Managing AI Agents Across Multiple Frameworks (2026)
One control plane for CrewAI, LangChain, ADK, OpenAI, and custom agents. Unified monitoring, governance, and cost tracking across any framework.
What you will learn
- Design a unified management layer for agents from different frameworks
- Standardize monitoring and cost tracking across CrewAI, LangChain, ADK, and custom agents
- Apply consistent governance policies regardless of agent framework
- Migrate from single-framework to multi-framework architecture
- Avoid the 4 common failure modes of multi-framework rollouts
TL;DR — Multi-framework is the norm, not the exception. The goal is not to pick one framework — it is to make any framework behave consistently under one governance layer. One gateway, one fleet view, one audit trail.
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
- ADK agents → Gateway / MCP / A2A → 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, ADK, or any new framework. They plug in through the same Gateway — no new infrastructure needed.
4 Failure Modes to Avoid
- The big-bang migration — moving everything at once is risky. Go one framework at a time.
- Framework envy — chasing the newest framework instead of consolidating on what works. The control plane makes this cheap.
- Policy drift — different policies per framework defeats the point. Enforce at the org level.
- Naming chaos — Agent A in CrewAI and Agent A in LangChain are two different things. Use globally unique agent IDs from day one.
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.
Frequently Asked Questions
Can I run two versions of the same framework side by side?
Yes. Tag agents with framework_version metadata at registration and the fleet view filters by version. This makes blue-green framework upgrades straightforward.
Which framework should we standardize on?
Wrong question. Standardize on the control plane and let teams pick the framework that fits the task. Most mature fleets use 2-3 frameworks deliberately — the governance layer makes the heterogeneity harmless.
Does the Gateway add enough value if we only use one framework today?
Yes — even with one framework, you still get cost tracking, policy enforcement, kill-switch, and audit trails. The multi-framework story is a bonus when you inevitably add a second framework later.