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–32B) 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.

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 · balanced (100% accuracy)
ollama pull dcostenco/prism-coder:14b   # 8.4 GB · Mac default (100% accuracy)
ollama pull dcostenco/prism-coder:32b   # 16 GB  · complex tasks (100% accuracy)

Prism detects both the namespaced (dcostenco/prism-coder:14b) and bare (prism-coder:14b) 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.

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. The 14B and 32B are fine-tuned from Qwen3; the 2B and 4B slots use stock Qwen3.5-4B with prompt engineering at different quantization levels (100% routing accuracy without fine-tuning). They are not general-purpose chat models — they route reliably and run offline; Claude and other frontier models remain better at reasoning, coding, and open-domain work. The intended pattern is local routing with an optional cloud fallback for hard cases.

Weights: huggingface.co/dcostenco (public GGUF). Latency depends on model size and hardware — see Benchmarks to measure it on your own machine rather than trusting a printed number.

Cascade

query → prism-coder:14b (local router, Mac default)
      → qwen3.5:4b (grounding verifier)
      → prism-coder:2b (iPhone / mobile, auto-selected by RAM)
      → prism-coder:32b (complex tasks, on demand)
      → cloud fallback (paid tiers, for max quality)

Benchmarks

Reproduce every number yourself. All evals are open-source and self-contained:

git clone https://github.com/dcostenco/prism-coder && cd prism-coder
pip install anthropic requests
python3 tests/benchmarks/prism-routing-100/benchmark.py --models 2b 4b 14b 32b

Routing eval (115 cases, 12 categories, 3-seed mean). On this narrow tool-routing task all fleet models achieve near-perfect accuracy. Be honest with yourself about what that means: the eval is near-saturated for this taxonomy — it measures whether the right one of a small set of MCP tools is selected, not general capability. The useful takeaway is offline routing reliability at zero cost, not that a 2.3 GB model rivals a frontier model in general.

Memory uplift (LoCoMo-Plus, self-published). A separate long-context dialogue benchmark (dcostenco/Locomo-Plus) measures how much structured memory helps a base model retain multi-day context. Results show large gains when a model is paired with Prism memory versus running raw. Note this benchmark is authored, run, and LLM-judged by this project — treat it as a reproducible demonstration, not an independen