_root(log_config)

# Configure LLM backend
llm_config = DotDict({
    "default": "local",
    "backends": {
        "local": {
            "type": "openai_compatible",
            "base_url": "http://localhost:8000/v1",
            "model": "default",
        }
    },
})

# Create agent with traits
identity = Identity(domain=None, workspace="demo", name="my-agent")
config = Config(identity=identity)
agent = Agent(lg, config)

# Add capabilities via traits
agent.add_trait(DirectiveTrait(agent, directive="You are a helpful assistant."))
agent.add_trait(LLMTrait(agent, llm_config))

# Start and use agent
agent.start()
llm = agent.require_trait(LLMTrait)
result = llm.complete([{"role": "user", "content": "Hello!"}])
print(result.content)
agent.stop()

Core Concepts

Agents

An Agent is a container for traits with lifecycle management. Agents have an identity (domain/workspace/name) and can be started, stopped, and run in cycles.

Traits

Traits provide specific capabilities to agents:

Trait	Purpose
LLMTrait	LLM completions with multi-backend routing
DirectiveTrait	System prompts and agent instructions
StorageTrait	PostgreSQL persistence with migrations
RatingTrait	Automated LLM-based content evaluation
LearnTrait	Training data collection (SFT/DPO)
ToolsTrait	Tool/function calling support

Tools

Built-in tools for agentic workflows:

• ShellTool - Execute shell commands
• FileReadTool / FileWriteTool - File operations
• HTTPFetchTool - HTTP requests
• RecallTool / RememberTool - Memory operations

Running as a Service

# Start agent server
llm-gent serve

# Or with specific config
llm-gent -c etc/llm-gent.yaml serve

Related Projects

• llm-infer - LLM inference server and client
• llm-kelt - Training infrastructure (SFT/DPO)
• appinfra - Application infrastructure utilities

License

Apache-2.0