feat: sample agent folder, remove docker file in Readme
This commit is contained in:
Richard Tang
@@ -15,7 +15,6 @@
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[](https://github.com/adenhq/hive/blob/main/LICENSE)
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[](https://www.ycombinator.com/companies/aden)
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[](https://hub.docker.com/u/adenhq)
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[](https://discord.com/invite/MXE49hrKDk)
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[](https://x.com/aden_hq)
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[](https://www.linkedin.com/company/teamaden/)
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+1
-1
@@ -268,7 +268,7 @@ classDef done fill:#9e9e9e,color:#fff,stroke:#757575
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- [ ] Wake-up Tool (resume agent tasks)
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### Deployment (Self-Hosted)
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- [ ] Docker container standardization
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- [ ] Workder agent docker container standardization
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- [ ] Headless backend execution
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- [ ] Exposed API for frontend attachment
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- [ ] Local monitoring & observability
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@@ -14,7 +14,6 @@
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[](https://github.com/adenhq/hive/blob/main/LICENSE)
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[](https://www.ycombinator.com/companies/aden)
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[](https://hub.docker.com/u/adenhq)
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[](https://discord.com/invite/MXE49hrKDk)
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[](https://x.com/aden_hq)
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[](https://www.linkedin.com/company/teamaden/)
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@@ -66,7 +65,6 @@ Aden es una plataforma para construir, desplegar, operar y adaptar agentes de IA
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### Prerrequisitos
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- [Python 3.11+](https://www.python.org/downloads/) - Para desarrollo de agentes
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- [Docker](https://docs.docker.com/get-docker/) (v20.10+) - Opcional, para herramientas en contenedores
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### Instalación
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@@ -0,0 +1,41 @@
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# Examples
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This directory contains two types of examples to help you build agents with the Hive framework.
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## Recipes vs Templates
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### [recipes/](recipes/) — "How to make it"
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A recipe is a **prompt-only** description of an agent. It tells you the goal, the nodes, the prompts, the edge routing logic, and what tools to wire in — but it's not runnable code. You read the recipe, then build the agent yourself.
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Use recipes when you want to:
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- Understand a pattern before committing to an implementation
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- Adapt an idea to your own codebase or tooling
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- Learn how to think about agent design (goals, nodes, edges, prompts)
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### [templates/](templates/) — "Ready to eat"
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A template is a **working agent scaffold** that follows the standard Hive export structure. Copy the folder, rename it, swap in your own prompts and tools, and run it.
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Use templates when you want to:
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- Get a new agent running quickly
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- Start from a known-good structure instead of from scratch
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- See how all the pieces (goal, nodes, edges, config, CLI) fit together in real code
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## How to use a template
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```bash
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# 1. Copy the template
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cp -r examples/templates/marketing_agent exports/my_agent
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# 2. Edit the goal, nodes, and edges in agent.py and nodes/__init__.py
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# 3. Run it
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PYTHONPATH=core python -m exports.my_agent --help
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```
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## How to use a recipe
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1. Read the recipe markdown file
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2. Use the patterns described to build your own agent — either manually or with the builder agent (`/agent-workflow`)
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3. Refer to the [core README](../core/README.md) for framework API details
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@@ -0,0 +1,27 @@
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# Recipes
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A recipe describes an agent's design — the goal, nodes, prompts, edge logic, and tools — without providing runnable code. Think of it as a blueprint: it tells you *how* to build the agent, but you do the building.
