Chat-first Streamlit app that turns Hacker News demand signals, DEV.to supply gaps, and Weaviate Engram memory into a developer trend digest plus ranked talk, blog, and tutorial ideas.

An Agno-powered DevRel research assistant for developer-tool companies. It plans HN and DEV.to searches with Nebius GLM, gathers evidence in parallel, writes a structured ideation report, and remembers prior research across sessions with Engram.

Project path: memory_agents/engineering_content_agent

Features

Chat-first Streamlit UI with sidebar API key controls and live pipeline progress
GLM query planning for high-intent HN and DEV.to searches
Parallel evidence gathering from Hacker News Algolia, DEV/Forem API, and Engram Memory
DevRel ideation report with trend digest plus ranked talk/blog/tutorial ideas
Report guardrails for malformed JSON, stale-topic bleed, repeated DEV links, and raw HN comment fragments
Cross-session memory via Weaviate Engram (compact product context + research summaries)
Markdown download for the latest report

Prerequisites

Python 3.11+
uv or pip
Nebius Token Factory API key (required)
Weaviate Engram API key (optional but recommended)
DEV API key (optional; public search works without it)

Installation

Clone the repository:

git clone https://github.com/Arindam200/awesome-ai-apps.git
cd awesome-ai-apps/memory_agents/engineering_content_agent

Install dependencies:

# Using uv (recommended)
uv sync

# Or using pip
pip install -r requirements.txt

Create a .env file:

cp .env.example .env

Required:

NEBIUS_API_KEY=your_nebius_api_key_here
NEBIUS_MODEL=zai-org/GLM-5.2

Optional:

ENGRAM_API_KEY=your_engram_api_key_here
ENGRAM_NAMESPACE=default
ENGRAM_USER_ID=engineering-content-agent-user
ENGRAM_CONVERSATION_ID=
DEV_API_KEY=optional_dev_api_key_here
LOG_LEVEL=INFO

Note: This app uses Nebius Token Factory via Agno's Nebius provider (Chat Completions). Get your API key from Nebius Token Factory.

Usage

Start the Streamlit app:
```
uv run streamlit run app.py
```
Open your browser at http://localhost:8501.
Add API keys in the sidebar if they are not already loaded from .env.

Describe your product and ask for research. Example:

I run raah.dev, a web analytics and network observability tool. My audience is backend engineers who care about latency, error rates, and user-side ISP behavior. Research what developers are discussing on HN, check DEV.to saturation, and suggest talk and blog ideas around debugging production services.

Follow up after a report: What did we find? · Show evidence for idea 1 · What topics have we researched before?

How It Works

Context extraction — The chat router infers company, product, audience, and seed keywords from natural language.
Query planning — GLM selects HN queries, DEV queries, and tags for developer demand and supply research.
Parallel research — HN Algolia, DEV/Forem, and Engram memory search run concurrently.
Report writing — The DevRel Ideation Writer produces a trend digest and up to five ranked content ideas from gathered facts.
Guardrails — Local validation repairs links, filters noisy source fragments, and enforces product-context relevance.
Memory storage — A compact research summary is stored in Engram for future sessions.

The app shows a live pipeline stepper while query planning, parallel research, and report writing run.

Engram vs local artifacts

Engram stores compact product context and research summaries (top trends + idea titles).
Chat history is the current Streamlit session transcript only.
outputs/ holds local runtime files such as latest_ideation_report.json. This folder is gitignored except for outputs/.gitignore.
To drop legacy memories, use a new ENGRAM_NAMESPACE or clear memories in the Engram dashboard.

Project Structure

engineering_content_agent/
├── app.py                 # Streamlit UI, routing, pipeline stepper
├── agents.py              # Query planning, report writing, guardrails
├── chat.py                # Intent detection and follow-up helpers
├── config.py              # Settings and env loading
├── engram_memory.py       # Engram Memory store adapter
├── llm.py                 # Nebius model setup for Agno agents
├── models.py              # Dataclass domain models
├── sources.py             # HN Algolia and DEV.to search
├── tests/                 # Unit tests
├── assets/                # Logos and UI assets
├── outputs/               # Gitignored runtime artifacts
├── .streamlit/            # Streamlit theme config
├── .env.example
├── pyproject.toml
└── requirements.txt

Tech Stack

Agno >= 2.2.3 — specialist agents and Nebius model provider
Nebius Token Factory — LLM inference via Agno Nebius (Chat Completions)
Weaviate Engram — persistent cross-session memory
Streamlit — chat UI
HN Algolia API — developer demand signals
DEV API — article supply analysis

Testing

cd memory_agents/engineering_content_agent
python -m pytest tests/ -q

Provider Links

Contributing

Contributions are welcome. See the repository CONTRIBUTING.md for guidelines. Submit one project per pull request.

License

This project is part of awesome-ai-apps and is licensed under the MIT License.