Prime-Silo MCP server
Commit Nov 14, 2026An open-source agent OS and runtime harness that enables deterministic AI execution, agent-to-agent swarm collaboration, and auditable lineage.
Also known as binary16labs/prime-silo
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What is Prime-Silo MCP?
Prime-Silo is an open-source agent OS and runtime harness that enables deterministic AI execution, agent-to-agent swarm collaboration, and auditable lineage.
Summary generated from the project's README
About Prime-Silo MCP server
Executing deterministic AI reasoning and managing agent swarms is what Prime-Silo enables for organizations requiring data sovereignty and verifiable lineage. It functions as an open-source agent operating system and runtime harness that combines the Benny deterministic substrate with the Space-Agent adaptive shell. Users can interact with the system via a desktop application, a browser-resident runtime, or a command-line interface using node space and benny commands.
The platform is built to be MCP native, allowing for universal tool connectivity without custom wrappers. It utilizes Pypes transformation algebra to run operations locally, which helps eliminate recursive cloud context dumps and reduces token overhead. Every decision trace and state transition is cryptographically signed using HMAC SHA-256 to ensure an auditable history. Developers and institutional operators can manage complex workflows through the Bridge cockpit, which unifies memory, documents, code, and execution flows into a single interface.
What you can do with it
- 1Audit agent decision historyA developer can verify the cryptographic integrity of every state transition and decision trace made by an agent.
- 2Reduce cloud token costsAn organization can run deterministic transformations locally to avoid expensive recursive context dumps to the cloud.
- 3Coordinate specialized agent swarmsUsers can deploy multiple specialized agents that use open communication standards to discover and complete tasks together.
- 4Query historical session memoryAn operator can ask an agent to recall specific details about previous work sessions or file interactions.
Written from the project's README. Every use case is checked against a quote from it.
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README
View on GitHubPrime-Silo // Sovereign AI Agent OS & Harness
The canonical Sovereign AI Agent OS and runtime harness — engineered for institutional cognition by Binary 16 Labs & Agent Benny.
Explore Live Web Substrate • Read Architecture Spine • View Token Economics
"Stop gambling with probabilistic cloud wrappers. Start executing with deterministic institutional reasoning."
Prime-Silo is an open-source typed execution substrate and Agent OS harness designed for organizations requiring data sovereignty, zero token tax, and verifiable lineage. It fuses two breakthrough architectures:
- Benny — the deterministic substrate (Pypes Layer 0 transformation algebra, swarm-executed cognitive operations, triple-lineage governance, manifest signing). Vendored here under
runtime/. - Space-Agent — the adaptive shell (browser-resident agent runtime, modular puzzle-piece UI, Git-backed workspace versioning).
🏛️ Why Prime-Silo is Trending in 2026
While traditional agent frameworks suffer from context bloat, cloud gatekeeper fees, and brittle custom code, Prime-Silo delivers:
- Model Context Protocol (MCP) Native: Universal tool connectivity ("USB-C for AI") without code fragmentation or custom wrappers.
- Agent-to-Agent (A2A) Swarm Collaboration: Built-in open communication standards for specialized agent swarms to discover and execute tasks autonomously.
- Zero Token Tax Execution: Deterministic Pypes transformation algebra runs locally, eliminating recursive cloud context dumps and saving up to 90% in token overhead.
- HMAC SHA-256 Auditable Lineage: Every state transition, UI layout, and decision trace is cryptographically signed and auditable.
The goal: a single Agent OS shell that gives institutional operators one nav, one theme, one run timeline, and one audit story across documents, code, tabular data, and post-run review — without ever compromising determinism or burning capital on cloud context bloat.
Determinism boundary (ADR-001)
Prime-Silo splits surfaces into two zones, with different agent authority in each:
| Zone | Surfaces | Agent authority | Where it lives |
|---|---|---|---|
| Deterministic | Manifest authoring, run execution, KG/code graph mutation, L3 writes, skill registry | Read-only. Drafts → HITL → sign_manifest() → run |
Static React widgets |
| Review (fluid) | Post-run drill-down, frame inspection, reasoning trace, audit query, agent-composed analyst reports | Read everything; write only to agent_sandbox/{views,notes,drafts,skills}/ |
Adaptive composed canvas |
Pinned agent-composed layouts become .aamp.view bundles, HMAC-signed via the existing skin-pack signing path. Replaying a layout is deterministic and auditable.
