Your agents forget. Continuum remembers.
Point any MCP client at Continuum and tell it to read the plot. The client becomes an orchestrator — it researches the codebase, verifies the plan, persists a dispatch record, and hands you a ready-to-paste prompt for a fresh agent in an isolated git worktree. Every other Claude Code / Codex / Cline chat on the project sees what's reserved, shares what's been learned, and never re-explains the codebase. One local server: vector knowledge base, orchestration protocol, agent registry — shared across every MCP-speaking app on your machine.
flowchart TD
CC["Claude Code"]
CX["Codex"]
CL["Cline"]
YOU["YOU"]
subgraph CONTINUUM["CONTINUUM ORCHESTRATOR"]
direction LR
PLOT["PLOT.md<br/>protocol"]
KNOW["KNOWLEDGE<br/>(vectorized)"]
AGENTS["AGENTS<br/>registry"]
PANEL["WEB PANEL<br/>(humans)"]
end
DB[("SQLite + sqlite-vec<br/>one file, your disk")]
CC -- MCP --> CONTINUUM
CX -- MCP --> CONTINUUM
CL -- MCP --> CONTINUUM
YOU -- HTTP --> CONTINUUM
CONTINUUM --> DB
No SaaS. No telemetry. No keys to manage. Boots in seconds. Survives reboots. Scales with you.
What you get
| |
|---|
| 🧠 Vectorized memory | Every lesson your agents learn becomes a 768-dim embedding indexed in sqlite-vec. Recall is semantic, fuzzy, and instant — no exact-match games. |
| 🔍 RAG-native search | knowledge_search({q: "how do we handle webhook retries?"}) returns ranked metadata (slug + kind + agentSlug + timestamps); follow up with knowledge_get({slug}) for the body. Metadata first keeps top-K context cheap; only fetch what's worth reading. |
| 🗺️ Canonical workflow | Every project gets a PLOT.md seeded with a 4-phase dispatch protocol — Intake → Research → Verify → Handoff. Stop re-explaining your process. |
| 🤖 Multi-agent registry | State machine + reserved-path tracking stops parallel agents from clobbering each other across git worktrees. |
| 📚 Two-tier knowledge | fundamental lessons are binding rules loaded on every dispatch. situational lessons surface via semantic search when relevant. |
| 🖥️ Human web panel | Astro + React UI to browse projects, agents, plot, and knowledge while AI clients drive everything via MCP. |
| 🔌 Client-agnostic | Standard MCP / Streamable HTTP. Works with Claude Code, Codex, Cline, Cursor, or anything that speaks the protocol. |
| 🔒 Local-first | One SQLite file. WAL mode. No cloud. Optional embedder is your call (Ollama, TEI, anything OpenAI-shaped). |
| 📦 One container in prod | Single image, one HTTP port, one mounted volume. Drop on any host, point your MCP clients at it. |
Why Continuum exists
| Without Continuum | With Continuum |
|---|
| Session ends → context gone. | Lessons persist as embedded vectors. Recall survives reboots, models, and clients. |
| You re-explain conventions every session. | knowledge_search ranks lessons by relevance; knowledge_get pulls the body when needed. |
| Two parallel agents edit the same file. | reserved_paths + status state machine surfaces collisions before dispatch. |
| Each new task improvises orchestration. | PLOT.md protocol seeded into every project — Intake → Research → Verify → Handoff. |
| "What did Claude do last week?" | registry_list paginates dispatch metadata (slug, status, branch, reserved paths). agent_get({slug}) pulls the full record. |
| Knowledge scattered across chat logs. | One SQLite database. One vector index. One source of truth. |
Quick start
🐳 The easy path — one Docker command
docker compose -f docker-compose.dev.yml up
That's it. The whole stack comes up with hot reload, a SQLite browser, and a single shared volume — no Node, no pnpm, no native-build dance on your host machine.
