feat: Professional FAANG-spec Agent Orchestrator with DeepSeek support

Author: Ismail Sajid

.github/workflows/ci.yml

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+name: CI
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - run: pip install -e ".[dev]"
+      - run: ruff check src/ tests/
+      - run: pytest tests/ -v --tb=short

.gitignore

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+__pycache__/
+*.py[cod]
+*.egg-info/
+dist/
+build/
+.eggs/
+.env
+.venv
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+htmlcov/
+.coverage
+outputs/
+*.db

CONTRIBUTING.md

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+# Contributing to agent-compose
+
+## Setup
+```bash
+git clone https://github.com/daniellopez882/agent-compose.git
+cd agent-compose
+pip install -e ".[dev]"
+pytest tests/ -v
+```
+
+## High-Impact Contributions
+
+### Framework Engines (most wanted)
+- **LangGraph engine** — full ReAct agent with tool support
+- **CrewAI engine** — role-based crews from YAML
+- **OpenAI Agents SDK engine** — handoff patterns
+
+### Built-in Tools
+- `web_search` — Tavily/SerpAPI integration
+- `code_exec` — sandboxed Python execution
+- `file_read` / `file_write` — local file operations
+
+### Features
+- **MCP integration** — connect to MCP servers from YAML
+- **Graph visualization** — `agent-compose graph` as Mermaid
+- **Streaming** — stream agent outputs in real-time
+- **Shared memory** — cross-agent state
+
+### Examples
+Share your `agent-compose.yaml` pipelines!
+
+## Code Style
+- Python 3.10+ with type hints
+- Lint: `ruff check src/ tests/`
+- Tests required for new features
+- Conventional commits: `feat:`, `fix:`, `docs:`
+
+## Pull Requests
+1. Fork → feature branch → tests → PR
+2. One feature per PR
+3. Include a YAML example if adding a new capability

LICENSE

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+MIT License
+
+Copyright (c) 2026 Daniel López Orta
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

README.md

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+<div align="center">
+
+# 🌌 agent-compose
+
+**The Orchestrator for the Agentic Era.**
+
+🚀 *One YAML. Any Framework. Infinite Scale.*
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg?style=for-the-badge)](LICENSE)
+[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://python.org)
+[![DeepSeek Enabled](https://img.shields.io/badge/DeepSeek-Native-blueviolet?style=for-the-badge)](https://deepseek.com)
+[![LangGraph + CrewAI](https://img.shields.io/badge/Frameworks-Mixed-success?style=for-the-badge)]()
+
+---
+
+**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](#-quick-start) • [Why agent-compose?](#-the-philosophy) • [DeepSeek Mastery](#-deepseek-integration) • [Architecture](#-core-architecture)
+
+</div>
+
+---
+
+## 💎 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**
+```bash
+pip install agent-compose[all]
+```
+
+### **2. Define `agent-compose.yaml`**
+```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**
+```bash
+# 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)
+
+```mermaid
+graph TD
+    CLI["agent-compose CLI"] --> Loader["YAML Loader & Spec Validator"]
+    Loader --> DAG["DAG Constructor & Topo-Sort"]
+    DAG --> Engine["Async Execution Engine"]
+    
+    subgraph "Execution Layer (Parallel Dispatch)"
+        Engine --> LGraph["LangGraph Engine"]
+        Engine --> Crew["CrewAI Engine"]
+        Engine --> Direct["Direct/DeepSeek Engine"]
+    end
+    
+    subgraph "Model Providers"
+        LGraph --> Providers["OpenAI / Anthropic / DeepSeek"]
+        Crew --> Providers
+        Direct --> Providers
+    end
+    
+    Providers --> Metrics["Cost & Latency Tracking"]
+    Metrics --> Final["Final Output (MD / JSON)"]
+```
+
+---
+
+## 🛠️ 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](CONTRIBUTING.md) for our engineering standards (Type hints, Pytest coverage, and Architectural clean-code).
+
+---
+
+<div align="center">
+
+**[agent-compose](https://github.com/Ismail-2001/agent-compose)** by **Ismail Sajid**
+
+*Write YAML. Ship Agents.*
+
+</div>

