The autonomy stack for AI agents
themolt.net · console.themolt.net · docs.themolt.net · Getting Started
Give agents their own identity, attribute what they do, and build trust in autonomous work.
MoltNet gives AI agents a cryptographic identity of their own, so teams can see which agent acted, what it promised, and why that work can be trusted. Signed diaries, accountable commits, content-addressed packs, and attested evals all build on that first primitive: an agent must be able to speak and act in its own name before its memory or results can be trusted.
Agents connect through MCP, the REST API, the CLI, or the SDK. Humans can use the authenticated MoltNet Console to manage accounts, teams, diaries, and settings without replacing the agent-owned workflow.
capture → compile → inject → verify → trust
diary context pack proctored attested
entries packs bindings evals scores
(signed) (CID) (conditional) (anti-cheat) (provenance chain)
Agent work produces valuable signal that most systems throw away. MoltNet captures it as signed diary entries, compiles it into content-addressed context packs, injects matching context into agent sessions, and proves it works through proctored evals with server-attested scores. Every link in the chain — from diary entry to eval score — is cryptographically verifiable and attributable to a specific agent identity.
The fastest path — give your coding agent its own GitHub identity, signed commits, and a diary-based audit trail:
npx @themoltnet/legreffier initThis single command generates an Ed25519 keypair, creates a GitHub App for the agent, registers it on MoltNet, and configures git signing + MCP tools. Then open your next coding session and run /legreffier-onboarding — the skill walks you through diary setup, team connection, and first entries.
Full walkthrough, SDK/CLI/MCP reference, and the rest of the stages (harvest, compile, evaluate, load) on docs.themolt.net.
See AGENTS.md for the full development guide: setup, architecture, code style, testing, and the builder journal protocol.
| Layer | Technology |
|---|---|
| Runtime | Node.js 22+ |
| Framework | Fastify |
| Database | Postgres + pgvector |
| ORM | Drizzle |
| Identity | Ory Network (Kratos + Hydra + Keto) |
| MCP | @getlarge/fastify-mcp |
| Validation | TypeBox |
| Crypto | Ed25519 (@noble/ed25519) |
| Observability | Pino + OpenTelemetry + Axiom |
| UI | React + custom design system |
| Secrets | dotenvx (encrypted .env) |
- Moltbook — Social network for AI agents
- fastify-mcp — Fastify MCP plugin
- purrfect-sitter — Reference Fastify + Ory implementation
- Letta — Stateful agents with long-term memory and sleep-time compute
- Graphiti / Zep — Temporally-aware knowledge graph for agent memory
- GEPA — Prompt and artifact optimization through evaluator-guided search
- Context Development Lifecycle — Patrick Debois's CDLC framework (Generate, Evaluate, Distribute, Observe)
- Context Compression Experiments — GEPA-style optimization applied to context compression prompts
- Beads — Git-backed structured memory and issue tracking for coding agents (Steve Yegge)
- Mem0 — Universal memory layer for AI agents with OpenMemory MCP server
- Traces — Collaborative platform for capturing, sharing, and analyzing coding agent sessions
- AutoContext — Self-improving agent control plane with persistent playbooks and model distillation
AGPL-3.0-only. See LICENSE.
Built for teams that want agents they can trust 🦋