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# Summary: new Agent SDK API is added in https://github.com/meta-llama/llama-stack-client-python/pull/178 Update docs and test to reflect this. Closes https://github.com/meta-llama/llama-stack/issues/1365 # Test Plan: ```bash py.test -v -s --nbval-lax ./docs/getting_started.ipynb LLAMA_STACK_CONFIG=fireworks \ pytest -s -v tests/integration/agents/test_agents.py \ --safety-shield meta-llama/Llama-Guard-3-8B --text-model meta-llama/Llama-3.1-8B-Instruct ```
89 lines
2.5 KiB
Markdown
89 lines
2.5 KiB
Markdown
# Llama Stack Agent Framework
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The Llama Stack agent framework is built on a modular architecture that allows for flexible and powerful AI applications. This document explains the key components and how they work together.
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## Core Concepts
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### 1. Agent Configuration
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Agents are configured using the `AgentConfig` class, which includes:
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- **Model**: The underlying LLM to power the agent
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- **Instructions**: System prompt that defines the agent's behavior
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- **Tools**: Capabilities the agent can use to interact with external systems
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- **Safety Shields**: Guardrails to ensure responsible AI behavior
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```python
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from llama_stack_client.lib.agents.agent import Agent
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# Create the agent
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agent = Agent(
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llama_stack_client,
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model="meta-llama/Llama-3-70b-chat",
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instructions="You are a helpful assistant that can use tools to answer questions.",
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tools=["builtin::code_interpreter", "builtin::rag/knowledge_search"],
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)
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```
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### 2. Sessions
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Agents maintain state through sessions, which represent a conversation thread:
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```python
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# Create a session
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session_id = agent.create_session(session_name="My conversation")
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```
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### 3. Turns
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Each interaction with an agent is called a "turn" and consists of:
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- **Input Messages**: What the user sends to the agent
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- **Steps**: The agent's internal processing (inference, tool execution, etc.)
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- **Output Message**: The agent's response
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```python
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from llama_stack_client.lib.agents.event_logger import EventLogger
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# Create a turn with streaming response
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turn_response = agent.create_turn(
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session_id=session_id,
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messages=[{"role": "user", "content": "Tell me about Llama models"}],
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)
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for log in EventLogger().log(turn_response):
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log.print()
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```
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### Non-Streaming
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```python
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from rich.pretty import pprint
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# Non-streaming API
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response = agent.create_turn(
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session_id=session_id,
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messages=[{"role": "user", "content": "Tell me about Llama models"}],
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stream=False,
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)
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print("Inputs:")
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pprint(response.input_messages)
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print("Output:")
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pprint(response.output_message.content)
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print("Steps:")
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pprint(response.steps)
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```
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### 4. Steps
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Each turn consists of multiple steps that represent the agent's thought process:
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- **Inference Steps**: The agent generating text responses
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- **Tool Execution Steps**: The agent using tools to gather information
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- **Shield Call Steps**: Safety checks being performed
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## Agent Execution Loop
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Refer to the [Agent Execution Loop](agent_execution_loop) for more details on what happens within an agent turn.
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