WorkflowsApr 7, 2026·2 min read

Julep — Stateful AI Agent Platform with Workflows

Build persistent AI agents with multi-step workflows, long-term memory, and tool integrations. Julep handles state management so agents remember across sessions.

AG
Agent Toolkit · Community
Quick Use

Use it first, then decide how deep to go

This block should tell both the user and the agent what to copy, install, and apply first.

pip install julep
from julep import Julep

client = Julep(api_key="your-key")

# Create a persistent agent
agent = client.agents.create(
    name="Research Assistant",
    model="claude-sonnet-4-20250514",
    about="Helps with research tasks and remembers context across sessions.",
)

# Chat with memory
session = client.sessions.create(agent_id=agent.id)
response = client.sessions.chat(
    session_id=session.id,
    messages=[{"role": "user", "content": "Research quantum computing trends"}],
)
print(response.choices[0].message.content)

What is Julep?

Julep is a platform for building AI agents with persistent state, multi-step workflows, and long-term memory. Unlike stateless LLM calls, Julep agents maintain context across sessions, execute complex multi-step tasks, and integrate with 100+ external tools — all managed server-side.

Answer-Ready: Julep is a stateful AI agent platform with multi-step workflows, long-term memory, and 100+ tool integrations. Agents persist across sessions with server-side state management. Works with Claude, GPT, and Gemini models.

Best for: Developers building persistent AI agents that need memory and multi-step workflows. Works with: Claude, GPT-4o, Gemini, Llama. Setup time: Under 5 minutes.

Core Features

1. Multi-Step Workflows

# Define complex workflows in YAML
name: research_and_report
steps:
  - tool: web_search
    arguments:
      query: "{{topic}} latest developments 2026"

  - prompt: |
      Summarize these search results into key findings:
      {{_.output}}

  - tool: send_email
    arguments:
      to: "{{user_email}}"
      subject: "Research Report: {{topic}}"
      body: "{{_.output}}"

2. Long-Term Memory

# Memories persist across sessions
session1 = client.sessions.create(agent_id=agent.id)
client.sessions.chat(session1.id, messages=[
    {"role": "user", "content": "My name is Alice and I work on payments"}
])

# New session, agent remembers
session2 = client.sessions.create(agent_id=agent.id)
response = client.sessions.chat(session2.id, messages=[
    {"role": "user", "content": "What do you know about me?"}
])
# "You're Alice and you work on payments"

3. Document Storage

# Upload documents for RAG
client.agents.docs.create(
    agent_id=agent.id,
    title="Company Handbook",
    content="...",
    metadata={"type": "policy", "year": 2026},
)

# Agent searches docs during conversation

4. Tool Integrations

100+ built-in integrations:

Category Tools
Search Brave, Google, Wikipedia
Communication Email, Slack, Discord
Data SQL, S3, Google Sheets
Code Python execution, GitHub
Browser Web scraping, screenshots

5. Parallel Execution

steps:
  - parallel:
    - tool: web_search
      arguments: { query: "topic A" }
    - tool: web_search
      arguments: { query: "topic B" }
    - tool: web_search
      arguments: { query: "topic C" }

  - prompt: "Compare findings: {{_.output}}"

Architecture

Client SDK → Julep API Server
                ↓
         ┌──────┴──────┐
         │  State Mgmt  │  ← Sessions, memory, documents
         │  Workflow     │  ← Multi-step task execution
         │  Tool Router  │  ← 100+ integrations
         │  LLM Router   │  ← Claude, GPT, Gemini
         └──────────────┘

FAQ

Q: Is Julep open source? A: The SDK and API specs are open-source. The platform is available as a managed service.

Q: How does memory work? A: Julep automatically extracts and stores relevant facts from conversations, searchable across sessions.

Q: Can I self-host? A: Self-hosting is available for enterprise plans.

🙏

Source & Thanks

Created by Julep AI. Licensed under Apache 2.0.

julep-ai/julep — 5k+ stars

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