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## What's in a recipe
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Each recipe is a markdown file (or folder with a markdown file) containing:
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- **Goal**: What the agent accomplishes, including success criteria and constraints
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- **Nodes**: Each step in the workflow, with the system prompt, node type, and input/output keys
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- **Edges**: How nodes connect, including conditions and routing logic
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- **Tools**: What external tools or MCP servers the agent needs
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- **Usage notes**: Tips, gotchas, and suggested variations
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## How to use a recipe
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1. Read through the recipe to understand the design
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2. Create a new agent using the standard export structure (see [templates/](../templates/) for a scaffold)
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3. Translate the recipe's goal, nodes, and edges into code
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4. Wire in the tools described
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5. Test and iterate
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## Available recipes
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| Recipe | Description |
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|--------|-------------|
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| [marketing_agent](marketing_agent/) | Multi-channel marketing content generator with audience analysis and A/B copy variants |
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@@ -0,0 +1,156 @@
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# Recipe: Marketing Content Agent
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A multi-channel marketing content generator. Given a product description and target audience, this agent analyzes the audience, generates tailored copy for multiple channels, and produces A/B variants.
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## Goal
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```
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Name: Marketing Content Generator
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Description: Generate targeted marketing content across multiple channels
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for a given product and audience.
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Success criteria:
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- Audience analysis is produced with demographics and pain points
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- At least 2 channel-specific content pieces are generated
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- A/B variants are provided for each piece
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- All content aligns with the specified brand voice
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Constraints:
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- (hard) No competitor brand names in generated content
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- (soft) Content should be under 280 characters for social media channels
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```
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## Input / Output
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**Input:**
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- `product_description` (str) — What the product is and does
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- `target_audience` (str) — Who the content is for
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- `brand_voice` (str) — Tone and style guidelines (e.g., "professional but approachable")
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- `channels` (list[str]) — Target channels, e.g. `["email", "twitter", "linkedin"]`
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**Output:**
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- `audience_analysis` (dict) — Demographics, pain points, motivations
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- `content` (list[dict]) — Per-channel content with A/B variants
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## Workflow
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```
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[analyze_audience] → [generate_content] → [review_and_refine]
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(conditional)
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needs_revision == True → [generate_content]
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needs_revision == False → (done)
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```
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## Nodes
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### 1. analyze_audience
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| Field | Value |
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|-------|-------|
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| Type | `llm_generate` |
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| Input keys | `product_description`, `target_audience` |
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| Output keys | `audience_analysis` |
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| Tools | None |
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**System prompt:**
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```
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You are a marketing strategist. Analyze the target audience for a product.
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Product: {product_description}
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Target audience: {target_audience}
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Produce a structured analysis in JSON:
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{{
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"audience_analysis": {{
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"demographics": "...",
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"pain_points": ["..."],
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"motivations": ["..."],
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"preferred_channels": ["..."],
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"messaging_angle": "..."
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}}
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}}
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```
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### 2. generate_content
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| Field | Value |
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|-------|-------|
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| Type | `llm_generate` |
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| Input keys | `product_description`, `audience_analysis`, `brand_voice`, `channels` |
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| Output keys | `content` |
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| Tools | None |
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**System prompt:**
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```
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You are a marketing copywriter. Generate content for each channel.
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Product: {product_description}
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Audience analysis: {audience_analysis}
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Brand voice: {brand_voice}
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Channels: {channels}
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For each channel, produce two variants (A and B).
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Output as JSON:
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{{
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"content": [
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{{
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"channel": "twitter",
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"variant_a": "...",
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"variant_b": "..."
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}}
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]
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}}
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```
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### 3. review_and_refine
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| Field | Value |
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|-------|-------|
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| Type | `llm_generate` |
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| Input keys | `content`, `brand_voice` |
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| Output keys | `content`, `needs_revision` |
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| Tools | None |
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**System prompt:**
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```
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You are a senior marketing editor. Review the following content for brand
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voice alignment, clarity, and channel appropriateness.
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Content: {content}
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Brand voice: {brand_voice}
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If any piece needs revision, fix it and set needs_revision to true.
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If everything looks good, return the content unchanged with needs_revision false.
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Output as JSON:
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{{
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"content": [...],
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"needs_revision": false
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}}
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```
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## Edges
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| Source | Target | Condition | Priority |
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|--------|--------|-----------|----------|
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| analyze_audience | generate_content | `on_success` | 0 |
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| generate_content | review_and_refine | `on_success` | 0 |
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| review_and_refine | generate_content | `conditional: needs_revision == True` | 10 |
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The `review_and_refine → generate_content` loop has higher priority so it's checked first. If `needs_revision` is false, execution ends at `review_and_refine` (terminal node).
