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🛡️ AgentSeed

Anti-hallucination guardrails for AI coding agents.

A hybrid Agent Plugins plugin (Skill + MCP Server) that forces spec-driven development and verifies code before it is marked done — so "Done, all tests pass" becomes an observed fact, not a claim.

English · 中文 · 日本語

⭐ Like this project? Consider giving it a star — it helps developers find guardrails before they ship hallucinated code.

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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:

Gap in Agent Plugins 1.0.0	What AgentSeed does
No enforcement mechanism (skills are optional to follow)	verify-before-code skill makes verification non-skippable
No official conformance linter	check_plugin is the first strict 1.0.0 linter

What it does

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

Tool	Catches	Technique
verify_code	Invented APIs / undefined symbols	Python AST + TS/JS lexical pass
scan_hallucination	Placeholder code, overclaims, fabricated content	28+ signals in 3 groups
check_plugin	Non-conformant plugin packaging	Strict 1.0.0 linter
sandbox_run	"Tests pass" without running anything	Deterministic execution channel
schema_validate	Invalid structured output	JSON Schema validation
record_verification	No persistent evidence trail	Appends a JSONL audit entry under PLUGIN_DATA

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

Extra	Upgrades	Without it
jsonschema	schema_validate → full Draft 2020-12 validation	built-in subset validator
pyflakes	verify_code → pyflakes F821 undefined-name analysis	built-in AST walk
pyyaml	SKILL.md frontmatter parsing → full YAML	built-in lite parser

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:

Host capability	What you get	Setup
Full Agent Plugins	drop-in: skill + MCP auto-discovered, ${PLUGIN_DATA} config honored	copy the plugin directory
MCP-capable client	all 6 tools via registration	exact snippets above
Skills-only client	skill workflow; verification degrades to guard_cli.py via shell (the skill contains the fallback instructions)	copy skills/verify-before-code flat
Plain terminal / CI / no agent at all	CLI gates with exit codes	python server/guard_cli.py check . --ci

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

Client	Agent Plugins 1.0.0	Status	Notes
Claude Code	skills + MCP config	verified	skills via ~/.claude/skills, server via claude mcp add
opencode	skills + MCP config	verified	~/.config/opencode/opencode.json — exact snippet below
Cursor	skills + mcp.json	untested*	copy into project; no stable plugin dir yet
VS Code (+Copilot)	MCP support rolling out	untested*	use mcp.json fields as-is
Cline / Windsurf	MCP config compatible	untested*	stdio server entry maps directly

* 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)

Key	Type	Effect
allowlist	string[]	scan exclusions (replaces built-in test-idiom list)
severities	{group: error|warning|info}	per-group severity override
timeout	int	default sandbox_run timeout, seconds (clamped 1–120)
extra_tokens	{group: string[]}	extend the hallucination word pool at runtime
suppress_symbols	string[]	names verify_code never flags (reported in suppressed)
sandbox_allowed_prefixes	string[]	allowlist of executables sandbox_run may launch (absent = unrestricted). Entries without a path separator match the command's PATH-resolved basename (python also accepts python.exe); entries WITH a separator must equal or be a directory-prefix of the resolved absolute path (separator boundary enforced)
sandbox_env	"inherit" | "scrub"	child environment policy: scrub drops credential-looking variable names (TOKEN/SECRET/PASSWORD/API_KEY/…) before spawn — best-effort denylist, not a security boundary

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

Language coverage (honest scope)

Language	verify_code analysis
Python	full AST scope walk (+ pyflakes when installed), line numbers
TypeScript / JavaScript	lexical regex pass (documented false-positive classes)
Go / Java / Rust / C/C++ / others	not analyzed yet — returns an empty result

⚠️ 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)

Resource	Contents
PROMPT-POOL	20+ copy-paste guardrail prompts: completion evidence, verify-before-claim, uncertainty, API verification, citation rules…
HALLUCINATION-PATTERNS	Failure-mode catalog: 5-class code taxonomy + SoK findings + real legal/chat cases
VERIFICATION-CHECKLIST	Executable end-of-task checklist: risk class → contract → evidence → language audit
SDD-CONTRACT	The contract every coding task must satisfy
VENDOR-SOLUTIONS	Adoption map of vendor techniques (Anthropic, OpenAI, AWS, NVIDIA, IBM, Guardrails AI, Vectara)

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:

Norm	Enforced by
Contract before code (goal / interface / non-goals / verification)	Gate 1 of verify-before-code
No invented APIs — never call an undefined symbol	verify_code suspects gate
Real implementations only — no stubs/placeholders/fakes	scan_hallucination stub signals
Verification before completion claims — run it, then say it	Gate 3 + sandbox_run exit codes
Evidence-backed reports — file:line you read, output you saw	Gate 4 audit + record_verification JSONL
Smallest diff, no drive-by refactors; surface ambiguity, ask once	contract non-goals + CI guard_cli gate

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

	Prompt-only guardrail skills (e.g. superpowers)	Static import linters (MCP)	AgentSeed
Touches code	❌ prompt only	✅ import-graph analysis	✅ AST + lexical analysis
Runs verification tools	❌	lint gates	✅ 6 MCP tools incl. sandboxed execution
Hallucination-language scan	❌	❌	✅ stub / oversold / fabricated signals (EN + CJK)
Enforcement	soft (skill text)	CI gate	hard gate: skill + MCP + CLI exit codes
Agent Plugins 1.0.0 conformance linter	❌	❌	✅ first

Roadmap

• [x] Hybrid Skill + MCP guardrail, 6 tools — first strict 1.0.0 linter
• [x] Prompt pool + pattern library + grouped signals + vendor techniques
• [x] 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.

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⭐ If AgentSeed saved you from shipping hallucinated code, star the repo — it's the best signal that guardrails matter.

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