Managing AI Agents Across Multiple Frameworks
One control plane for CrewAI, LangChain, OpenAI, and custom agents. Unified monitoring, governance, and cost tracking.
What you will learn
- Design a unified management layer for agents from different frameworks
- Standardize monitoring and cost tracking across CrewAI, LangChain, and custom agents
- Apply consistent governance policies regardless of agent framework
- Migrate from single-framework to multi-framework architecture
The Multi-Framework Reality
Most organizations do not choose one AI agent framework and stick with it. The ML team built agents in LangChain. The DevOps team uses CrewAI for infrastructure automation. The product team has custom Python agents. And the CEO just approved a pilot with OpenAI Assistants.
This is normal. Different frameworks excel at different tasks. The problem is not having multiple frameworks — it is managing them without a unified layer.
4 frameworks, 4 dashboards, 4 cost tracking systems, 4 approval workflows. Nobody knows the total AI spend. An audit requires collecting data from every system manually.
4 frameworks, 1 dashboard. All costs tracked in one place. One set of governance policies. One audit trail. Agents from any framework appear in the same fleet view.
The Control Plane Approach
A control plane sits above individual frameworks. It does not replace CrewAI or LangChain — it connects them. Every LLM call from every framework routes through the same gateway. Every agent, regardless of origin, appears in the same fleet dashboard.
- CrewAI agents → Gateway → tracked, governed, audited
- LangChain agents → Gateway → tracked, governed, audited
- OpenAI Assistants → Gateway → tracked, governed, audited
- Custom REST agents → Gateway → tracked, governed, audited
Unified Connection Patterns
- Gateway (LLM proxy) — route LLM calls from any SDK through one endpoint. Standard OpenAI format.
- A2A Protocol — agent-to-agent communication for cross-framework collaboration
- MCP — tool access for agents that need to call external services
- REST/Webhooks — for custom agents and CI/CD integration
Dobby auto-discovers agents on their first Gateway call. A CrewAI agent, a LangChain chain, and a custom Python script all appear in the same Agent Fleet dashboard automatically — with their own cost tracking, health status, and activity timeline.
Consistent Governance Across Frameworks
The biggest risk of multi-framework is inconsistent governance. If your CrewAI agents have approval gates but your LangChain agents do not, you have a governance gap. A control plane enforces the same policies regardless of framework.
# Same governance policies apply to ALL agents
# regardless of framework origin
# Per-org policy (applies to all frameworks)
{
"max_tokens_per_request": 4096,
"allowed_models": ["gpt-4o", "claude-sonnet-4-20250514", "gemini-2.5-flash"],
"require_approval_above_cost": 5.00,
"data_residency": "EU",
"dlp_enabled": true,
"kill_switch_available": true
}Migration Path
Start with one framework. Connect your largest agent fleet (e.g., CrewAI) to the Gateway. Get comfortable with the dashboard, cost tracking, and approval workflows.
Add the second framework. Connect your LangChain agents. Both frameworks now appear in the same fleet view with unified cost tracking.
Standardize policies. Apply the same governance rules across both frameworks. Set organization-wide budgets and approval gates.
Scale. Add custom agents, OpenAI Assistants, or any new framework. They plug in through the same Gateway — no new infrastructure needed.
The migration is incremental. You do not need to move everything at once. Start with Gateway routing for cost visibility, then add governance policies as you scale.