# Julep — Serverless AI Agent Workflows

> Build production-ready AI agent workflows with persistent memory, parallel execution, and 100+ tool integrations. 6.6K+ stars.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

# Julep — Serverless AI Agent Workflows

## Quick Use

```bash
pip install julep
```

```python
from julep import Julep

client = Julep(api_key="YOUR_KEY")

# Create an agent with memory
agent = client.agents.create(
    name="Research Assistant",
    model="claude-sonnet-4-6",
    about="A research assistant that remembers past conversations."
)

# Create a task (workflow)
task = client.tasks.create(
    agent_id=agent.id,
    name="web_research",
    main=[
        {"tool": "web_search", "arguments": {"query": "_.topic"}},
        {"prompt": [{"role": "user", "content": "Summarize: {{results}}"}]},
    ]
)

# Execute
execution = client.executions.create(task_id=task.id, input={"topic": "AI agents 2026"})
print(client.executions.get(execution.id))
```

---

## Intro

Julep is an open-source serverless platform for building production-ready AI agent workflows — often called "Firebase for AI agents." With 6,600+ GitHub stars, it lets developers orchestrate complex multi-step processes using YAML or Python code, with built-in persistent memory, parallel execution, automatic retries, and 100+ tool integrations. Agents remember context across conversations and self-heal on failures, making Julep ideal for long-running production pipelines.

Works with: GPT-4, Claude, Llama, Gemini, any OpenAI-compatible API. Best for teams building production AI workflows that need reliability, memory, and orchestration. Setup time: under 5 minutes.

---

## Julep Architecture & Workflow Design

### Core Concepts

| Concept | Description |
|---------|-------------|
| **Agent** | An AI entity with a model, instructions, and memory |
| **Task** | A multi-step workflow definition (YAML or code) |
| **Execution** | A running instance of a task |
| **Session** | A conversation context with persistent memory |
| **Tool** | An external integration (API, function, webhook) |

### Workflow Definition (YAML)

```yaml
name: research_and_report
description: Research a topic and generate a report

tools:
  - name: web_search
    type: integration
    integration:
      provider: brave
      method: search

main:
  # Step 1: Search the web
  - tool: web_search
    arguments:
      query: "{{inputs.topic}} latest developments 2026"

  # Step 2: Analyze results in parallel
  - parallel:
    - prompt:
      - role: user
        content: "Summarize key findings: {{_}}"
    - prompt:
      - role: user
        content: "List open questions: {{_}}"

  # Step 3: Generate final report
  - prompt:
    - role: user
      content: |
        Create a research report combining:
        Summary: {{outputs[0]}}
        Open Questions: {{outputs[1]}}
```

### Persistent Memory

Agents maintain context across sessions:

```python
# Session 1
session = client.sessions.create(agent_id=agent.id)
client.sessions.chat(session.id, messages=[
    {"role": "user", "content": "My name is Alice, I work on ML pipelines"}
])

# Session 2 — agent remembers Alice
client.sessions.chat(session.id, messages=[
    {"role": "user", "content": "What do you know about me?"}
])
# -> "You're Alice, you work on ML pipelines..."
```

### 100+ Tool Integrations

Pre-built integrations include:
- **Search**: Brave, Wikipedia, arXiv
- **Communication**: Email, Slack, Discord
- **Storage**: S3, Google Drive
- **Dev Tools**: GitHub, Linear, Jira
- **Data**: SQL databases, REST APIs
- **Custom**: Any webhook or function

### Error Handling & Self-Healing

```yaml
main:
  - tool: flaky_api
    arguments: { query: "{{inputs.q}}" }
    on_error:
      retry:
        max_retries: 3
        backoff: exponential
      fallback:
        - prompt:
          - role: user
            content: "API failed. Use cached data instead."
```

### Parallel Execution

Run multiple steps concurrently for faster workflows:

```yaml
main:
  - parallel:
    - tool: search_arxiv
      arguments: { query: "{{inputs.topic}}" }
    - tool: search_web
      arguments: { query: "{{inputs.topic}}" }
    - tool: search_github
      arguments: { query: "{{inputs.topic}}" }
  - prompt:
    - role: user
      content: "Combine results from all 3 sources: {{outputs}}"
```

---

## FAQ

**Q: What is Julep?**
A: Julep is an open-source serverless platform for building AI agent workflows with persistent memory, parallel execution, 100+ tool integrations, and automatic error recovery. Think "Firebase for AI agents."

**Q: How is Julep different from LangChain or CrewAI?**
A: Julep is a managed platform, not a library. It handles infrastructure (execution, storage, retries, scaling) so you focus on workflow logic. LangChain/CrewAI are code frameworks you host yourself.

**Q: Is Julep free?**
A: The open-source version is free. Julep also offers a hosted cloud service with a free tier.

---

## Source & Thanks

> Created by [julep-ai](https://github.com/julep-ai). Licensed under Apache-2.0.
>
> [julep](https://github.com/julep-ai/julep) — ⭐ 6,600+

Thanks to the Julep team for making production AI workflows accessible to all developers.


---
Source: https://tokrepo.com/en/workflows/julep-serverless-ai-agent-workflows-70982ab2
Author: Agent Toolkit