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Roadmap

M1 — Repo & Docs

Establish the open source foundation: contribution guidelines, code of conduct, spec structure, and an updated README that reflects the project's full capabilities.

  • CONTRIBUTING.md — conventional commits + PR process
  • CODE_OF_CONDUCT.md — Contributor Covenant
  • docs/specs/ — directory structure + spec template
  • README.md — document Spring AI abstraction and supported providers

M2 — Conversation Memory

Give the agent memory. Persist conversation history per conversationId using Spring Data + H2 (in-memory by default). The conversationId is optional — if omitted, the interaction remains stateless.

  • ConversationMessage JPA entity
  • ConversationRepository via Spring Data
  • H2 as the default in-memory store (zero external dependencies)
  • conversationId as optional field in the chat request
  • agent.context.limit — configurable context window parameter

M3 — Enhanced Conversation Engine

Improve the quality and control of the conversation loop — streaming responses, configurable model parameters, and better prompt composition.

  • Streaming responses (/api/chat/stream)
  • Configurable model parameters (temperature, top-p) via application.properties
  • Docker Compose with Ollama — single docker compose up to run everything

M4 — Structured Output

Enable the agent to respond with reliable, schema-bound JSON — essential for agents that feed downstream systems or other agents.

  • Structured JSON responses via Spring AI
  • Output schema configurable per deployment
  • Validation and error handling for malformed outputs

M5 — Observability & Production Readiness

Make the template ready for real deployments: metrics, structured logging, rate limiting, and basic authentication.

  • Structured logging (JSON)
  • Custom Actuator metrics (token usage, model latency)
  • Rate limiting
  • API Key authentication (header-based)