Creating a Multi-Agent System with Weaviate
We’re building a multi-agent system with Weaviate because it’s an impressive database optimized for handling vector search and semantic queries. With its ability to efficiently store and retrieve data, it becomes a crucial part of any modern application. So, how do weaviate create a multi-agent system exactly? Let’s get into the details.
Prerequisites
- Go 1.18+
- Docker 20.10+
- Python 3.9+
- Weaviate v1.19.0
- Basic knowledge of REST APIs
Step 1: Setting Up Weaviate with Docker
To kick things off, we’ll set up Weaviate using Docker. I know some of you might be thinking, “Docker again? Really?” but it’s still the easiest way to get things running.
docker run -p 8080:8080 \
-e WEAVIATE_HOST=localhost \
-e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
--name weaviate \
semitechnologies/weaviate:1.19.0
Why do this? Running Weaviate in a container isolates it and allows you to start using it almost immediately without worrying about local environment issues. However, don’t forget to monitor the container logs! You can do this using:
docker logs -f weaviate
If you get an error saying “Cannot start container,” make sure to check your Docker installation and configuration. I once spent hours trying to figure out why a container wouldn’t start, only to discover that Docker wasn’t running.
Step 2: Configuring Schema for The Multi-Agent System
Next, let’s create a schema for our multi-agent setup. Think of a schema as the blueprint for your data structure.
import requests
url = "http://localhost:8080/v1/schema"
data = {
"classes": [{
"class": "Agent",
"properties": [{
"name": "name",
"dataType": ["string"]
}, {
"name": "role",
"dataType": ["string"]
}]
}]
}
response = requests.post(url, json=data)
if response.status_code == 200:
print("Schema created successfully.")
else:
print("Error:", response.json())
This initializes an `Agent` class with properties for `name` and `role`. The challenge here lies in the way data types are constructed. Mislabeling the data type results in errors that might make you think your code is broken. Keep an eye on the API response.
Step 3: Inserting Agents into Weaviate
Now that we’ve got our schema set up, it’s time to add some agents. This is where the magic happens.
agents = [
{"name": "Agent A", "role": "Researcher"},
{"name": "Agent B", "role": "Analyst"},
]
url = "http://localhost:8080/v1/objects"
for agent in agents:
response = requests.post(url, json={"class": "Agent", "properties": agent})
if response.status_code == 201:
print(f"{agent['name']} added.")
else:
print("Error:", response.json())
Use this snippet to batch insert agents. You might hit a bump with duplicate entries or incorrect property names. Pay attention! I’ve accidentally caused duplicates that skewed my results. Nobody wants to work with Agent A twice!
Step 4: Querying Agents
Having the agents in the database is all fine and dandy, but how do we get information back out? Here’s where querying comes into play.
query = """
{
Get {
Agent {
name
role
}
}
}
"""
response = requests.post("http://localhost:8080/v1/graphql", json={"query": query})
if response.status_code == 200:
data = response.json()
for agent in data['data']['Get']['Agent']:
print(f"Name: {agent['name']}, Role: {agent['role']}")
else:
print("Error:", response.json())
This GraphQL query retrieves all agents. If the query fails, ensure that your schema matches what you’re trying to access. It’s common to forget to update the schema which causes queries to return nothing. I’ve lost count of how many times I’ve tried querying only to realize I ran a different version of the schema.
The Gotchas
Here are some nuggets to consider that most tutorials brush aside:
- Versioning Problems: Weaviate’s schema evolution isn’t perfect. If you change your schema, re-create classes carefully, or you might lose data.
- Types and Properties: Being inconsistent with property types leads to frustrating errors. Always double-check your schema and data entries before querying.
- Error Handling: Not all API errors are self-explanatory. Use debug output liberally to catch what’s going wrong.
- Data Format: When sending data, ensure it’s in the right format, particularly with dates or nested data types. Mismatches mean lost time chasing dead ends.
- Scaling Issues: Performance might degrade with too many agents or queries. Monitor your Docker resources. I once brought my laptop to its knees with a simple loop of inefficient queries!
Full Code Example
Combining all our steps, here’s the complete, runnable code for ensuring you’ve got a working multi-agent system.
import requests
# Step 1: Set up Weaviate with Docker
# (Run the previous docker command in your CLI)
# Step 2: Configure Schema
schema_url = "http://localhost:8080/v1/schema"
schema_data = {
"classes": [{
"class": "Agent",
"properties": [{
"name": "name",
"dataType": ["string"]
}, {
"name": "role",
"dataType": ["string"]
}]
}]
}
requests.post(schema_url, json=schema_data)
# Step 3: Insert Agents
agents = [
{"name": "Agent A", "role": "Researcher"},
{"name": "Agent B", "role": "Analyst"},
]
insert_url = "http://localhost:8080/v1/objects"
for agent in agents:
requests.post(insert_url, json={"class": "Agent", "properties": agent})
# Step 4: Query Agents
query = """
{
Get {
Agent {
name
role
}
}
}
"""
response = requests.post("http://localhost:8080/v1/graphql", json={"query": query})
if response.status_code == 200:
data = response.json()
for agent in data['data']['Get']['Agent']:
print(f"Name: {agent['name']}, Role: {agent['role']}")
else:
print("Error:", response.json())
What’s Next
After establishing your multi-agent system, consider expanding functionalities. Integrate a real-time messaging system between agents, or start linking actions between agents using an orchestration engine. It’s rewarding to see the interactions come to life.
FAQ
How do I troubleshoot issues with Weaviate?
Start by checking the logs using Docker. Also, make sure your API endpoints are responding as expected. Internet connectivity or firewall settings can sometimes interfere with network calls.
Can I run Weaviate on a cloud provider?
Absolutely! Weaviate can be deployed on AWS, GCP, or Azure using Docker or the official managed service offerings. Just watch your cloud bill, or you’ll end up crying like I did!
Is Weaviate suitable for production workloads?
It can be if set up correctly, but you need to ensure appropriate monitoring, scaling, and backups in place. Testing in your environment helps in avoiding surprises.
Data Sources
Last updated March 30, 2026. Data sourced from official docs and community benchmarks.
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