What you get:
The API embeds in-process by default (Snowflake/snowflake-arctic-embed-m-v1.5 via Transformers.js, 768-dim, ONNX q8). No external service required for semantic search. Override via EMBEDDER_URL to point at any HTTP-based embedder (Ollama, TEI, OpenAI-compatible). Turn the bundled TEI container on with the embedder profile:
HF_TOKEN=hf_xxx docker compose -f docker-compose.dev.yml --profile embedder up
Bind mounts keep the source tree on your host — edits are instant. Named volumes hold node_modules so installs survive restarts. Database lives at ./.local-data/dev-orchestrator.db on your host, gitignored, safe to wipe.
Stop everything:
docker compose -f docker-compose.dev.yml down
docker compose -f docker-compose.dev.yml down -v
Dev SQLite at ./.local-data/orchestrator.db. Gitignored. Wipe to reset — next boot recreates and migrates forward.
Requires Node >=22.12.0 and pnpm 10.33+. The compose path skips both.
Memory & vectors
This is the headline. Continuum's knowledge layer is what makes your agents stop being amnesiac.
How a lesson becomes memory
agent finishes a dispatch
│
▼
knowledge_create({
project, agentSlug, slug,
content: "When wiring rate-limit guards in Nest,
register them in app.module.ts AFTER
the auth guard, not before. Order matters.",
kind: 'situational'
})
│
▼
content → embedder (in-process by default, or Ollama / TEI / OpenAI-compatible)
│ Snowflake Arctic Embed v1.5 → 768-dim vector
▼
SQLite row + sqlite-vec index entry
│ freshness profile = (URL, model, dim)
▼
forever queryable by ANY future agent on this project
How recall works
Phase 1 of every dispatch (the orchestrator does this automatically):
knowledge_list({ project, kind: 'fundamental' })
└─ returns metadata for every binding lesson — slug + agentSlug + timestamps.
knowledge_get({ project, slug }) // for each fundamental slug
└─ full body — base branch, worktree convention, no-AI-attribution, etc.
knowledge_search({ project, q: "rate limit middleware nest interceptor order" })
└─ vector similarity over your prose
└─ returns top-K ranked metadata (no content)
└─ does NOT need exact wording — natural language works
knowledge_get({ project, slug }) // for each relevant hit
└─ pulls the full lesson body
Search and list return metadata only — slug + kind + agentSlug + timestamps. Call knowledge_get for the body of any lesson worth reading. Keeps top-K context cheap; the orchestrator triages first, fetches second.
The result: the agent that's about to plan your task already knows what your last five agents learned the hard way. No prompt engineering. No "here's our convention" copy-paste.
Two kinds of knowledge
| Kind | Loaded when | Use for |
|---|
fundamental | Every dispatch, unconditionally | Project-wide rules: base branch, worktree naming, commit conventions, append-only files |
situational | Only if semantically relevant to the task | Specific gotchas: "auth guard ordering," "regen artifact merge strategy," "Stripe webhook idempotency keys" |
The freshness profile
Vectors are pinned to a (EMBEDDER_URL, EMBEDDER_MODEL, EMBEDDER_DIM) triple. Change any one and Continuum marks stale vectors for rebuild on next boot. You stay in control of which embedder owns your memory — local Ollama, self-hosted TEI, or anything OpenAI-shaped.
Pluggable embedders
The embedder is required and runs in-process by default (Snowflake/snowflake-arctic-embed-m-v1.5, 768-dim, ONNX q8). The model weights are bundled in the API image — no extra container, no extra config, semantic search works out of the box.
Override via EMBEDDER_URL to delegate embedding to an HTTP service:
ollama pull <embedding-model>
HF_TOKEN=hf_xxx docker compose -f docker-compose.dev.yml --profile embedder up embedder
Built on sqlite-vec
The vector index lives in the same SQLite file as your projects, agents, and plot. One file. One backup. One restore. No separate vector DB to provision, no Pinecone bill, no Chroma daemon to babysit. WAL mode keeps reads concurrent with writes; foreign keys keep your registry consistent; busy_timeout = 5000 keeps things calm under contention.
