Continuum

Shared memory + orchestration for your coding agents.

One MCP server. Tell any client to read the plot — it becomes the orchestrator. Dispatches fresh agents in isolated worktrees, shares lessons across every Claude Code / Codex / Cline chat on the project.

Quick Start • Memory & Vectors • Workflow • MCP Tools • Wire It In • Configuration

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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.mdprotocol"]
        KNOW["KNOWLEDGE(vectorized)"]
        AGENTS["AGENTSregistry"]
        PANEL["WEB PANEL(humans)"]
    end

    DB[("SQLite + sqlite-vecone 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.

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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.

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Why Continuum exists