See runtime/architecture/ADR-001-prime-silo-shell-fork.md for the full decision record, architecture/ROADMAP.md for the rolling phase status, and architecture/OPERATING_PLAN.md for the test runbook + dev loop.
Documentation by Role
👤 Desktop App Users — Start here:
- QUICKSTART-EXE.md — Download, install, and launch the desktop app
- HOME-DIRECTORY.md — Configure where your data is stored
- GUIDE.md — Feature walkthroughs and UI help
💻 Command-Line Users — Start here:
- CLI.md — Complete reference for
node spaceandbennycommands - QUICKSTART-EXE.md — Desktop setup (if needed)
- GUIDE.md — Step-by-step usage examples
🤖 AI Agents — Start here:
- AGENT-AWARENESS.md — System state, API endpoints, sandbox permissions
- CLI.md — Available CLI tools for agents
- HOME-DIRECTORY.md — Workspace file structure
🔧 Developers — Start here:
- AGENTS.md — Architecture rules and contracts
runtime/architecture/ADR-001-prime-silo-shell-fork.md— Design decisionsarchitecture/OPERATING_MANUAL.md— Deep-dive dev setup
📚 Full Documentation Index:
- See INDEX.md for the complete navigation map
Interactive demo site: site/ — self-contained website that tours every feature (with layer-by-layer deconstruction views), embeds the operating manual, and ships a configuration wizard (generates your .env + prime-silo.config.json + launch commands, each setting mapped to the process that consumes it) plus a live operator dashboard that health-checks your running stack. No build step:
.\scripts\site.ps1 # or: python -m http.server 4173 --directory site
# → open http://localhost:4173
Companion — Memo-Ray: the memory graph of the cognitive mesh — the third first-class graph beside the knowledge graph (documents) and code graph (AST). It X-rays Claude + Antigravity session logs into an explorable organic lineage map so the operator never has to be the institutional memory. Clone it beside this repo (or set MEMORAY_DIR); .\scripts\dev.ps1 auto-boots it when enabled.
As of Phase M1 the memory graph is built into the shell — one capability on four surfaces, all over a single configurable proxy (/api/memoray, endpoint via MEMORAY_BASE_URL or the wizard manifest):
- Page —
#/_prime_silo/memory: Command Center cards, session list, lineage graph, node inspector, a conformance strip, and first-class offline/disabled screens. - Agent skill —
memory-recall: the onscreen agent answers "what was I working on / which sessions touched file X" by querying the graph itself. - CLI —
node space memory <status|sync|sessions|search|audit>. - Self-audit — the integration is declared in
manifests/integrations/memoray.integration.json(data model + process map + config surface, HMAC-signed).GET /api/integration_audit/node space memory audit/node scripts/audit-integrations.mjsprobe live reality against that declaration and emit a drift report whose findings carry the owner path to fix — so an agent (or a local LLM via Lemonade) can maintain it without spelunking. Seemanifests/integrations/AGENTS.md.
Bridge — one cockpit for the whole mesh
Phase B ropes the scattered pages into a single calm surface at #/_prime_silo/bridge — designed for one mental model, not six tabs. A mode rail (Pulse · Memory · Documents · Code 3D · Flows · Runs), one stage, and Benny (the onscreen agent) in the dock, grounded in whatever's on screen so you can ask "explain this" / "what did I work on" and get a real answer with a deep link back.
- Golden paths, not copy-paste — Flows: type a requirement → Plan renders the DAG → Run flips to live observability. Documents: pick a workspace → manage files → Ingest turns documents into semantic triples (rendered as the knowledge graph) → Correlate links them to the code graph.
- 3D when you want it — Code 3D and the knowledge graph default to an offline-safe 2D layout with a one-click 3D (WebGL) toggle.
- Same thesis — declared as a signed manifest (
manifests/integrations/bridge.integration.json) covered by the self-audit, and ubiquitous: page +benny-pilotagent skill +node space bridge <status|plan|run|ingest|open>. Configurable landing viaBRIDGE_DEFAULT_MODE. Pulse carries Memo-Ray's/api/lifelogactivity feed (sessions + artifacts + git commits).
