Julep — Stateful AI Agent Platform with Workflows

What it is

Julep is an AI agent platform that handles state management, multi-step workflows, and long-term memory so your agents remember context across sessions. Instead of building custom persistence layers and workflow orchestration, you define agent behavior declaratively and Julep manages execution.

Julep targets developers building production AI agents that need to maintain context over days or weeks: customer support bots, research assistants, or automated workflows that span multiple interactions.

How it saves time or tokens

Julep eliminates the boilerplate of building agent memory and workflow state from scratch. Without it, you write custom database schemas, session management, and retry logic. Julep provides these as primitives. The estimated token cost for a typical workflow definition is around 4,000 tokens, but it replaces thousands of lines of custom orchestration code.

Long-term memory means agents do not re-process context from previous sessions, reducing redundant token consumption.

How to use

1. Install the Julep 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='Helps users research technical topics'
)

1. Define a multi-step workflow:

task = client.tasks.create(
    agent_id=agent.id,
    name='research_workflow',
    main=[
        {'prompt': 'Search for: {{topic}}'},
        {'tool': 'web_search', 'args': {'query': '{{topic}}'}},
        {'prompt': 'Summarize findings from search results'}
    ]
)

Example

A workflow that researches a topic and saves findings:

# Execute the workflow
execution = client.executions.create(
    task_id=task.id,
    input={'topic': 'vector database benchmarks 2026'}
)

# Check status
result = client.executions.get(execution.id)
print(result.output)

Julep handles retries, tool calls, and state persistence automatically. If the execution fails mid-step, it resumes from the last checkpoint.

Related on TokRepo

• AI tools for agents -- Frameworks and platforms for building AI agents
• Multi-agent frameworks -- Compare orchestration approaches across agent platforms

Common pitfalls

• Overloading agent memory with irrelevant context. Julep stores all session data by default. Prune old sessions periodically to keep retrieval relevant.
• Defining overly long workflows without breakpoints. If a 10-step workflow fails at step 8, you want granular retry. Split complex workflows into sub-tasks.
• Hardcoding model names. Julep supports model switching, so parameterize the model choice for easy upgrades.