A toolkit for creating specialized, self-improving agent systems from a specification and the knowledge your work already has.
ZeneraNeo turns a clear description of a job into an agentic system built for that job. Give it the documents, policies, API schemas, files and integrations the work depends on; describe the outcome and its constraints; then let the meta-agent design the specialists, tools, skills, memory, handoffs, retrieval and execution strategy that fit.
This is for recurring work that needs more than one general-purpose chat: technical investigation, policy review, operations triage, software delivery, analysis and workflows that must show their evidence. The result is a project you can run, inspect, improve, review and share.
This is an open-source side project for experimentation and chore work. It is not the official Zenera AI Platform, and it carries no support or stability promises.
People define the job; they should not have to hand-design every agent, prompt,
handoff, tool, memory rule and evaluation loop required to do it well.
SPECIFICATION.md is the source of intent: the work to perform, the knowledge
to use, the boundaries to keep, and the criteria that define success.
ZeneraNeo's meta-agent turns that intent into a working system, then autonomously tests it, examines its observable trajectory, and proposes improvements to the specification. The next system is generated from that improved intent. This is a continuous loop for making a specialized system more effective without losing the human-defined purpose and constraints.
The loop is deliberate. A run is not only an answer; it is evidence about the system that produced it. ZeneraNeo records the trajectory, evaluates the result against the specification, identifies changes that improve quality, accuracy, latency or token use, and turns those findings into reviewable updates.
Projects are ordinary Markdown and YAML, so the intent, generated system and proposed changes can be versioned in Git and reviewed like any other software. Each run leaves an inspectable trajectory of the prompts, agent handoffs, tool calls, results and usage that produced it. Teams can examine that record themselves or give it to a meta-agent to evaluate outcomes, diagnose weak steps and propose the next improvement with evidence.
- Specialized agents with distinct responsibilities, models and execution boundaries instead of one prompt asked to do everything.
- Teams that coordinate through explicit handoffs, parallel work and strategies matched to the task.
- Knowledge systems over documentation and API schemas, with retrieval that can discover, inspect and verify evidence.
- Tools and integrations selected for the job, with file access, sandboxed commands and generated integrations where they are needed.
- Durable memory that carries useful context between runs without relying on an ever-growing chat transcript.
- Evaluation and observability through self-tests, success criteria and trajectories that explain what happened, how long it took and what it used.
- Shareable projects expressed as Markdown and YAML, ready for source control, code review and reuse by another team.
npm i -g @zenera/cli
zen key add openai
zen init my-project
zen open my-projectDescribe the system in SPECIFICATION.md, then run /spec-sync-project from your
editor's agent chat. It generates and updates the system. Use zen check to
verify it, zen run to exercise it, and zen inspect to examine the resulting
trajectory and improvement opportunities.
The complete project workflow, command reference and examples are in
@zenera/cli.
| Package | Role |
|---|---|
@zenera/cli |
The zen command-line workflow for creating projects, generating agent systems, running them, testing them and inspecting their trajectories. |
@zenera/neo |
The TypeScript runtime for applications that need agents, models, tools, skills, memory, trajectories and project loading directly in code. |
@zenera/rag |
Advanced retrieval for agent knowledge: semantic, full-text and hybrid search over documents, plus graph-aware API schema indexing and search. |
@zenera/faker |
Mock API generation from OpenAPI and Swagger descriptions, with generated behavior validated against the API contract. |
@zenera/cli installs zen; @zenera/rag and @zenera/faker extend it with
their own subcommands. Install only the capabilities a project needs.
- Specification-driven projects
- Agent configuration
- Projects and sharing
- Agent knowledge and document retrieval
- API integrations
- Memory
Early days and moving fast - issues, questions and pull requests are welcome. MIT.