docs: OpenAI Agents integration update (#1608)

Signed-off-by: Anush008 <anushshetty90@gmail.com>
2026-10-05 10:58:32 +02:00 · 2025-04-30 14:10:48 +05:30
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+---
+title: OpenAI Agents
+aliases: [ /documentation/frameworks/swarm/ ]
+---
+
+# OpenAI Agents
+
+[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).
+
+## Getting Started
+
+To start using OpenAI Agents, follow these steps:
+
+- Install the package
+
+```bash
+pip install openai-agents
+```
+
+- Set up your OpenAI API key
+
+```bash
+export OPENAI_API_KEY="<YOUR_KEY>"
+```
+
+## How It Works
+
+The Agents SDK has a very small set of primitives:
+
+- `Agents`, which are LLMs equipped with instructions and tools
+- `Handoffs`, which allow agents to delegate to other agents for specific tasks
+- `Guardrails`, which enable the inputs to agents to be validated
+
+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.
+
+## Creating Your First Agents
+
+Here’s a basic example of three agents:
+
+- 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.
+- Math Tutor: A specialist agent designed to help with math-related questions.
+- History Tutor: A specialist agent focused on historical topics.
+
+```python
+from agents import Agent, Runner
+
+
+math_tutor_agent = Agent(
+    name="Math Tutor",
+    handoff_description="Specialist agent for math questions",
+    instructions="You provide help with math problems. Explain your reasoning at each step and include examples",
+)
+
+history_tutor_agent = Agent(
+    name="History Tutor",
+    handoff_description="Specialist agent for historical questions",
+    instructions="You provide assistance with historical queries. Explain important events and context clearly.",
+)
+
+triage_agent = Agent(
+    name="Triage Agent",
+    instructions="You determine which agent to use based on the user's homework question",
+    handoffs=[history_tutor_agent, math_tutor_agent],
+)
+
+# Run the interaction
+result = Runner.run_sync(triage_agent, "I want some help with WW1.")
+print(result.final_output)
+```
+
+## Integrating with Qdrant
+
+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.
+
+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.
+
+```python
+import qdrant_client
+from openai import OpenAI
+from agents import Agent, function_tool
+
+# Initialize clients
+openai_client = OpenAI()
+qdrant = qdrant_client.QdrantClient(host="localhost")
+
+# Configuration
+EMBEDDING_MODEL = "text-embedding-3-small"
+COLLECTION_NAME = "help_center"
+LIMIT = 5
+SCORE_THRESHOLD = 0.7
+
+@function_tool
+def query_qdrant(query: str) -> str:
+    """Retrieve semantically relevant content from Qdrant.
+
+    Args:
+        query: The query to search.
+    """
+    embedded_query = openai_client.embeddings.create(
+        input=query,
+        model=EMBEDDING_MODEL,
+    ).data[0].embedding
+
+    results = qdrant.query_points(
+        collection_name=COLLECTION_NAME,
+        query=embedded_query,
+        limit=LIMIT,
+        score_threshold=SCORE_THRESHOLD,
+    ).points
+
+    if results:
+        return "\n".join([point.payload["text"] for point in results])
+    else:
+        return "No results found."
+
+qdrant_agent = Agent(
+    name="Qdrant searcher",
+    handoff_description="Specialist agent for retrieving info from a Qdrant collection",
+    instructions="You help find answers for user queries using Qdrant. Do not make up any info on your own.",
+    tools=[query_qdrant],  
+)
+```
+
+Our `qdrant_agent` can now query a Qdrant collection whenever deemed necessary to answer a user query.
+
+## Further Reading
+
+- [Agents Documentation](https://openai.github.io/openai-agents-python/)
+- [Agents Examples](https://github.com/openai/openai-agents-python/tree/main/examples)