Wire it into your AI client
Claude Code
{
"mcpServers": {
"continuum": {
"transport": "streamable-http",
"url": "http://127.0.0.1:7776/mcp"
}
}
}
Restart Claude Code. Tools appear under mcp__continuum__*.
Codex
[mcp_servers.continuum]
transport = "streamable-http"
url = "http://127.0.0.1:7776/mcp"
Anything else (Cline, Cursor, custom)
Point any MCP-compatible client at http://127.0.0.1:7776/mcp. Streamable HTTP, stateful sessions (UUID-keyed). Server name continuum.
Your first project
Inside any wired client:
Create a Continuum project for /absolute/path/to/my/repo,
then read the PLOT.md and act as orchestrator
for the following task: <describe what you want done>.
What happens:
- Client calls
project_create({project: "/absolute/path/..."})
- Server seeds PLOT.md (the orchestration protocol) and an empty knowledge base
- Client calls
plot({project}), reads the protocol, enters orchestrator mode
- Client follows the 4-phase workflow — researches, verifies, persists a dispatch record, hands you a ready-to-paste prompt for a fresh agent in a git worktree
You paste the prompt into a new Claude Code / Codex session. That agent codes inside an isolated worktree against its reserved paths. Other parallel agents see the new agent's reservations via the coordination brief.
When work merges, the orchestrator records what was learned via knowledge_create — and every future dispatch on this project benefits from it forever.
The workflow
1. Intake
registry_list({project, status:'active'}) // paginated metadata: slug + reservedPaths + timestamps
agent_get({project, slug}) // bodies (request/plan/implPrompt/coordinationBrief) per active agent
knowledge_list({project, kind:'fundamental'}) // binding-rule metadata
knowledge_get({project, slug}) // body per fundamental slug — read every one
knowledge_search({project, q: "<task intent>"}) // RAG → ranked metadata
knowledge_get({project, slug}) // body for each relevant hit
2. Research
Spawn an Explore / Plan subagent. It reads the codebase against the loaded knowledge. Produces a file-by-file plan: create / modify / delete. Plan only — no code.
3. Verify
Spawn a critique agent. It challenges every file touched, looks for orphans and broken call sites, cross-checks against active agents' reserved paths. Returns annotated deltas.
4. Handoff
agent_create({
project, slug, branch, worktree,
reservedPaths, // paths this agent owns
request, // verbatim human task
plan, // final agreed plan
implPrompt, // self-contained prompt for the dispatched agent
coordinationBrief, // short block for every other active agent
})
Agent lands as draft. Promote with agent_update({status:'active'}) when you dispatch. On merge, record the SHA and bank the lesson:
agent_update({ project, slug, status:'merged', mergedCommit, postMergeNotes })
knowledge_create({ project, agentSlug, slug, content, kind:'situational' })
The orchestrator never edits source, runs regen, merges branches, or pushes commits. It researches, verifies, coordinates, and persists. Code lives in fresh agents inside isolated worktrees.
Every tool takes project (canonical absolute path) as its first argument.
Project
| Tool | Purpose |
|---|
project_list | Enumerate registered projects |
project_get | Fetch one project |
project_create | Register project, seed PLOT.md + knowledge |
project_rename | Rename a project |
project_delete | Cascade delete (project + agents + lessons) |
Plot — the orchestration protocol
| Tool | Purpose |
|---|
plot | Read PLOT.md |
plot_update | Apply a unified git-format diff (zero fuzz, exact context match) |
plot_update validates --- a/PLOT.md / +++ b/PLOT.md / @@ headers strictly. Context mismatch throws hunk_mismatch with the failing hunk header. Re-read with plot, regenerate the diff, retry. No silent overwrites.
