add structured planning agent (#13149)

run-llama · Apr 30, 2024 · af8c8eb · af8c8eb
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diff --git a/docs/docs/examples/agent/structured_planner.ipynb b/docs/docs/examples/agent/structured_planner.ipynb
diff --git a/docs/docs/module_guides/deploying/agents/modules.md b/docs/docs/module_guides/deploying/agents/modules.md
@@ -16,6 +16,7 @@ For more detailed guides on how to use specific tools, check out our [tools modu
 - [Multi Document Agents](../../../examples/agent/multi_document_agents.ipynb)
 - [Agent Builder](../../../examples/agent/agent_builder.ipynb)
 - [Parallel Function Calling](../../../examples/agent/openai_agent_parallel_function_calling.ipynb)
+- [Agent with Planning](../../../examples/agent/structured_planner.ipynb)
 
 ## [Beta] OpenAI Assistant Agent
 
@@ -49,4 +50,5 @@ For more detailed guides on how to use specific tools, check out our [tools modu
 
 - [Agent Runner](../../../examples/agent/agent_runner/agent_runner.ipynb)
 - [Agent Runner RAG](../../../examples/agent/agent_runner/agent_runner_rag.ipynb)
+- [Agent with Planning](../../../examples/agent/structured_planner.ipynb)
 - [Controllable Agent Runner](../../../examples/agent/agent_runner/agent_runner_rag_controllable.ipynb)
diff --git a/docs/docs/module_guides/deploying/agents/usage_pattern.md b/docs/docs/module_guides/deploying/agents/usage_pattern.md
@@ -108,6 +108,27 @@ query_engine_tools = [
 outer_agent = ReActAgent.from_tools(query_engine_tools, llm=llm, verbose=True)
 ```
 
+## Agent With Planning
+
+Breaking down an initial task into easier-to-digest sub-tasks is a powerful pattern.
+
+LlamaIndex provides an agent planning module that does just this:
+
+```python
+from llama_index.agent.openai import OpenAIAgentWorker
+from llama_index.core.agent import (
+    StructuredPlannerAgent,
+    FunctionCallingAgentWorker,
+)
+
+worker = FunctionCallingAgentWorker.from_tools(tools, llm=llm)
+agent = StructuredPlannerAgent(worker)
+```
+
+In general, this agent may take longer to respond compared to the basic `AgentRunner` class, but the outputs will often be more complete. Another tradeoff to consider is that planning often requires a very capable LLM (for context, `gpt-3.5-turbo` is sometimes flakey for planning, while `gpt-4-turbo` does much better.)
+
+See more in the [complete guide](../../../examples/agent/structured_planner.ipynb)
+
 ## Lower-Level API
 
 The OpenAIAgent and ReActAgent are simple wrappers on top of an `AgentRunner` interacting with an `AgentWorker`.

diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
@@ -85,6 +85,7 @@ nav:
           - ./examples/agent/coa_agent.ipynb
           - ./examples/agent/lats_agent.ipynb
           - ./examples/agent/llm_compiler.ipynb
+          - ./examples/agent/structured_planner.ipynb
       - Callbacks:
           - ./examples/callbacks/HoneyHiveLlamaIndexTracer.ipynb
           - ./examples/callbacks/PromptLayerHandler.ipynb

diff --git a/llama-index-core/llama_index/core/agent/__init__.py b/llama-index-core/llama_index/core/agent/__init__.py
@@ -7,13 +7,15 @@
 from llama_index.core.agent.react.step import ReActAgentWorker
 from llama_index.core.agent.react_multimodal.step import MultimodalReActAgentWorker
 from llama_index.core.agent.runner.base import AgentRunner
+from llama_index.core.agent.runner.planner import StructuredPlannerAgent
 from llama_index.core.agent.runner.parallel import ParallelAgentRunner
 from llama_index.core.agent.types import Task
 from llama_index.core.chat_engine.types import AgentChatResponse
 from llama_index.core.agent.function_calling.step import FunctionCallingAgentWorker
 
 __all__ = [
     "AgentRunner",
+    "StructuredPlannerAgent",
     "ParallelAgentRunner",
     "ReActAgentWorker",
     "ReActAgent",