Agent Operating System — Production-ready multi-model agent framework with Tool-Using Agent, LLM Provider abstraction (OpenAI/DeepSeek/Anthropic), Function Calling, streaming, retry, checkpoint/resume, A2A protocol, swarm coordination, and comprehensive observability.
Project description
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AgentOS v1.0
Production-ready multi-model agent framework.
Quick Start
pip install agentos
With an API key:
export OPENAI_API_KEY=sk-...
agentos "What's the weather in Beijing?"
Without an API key (mock demo mode):
agentos demo
Features
- LLM Provider: OpenAI / DeepSeek / Anthropic, unified interface
- Tool-Using Agent: Autonomous multi-step reasoning with Function Calling
- Streaming:
run_stream()for real-time step yielding - Retry & Checkpoint: Configurable retry + JSON checkpoint/resume
- Mock Mode: Test without API keys
- A2A Protocol: Agent-to-agent communication
- Swarm Coordination: Multi-agent topologies
CLI
agentos <task> Run a task
agentos demo Weather agent demo
agentos serve Start API server
agentos version Show version
Provider Auto-Detection
| Env Var | Provider | Default Model |
|---|---|---|
OPENAI_API_KEY |
OpenAI | gpt-4o-mini |
DEEPSEEK_API_KEY |
DeepSeek | deepseek-chat |
ANTHROPIC_API_KEY |
Anthropic | claude-sonnet-4 |
Python API
from agentos.llm import create_provider
from agentos.llm.base import Tool, ToolParameter
from agentos.agent import ToolAgent, ToolExecutor, AgentConfig
provider = create_provider("openai")
executor = ToolExecutor()
executor.register(
Tool.from_function("get_weather", "Get weather", {
"city": ToolParameter(type="string", description="City name"),
}),
lambda city: f"{city}: 22°C sunny",
)
agent = ToolAgent(provider, executor, config=AgentConfig())
result = agent.run("What's the weather in Tokyo?")
print(result.final_answer)
(内容由AI生成,仅供参考)
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