Knowledge — the vector memory
| Tool | Purpose |
|---|
knowledge_list | Lesson metadata (slug, kind, agentSlug, timestamps). Optional kind filter. No content. |
knowledge_search | Semantic query → ranked metadata. q free-text intent, optional kind, optional limit. No content. |
knowledge_get | Read one lesson by slug — full body |
knowledge_create | Record a new lesson — kind: 'fundamental' | 'situational'. Embedded automatically. |
knowledge_update | Replace lesson content. Re-embeds on save. |
knowledge_delete | Retire a lesson |
knowledge_search runs vector lookup over the sqlite-vec index. Pass natural prose — questions, task statements, descriptions all work. SQL wildcard syntax does not. Response is ranked metadata; call knowledge_get({project, slug}) for the body of any hit worth reading. Same pattern for knowledge_list (use kind: 'fundamental' to enumerate the binding set, then get each).
Agents
| Tool | Purpose |
|---|
registry_list | Paginated agent metadata — {data, page, limit, hasNextPage}. Each data row: slug, status, branch, worktree, reservedPaths, timestamps, mergedCommit, abandonedReason. Optional status filter; default limit 10, max 50. No artifact bodies. |
agent_get | Full record by slug — includes request, plan, implPrompt, coordinationBrief, postMergeNotes |
agent_create | Persist a draft dispatch record |
agent_update | Transition status, edit fields |
Allowed transitions: draft → active → merged | abandoned. merged requires a 7–40 char commit SHA. abandoned requires a reason. Only active agents reserve paths.
registry_list returns the collision matrix metadata in one paginated envelope. To inspect a dispatch's plan or implPrompt, follow with agent_get({project, slug}). Page through with page until hasNextPage is false.
Example session
human: orchestrate adding rate-limit middleware to /api routes
claude: → registry_list({project, status:'active'}) // 0 active
→ knowledge_list({project, kind:'fundamental'})
// metadata for binding rules
→ knowledge_get({project, slug:'base-branch'}) // → main
→ knowledge_get({project, slug:'worktree-convention'}) // → ../<slug>/
→ knowledge_search({project, q:'rate limit middleware nest interceptor'})
// ✨ 2 ranked hits on guard ordering (metadata)
→ knowledge_get({project, slug:'rate-limit-guard-ordering'}) // bodies fetched
→ spawn Plan subagent → plan returned
→ spawn critique subagent → 1 issue: missed `app.module.ts` registration → fixed
→ agent_create({
slug: 'rate-limit-middleware',
branch: 'feature/rate-limit',
worktree: '/abs/path/../rate-limit-middleware',
reservedPaths: ['apps/api/src/common/rate-limit/**', 'apps/api/src/app.module.ts'],
request: 'orchestrate adding rate-limit middleware to /api routes',
plan: '...',
implPrompt: '...',
coordinationBrief: 'reserves common/rate-limit/**, edits app.module.ts (additive)'
})
→ here's the impl prompt to paste into a fresh session: ...
[ task ships ]
claude: → agent_update({slug:'rate-limit-middleware', status:'merged', mergedCommit:'a1b2c3d'})
→ knowledge_create({
agentSlug: 'rate-limit-middleware',
slug: 'rate-limit-guard-ordering',
kind: 'situational',
content: 'Rate-limit guards must register AFTER auth guard in app.module.ts.
Order in providers array decides execution order. Reverse → 401s
bypass rate limiting.'
})
// ✅ lesson embedded → vector indexed → permanent memory
Next time anyone — you, Claude, Codex — touches anything rate-limit-shaped on this project, that lesson surfaces automatically.
Web panel
Astro 6 + React 19 islands. Tailwind v4. shadcn/ui.
| Page | What |
|---|
/ | Landing |
/projects | Projects list — TanStack Table, sortable, filterable |
/agents | Registry / status board across projects |
/knowledge | Markdown viewer — GFM, syntax highlight, anchored sections, search |
/plot | Plot viewer + editor with diff preview |
/settings | Embedder URL + model + dim, tokenizer key, REST gate, log level |
Settings stored in app_settings (SQLite) override env vars. Headless deploys still work via env alone.
