Why AgentSeed

LLMs hallucinate — in code, that means invented APIs, undefined identifiers, fake test passes, and confident overclaims. The numbers:

• 15.1% of code hallucinations are knowledge-conflicting: calling APIs that don't exist or were never imported (arXiv:2404.00971).
• <10% of hallucinated code fails tests — most slips through CI (arXiv:2404.00971).
• 60%+ of model-output errors are unverifiable — no way to tell fact from fiction (FAVA, cited in SoK).

Prompt-only guardrails are soft: a model can agree to verify and then skip it. AgentSeed binds the instruction to a hard MCP gate — the evidence comes from running code, not from the model's self-report.

It also fills two gaps the 1.0.0 spec deliberately leaves open:

What it does

Six MCP tools — zero required dependencies, enhanced by optional extras:

Measured on a seeded synthetic corpus (5 defect classes): precision 1.0, recall 1.0 (tp=100, fp=0, fn=0) with the regression test locking it in — methodology and honest scope in docs/BENCHMARK.md.

Live demo

$ verify_code(source="def f():\n    return magic_unknown()\n", language="python")
{
  "language": "python",
  "suspects": ["magic_unknown"]      # ← hallucinated API caught
}

$ scan_hallucination(source="The feature is production ready, all tests pass. Trust me.")
{
  "hits": [
    {"word": "all tests pass", "group": "oversold", "line": 1},
    {"word": "production ready", "group": "oversold", "line": 1},
    {"word": "trust me", "group": "oversold", "line": 1}
  ],
  "clean": false                      # ← overclaim caught
}

$ check_plugin(path="/path/to/AgentSeed")
{ "ok": true, "errors": [], "warnings": [] }   # ← strict 1.0.0 conformance

Quick start

Option A — download a release (no git needed):

# grab the latest asset from https://gitcode.com/badhope/AgentSeed/releases
# or use the installer, which drops it into a client of your choice:
bash install.sh --client auto        # macOS / Linux
./install.ps1 -Client auto           # Windows PowerShell
# --client: claude | opencode | cursor | manual

Option B — clone:

git clone https://gitcode.com/badhope/AgentSeed.git
# or: https://gitcode.com/badhope/AgentSeed · https://gitee.com/badhope/AgentSeed

1. Drop the AgentSeed/ directory into any client that supports Agent Plugins (Cursor, VS Code, Claude Code, Copilot…). No build, no install; zero required dependencies (optional extras below).
2. The client auto-discovers the verify-before-code skill and the agentseed MCP server from plugin.json + mcp.json.
3. That's it. The skill now gates every coding task: contract → implement → verify → evidence.

Run it standalone for a self-check:

python3 server/guard_engine.py              # self-check: demo verify_code + scan_hallucination
python3 -m unittest discover -s server      # 90+ unit tests (also: `pytest` in CI)

Gate a human PR with the same rules (CI mode):

python3 server/guard_cli.py gate --root .    # composite hard gate: conformance
                                             # + symbols + baseline scan, exit 1 on any failure
python3 server/guard_cli.py check . --ci     # plugin conformance only, exit 1 on errors
python3 server/guard_cli.py scan src/ --strict   # hallucination scan, blocking severities only

Windows note: mcp.json launches the server via python3. On many Windows installs that alias is a Microsoft Store stub; if the server fails to start, change command to ["python", "server/guard_server.py"] or point it at your interpreter's absolute path.

Optional dependencies

AgentSeed runs on the Python standard library alone. Installing the extras upgrades two tools to industry-standard engines (auto-detected, graceful fallback either way):

pip install -r server/requirements.txt

Use an absolute path to guard_server.py; the server resolves everything else from its own location, so no special cwd is required.

Compatibility & graceful degradation

AgentSeed adapts to whatever the host supports, degrading one level at a time — never silently skipping verification:

The skill itself carries the degradation path: when the MCP tools are absent, it instructs the agent to run guard_cli.py verify/scan through the shell and apply the same blocking rules to exit codes.

Platform support

* honest states: the formats are spec-compatible and expected to work, but we have not run AgentSeed in these clients ourselves. Verified = actually exercised by the maintainers. If you verify one, open a PR updating this table.

Clients honoring the full spec also set ${PLUGIN_DATA}; AgentSeed reads agentseed.config.json from there.

