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docs: OpenAI Agents integration update (#1608)
Signed-off-by: Anush008 <anushshetty90@gmail.com>
This commit is contained in:
Anush
@@ -34,6 +34,7 @@ aliases: ["/documentation/frameworks/memgpt/"]
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| [Mirror Security](/documentation/frameworks/mirror-security/) | Python framework for vector encryption and access control. |
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| [Mem0](/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
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| [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
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| [OpenAI Agents](/documentation/frameworks/openai-agents/) | Python framework for managing multiple AI agents that can work together. |
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| [Pandas-AI](/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
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| [Ragbits](/documentation/frameworks/ragbits/) | Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. |
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| [Rig-rs](/documentation/frameworks/rig-rs/) | Rust library for building scalable, modular, and ergonomic LLM-powered applications. |
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@@ -42,7 +43,6 @@ aliases: ["/documentation/frameworks/memgpt/"]
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| [Solon](/documentation/frameworks/solon/) | A lightweight, high-performance Java enterprise framework |
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| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
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| [Superduper](/documentation/frameworks/superduper/) | Framework for building flexible, compositional AI apps which may be applied directly to databases. |
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| [Swarm](/documentation/frameworks/swarm/) | Python framework for managing multiple AI agents that can work together. |
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| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
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| [Testcontainers](/documentation/frameworks/testcontainers/) | Framework for providing throwaway, lightweight instances of systems for testing |
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| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
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@@ -0,0 +1,129 @@
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---
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title: OpenAI Agents
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aliases: [ /documentation/frameworks/swarm/ ]
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---
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# OpenAI Agents
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[OpenAI Agents](https://github.com/openai/openai-agents-python) is a Python framework to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of the experimental framework, [Swarm](https://github.com/openai/swarm).
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## Getting Started
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To start using OpenAI Agents, follow these steps:
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- Install the package
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```bash
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pip install openai-agents
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```
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- Set up your OpenAI API key
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```bash
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export OPENAI_API_KEY="<YOUR_KEY>"
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```
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## How It Works
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The Agents SDK has a very small set of primitives:
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- `Agents`, which are LLMs equipped with instructions and tools
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- `Handoffs`, which allow agents to delegate to other agents for specific tasks
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- `Guardrails`, which enable the inputs to agents to be validated
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Used with Python, these building blocks make it easy to create real-world apps with tool-agent interactions and minimal learning curve. Plus, the SDK also includes tracing to help you debug, evaluate, and fine-tune your agent workflows.
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## Creating Your First Agents
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Here’s a basic example of three agents:
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- Triage Agent: Acts as the initial point of contact. It analyzes the user's question and decides whether to route it to a specialized agent.
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- Math Tutor: A specialist agent designed to help with math-related questions.
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- History Tutor: A specialist agent focused on historical topics.
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```python
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from agents import Agent, Runner
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math_tutor_agent = Agent(
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name="Math Tutor",
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handoff_description="Specialist agent for math questions",
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instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
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)
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history_tutor_agent = Agent(
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name="History Tutor",
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handoff_description="Specialist agent for historical questions",
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instructions="You provide assistance with historical queries. Explain important events and context clearly.",
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)
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triage_agent = Agent(
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name="Triage Agent",
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instructions="You determine which agent to use based on the user's homework question",
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handoffs=[history_tutor_agent, math_tutor_agent],
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)
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# Run the interaction
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result = Runner.run_sync(triage_agent, "I want some help with WW1.")
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print(result.final_output)
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```
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## Integrating with Qdrant
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You can connect agents to retrieve or ingest data into a Qdrant collection. Thereby building your knowledge base. Here’s how to enable an agent to retrieve information from Qdrant.
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Assume you have a Qdrant [collection created](https://qdrant.tech/documentation/concepts/collections/#create-a-collection) using the `"text-embedding-3-small"` model. The payload structure includes a `text` field for knowledge storage.
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```python
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import qdrant_client
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from openai import OpenAI
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from agents import Agent, function_tool
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# Initialize clients
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openai_client = OpenAI()
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qdrant = qdrant_client.QdrantClient(host="localhost")
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# Configuration
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EMBEDDING_MODEL = "text-embedding-3-small"
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COLLECTION_NAME = "help_center"
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LIMIT = 5
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SCORE_THRESHOLD = 0.7
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@function_tool
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def query_qdrant(query: str) -> str:
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"""Retrieve semantically relevant content from Qdrant.
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Args:
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query: The query to search.
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"""
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embedded_query = openai_client.embeddings.create(
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input=query,
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model=EMBEDDING_MODEL,
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).data[0].embedding
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results = qdrant.query_points(
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collection_name=COLLECTION_NAME,
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query=embedded_query,
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limit=LIMIT,
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score_threshold=SCORE_THRESHOLD,
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).points
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if results:
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return "\n".join([point.payload["text"] for point in results])
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else:
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return "No results found."
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qdrant_agent = Agent(
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name="Qdrant searcher",
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handoff_description="Specialist agent for retrieving info from a Qdrant collection",
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instructions="You help find answers for user queries using Qdrant. Do not make up any info on your own.",
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tools=[query_qdrant],
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)
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```
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Our `qdrant_agent` can now query a Qdrant collection whenever deemed necessary to answer a user query.
