daari

Local-first execution router — cache, tools, and local models before frontier APIs.

Status: v1.4.0 — docs site · tracker · release notes

Route Cursor, Claude Code, and any OpenAI-compatible client through local tiers instead of paying frontier for repeat work. Not a proxy — a cost optimizer you run. Apache 2.0.

Quick start

pip:

pip install daari
daari onboard --yes --serve

Docker (bundles Ollama):

docker compose up

First start pulls the L3 model (~2 GB), then daari listens on http://127.0.0.1:11435 (readiness: GET /ready). Prebuilt image: ghcr.io/naveenreddyalka/daari. Package: pypi.org/project/daari.

Docs — Developer documentation (install, concepts, guides, reference). Contributors: docs/DEVELOPING.md. Community: Discussions (templates).

One-click demo (install, serve, smoke curl, stats):

./scripts/demo.sh

Manual steps:

python3.12 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
ollama pull llama3.2:3b
daari serve

curl http://127.0.0.1:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"llama3.2:3b","messages":[{"role":"user","content":"Say hi"}]}'

Run the same curl twice — the second response should show "tier": "L0" in daari_meta.

Feature snapshot (v1.2.0)

Routing & caching

• Local-first routing chain: L0 exact cache → L1 semantic cache (with draft injection) → CCS/L2 rules → Lt CLI tools → L3–L5 local models (Ollama or MLX) → L6 frontier, with confidence-based escalation
• Cache trust you can measure: shadow-sampled false-hit rate, response-diversity monitoring, input normalization, per-category TTLs
• Prompt intelligence: category/complexity profiling, per-category policies, latency budgets, warm-model preference, learned routing from your own outcomes

Learning (on-device)

• Implicit outcome capture + explicit accept/reject feedback → daari learn stats/recommend
• Auto-tuned per-category confidence thresholds, opt-in example capture → daari learn finetune (MLX LoRA) → daari learn deploy
• Opt-in, review-first anonymized stats export (daari learn export-stats) — metadata only, never prompts

Clients (one-click)

• Cursor (BYOK via tunnel + API-key auth), Claude Code (full tool passthrough), Claude Desktop (third-party gateway mode), ChatGPT Desktop / JetBrains AI Assistant (Ollama-compatible facade), VS Code, any OpenAI/Anthropic SDK
• Per-project profiles (.daari.yaml): tier caps, no-frontier, latency budgets per repo

Observability & spend

• Per-request traces (daari trace), usage ledger with estimated savings (daari report, Markdown export), web dashboard (daari web-ui serve)
• Monthly/daily frontier budgets with soft warnings, per-client cost attribution, optional pre-frontier PII scrub

Platform

• MCP server at POST /mcp (initialize / tools/list / tools/call), Sourcegraph/GHE/GitLab providers, org shared cache + collective learning (tracer), gateway API-key auth, MLX backend for Apple Silicon

Docs

Principles

• Open source — Apache 2.0; run it, fork it, sell it
• Local-first — on-device by default
• Cost-minimize — cheapest capable path for every task
• AI optional — many tasks use IDE/CLI tools, not models

License

Daari is licensed under the Apache License 2.0. Decision: ADR-0016.

Repo

https://github.com/naveenreddyalka/daari