The only MIT-licensed, graph-native skill runtime with zero external dependencies and every skill quality-gated on every commit.
A 9-second tour of Agent Foundry, produced by HyperFrames (Apache-2.0, 36k★) from HTML to MP4 with FFmpeg + Puppeteer. hyperframes render . --quality draft
Agent Foundry runs entirely locally. Install, run, verify, no cloud, no SaaS, no telemetry.
Each ≤150 lines with trigger phrases, anti-patterns, and verification checklists. Disciplines, not domain knowledge.
af-planner decomposes requests. af-critic scores output. af-orchestrator dispatches subtasks to agents and skills.
396 nodes, 626 edges, 44 communities. The knowledge graph feeds back into the planner, boosting related skills 15% higher in the ranking.
Rate outputs, run agent-foundry learn, and the system suggests new trigger patterns. Instincts are user-approvable and MIT.
Agent Foundry upgrades your workflow without forcing a tool reset.
Real feedback from early users.
Skills, agents, orchestrator, graph, self-improvement, built to work together.
Disciplines the model applies to its own work, how to think, not what to know. Each ≤150 lines with trigger phrase, anti-patterns, and verification. 24 core + 6 optional.
Roles the orchestrator dispatches. af-planner, af-critic, and the new af-orchestrator which decomposes → dispatches → merges subtasks across skills and agents.
Local daemon: ranks, dispatches, executes, logs, judges. FastAPI, SQLite, lazy-started. HTTP endpoints for everything: /plan, /execute, /loop, /health.
Adopted from DeepSeek's Engram paper. O(1) N-gram hash → SQLite lookup for hot prompts. Plan + execute the same task in microseconds when the pattern is recognized.
Rate executions, run agent-foundry learn, and the system suggests new trigger patterns from successful runs. Instincts stored in SQLite, user-approvable, MIT.
396 nodes, 626 edges, 44 communities. Tree-sitter AST extraction, community detection, god nodes, surprising connections. Graph-aware planning boosts related skills 15%.
Three gates on every commit: 34/34 eval, 31/31 validate, 0 external references. Every skill is original, verified, and MIT-licensed.
Install targets for Claude Code, Hermes, Codex, Cursor, OpenCode, Gemini. 3 tested end-to-end on this machine. Smoke tests verify every install path.
Live from the repo, regenerated on every build, no hand-maintained counts.
An honest tested / documented / untested matrix. We never claim what we haven't verified.
A taste of the catalog. See all 30 →
Quick answers to common questions.
Agent Foundry ships 30 original skills with a hard zero-external-references gate, every skill is original MIT-licensed work. We also ship a live knowledge graph of the codebase and a 3-gate audit pipeline on every commit, including an external-reference scanner that fails the build if any third-party name leaks in.
Every commit runs a tree-sitter AST extraction of the entire codebase. The resulting graph (396 nodes, 626 edges, 44 communities) feeds back into the orchestrator, related skills get a 15% boost in planning. The graph is a first-class feature, not a visualization afterthought.
Yes. MIT-licensed, all local, no cloud. You supply the API key for your LLM provider. No SaaS, no telemetry, no subscription.
Claude Code, Hermes, and Codex are tested end-to-end. Cursor, OpenCode, and Gemini CLI are documented. Each has a --harness=NAME install path.
Absolutely. Fork the repo, add a skill under skills/core/ following the template in community/README.md, and open a PR. Every skill must pass foundry-eval.py and the nox.sh external-reference gate.
The daemon is lazy-started on first command. No systemd, no launchd, no Docker. Everything runs locally in ~/.config/agent-foundry/executions.db.
30 curated skills, 3 agents, a live knowledge graph, a self-improvement loop, and three quality gates, all MIT, all local.