Seven Frameworks.
One AI Control Plane.
Framework Ecosystem
Seven Frameworks.One AI Control Plane.
CARDIAC-PURR AI Control Plane integrates with seven agent frameworks and routes each request through the same capability-aware model qualification and tier selection used across the platform. Agent workloads route through the same governed, capability-aware layer as every other request — no separate integration tier, no separate rules. This page covers the architecture and integration paths — for the full AI Control Plane provider benchmark, see Benchmarks.
LangChain
Routed through the standard CARDIAC-PURR AI Control Plane model interface.
LlamaIndex
Integrated via its ReAct and FunctionTool agent interfaces — the most specifically-documented integration path of the seven.
AutoGen
Multi-agent conversations routed per request through the same capability-aware layer.
CrewAI
Crew and task-based agent workflows route through the control plane per step.
Haystack
Pipeline-based agent components route through the same model qualification layer.
DSPy
Programmatic prompting pipelines route through CARDIAC-PURR AI Control Plane's tier selection.
Semantic Kernel
Plugin and planner-based agent execution routes through the control plane.
Compatible integration path
An OpenAI-compatible endpoint is available for applicable frameworks — point your existing client at the CARDIAC-PURR AI Control Plane base URL without a framework-specific adapter.
Architecture
The AgenticControl Plane.
Routing
Automatic.Or Explicit.
Live tier-resolution trace
A current v114 validation call using explicit LARGE-tier routing:
Reflects current production configuration (build cardiac-purr:v114). This is a capability trace, not a benchmark result, and is separate from the dated benchmark evidence below.
What "auto" does today
model="auto" evaluates the request and selects a tier without manual specification. In the published agentic benchmark below, this resulted in every one of the 350 factual-QA requests routing to the small tier — consistent with a workload that didn't require medium- or large-tier capability.
Recorded Benchmark Evidence
One Dated Run.Clearly Scoped.
What this benchmark does & doesn’t show
- 01These figures apply only to the published 350-request factual-QA workload — they are not a general accuracy or savings claim.
- 02Every request in this benchmark routed to the small tier; medium and large tiers were not exercised by this run.
- 03Caching affected the workload. This benchmark reflects a cached-influenced result, not a purely cold-inference measurement.
- 04Results are reported as an aggregate across all 7 frameworks combined — no individual per-framework breakdown is published from this run.
Evidence Boundaries
What This PageDoes Not Claim.
Structured-output, reasoning, context-window, and vision awareness are tracked as capability metadata dimensions within the routing layer. No benchmark results are currently published for these specific dimensions — only the factual-QA workload above has been benchmarked.
Streaming is supported in current integration code. No streaming-specific benchmark results are published on this page.
No per-provider capability matrix with scored values is published. The architecture diagram above reflects qualification stages that exist in the routing layer, not a comparative scorecard.
No individual framework (LangChain, CrewAI, etc.) has a published standalone accuracy or savings figure — only the 7-framework aggregate from the August 12, 2026 benchmark.
FAQ
CommonQuestions.
Which agent frameworks does CARDIAC-PURR AI Control Plane integrate with?
Seven frameworks: LangChain, LlamaIndex, AutoGen, CrewAI, Haystack, DSPy, and Semantic Kernel. Integration mechanics differ by framework — LlamaIndex, for example, is integrated via its ReAct and FunctionTool agent interfaces. An OpenAI-compatible endpoint is also available for applicable frameworks.
Does the router pick the model automatically, or can I choose?
Both. model="auto" lets the router select a tier automatically. You can also force an explicit tier with model="small", model="medium", or model="large", or override per-call with force_tier in router.query().
What does the published agentic benchmark actually measure?
A 350-request factual-QA workload (50 requests across each of the 7 frameworks), run August 12, 2026, entirely on the small tier via Claude Haiku 4.5, with caching present. It does not measure medium- or large-tier behaviour, reasoning-heavy tasks, or tool-use-heavy workloads.
Is this the same benchmark as the AI Control Plane benchmarks page?
No. The AI Control Plane benchmark measures routing accuracy and cost savings across 9 providers on general enterprise queries, run July 12, 2026. The agentic benchmark on this page is a separate, framework-specific study run on a different date against a different workload.
What's the difference between the benchmark model and the current platform's large-tier model?
The August 12, 2026 benchmark used claude-haiku-4-5-20251001 at the small tier. The current production platform's large tier routes to claude-opus-4-8. These are separate and should not be compared as if they were the same test.
Evaluate Agentic Routing on Your Own Workflows.
Technical documentation and a scoped evaluation plan are provided at the start of every engagement.