flowise_agentmesh 3.3.0

AgentMesh governance nodes for Flowise — policy enforcement, trust gating, audit logging, and rate limiting for visual AI flows

flowise-agentmesh

AgentMesh governance nodes for Flowise — policy enforcement, trust gating, audit logging, and rate limiting for visual AI flows.

Deterministic, LLM-independent governance that plugs into any Flowise chatflow or agentflow.

What It Does

This package provides four governance nodes designed to sit inline within Flowise flows:

Node	Purpose
GovernanceNode	Evaluate tool calls against YAML policy (allowlist/blocklist, content patterns, argument scanning)
TrustGateNode	Route agents to trust tiers (trusted / review / blocked) based on score thresholds
AuditNode	Log all actions to a hash chain audit trail with SHA-256 tamper evidence
RateLimiterNode	Token bucket rate limiting per agent and per action

All nodes are zero-dependency on LLMs, fully deterministic, and composable.

Installation

pip install flowise-agentmesh

Or install from source:

git clone https://github.com/microsoft/agent-governance-toolkit.git
cd agent-governance-python/agentmesh-integrations/flowise-agentmesh
pip install -e ".[dev]"

Quick Start

1. Define a Governance Policy (YAML)

# policy.yaml
allowed_tools:
  - search_*
  - read_file
blocked_tools:
  - rm_*
  - delete_*
blocked_content_patterns:
  - "DROP\\s+TABLE"
  - "rm\\s+-rf"
blocked_argument_patterns:
  - "\\.\\./"
  - "/etc/passwd"
default_action: deny

2. Use in Python (Custom Flowise Node)

from flowise_agentmesh import GovernanceNode, TrustGateNode, AuditNode, RateLimiterNode

# Governance check
gov = GovernanceNode(policy_path="policy.yaml")
result = gov.run({"tool": "search_web", "content": "latest news"})
# result["allowed"] == True

# Trust gate
gate = TrustGateNode(min_trust_score=0.7, review_threshold=0.4)
result = gate.run({"agent_id": "agent-1", "trust_score": 0.85})
# result["tier"] == "trusted"

# Audit logging
audit = AuditNode(storage="file", file_path="audit.jsonl", export_format="jsonl")
result = audit.run({"action": "search", "query": "hello"})
# result["chain_valid"] == True

# Rate limiting
limiter = RateLimiterNode(max_requests=10, window_seconds=60)
result = limiter.run({"agent_id": "agent-1", "action": "search"})
# result["allowed"] == True

Flowise Integration

Where These Nodes Fit in a Flow

┌─────────────┐    ┌─────────────────┐    ┌──────────────┐    ┌──────────┐
│  User Input  │───▶│  RateLimiterNode │───▶│ GovernanceNode│───▶│ LLM/Agent│
└─────────────┘    └─────────────────┘    └──────────────┘    └──────────┘
                                                │                    │
                                                ▼                    ▼
                                          ┌───────────┐      ┌───────────┐
                                          │ Block Flow │      │ AuditNode │
                                          └───────────┘      └───────────┘
                                                              │
                                                              ▼
                                                        ┌──────────────┐
                                                        │ TrustGateNode │
                                                        └──────────────┘
                                                         ╱      │      ╲
                                                        ▼       ▼       ▼
                                                    Trusted  Review  Blocked

Using as a Flowise Custom Node

Each node implements a run(input_data: dict) -> dict interface compatible with Flowise's custom node pattern:

Input: A dictionary from the previous node in the flow Output: A dictionary with the governance decision plus output key for the next node

Example run() return for GovernanceNode:

{
  "allowed": true,
  "reason": null,
  "tool": "search_web",
  "output": { "tool": "search_web", "content": "latest news" }
}

When blocked, output is null and reason explains why.

Example: Governance Pipeline

from flowise_agentmesh import GovernanceNode, TrustGateNode, AuditNode, RateLimiterNode

# Set up pipeline
limiter = RateLimiterNode(max_requests=100, window_seconds=60)
gov = GovernanceNode(policy_path="policy.yaml")
audit = AuditNode(storage="memory")
gate = TrustGateNode(min_trust_score=0.7)

def governance_pipeline(input_data: dict) -> dict:
    # Step 1: Rate limit
    rate_result = limiter.run(input_data)
    if not rate_result["allowed"]:
        return rate_result

    # Step 2: Policy check
    gov_result = gov.run(input_data)
    if not gov_result["allowed"]:
        audit.run({"decision": "blocked", **gov_result})
        return gov_result

    # Step 3: Audit log
    audit.run(input_data)

    # Step 4: Trust gate
    trust_result = gate.run(input_data)
    return trust_result

Components

GovernanceNode

Parameter	Type	Default	Description
policy	Policy | str | dict	None	Policy object, YAML string, or dict
policy_path	str	None	Path to YAML policy file
strict_mode	bool	True	Require at least one check input
log_level	str	"INFO"	Logging level

TrustGateNode

Parameter	Type	Default	Description
min_trust_score	float	0.7	Minimum score for "trusted" tier
review_threshold	float	0.4	Minimum score for "review" tier

AuditNode

Parameter	Type	Default	Description
storage	str	"memory"	Storage backend: memory or file
file_path	str	None	File path (required when storage="file")
export_format	str	"json"	Export format: json or jsonl

RateLimiterNode

Parameter	Type	Default	Description
max_requests	int	10	Maximum requests per window
window_seconds	float	60.0	Time window in seconds

Development

pip install -e ".[dev]"
pytest tests/ -v

License

MIT