Configuration reference (agentseed.config.json)

Unknown keys are warned about on stderr — a typo'd key is never silently ignored.

Language coverage (honest scope)

⚠️ Security note: sandbox_run executes real processes with your user's permissions. Clients must gate it behind user approval; set sandbox_allowed_prefixes in shared/CI environments. When an allowlist is configured, commands resolve through PATH to their absolute path before execution — a hostile working directory cannot shadow an allowlisted basename with a planted executable, and unmatched/unresolvable commands are refused (exit -10) without running.

Client setup — exact configuration

AgentSeed has two halves; both are needed for the full gate:

1. Skill (skills/verify-before-code/) — teaches the agent the workflow.
2. MCP server (server/guard_server.py) — provides the 6 tools.

The installers wire step 1 and print step 2 for your client. Manual setup:

Claude Code

# skill: copy it flat so SKILL.md sits directly in the folder
cp -R skills/verify-before-code ~/.claude/skills/verify-before-code
# MCP server:
claude mcp add agentseed -- python /path/to/AgentSeed/server/guard_server.py

opencode — copy skills/verify-before-code/ to ~/.config/opencode/skill/verify-before-code, then add to opencode.json:

{
  "mcp": {
    "agentseed": {
      "type": "local",
      "command": ["python", "/path/to/AgentSeed/server/guard_server.py"],
      "enabled": true
    }
  }
}

Cursor / other MCP clients — register a stdio server with command: python, args: ["/path/to/AgentSeed/server/guard_server.py"], and copy the skill folder per your client's skills location.

Use an absolute path to guard_server.py; the server resolves everything else from its own location, so no special cwd is required.

Changelog

See CHANGELOG.md.

Built-in guardrail library (EN / 中文 / 日本語)

How the gate works

1. Before coding — load the SDD contract, state it in one sentence.
2. Implement — real code only: no placeholders, no invented APIs.
3. Before "done" — call verify_code + scan_hallucination; prove runtime claims with sandbox_run; validate structure with schema_validate.
4. Language audit — completion reports attach evidence; overclaim vocabulary is banned.
5. Only when all checks pass may the task be marked complete.

The enforced norms (how the AI is constrained)

The skill does not just suggest behavior — each norm maps to a gate that observes compliance:

These synthesize what strong agent operators converged on publicly — the AGENTS.md open standard, Anthropic's Claude Code best practices, and community disciplines like FerroxLabs/agents-md (senior- engineer stance, anti-sycophancy, forced verification loops). The difference: there they are prose; here every norm has an enforcing tool or exit code. Full table with rationale: skills/verify-before-code/references/DEFAULT-NORMS.md.

Works alongside your agent config files

AgentSeed complements — not replaces — the context files your team already maintains for AI coding agents (CLAUDE.md, AGENTS.md, .cursor/rules/, .github/copilot-instructions.md, CONTRIBUTING.md conventions):

• Those files carry project facts: stack, commands, layout, style. They are prose — persuasive but soft.
• AgentSeed carries the behavior contract and the enforcement: hallucination detection, verification gates, evidence trails — hard MCP tools plus CI exit codes that cannot be quietly deprioritized.
• Keep one source of truth per concern: point your AGENTS.md at this skill's norms instead of copying them; the plugin updates and the norm stays binding.

Why AgentSeed vs. alternatives

Roadmap

• Hybrid Skill + MCP guardrail, 6 tools — first strict 1.0.0 linter
• Prompt pool + pattern library + grouped signals + vendor techniques
• verify_code for TypeScript / JavaScript (zero-dependency lexical pass)
• verify_code for Go
• Grammar-constrained decoding for structured outputs
• Optional remote fact-checker (HHEM-style) MCP server

FAQ

Does it need a specific LLM? No — it's client-agnostic and model-agnostic. The gate is enforced by the skill + MCP server, not by any model.

Zero dependencies? Yes. The entire MCP server is pure Python standard library.

Conformant? check_plugin validates the plugin against the spec (§5/§6/§7) — and AgentSeed passes its own linter (ok: true).

Contributing

Issues, PRs and ideas welcome. See the roadmap for directions — or open an issue for a hallucination pattern we haven't catalogued yet.

License

Apache-2.0 © AgentSeed. See LICENSE.