Shared context, model-agnostic routing, and human oversight at every critical decision — taking multi-agent systems from prototype to production in weeks, not quarters.
Every interaction orchestrated. Every decision auditable. Every outcome measured.
Scroll to explore the full architecture.
Human Agents
Slack / Teams / Custom Apps
Custom Agents
LeafMesh Native / Python / Node.js
System Agents
SAP / Oracle / Workday / CRM
External Agents
LangGraph / CrewAI / Agentforce
Orchestrated mesh routing every interaction
Enterprise Processes
Workflow / Approvals / Escalations
Business Automation
Rules Engine / Event-Driven / Scheduled
Enterprise Data
Analytics / Audit Trail / Reporting
Orchestrated mesh routing
Agent Ops
Not months. Not quarters.
Your agent topology lives in a YAML config file — version-controlled, diffable, auditable. No drag-drop UI, no boilerplate. Change a rule, commit, deploy.
No rip-and-replace.
The fabric sits on top of what you already have. OpenAI, Anthropic, custom agents, legacy systems — all coordinated through one runtime. Switch providers with one line in the YAML.
Built in, not bolted on.
Human oversight at critical decisions. Policy enforcement. Full audit trails. Override rates tracked. When the board asks 'what happened?' — you have the answer in the logs.
Not just POCs.
Orchestration patterns, escalation rules, and governance policies validated across live deployments in AdTech, Manufacturing, Logistics, Healthcare, and Financial Services.
Config file to orchestrated agent mesh — no infra wrangling, no long rollouts.
Define agents, tools, rules, and guardrails in one config file. No boilerplate, no lock-in.
LeafMesh validates, wires the mesh, enables observability, and deploys — seconds, not sprints.
Every interaction routed through the control plane — validated, logged, auto-healed on failure.
Your entire agent topology lives in a YAML config file. Every change tracked in git. Your team extends behavior in Python. No black boxes.
name: "customer_service_swarm"
version: "1.0.0"
architecture: "managed_mesh"
manager:
enabled: true
model: "gpt-4o"
coordination_rules:
escalation_threshold: 2
max_response_time: 30
summarizer:
enabled: true
model: "gpt-4o-mini"
domain: "customer_service"
agents:
conversation_agent:
name: "conversation_agent"
model: "gpt-4o"
prompt: |
You are a friendly customer service agent.
Help customers with their inquiries.
tools:
- "current_time"
- "web_request"
- "json_formatter"
yields:
response: "string"
confidence: "number"
needs_escalation: "boolean"
can_call:
- agent: "technical_agent"
condition: "needs_technical_help == true"
- agent: "supervisor_agent"
condition: "needs_escalation == true"
technical_agent:
name: "technical_agent"
model: "gpt-4o"
prompt: "You are a technical support specialist."
tools: ["current_time", "web_request"]
yields:
solution: "string"
complexity: "string"The details that matter when your board asks "is this safe to run in production?"
Every agent interaction flows through one orchestrated runtime. LeafMesh routes, enforces policies, observes, intervenes, and self-heals. No agent-to-agent communication without oversight.
Six validation layers before any downstream action. Four are fully deterministic — yield parsing, condition evaluation, schema enforcement, and type-safe contracts.
Deterministic context assembly before any LLM call. Three processors ensure clean, structured input. Prevents prompt injection, manages context windows, anchors ground truth.
When an agent fails, the fabric responds automatically: restart, reroute, scale, quarantine, rollback. Your team wakes up to resolved incidents, not outage alerts.
Each request is routed to the optimal model — balancing cost, latency, and capability across eight dimensions. Automatic fallback. No wasted spend on overqualified models.
YAML routing conditions evaluated through Python's AST module — never eval(). Whitelist of allowed operations: comparisons, boolean logic, arithmetic. Code injection through YAML configuration is architecturally impossible.
Switch providers with a single YAML config change. Bring your own agents, models, and systems.
Start with the documentation or a half-day workshop. Either way, you'll know in weeks — not months.
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