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## Further Reading
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- [Agents Documentation](https://openai.github.io/openai-agents-python/)
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- [Agents Examples](https://github.com/openai/openai-agents-python/tree/main/examples)
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@@ -1,171 +0,0 @@
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---
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title: OpenAI Swarm
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---
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# Swarm
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[OpenAI Swarm](https://github.com/openai/swarm) is a Python framework for managing multiple AI agents that can work together. Instead of relying on a single LLM instance to perform all tasks, Swarm allows you to build specialized agents that communicate and collaborate, like a team of experts with unique skills.
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## Getting Started
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To start using Swarm, follow these steps:
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- Install Swarm
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```bash
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pip install git+https://github.com/openai/swarm.git
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```
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- Set up your OpenAI API key
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```bash
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export OPENAI_API_KEY="<YOUR_KEY>"
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```
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## How Swarm Works
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In Swarm, agents represent individual team members with specific roles and instructions. Each agent can execute tasks or hand off the conversation to another agent, depending on the situation.
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An `Agent` instance simply encapsulates a set of instructions with a set of functions, and has the capability to hand off execution to another Agent.
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These simple building blocks allow you to create complex workflows with a simple mental model.
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## Creating Your First Agents
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Here’s a basic example of two agents:
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- **Agent A**: A helpful assistant.
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- **Agent B**: An arithmetric specialist.
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Agent A transfers the conversation to Agent B when requested.
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```python
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from swarm import Swarm, Agent
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client = Swarm()
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# Define Agent B
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agent_b = Agent(
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name="Agent B",
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instructions="Arithmetic solving expertise holder.",
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)
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def transfer_to_agent_b():
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return agent_b
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# Define Agent A
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agent_a = Agent(
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name="Agent A",
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instructions="You are a helpful agent.",
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functions=[transfer_to_agent_b],
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)
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# Run the interaction
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response = client.run(
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agent=agent_a,
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messages=[{"role": "user", "content": "I want some help with numbers."}],
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)
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print(response)
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```
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In this example, Agent A passes the conversation to Agent B when deemed necessary.
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## Features of an agent
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### 1. **Instructions**
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Agent instructions define their behavior. These are translated into system prompts for conversations. Only the active agent's instructions are used during an interaction.
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### 2. **Functions**
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Agents can execute Python functions, enabling them to perform tasks like processing data or querying databases. Swarm automatically converts functions into a JSON Schema that is passed into Chat Completions tools.
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Example:
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```python
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def greet(context_variables, language):
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user_name = context_variables["user_name"]
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greeting = "Hola" if language.lower() == "spanish" else "Hello"
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print(f"{greeting}, {user_name}!")
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return "Done"
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agent = Agent(
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name="Greeter Agent",
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functions=[greet],
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)
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client.run(
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agent=agent,
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messages=[{"role": "user", "content": "Greet me in Spanish."}],
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context_variables={"user_name": "John"},
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)
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```
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Errors are handled gracefully by appending an error response to the conversation.
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### 3. **Handoffs**
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If a function returns another agent, the system transfers control to that agent.
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## Integrating Swarm with Qdrant
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You can connect you Swarm agents to retrieve or ingest data into a Qdrant collection. Thereby building you knowledge base. Here’s how to enable an agent to retrieve information from Qdrant.
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Assume you have a Qdrant [collection created](https://qdrant.tech/documentation/concepts/collections/#create-a-collection) using the `"text-embedding-3-small"` model. The payload structure includes a `text` field for knowledge storage.
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```python
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import qdrant_client
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from openai import OpenAI
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# Initialize clients
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openai_client = OpenAI()
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qdrant = qdrant_client.QdrantClient(host="localhost")
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# Configuration
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EMBEDDING_MODEL = "text-embedding-3-small"
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COLLECTION_NAME = "help_center"
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LIMIT = 5
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SCORE_THRESHOLD = 0.7
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# Function to query Qdrant
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def query_qdrant(query):
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"""Retrieve semantically relevant content from Qdrant."""
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embedded_query = openai_client.embeddings.create(
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input=query,
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model=EMBEDDING_MODEL,
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).data[0].embedding
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results = qdrant.query_points(
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collection_name=COLLECTION_NAME,
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query=embedded_query,
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limit=LIMIT,
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score_threshold=SCORE_THRESHOLD,
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).points
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if results:
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return {"response": "\n".join([point.payload["text"] for point in results])}
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else:
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return {"response": "No results found."}
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# Define agents
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qdrant_agent = Agent(
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name="Qdrant Agent",
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instructions="Retrieve relevant info from a knowledge base stored in Qdrant.",
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functions=[query_qdrant],
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)
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def transfer_to_qdrant():
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return qdrant_agent
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main_agent = Agent(
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name="Main Agent",
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instructions="Handle user queries and delegate searches to Qdrant.",
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functions=[transfer_to_qdrant],
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)
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```
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Our `qdrant_agent` can now query a Qdrant collection whenever deemed necessary to answer a user query.
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## Further Reading
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You can find more usage examples in the [Swarm repo](https://github.com/openai/swarm/blob/main/examples/) that further describe its capabilities.
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