Prism Coder

Give your AI agent memory that lasts. Persistent sessions, knowledge graphs, and offline tool-routing — fully local and free.

Prism Coder is an MCP server that gives Claude, Cursor, and other AI tools long-term memory that survives across sessions. It ships with the open-weight prism-coder model fleet (2B–27B) for fast, offline tool-routing — no cloud required.

No account needed. No API keys. Runs on your machine.
A paid subscription adds cloud sync, higher model tiers, and team features through the Synalux portal.

What's New in v20

License: AGPL-3.0 → Apache-2.0

Prism MCP is now Apache-2.0. The thin-client architecture means all proprietary value (skill resolution, tier gating, billing, cloud inference) lives server-side — the open client carries no moat to protect. Apache-2.0 removes the enterprise adoption friction that AGPL caused.

Thin Client Architecture

Skill routing, budget management, and content resolution have moved server-side to the Synalux portal. The MCP client is now a thin API caller — simpler, smaller, and portable across any host (Claude Code, Gemini, Cursor, autonomous scripts). Offline fallback reads the last successful response from local SQLite.

Clean-Room Voyage AI Adapter

The Voyage AI embedding adapter was independently reimplemented from the Voyage API docs to ensure 100% project-owned copyright. Default model updated to voyage-3.5. See PROVENANCE.md for details.

Server-Side Drift Detection

Session drift detection (GATE 5) no longer requires Claude Code hooks. The timer runs server-side per conversation, piggybacked on every MCP tool response. Works for any host.

CLA Requirement

External contributions now require signing the Individual CLA. The CLA check is merge-blocking on the main branch.

Quickstart

The free tier needs no account, no API key, and no cloud. Add the server to your MCP client:

{
  "mcpServers": {
    "prism": {
      "command": "npx",
      "args": ["-y", "prism-mcp-server"]
    }
  }
}

Open Claude Desktop or Cursor and your agent now has memory backed by a local SQLite database (~/.prism-mcp/data.db).

Optional — local model fleet for offline tool-routing. Pull whichever fits your hardware:

ollama pull dcostenco/prism-coder:2b    # 2.3 GB · mobile / lightweight (99.1% routing accuracy)
ollama pull dcostenco/prism-coder:4b    # 3.4 GB · verifier (100% accuracy)
ollama pull dcostenco/prism-coder:9b    # 5.8 GB · default router (100% accuracy, Qwen3.5)
ollama pull dcostenco/prism-coder:27b   # 16 GB  · complex tasks (100% accuracy)

Prism detects both the namespaced (dcostenco/prism-coder:9b) and bare (prism-coder:9b) Ollama tags automatically.

What it does

Your AI agent forgets everything between sessions. Prism fixes that — and adds verification, drift detection, and multi-agent coordination on top.

Mind Palace — persistent memory that survives across sessions

Every conversation feeds a persistent store. The next session loads the right context automatically — no re-explaining.

The dashboard shows your current project state, pending TODOs, intent health, and a neural knowledge graph — all built automatically from your agent sessions.

Knowledge Graph — semantic + keyword + graph search

Ask "what did I decide about the auth flow last month?" and get an answer with citations, combining vector similarity, full-text search, and graph traversal.

Session History — immutable audit trail

Every session is logged with files changed, decisions made, and TODOs. Search, filter, and replay any past session.

Inference Metrics — see where your tokens go

Every prism_infer call tracks which model handled it (local Ollama vs cloud) and how many tokens were consumed. When you save a session, Prism shows a summary:

📊 Inference Metrics (this session):
  Total calls: 12 — Local: 10 (83%) | Cloud: 2 (17%)
  Prompt tokens: 7,840 evaluated / 8,420 submitted est.
  Completion tokens: 3,150
  Cloud tokens saved (est.): 11,570 — token volume handled locally instead of cloud
  Avg latency: 1,240ms
  By model:
    prism-coder:27b: 6 calls, 7,200 tokens, avg 1,800ms
    prism-coder:9b: 4 calls, 2,870 tokens, avg 620ms
    synalux-27b: 2 calls, 1,500 tokens, avg 1,100ms

Cloud tokens saved is the honest routing metric — it accrues only when local Ollama handles a call that would otherwise have gone to Claude or the Synalux portal. A compact version appears inline after every 5th prism_infer call: 📊 local 10 (83%) · cloud 2 (17%) · ~11,570 tok · avg 1,240ms · 11,570 cloud tok saved.

Local calls use actual Ollama token counts (prompt_eval_count / eval_count from Ollama); cloud calls use char/4 estimates. Metrics are tracked locally — no portal dependency, no env vars, works offline. Per-call data is also forwarded to the Synalux portal as best-effort analytics (independent of the display).

Session Drift Detection

Long agent sessions can wander from their original goal. session_detect_drift compares current work against the stated goal and returns on_track / minor_drift / major_drift so the agent can self-correct.

Behavioral Verification — catch bad edits before they happen

AI agents apply patterns from checklists without understanding the real-world impact. The verify_behavior tool challenges the agent with a scenario it must answer before editing — forcing it to think through what the end user will experience.

Agent: "I'll revert this kitchen display change"
Prism: "⚠️ Scenario: A cook sees a 3-item ticket. One item is voided.
        What should the cook see after the void?"
Agent: "The ticket stays visible with the remaining 2 items."
Prism: "Correct — your revert would hide the ticket entirely."

17 built-in domains (billing, auth, ordering, clinical, HR, and more). Custom domains per workspace on Enterprise. No hooks needed — works in any MCP client.

Time Travel

Roll back to any previous session state. Compare diffs between versions. Restore a known-good state with one click.

Cognitive Routing

Three memory types, automatically sorted: episodic (what happened — session logs, decisions), semantic (what's true — facts, architecture), and procedural (how to do X — workflows, patterns). When you search, the router picks the right store instead of dumping everything.

Multi-Agent Hivemind

Coordinate multiple AI agents working on the same project. Each agent has its own session, but they share memory through the knowledge graph. The Hivemind Radar shows real-time agent status, tasks, and activity.

Neural Search

Search across all memories with highlighted results, knowledge graph editing, and memory density metrics.

Local-first and privacy

The free tier runs entirely on your machine. Paid tiers add cloud sync through the Synalux portal, which is what enables cross-device memory and team sharing.

Handling sensitive data. All cloud writes pass through automatic redaction (SSNs, dates of birth, medical record numbers, phone numbers, emails, and clinical identifiers are stripped before transit). For regulated workloads, run the local tier for full air-gap, or use Enterprise which includes a HIPAA Business Associate Agreement.

Models

The prism-coder fleet uses Qwen3.5 for MCP tool-routing AND general inference. The 9B and 27B are fine-tuned with LoRA (r=128, all 64 layers including DeltaNet); the 2B and 4B use stock Qwen3.5-4B at different quantization levels. The 27B scored 100% on BFCL function-calling and 100% on an internal 15-problem coding eval at $0 inference cost.

prism_infer supports three modes: route (tool rout