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## Tools
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This recipe uses no external tools — all nodes are `llm_generate`. To extend it, consider adding:
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- A web search tool for competitive analysis in the `analyze_audience` node
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- A URL shortener tool for social media content
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- An image generation tool for visual content variants
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## Variations
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- **Single-channel mode**: Remove the `channels` input and hardcode to one channel for simpler output
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- **With approval gate**: Add a `human_input` node between `review_and_refine` and the terminal to require human sign-off
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- **With analytics**: Add a `function` node that logs generated content to a tracking system
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@@ -0,0 +1,38 @@
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# Templates
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A template is a working agent scaffold that follows the standard Hive export structure. Copy it, rename it, customize the goal/nodes/edges, and run it.
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## What's in a template
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Each template is a complete agent package:
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```
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template_name/
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├── __init__.py # Package exports
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├── __main__.py # CLI entry point
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├── agent.py # Goal, edges, graph spec, agent class
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├── config.py # Runtime configuration
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├── nodes/
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│ └── __init__.py # Node definitions (NodeSpec instances)
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└── README.md # What this template demonstrates
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```
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## How to use a template
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```bash
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# 1. Copy to your exports directory
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cp -r examples/templates/marketing_agent exports/my_marketing_agent
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# 2. Update the module references in __main__.py and __init__.py
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# 3. Customize goal, nodes, edges, and prompts
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# 4. Run it
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PYTHONPATH=core python -m exports.my_marketing_agent --input '{"product_description": "..."}'
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```
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## Available templates
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| Template | Description |
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|----------|-------------|
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| [marketing_agent](marketing_agent/) | Multi-channel marketing content generator with audience analysis, content generation, and editorial review nodes |
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@@ -0,0 +1,57 @@
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# Template: Marketing Content Agent
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A multi-channel marketing content generator. Given a product and audience, this agent analyzes the audience, generates tailored copy for multiple channels with A/B variants, and reviews the output for quality.
|
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## Workflow
|
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```
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[analyze-audience] → [generate-content] → [review-and-refine]
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|
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(conditional)
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|
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needs_revision == True → [generate-content]
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needs_revision == False → (done)
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```
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## Nodes
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| Node | Type | Description |
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|------|------|-------------|
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| `analyze-audience` | `llm_generate` | Produces structured audience analysis |
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| `generate-content` | `llm_generate` | Creates per-channel copy with A/B variants |
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| `review-and-refine` | `llm_generate` | Reviews and optionally revises content |
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## Usage
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```bash
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# From the repo root
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PYTHONPATH=core python -m examples.templates.marketing_agent
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# With custom input
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PYTHONPATH=core python -m examples.templates.marketing_agent --input '{
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"product_description": "A fitness tracking app",
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"target_audience": "Health-conscious millennials",
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"brand_voice": "Energetic and motivational",
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"channels": ["instagram", "email"]
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}'
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```
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## Customization ideas
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- Add a `function` node to call an analytics API and inform audience analysis with real data
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- Add a `human_input` pause node before final output for editorial approval
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- Swap `llm_generate` nodes to `llm_tool_use` and add web search tools for competitive research
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- Add an image generation tool to produce visual assets alongside copy
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## File structure
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```
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marketing_agent/
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├── __init__.py # Package exports
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├── __main__.py # CLI entry point
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├── agent.py # Goal, edges, graph spec, MarketingAgent class
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├── config.py # RuntimeConfig and AgentMetadata
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├── nodes/
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│ └── __init__.py # NodeSpec definitions
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└── README.md # This file
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```
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@@ -0,0 +1,6 @@
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"""Marketing Content Agent — template example."""
