Julep — Serverless AI Agent Workflows

What it is

Julep is a platform for building production-ready AI agent workflows. It provides persistent memory across sessions, parallel task execution, and integrations with over 100 tools out of the box. Agents are defined in Python and run on Julep's serverless infrastructure.

Julep targets developers building complex AI agents that need to maintain state across conversations, execute multi-step tasks in parallel, and integrate with external services. It handles the infrastructure so you focus on agent logic rather than memory management and orchestration.

How it saves time or tokens

Building agent memory, task queues, and tool integrations from scratch requires significant engineering effort. Julep provides these as managed services. Persistent memory means agents remember context across sessions without you implementing a vector store. Parallel execution runs multiple agent tasks concurrently without custom threading code. The pre-built tool integrations eliminate the boilerplate of writing API connectors for common services.

How to use

1. Install the Python SDK:

pip install julep

1. Create an agent with memory:

from julep import Julep

client = Julep(api_key='YOUR_KEY')

agent = client.agents.create(
    name='Research Assistant',
    model='claude-sonnet-4-20250514',
    about='A research agent with persistent memory'
)

1. Define and execute a task:

task = client.tasks.create(
    agent_id=agent.id,
    name='Research Topic',
    main=[
        {'prompt': 'Research {{topic}} and summarize key findings.'},
        {'tool': 'web_search', 'arguments': {'query': '{{topic}}'}},
        {'prompt': 'Synthesize the search results into a report.'}
    ]
)

execution = client.executions.create(
    task_id=task.id,
    input={'topic': 'quantum computing 2026'}
)

Example

# Multi-step agent workflow with parallel execution
from julep import Julep

client = Julep(api_key='YOUR_KEY')

# Create agent
agent = client.agents.create(
    name='Content Analyzer',
    model='claude-sonnet-4-20250514'
)

# Define parallel research task
task = client.tasks.create(
    agent_id=agent.id,
    name='Parallel Analysis',
    main=[
        {
            'parallel': [
                {'tool': 'web_search', 'arguments': {'query': '{{topic}} news'}},
                {'tool': 'web_search', 'arguments': {'query': '{{topic}} research'}},
            ]
        },
        {'prompt': 'Combine results into a briefing document.'}
    ]
)

Related on TokRepo

• AI Agent Tools — Frameworks and platforms for building AI agents
• Multi-Agent Frameworks — Compare multi-agent orchestration options

Common pitfalls

• Julep requires an API key and runs on their serverless infrastructure; it is not self-hostable, so consider data residency requirements before sending sensitive information.
• Agent memory persists by default, which is useful but can accumulate irrelevant context over time; implement periodic memory cleanup in long-running agents.
• Parallel task execution has concurrency limits that depend on your plan tier; test with your expected workload before going to production.