Use case
AI & machine learning
Track the frameworks, runtimes, and eval tooling agent and LLM teams are actually shipping on, from orchestration layers to inference serving.
Example brief
What you might type when setting this up
“Track agent frameworks and LLM inference tooling. Flag anything picking up real integration traction, skip one-off demo repos.”
- Agent frameworks and orchestration layers
- LLM inference and serving infrastructure
- Evaluation and observability tooling for AI systems
- Fine-tuning and post-training tooling
What a brief here tracks
LLM tooling, agent frameworks, and inference infrastructure.
- Agent frameworks and orchestration layers
- LLM inference and serving infrastructure
- Evaluation and observability tooling for AI systems
- Fine-tuning and post-training tooling
Questions it answers
Which agent framework is gaining real integration traction, not just stars?
Is a new inference runtime worth a build-vs-adopt review?
Which eval and observability tools are teams actually standardizing on?
What Dokeon would surface
The kind of signals and articles a brief here produces. Illustrative examples, not live data.
Signals
Breakout
An eval framework doubles its active contributors in 30 days
Rising
A small inference runtime gains steady integrations from larger projects
Leader
An orchestration framework keeps its lead on merged pull requests
An article the agents could write
Pitch
Agent frameworks consolidate around graph-based orchestration
Three of the most active projects shipped compatible state models this month. What it means for teams choosing a stack.
Projects a brief here would watch
Illustrative starting points, not a claim about what is trending right now. Once your brief is live, the ranking follows what actually moves.
langchain-ai/langgraph
Graph-based orchestration framework that has become a default choice for building stateful agents.
vercel/ai-sdk
TypeScript toolkit for building AI-powered applications, widely adopted across the JS ecosystem.
ollama/ollama
Local model runtime that made running open-weight LLMs a one-command experience.
Who uses this brief
AI platform leads
Shortlist the frameworks and runtimes worth a build-vs-adopt review.
Investors
Spot which layers of the AI stack are consolidating and where white space remains.
Product strategy
Track how fast the tooling your competitors rely on is moving.
Questions about this market
Does Dokeon track model releases too?
Dokeon tracks the open-source tooling around models: frameworks, runtimes, eval and observability projects. Model announcements come in as web context when they move that tooling.
Can I narrow it to agents only?
Yes. Your brief decides the scope: write “agent frameworks only” and everything else is ranked down.
Ready to track AI & machine learning?
Start free on one brief, no card required. Use the example above, or write your own.