🌌 agent-compose

The Orchestrator for the Agentic Era.

🚀 One YAML. Any Framework. Infinite Scale.

---

Build multi-agent AI systems like you build infrastructure with Docker Compose.
Stop writing brittle orchestration glue code. Define your topology in YAML and ship.

Quick Start • Why agent-compose? • DeepSeek Mastery • Architecture

---

💎 The Philosophy

Multi-agent development is currently in its "Manual Era." Teams spend 70% of their time writing orchestration logic—handling state, resolving dependencies, and tracking costs.

agent-compose brings order to the chaos. It is the first framework-agnostic orchestrator that allows you to mix LangGraph, CrewAI, and OpenAI SDK agents in a single, declarative pipeline.

The "Killer Feature": Framework Mixing

Prototype an agent in CrewAI, refine another in LangGraph, and keep a simple generating agent as a raw LLM call. agent-compose handles the data flow, dependency resolution, and parallel execution.

---

⚡ Quick Start

1. Install

pip install agent-compose[all]

2. Define agent-compose.yaml

name: executive-research-pipeline
description: "LangGraph (Research) → CrewAI (Analysis) → DeepSeek (Writing)"

agents:
  researcher:
    framework: langgraph
    model: deepseek-chat
    system_prompt: "Thoroughly research the given topic and extract data points."
    tools: [web_search]
    connects_to: [analyst]

  analyst:
    framework: crewai
    role: "Senior Strategic Analyst"
    goal: "Extract competitive insights and second-order effects."
    model: deepseek-chat
    connects_to: [writer]

  writer:
    framework: raw
    model: deepseek-chat
    system_prompt: "Transform findings into a C-Suite executive brief."
    output: report.md

3. Launch

# Set your key
export DEEPSEEK_API_KEY="your_api_key"

# Run the pipeline
agent-compose up --input "The state of AI orchestration in 2026"

---

🥷 DeepSeek Native Integration

In 2026, cost-efficiency is the differentiator. agent-compose is optimized for DeepSeek's high-performance, low-cost reasoning.

• Unified Billing: Track per-agent token costs even when mixing providers.
• BaseURL Auto-Routing: Automatically routes to api.deepseek.com when a deepseek-* model is detected.
• Failover Support: Gracefully fallback from expensive flagship models to DeepSeek if budgets are exceeded.

---

🏗️ Core Architecture (FAANG-Spec)

graph TD
    CLI[agent-compose CLI] --> Loader[YAML Loader]
    Loader --> DAG[DAG Constructor]
    DAG --> Engine[Async Engine]
    
    subgraph Execution_Layer
        Engine --> LGraph[LangGraph Engine]
        Engine --> Crew[CrewAI Engine]
        Engine --> Direct[DeepSeek Engine]
    end
    
    subgraph Model_Providers
        LGraph --> Providers[Cloud LLMs]
        Crew --> Providers
        Direct --> Providers
    end
    
    Providers --> Metrics[Tracking]
    Metrics --> Final[Final Report]

Loading

---

🛠️ CLI Reference

Command	Description
up	Spin up the entire pipeline (Parallel by default).
validate	Perform strict schema validation and cycle detection.
graph	Visualize the agent topology in ASCII or Mermaid.
run <agent>	Debug a single agent in isolation with custom input.
costs	View full token and USD breakdown of the last run.

---

🤝 Contributing

We welcome senior-level contributions to the core engine. Check CONTRIBUTING.md for our engineering standards (Type hints, Pytest coverage, and Architectural clean-code).

---

agent-compose by Ismail Sajid

Write YAML. Ship Agents.