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from .agent import MarketingAgent, goal, edges, nodes
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from .config import default_config
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__all__ = ["MarketingAgent", "goal", "edges", "nodes", "default_config"]
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@@ -0,0 +1,31 @@
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"""CLI entry point for Marketing Content Agent."""
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import asyncio
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import json
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import sys
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def main():
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from .agent import MarketingAgent
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from .config import default_config
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# Simple CLI — replace with Click for production use
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input_data = {
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"product_description": "An AI-powered project management tool for remote teams",
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"target_audience": "Engineering managers at mid-size tech companies",
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"brand_voice": "Professional but approachable, concise, data-driven",
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"channels": ["email", "twitter", "linkedin"],
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}
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# Accept JSON input from command line
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if len(sys.argv) > 1 and sys.argv[1] == "--input":
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input_data = json.loads(sys.argv[2])
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agent = MarketingAgent(config=default_config)
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result = asyncio.run(agent.run(input_data))
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print(json.dumps(result, indent=2))
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,161 @@
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"""Marketing Content Agent — goal, edges, graph spec, and agent class."""
|
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|
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from pathlib import Path
|
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|
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from framework.graph import EdgeCondition, EdgeSpec, Goal, SuccessCriterion, Constraint
|
||||
from framework.graph.edge import GraphSpec
|
||||
from framework.graph.executor import GraphExecutor
|
||||
from framework.runtime.core import Runtime
|
||||
from framework.llm.anthropic import AnthropicProvider
|
||||
|
||||
from .config import default_config, RuntimeConfig
|
||||
from .nodes import all_nodes
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Goal
|
||||
# ---------------------------------------------------------------------------
|
||||
goal = Goal(
|
||||
id="marketing-content",
|
||||
name="Marketing Content Generator",
|
||||
description=(
|
||||
"Generate targeted marketing content across multiple channels "
|
||||
"for a given product and audience."
|
||||
),
|
||||
success_criteria=[
|
||||
SuccessCriterion(
|
||||
id="audience-analyzed",
|
||||
description="Audience analysis is produced with demographics and pain points",
|
||||
metric="output_contains",
|
||||
target="audience_analysis",
|
||||
),
|
||||
SuccessCriterion(
|
||||
id="content-generated",
|
||||
description="At least 2 channel-specific content pieces are generated",
|
||||
metric="custom",
|
||||
target="len(content) >= 2",
|
||||
),
|
||||
SuccessCriterion(
|
||||
id="variants-provided",
|
||||
description="A/B variants are provided for each content piece",
|
||||
metric="custom",
|
||||
target="all variants present",
|
||||
),
|
||||
],
|
||||
constraints=[
|
||||
Constraint(
|
||||
id="no-competitor-names",
|
||||
description="No competitor brand names in generated content",
|
||||
constraint_type="hard",
|
||||
category="safety",
|
||||
),
|
||||
Constraint(
|
||||
id="social-length",
|
||||
description="Social media content should be under 280 characters",
|
||||
constraint_type="soft",
|
||||
category="quality",
|
||||
),
|
||||
],
|
||||
input_schema={
|
||||
"product_description": {"type": "string"},
|
||||
"target_audience": {"type": "string"},
|
||||
"brand_voice": {"type": "string"},
|
||||
"channels": {"type": "array", "items": {"type": "string"}},
|
||||
},
|
||||
output_schema={
|
||||
"audience_analysis": {"type": "object"},
|
||||
"content": {"type": "array"},
|
||||
},
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Edges
|
||||
# ---------------------------------------------------------------------------
|
||||
edges = [
|
||||
EdgeSpec(
|
||||
id="analyze-to-generate",
|
||||
source="analyze-audience",
|
||||
target="generate-content",
|
||||
condition=EdgeCondition.ON_SUCCESS,
|
||||
description="After audience analysis, generate content",
|
||||
),
|
||||
EdgeSpec(
|
||||
id="generate-to-review",
|
||||
source="generate-content",
|
||||
target="review-and-refine",
|
||||
condition=EdgeCondition.ON_SUCCESS,
|
||||
description="After content generation, review and refine",
|
||||
),
|
||||
EdgeSpec(
|
||||
id="review-to-regenerate",
|
||||
source="review-and-refine",
|
||||
target="generate-content",
|
||||
condition=EdgeCondition.CONDITIONAL,
|
||||
condition_expr="needs_revision == True",
|
||||
priority=10,
|
||||
description="If revision needed, loop back to content generation",
|
||||
),
|
||||
]
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Graph structure
|
||||
# ---------------------------------------------------------------------------
|
||||
entry_node = "analyze-audience"
|
||||
entry_points = {"start": "analyze-audience"}
|
||||
terminal_nodes = ["review-and-refine"]
|
||||
pause_nodes = []
|
||||
nodes = all_nodes
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Agent class
|
||||
# ---------------------------------------------------------------------------
|
||||
class MarketingAgent:
|
||||
"""Multi-channel marketing content generator agent."""