Configuration
Both env vars and panel settings supported. Panel takes precedence.
| Var | Default | Purpose |
|---|
PORT | 7776 | HTTP port |
CORS_ORIGIN | unset | * or comma-separated origins |
ORCHESTRATOR_DB_PATH | /data/orchestrator.db | SQLite path. Dev scripts override to ./.local-data/orchestrator.db |
PANEL_REST_ENABLED | false | Mount /api/orchestrator/* REST surface (panel uses it) |
NODE_ENV | unset | Production blocks destructive migrations |
ORCHESTRATOR_ALLOW_DESTRUCTIVE_MIGRATE | unset | Set 1 to override in production |
ANTHROPIC_API_KEY | unset | Token-count endpoints (server-side only) |
ANTHROPIC_TOKENIZER_MODEL | claude-opus-4-7 | Model name for count_tokens |
EMBEDDER_URL | unset | HTTP embedding endpoint (Ollama / TEI / OpenAI-compatible). Unset → in-process embedder |
EMBEDDER_MODEL | Snowflake/snowflake-arctic-embed-m-v1.5 | Model id (in-process default; passed through to HTTP embedder when configured) |
EMBEDDER_DIM | 768 | Expected embedding dimension |
Stack
| Layer | Tech |
|---|
| Memory | sqlite-vec vector index • Snowflake Arctic Embed v1.5 in-process via Transformers.js (default) • Ollama / TEI / OpenAI-compatible embedders via EMBEDDER_URL • freshness-pinned vectors |
| API | NestJS 11 • @rekog/mcp-nest • @modelcontextprotocol/sdk 1.10 • Zod 4 • Swagger |
| Persistence | better-sqlite3 (WAL, FK, busy_timeout 5s) |
| Diff engine | diff 9 (zero-fuzz unified-diff applier) |
| Web | Astro 6 • React 19 • Tailwind 4 • shadcn/ui • TanStack Table • rehype/remark • highlight.js |
| Tooling | Turborepo 2 • pnpm 10.33 • TypeScript 5.9 • Prettier 3 • Jest |
Scripts
pnpm dev
pnpm build
pnpm lint
pnpm check-types
pnpm format
pnpm -F @continuum/api dev
pnpm -F @continuum/api mcp:dev
pnpm -F @continuum/api test
pnpm -F @continuum/api test:e2e
pnpm -F @continuum/web dev
pnpm -F @continuum/web build
Conventions
project is always a canonical absolute path. Pass the project root, never a worktree subdirectory. Worktree paths throw project_not_found.- Plot mutations flow through unified diffs. No full-text writes. Bad context → server rejects with the failing
@@ header.
- Knowledge updates are whole-content replace. Pass the full new
content. Re-embedded on save.
- Reserved paths are JSON arrays of globs. Only
active agents reserve.
- Migrations are forward-only on boot. Production blocks destructive migrations unless explicitly opted in.
Troubleshooting
| Symptom | Likely cause |
|---|
project_not_found | Passing a worktree path. Use the canonical project root registered with project_create. |
hunk_mismatch from plot_update | Stale diff. Re-read with plot, regenerate against current content, retry. |
knowledge_search / knowledge_list returns empty / weird results | Embedder unreachable, model mismatch, or dim mismatch (search only). Check /settings. Backfill stale vectors. Remember: both return metadata — call knowledge_get({project, slug}) for the body. |
| Vectors not regenerating after embedder swap | Freshness profile (URL + model + dim) must change. Restart server to trigger backfill. |
invalid_transition from agent_update | Allowed: draft → active → merged | abandoned. Anything else throws. |
missing_merged_commit / invalid_merged_commit | merged requires a 7–40 char SHA. |
| Schema migration refuses in production | Set ORCHESTRATOR_ALLOW_DESTRUCTIVE_MIGRATE=1 if you really mean it. |
Roadmap
Contributing
Contributions welcome. Fork, branch, test, PR. Match Prettier defaults (100-col, double quotes, semicolons, ES5 trailing commas). Keep MCP tool changes Zod-typed.
Support
Stop letting your agents forget.
pnpm install && pnpm dev — and they never will again.
