Generative AI
Zeta Alpha agents combine retrieval-augmented generation with tool use, memory, and multi-agent orchestration. There are two ways to work with them, depending on what you need.
What the Harness Provides
| Capability | What you get | Docs |
|---|---|---|
| Composable capabilities | Everything below is a pluggable source, enabled per agent with include/exclude filters: behavior is configured, not coded | Concepts |
| Retrieval and citations | Search, browse, and read any Zeta Alpha index, with resolved citations linking back to sources | Tools |
| Knowledge capture | Notes, tags, and private document ingestion through conversation | Note Tools |
| Data analysis | CSV/Excel and SQL exploration, aggregation, and inline charts | DataFrame Tools |
| Web access | Fetch and read public pages and PDFs, hardened against hostile content | Web Tools |
| Skills | Markdown-defined behaviors the agent discovers and applies on demand | Agent Skills |
| Memory | Facts persisted across sessions and loaded into the prompt | Memory |
| Multi-agent | Anonymous sub-agents and named specialist delegation | Delegation |
| MCP | External tool servers, including per-user OAuth authorization | MCP Tools |
| Model providers | OpenAI, Anthropic, Azure, Bedrock, Ollama, and any OpenAI-compatible endpoint (vLLM), with tool-calling accommodations for small models | LLM Configuration |
| Context management | Keeps as much history as fits by virtualizing old and large tool responses before older answers, so smaller and larger models swap in without code changes | Context Window Management |
| Resilient conversations | Server-owned turns that survive restarts: reattach from any replica, resume a crashed turn from its checkpoint | Conversations · Persistence |
| Streaming | SSE streaming with reconnection, turn discovery, and cancellation | Streaming |
| Tool streaming | Templated progress text while tools run, and per-tool shaping that keeps large results out of model context while the UI gets the full result | Tool Events |
| Testing and evaluation | Behavior specs, and RAGElo judging through the same provider layer, so the judge can be any OpenAI-compatible backend | Testing |
| Deployment | Run on the Zeta Alpha platform or self-host the REST API | Self-Hosted |
Configure Agents
Define agent behavior through JSON configuration: search tools, instructions, skills, memory, and sub-agents. No application code required for most use cases.
Start here if you want to:
- Create or customize agents for your domain
- Configure what tools, memory, and instructions an agent uses
- Deploy agents to the Zeta Alpha platform
- Test and debug agent behavior locally
Chat API
Call deployed agents from your application through REST or streaming endpoints. Send messages, pass context, and handle responses.
Start here if you want to:
- Integrate agent conversations into your product
- Use the streaming or REST Chat API
- Pass custom context or handle function call requests