mirror of
https://github.com/magnus919/agent-skills.git
synced 2026-09-18 06:56:59 +03:00
Magnus Hedemark
3e86349539
Comprehensive skill covering: - Core architecture (7 primitives, Settings, data flow) - RAG strategies (basic through advanced with hybrid retrieval, reranking) - Multi-agent orchestration (AgentWorkflow, handoff bug fix) - Event-driven workflows (durable execution, checkpoint/resume) - Production deployment (llama-deploy, debugging, observability) - PropertyGraphIndex (knowledge graphs, hybrid retrieval) - Evaluation and span-attached observability - Integration ecosystem (vector stores, LlamaHub, LlamaParse) 9 reference files, 4 templates, 1 verification script. MIT licensed. 100% AI agent portable (no platform-specific content). Signed-off-by: Magnus Hedemark <magnus919@pm.me>
93 lines
2.8 KiB
Markdown
93 lines
2.8 KiB
Markdown
# LlamaIndex Agent Patterns
|
|
|
|
## Agent Types
|
|
|
|
| Agent | Tool Calling | When to Use |
|
|
|-------|-------------|-------------|
|
|
| `FunctionAgent` | Native function calling API | Models with tool-calling support |
|
|
| `ReActAgent` | ReAct prompting pattern | Models without native tool support |
|
|
|
|
## Pattern 1: AgentWorkflow (Built-in Multi-Agent)
|
|
|
|
```python
|
|
from llama_index.core.agent.workflow import AgentWorkflow, FunctionAgent
|
|
|
|
research_agent = FunctionAgent(
|
|
name="ResearchAgent",
|
|
description="Searches and records notes",
|
|
system_prompt="You are a researcher. Hand off to WriteAgent when ready.",
|
|
tools=[search_web, record_notes],
|
|
can_handoff_to=["WriteAgent"],
|
|
)
|
|
|
|
write_agent = FunctionAgent(
|
|
name="WriteAgent",
|
|
description="Writes reports from notes",
|
|
can_handoff_to=["ReviewAgent"],
|
|
)
|
|
|
|
workflow = AgentWorkflow(
|
|
agents=[research_agent, write_agent],
|
|
root_agent=research_agent.name,
|
|
initial_state={"report_content": "Not written yet."},
|
|
)
|
|
|
|
response = await workflow.run(user_msg="Write a report on the history of the web...")
|
|
```
|
|
|
|
## Pattern 2: Orchestrator Agent (Sub-Agents as Tools)
|
|
|
|
Centralized control: one orchestrator calls sub-agents via tools.
|
|
|
|
```python
|
|
orchestrator = FunctionAgent(
|
|
name="Orchestrator",
|
|
system_prompt="You orchestrate research, writing, and review...",
|
|
tools=[call_research_agent, call_write_agent, call_review_agent],
|
|
)
|
|
response = await orchestrator.run(user_msg="Write a report...")
|
|
```
|
|
|
|
## Pattern 3: Custom Planner (DIY Orchestration)
|
|
|
|
```python
|
|
# LLM outputs structured plan in XML/JSON
|
|
# Python code parses and executes
|
|
for step in parse_plan(llm_response):
|
|
agent = agents[step.agent_name]
|
|
result = await agent.run(user_msg=step.input)
|
|
```
|
|
|
|
## Streaming Events
|
|
|
|
AgentWorkflow emits five event types:
|
|
|
|
```python
|
|
handler = workflow.run(user_msg="Your query")
|
|
async for event in handler.stream_events():
|
|
if isinstance(event, AgentStream):
|
|
print(event.delta, end="") # Real-time output
|
|
elif isinstance(event, AgentOutput):
|
|
print(f"{event.current_agent_name}: {event.response.content}")
|
|
```
|
|
|
|
## Known Bug: Agent Handoff
|
|
|
|
After handoff, the receiving agent may lose the user's original request because handoff messages push it out of ChatMemory.
|
|
|
|
**Fix**: Extend `FunctionAgent.take_step()`:
|
|
|
|
```python
|
|
class MyFunctionAgent(FunctionAgent):
|
|
async def take_step(self, ctx, llm_input, tools, memory):
|
|
last_msg = llm_input[-1].content if llm_input else ""
|
|
if "handoff_result" in last_msg:
|
|
for message in llm_input[::-1]:
|
|
if message.role == MessageRole.USER:
|
|
llm_input.append(message)
|
|
break
|
|
return await super().take_step(ctx, llm_input, tools, memory)
|
|
```
|
|
|
|
Also customize `handoff_output_prompt` with a `handoff_result` tag for reliable detection.
|