Roadmap

What's in Coulisse today, and what's coming.

Working today

  • Multi-agent routing via the model field.
  • Agents as tools — expose one agent to another under subagents: with a purpose: description. Nested invocations are bounded by a depth cap.
  • Per-user conversation history with isolation.
  • Long-term memory with semantic recall — persistent via SQLite and backed by a real embedder (OpenAI or Voyage AI; hash fallback for offline dev).
  • Auto-extraction — an optional background task pulls durable facts from each exchange and deduplicates them before storing.
  • Tunable memory budgets (context_budget, memory_budget_fraction, recall_k) in YAML.
  • Multi-backend support (Anthropic, OpenAI, Gemini, Cohere, Deepseek, Groq).
  • OpenAI-compatible HTTP API (/v1/chat/completions, /v1/models).
  • Read-only studio UI at /admin/ for browsing conversations, memories, and judge scores.
  • LLM-as-judge evaluation — background scoring of agent replies against YAML-defined rubrics, with per-judge sampling and per-user persistence.
  • Experiments (A/B testing) — wrap multiple agents under one addressable name and route traffic between them with sticky-by-user defaults. Three strategies: split (weighted random), shadow (primary serves the user, others run in the background and are scored), and bandit (epsilon-greedy on a single judge criterion).
  • Streaming responses over SSE (stream: true, with stream_options.include_usage).
  • MCP tool integration over stdio and HTTP, with per-agent filtering.
  • Per-user token rate limiting (hour / day / month).
  • YAML-driven config with startup validation.
  • Docker image with a volume-mounted SQLite store.

Planned

Durable rate-limit state

Current rate-limit counters live in memory — they reset on restart and don't span multiple instances. A durable, shared backend is planned so quotas survive reboots and horizontal scaling.

Workflow orchestration

Chaining agents into declarative pipelines (one agent's output feeds the next, with conditional routing) — all configured in YAML rather than app code.

Vector index for large memory stores

Recall currently does a linear cosine scan over all memories for the user. Fine at hundreds-to-low-thousands of memories per user, but a vector index will be needed if per-user memory counts grow into the tens of thousands.

Per-agent memory overrides

Today the memory: block is global. A future revision will allow per-agent scoping (different embedders or budgets per agent) for cases where one agent handles long-form research and another handles short user chat.

This list reflects what's on deck at the time of writing — check the repository for the current state.