Repo layout
prime-silo/
├── app/ # space-agent — browser frontend
├── server/ # space-agent — thin Node.js shell server
├── space/ # space-agent — agent runtime
├── packaging/ # space-agent — desktop builds
├── runtime/ # vendored from skybluecycology/benny
│ ├── benny/ # FastAPI backend, Pypes, swarm, governance
│ ├── manifests/ # Pypes/swarm manifests
│ ├── tests/ # pytest suite (~200 tests)
│ ├── docs/ # operator manuals, ADRs, requirements
│ └── architecture/ # ADR-001 lives here
├── scripts/ # prime-silo dev/launch scripts
└── README.md # this file
Status
- Phase A — agent sandbox boundary, scope guard middleware, widget registry contract, agent-authorship lineage emitter (in
runtime/benny/) - Phase B — fork bootstrap; Benny vendored under
runtime/, space-agent shell merged - Phase C — eight canvases migrated to the shell widget tree (text.markdown, run.reasoning_trace, run.lineage_timeline, run.drilldown_table, run.frame_inspector, kg3d.synoptic_web, codegraph.canvas, dag.canvas)
- Phase D / D2 / D3 — runtime transport, agent-context chokepoint, agent saved-views helpers
- Phase F / F2 / F2b —
.aamp.viewHMAC sign / verify / pin / load chokepoint; pinned views are self-describing signed JSON,GET /api/views/load/<ws>/<filename>returns{view, signature, valid}in one round-trip - Phase E — first deterministic-zone shell page (
manifest_explorer) lists registered swarm manifests and renders the selected one asdag.canvas. No agent context —runtimeFetchwith no scope. - Phase H1 — session checkpoints: save/restore/fork session state (history + skills + staged data + run refs); draft endpoints under the agent sandbox, human-only HMAC pinning
- Phase M1 — memory graph in-shell: Memo-Ray integration declared as a signed
aamp.integration/1manifest (data model + process map + config surface), surfaced on four surfaces (page#/_prime_silo/memory,memory-recallagent skill,node space memoryCLI,/api/integration_auditself-audit) over one configurable proxy - Phase B (Bridge) — one cockpit (
#/_prime_silo/bridge) unifying memory, documents→triples, code, flows and runs with Benny grounded in the stage; signedbridge.integration.json; ubiquitous (page +benny-pilotskill +node space bridge); carries Memo-Ray's lifelog feed - Phase M2 — LLM self-maintenance loop: a swarm manifest that runs the audit →
call_model()(Lemonade local) → drafts fixes intoagent_sandbox/drafts/for human pinning - Phase G — canvas consolidation; retire ManifestCanvas/PipelineCanvas/WorkflowCanvas duplication in the runtime frontend
Phase status is tracked rolling in architecture/ROADMAP.md. The original phase rationale lives in runtime/architecture/ADR-001-prime-silo-shell-fork.md §8.
Quickstart
# 1. Install Python + Node deps
cd runtime
python -m pip install -e .
cd ..\server
npm install
cd ..
# 2. Set required environment (HMAC key — runtime owns it; browser never sees it)
$env:BENNY_HMAC_KEY = "<64-hex-character key>" # see OPERATING_MANUAL.md §2.5 for how to generate
# 3. Boot runtime + shell in parallel
.\scripts\dev.ps1 # bash users: ./scripts/dev.sh
# 4. Verify the ADR-001 surfaces are live
curl http://localhost:8005/api/agent_sandbox/health
curl http://localhost:8005/api/widgets
# 5. Browse manifests in the shell:
# open http://localhost:3000/#/_prime_silo/manifest_explorer
A full setup-from-scratch walkthrough, including prerequisites, HMAC key generation, smoke tests, and a feature-by-feature usage guide, lives in architecture/OPERATING_MANUAL.md.
Updating the vendored Benny tree
git remote add benny https://github.com/skybluecycology/benny.git # one-time
git subtree pull --prefix=runtime benny master --squash
Fixes that originate in runtime/ go back upstream as PRs against skybluecycology/benny. Anything touching runtime/benny/ lands upstream first; the fork only owns:
- Space-agent shell customisations
- Migrated React widget components (Phase C onward)
.aamp.viewview bundles- Fork-specific docs
License
Space-agent shell code under LICENSE (inherited from upstream). The vendored Benny tree retains its own license at runtime/LICENSE where applicable.
Prime-Silo — engineered by Binary 16.
Prime-Silo MCP questions
What makes Prime-Silo different from traditional agent frameworks?
Unlike traditional frameworks that suffer from context bloat and cloud fees, Prime-Silo uses deterministic Pypes transformation algebra to run locally, saving up to 90% in token overhead.