delapan

The grounding engine behind context-aware AI tooling. Capture intent, ground every answer in a maintained knowledge base, and fill gaps from the web on demand — ground → grow → answer.

delapan runs fully local (SQLite + sqlite-vec, no cloud, no account) or behind your own storage via a small Store protocol. It ships as an MCP server, so any MCP client (Claude Code, etc.) can use it out of the box.

Install as a Claude Code plugin

Requires uv (curl -LsSf https://astral.sh/uv/install.sh | sh).

claude plugin marketplace add anthonysuherli/delapan
claude plugin install delapan@delapan

First launch materializes the Python environment (via uv) and seeds a bundled demo KB. With zero keys configured you can immediately run /delapan:projects and /delapan:resume against the demo (project delapan, kb demo). To unlock semantic search and web research on your own repos, copy .env.example to .env in the plugin directory and set AI_GATEWAY_API_KEY (plus TAVILY_API_KEY for /delapan:explore).

Skills: /delapan:resume, /delapan:search, /delapan:explore, /delapan:ingest, /delapan:backlog, /delapan:projects, /delapan:model.

Related MCP server: Grounded Code MCP

Quickstart — local, no credentials

pip install "delapan[local]"

# MCP server for Claude Code / any MCP client (resume, search, explore, projects)
python -m delapan.mcp.server

# or a loopback HTTP API on 127.0.0.1 (health, projects, KG read/write,
# findings, synopsis, resume, explore-over-SSE under /api/*)
python -m delapan.api.main

MCP tools: delapan_resume (tap a KB → resume card), delapan_search (semantic recall over findings), delapan_explore (gap-fill from the web, needs LLM + Tavily keys), delapan_backlog (ranked gap/sparse queries the KB was asked and couldn't answer), delapan_projects (cross-repo discovery). KG co-design seam: delapan_propose_kg_schema → delapan_set_kg_schema (draft a target ontology from the findings, then validate + persist the approved version) and delapan_build_graph / delapan_get_kg_schema (build the graph steered by the intent schema; compare intent vs emergent ontology).

# the engine, on SQLite, with no cloud creds:
from delapan.store import get_store
from delapan.mcp.tenancy import resolve_tenant

ctx = resolve_tenant("my-repo", "main", create=True)   # tenant on the local store
store = get_store()
print(store.count_findings(ctx.kb_id))

The local tier stores everything in ~/.delapan/delapan.db (override with DELAPAN_DB_PATH). No Supabase, no API key, loopback-only.

Status: the engine core (grounding, exploration, findings, KB/project persistence), the Store seam, the MCP server, and the local HTTP API (/api/* — mirrors the MCP surface plus KG read/write for a control-panel frontend) all run on SQLite today — see Roadmap.

What's inside

Project tracking

Solo initiative status and prioritized backlog live in docs/tracking/ (markdown source of truth). See the design spec.

Automatic sync

• Local: .git/hooks/post-commit (installed from .githooks/post-commit) runs scripts/tracking_sync.py after commits that touch docs/tracking/.

• CI: GitHub Action tracking-sync mirrors on push (needs secrets SUPABASE_URL + SUPABASE_SERVICE_ROLE_KEY).

Manual:

uv run python scripts/tracking_sync.py --dry-run
uv run python scripts/tracking_sync.py

Findings, KBs, and projects are not separate submodules — that persistence lives inside the Store implementations themselves (store/sqlite.py, store/supabase.py), behind the one Store protocol below.

Architecture — the storage seam

The engine never imports a storage client directly. It calls get_store(), which returns a backend selected by DELAPAN_BACKEND (local | cloud, auto-detected from creds when unset). Ship a new backend by implementing store/base.py::Store.

from delapan.store import get_store

store = get_store()          # SQLiteStore on the local tier
findings = store.match_findings(kb_id, embedding, limit=10)

Every write to findings goes through core/memory/persist.py::resolve_and_persist, not straight to insert_findings — a resolver decides per candidate whether it's genuinely new, refines an existing finding, merely corroborates one, or contradicts one, and applies that via the Store's update_finding/invalidate_finding/ supersede_finding primitives. Set memory.enabled: false in config.yaml to fall back to plain append-only ADD. scripts/dedup_backfill.py retires duplicates already sitting in an existing KB (dry-run by default); scripts/calibrate_bands.py recalibrates the coverage-band thresholds above for whichever embedding model is active. Schema changes for this land in migrations/ (cloud tier only — SQLite migrates itself in-process).