|
||||
|
||||
def __init__(self, config: RuntimeConfig | None = None):
|
||||
self.config = config or default_config
|
||||
self.goal = goal
|
||||
self.nodes = nodes
|
||||
self.edges = edges
|
||||
self.entry_node = entry_node
|
||||
self.terminal_nodes = terminal_nodes
|
||||
self.executor = None
|
||||
|
||||
def _build_graph(self) -> GraphSpec:
|
||||
return GraphSpec(
|
||||
id="marketing-content-graph",
|
||||
goal_id=self.goal.id,
|
||||
entry_node=self.entry_node,
|
||||
entry_points=entry_points,
|
||||
terminal_nodes=self.terminal_nodes,
|
||||
pause_nodes=pause_nodes,
|
||||
nodes=self.nodes,
|
||||
edges=self.edges,
|
||||
default_model=self.config.model,
|
||||
max_tokens=self.config.max_tokens,
|
||||
description="Marketing content generation workflow",
|
||||
)
|
||||
|
||||
def _create_executor(self):
|
||||
runtime = Runtime(storage_path=Path(self.config.storage_path).expanduser())
|
||||
llm = AnthropicProvider(model=self.config.model)
|
||||
self.executor = GraphExecutor(runtime=runtime, llm=llm)
|
||||
return self.executor
|
||||
|
||||
async def run(self, context: dict, mock_mode: bool = False) -> dict:
|
||||
graph = self._build_graph()
|
||||
executor = self._create_executor()
|
||||
result = await executor.execute(
|
||||
graph=graph,
|
||||
goal=self.goal,
|
||||
input_data=context,
|
||||
)
|
||||
return {
|
||||
"success": result.success,
|
||||
"output": result.output,
|
||||
"steps": result.steps_executed,
|
||||
"path": result.path,
|
||||
}
|
||||
|
||||
|
||||
default_agent = MarketingAgent()
|
||||
@@ -0,0 +1,24 @@
|
||||
"""Runtime configuration for Marketing Content Agent."""
|
||||
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
|
||||
@dataclass
|
||||
class RuntimeConfig:
|
||||
model: str = "claude-haiku-4-5-20251001"
|
||||
max_tokens: int = 2048
|
||||
storage_path: str = "~/.hive/storage"
|
||||
mock_mode: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentMetadata:
|
||||
name: str = "marketing_agent"
|
||||
version: str = "0.1.0"
|
||||
description: str = "Multi-channel marketing content generator"
|
||||
author: str = ""
|
||||
tags: list[str] = field(default_factory=lambda: ["marketing", "content", "template"])
|
||||
|
||||
|
||||
default_config = RuntimeConfig()
|
||||
metadata = AgentMetadata()
|
||||
@@ -0,0 +1,106 @@
|
||||
"""Node definitions for Marketing Content Agent."""