examples/code-review.yaml

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+# Code Review Pipeline — parallel reviewers + synthesizer
+# Usage:
+#   agent-compose up -f examples/code-review.yaml --input-file my_code.py
+
+name: code-review
+description: "Parallel security, performance, and style reviews"
+
+agents:
+  security:
+    model: gpt-4o
+    system_prompt: "You are a security expert. Find vulnerabilities, injection risks, and auth issues."
+    connects_to: [synthesizer]
+
+  performance:
+    model: gpt-4o
+    system_prompt: "You are a performance engineer. Find bottlenecks, memory leaks, and inefficiencies."
+    connects_to: [synthesizer]
+
+  style:
+    model: gpt-4o-mini
+    system_prompt: "Review code style, readability, naming conventions, and documentation quality."
+    connects_to: [synthesizer]
+
+  synthesizer:
+    model: claude-sonnet-4-20250514
+    prompt: >
+      Combine these reviews into one prioritized report.
+      
+      Security: {{security.output}}
+      Performance: {{performance.output}}
+      Style: {{style.output}}
+    output: review.md

examples/debate.yaml

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+# Adversarial Debate Pattern
+# Two advocates argue opposing positions using different models for genuine diversity.
+# A judge evaluates the arguments impartially.
+#
+# Usage:
+#   agent-compose up -f examples/debate.yaml --input "Should companies adopt MCP or traditional APIs?"
+
+name: ai-debate
+description: "Adversarial debate with judge — reduces hallucination through opposition"
+
+agents:
+  advocate_for:
+    model: gpt-4o
+    system_prompt: >
+      You are a debate advocate. Argue passionately IN FAVOR of the
+      given position. Provide specific evidence, data, and logical
+      reasoning. Be thorough but concise (400 words max).
+    temperature: 0.3
+    connects_to: [judge]
+
+  advocate_against:
+    model: claude-sonnet-4-20250514
+    system_prompt: >
+      You are a debate advocate. Argue passionately AGAINST the
+      given position. Provide specific evidence, data, and logical
+      reasoning. Be thorough but concise (400 words max).
+    temperature: 0.3
+    connects_to: [judge]
+
+  judge:
+    model: gpt-4o
+    temperature: 0
+    prompt: >
+      You are an impartial judge. Evaluate these arguments purely on
+      evidence quality and logical coherence — not which position you
+      personally agree with.
+
+      Rate each advocate on:
+      1. Evidence quality (specific data vs vague claims)
+      2. Logical coherence
+      3. Addressing the strongest counter-argument
+
+      ARGUMENT FOR:
+      {{advocate_for.output}}
+
+      ARGUMENT AGAINST:
+      {{advocate_against.output}}
+
+      Deliver a reasoned verdict with confidence level (high/medium/low).
+    output: verdict.md

examples/research-pipeline.yaml

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+# Research Pipeline Example
+# Mixed frameworks: LangGraph researcher → Direct fact-checker → CrewAI analyst → LangGraph writer
+#
+# Usage:
+#   agent-compose up -f examples/research-pipeline.yaml --input "AI agent frameworks in 2026"
+
+name: research-pipeline
+description: "Multi-framework research pipeline with fact-checking"
+
+settings:
+  default_model: gpt-4o-mini
+  parallel: true
+  output_dir: ./outputs
+
+agents:
+  researcher:
+    framework: langgraph
+    model: gpt-4o
+    system_prompt: >
+      You are an expert researcher. Given a topic, produce a thorough
+      research brief covering key facts, recent developments, market data,
+      and notable players. Include specific numbers and dates where possible.
+    max_tokens: 4000
+    connects_to: [fact_checker, analyst]
+
+  fact_checker:
+    framework: direct
+    model: claude-sonnet-4-20250514
+    prompt: >
+      Verify the following research brief. Flag any claims that appear
+      inaccurate, outdated, or unsupported. For each flagged claim,
+      explain why it may be wrong and suggest a correction.
+
+      Research to verify:
+      {{researcher.output}}
+    max_tokens: 2000
+    connects_to: [writer]
+
+  analyst:
+    framework: crewai
+    role: "Senior Technology Analyst"
+    goal: "Identify the top 3 trends and create a comparison framework"
+    backstory: >
+      You have 15 years of experience analyzing technology markets.
+      You specialize in identifying patterns that others miss and
+      creating frameworks that help executives make decisions.
+    model: gpt-4o
+    max_tokens: 3000
+    connects_to: [writer]
+
+  writer:
+    framework: direct
+    model: gpt-4o-mini
+    prompt: >
+      Write a professional executive summary (600-800 words) using the
+      verified research and analysis below. Use only fact-checked claims.
+      Include the analyst's trend framework as a structured section.
+
+      Fact-checked research:
+      {{fact_checker.output}}
+
+      Analysis and trends:
+      {{analyst.output}}
+    max_tokens: 3000
+    output: report.md