The open-core distribution ships the SQLite backend, at parity with the cloud Supabase/pgvector backend for both retrieval and the write-resolution path above.

Configuration

Copy .env.example and fill the local block (the cloud block is optional and only needed for a self-hosted multi-tenant deployment):

cp .env.example .env

Development

python3.11 -m venv .venv
.venv/bin/pip install -e ".[dev,local]"
pytest && ruff check .

Status & roadmap

Working today (verified on SQLite, no cloud deps):

• The Store seam — get_store() → SQLiteStore; tenancy, project listing, findings, synopsis, KG.

• The engine core — agent (preamble/synopsis/resume), exploration, memory (resolver + persist), knowledge_graph models.

• The tenancy gateway — resolve_tenant() resolves a local tenant through the store.

• The MCP server — delapan_resume / delapan_search / delapan_explore / delapan_backlog / delapan_projects / delapan_archive (whole package imports; all 6 tools register and run).

• python -m delapan.api.main → /health plus the /api/* surface: projects, per-KB graph read/write (nodes/edges CRUD, stats, schema), findings list/get/delete, synopsis, resume, explore over SSE, and canvas search/keep over SSE. CORS allows control-panel dev origins (:5173); scripts/seed_demo_kb.py seeds a credential-free demo KB to point a frontend at.

• Canvas phase 1 — /canvas/search (streamed candidates + grounded answer) and /canvas/keep (resolver-gated persistence) landed; includes two loud-failure fixes: explore now fails the run on provider quota/error (Tavily HTTP 432, etc.), and synopsis rebuild routes via gateway with status reporting (rebuilt/skipped/failed).

• Hosted-tier backend auth (build order phase 1 of the public-release design) — api.auth: none | supabase config fork; local JWT verification against SUPABASE_JWT_SECRET (delapan/api/auth.py), beta_members gate, org-scoped tenancy dependencies; slowapi rate limiting keyed by verified subject; an RLS audit script covering all 29 tenant tables; a two-user isolation acceptance test; build_combined_app() (delapan/mcp/cloud_server.py) serves REST /api beside the MCP server for a single Fly deploy. The local tier is unaffected (auth: none default).

• Eval pipeline — v1 ablation harness landed (spec: docs/truenorth/specs/2026-07-26-context-eval-pipeline-design.md); phase 2: LongMemEval adapter for externally comparable numbers.

• Claude Code plugin shell — shipped in-repo, marketplace-installable (2026-07-26): scripts/mcp-server.sh (uv-run launcher), seven skills under skills/ backing the /delapan:* slash commands, a bundled demo KB (data/demo.db, project delapan/kb demo) seeded on first local start, and first-run onboarding (delapan/mcp/onboarding.py). Zero-key surface is /delapan:projects + /delapan:resume against the demo; AI_GATEWAY_API_KEY (plus TAVILY_API_KEY) unlocks search/explore on real repos.

Next:

• Public release phases 2–3: frontend auth screens, /app guard + waitlist gate, landing/legal pages, GitHub OAuth, custom SMTP, Sentry/uptime/analytics wiring, and the Fly deploy of the combined MCP+REST app — none of this is done yet (see the spec's build order).

• The capture HTTP route (mirror the remaining MCP-adjacent surface over FastAPI).

• Concepts, drift, deepen, bridges, monitoring, user-profile, research reports, and the broader MCP tool surface.

• Store-route or gate the remaining cloud-coupled surfaces (userprofile, generic knowledge_graph/builder) — currently [cloud]-gated at call-time.

LLM-backed features need keys — exploration: TAVILY_API_KEY + AI_GATEWAY_API_KEY (the gateway covers LLM calls and embeddings; OPENAI_API_KEY is only the embeddings fallback), synopsis rebuild: ANTHROPIC_API_KEY. Browse/tenant/persistence work without them.

License

AGPL-3.0-or-later. Self-host freely; network-deployed modifications must be shared under the same license. For commercial / non-AGPL licensing, contact the maintainer.