|
||||
|
||||
from framework.graph import NodeSpec
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Node 1: Analyze the target audience
|
||||
# ---------------------------------------------------------------------------
|
||||
analyze_audience_node = NodeSpec(
|
||||
id="analyze-audience",
|
||||
name="Analyze Audience",
|
||||
description="Produce a structured audience analysis from the product and target audience description.",
|
||||
node_type="llm_generate",
|
||||
input_keys=["product_description", "target_audience"],
|
||||
output_keys=["audience_analysis"],
|
||||
system_prompt="""\
|
||||
You are a marketing strategist. Analyze the target audience for a product.
|
||||
|
||||
Product: {product_description}
|
||||
Target audience: {target_audience}
|
||||
|
||||
Produce a structured analysis as raw JSON (no markdown):
|
||||
{{
|
||||
"audience_analysis": {{
|
||||
"demographics": "...",
|
||||
"pain_points": ["..."],
|
||||
"motivations": ["..."],
|
||||
"preferred_channels": ["..."],
|
||||
"messaging_angle": "..."
|
||||
}}
|
||||
}}
|
||||
""",
|
||||
tools=[],
|
||||
max_retries=2,
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Node 2: Generate channel-specific content with A/B variants
|
||||
# ---------------------------------------------------------------------------
|
||||
generate_content_node = NodeSpec(
|
||||
id="generate-content",
|
||||
name="Generate Content",
|
||||
description="Create marketing copy for each requested channel with two variants per channel.",
|
||||
node_type="llm_generate",
|
||||
input_keys=["product_description", "audience_analysis", "brand_voice", "channels"],
|
||||
output_keys=["content"],
|
||||
system_prompt="""\
|
||||
You are a marketing copywriter. Generate content for each channel.
|
||||
|
||||
Product: {product_description}
|
||||
Audience analysis: {audience_analysis}
|
||||
Brand voice: {brand_voice}
|
||||
Channels: {channels}
|
||||
|
||||
For each channel, produce two variants (A and B).
|
||||
|
||||
Output as raw JSON (no markdown):
|
||||
{{
|
||||
"content": [
|
||||
{{
|
||||
"channel": "twitter",
|
||||
"variant_a": "...",
|
||||
"variant_b": "..."
|
||||
}}
|
||||
]
|
||||
}}
|
||||
""",
|
||||
tools=[],
|
||||
max_retries=2,
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Node 3: Review and refine content
|
||||
# ---------------------------------------------------------------------------
|
||||
review_and_refine_node = NodeSpec(
|
||||
id="review-and-refine",
|
||||
name="Review and Refine",
|
||||
description="Review generated content for brand voice alignment and channel fit. Revise if needed.",
|
||||
node_type="llm_generate",
|
||||
input_keys=["content", "brand_voice"],
|
||||
output_keys=["content", "needs_revision"],
|
||||
system_prompt="""\
|
||||
You are a senior marketing editor. Review the following content for brand
|
||||
voice alignment, clarity, and channel appropriateness.
|
||||
|
||||
Content: {content}
|
||||
Brand voice: {brand_voice}
|
||||
|
||||
If any piece needs revision, fix it and set needs_revision to true.
|
||||
If everything looks good, return the content unchanged with needs_revision false.
|
||||
|
||||
Output as raw JSON (no markdown):
|
||||
{{
|
||||
"content": [...],
|
||||
"needs_revision": false
|
||||
}}
|
||||
""",
|
||||
tools=[],
|
||||
max_retries=2,
|
||||
)
|
||||
|
||||
# All nodes for easy import
|
||||
all_nodes = [
|
||||
analyze_audience_node,
|
||||
generate_content_node,
|
||||
review_and_refine_node,
|
||||
]
|
||||
@@ -25,8 +25,6 @@ pip install playwright playwright-stealth
|
||||
playwright install chromium
|
||||
```
|
||||
|
||||
In Docker, add `RUN playwright install chromium --with-deps` to the Dockerfile.
|
||||
|
||||
## Environment Variables
|
||||
|
||||
This tool does not require any environment variables.
|
||||
|
||||
Reference in New Issue
Block a user