Your agent doesn't need another model. It needs one that finishes the job.
Flow AI is managed inference for agent harnesses — we make runs complete instead of stall, fail, or quietly narrate. Point your harness at us and keep your exact workflow.
Not a router. Not a raw API. The managed layer that sits between your agent and every model — and actually manages it.
Agent-first, literally: add our MCP server and your agent can query live model prices, delegate subtasks to cheaper models, and convene a council of different models to critique a plan — claude mcp add --transport http flow-ai https://api.flowaiapi.com/mcp. And any answer can carry a verdict: send verify: true and get pass/fail + confidence + issues alongside the response.
Run fails at 4.6M tokens — context overflow, 600s timeout, retries forever. No idea why.
Run completes — capped, cached, compacted to fit, with a per-run report of exactly what we did.
Every harness runs the same loop — and it breaks the same ways.
Think → call a tool → get the result → repeat. That loop quietly breaks in ways your provider dashboard never surfaces:
Runs balloon and die
Verbatim tool results pile up until the context overflows — the run hits the window, times out, and retries forever. We've measured single runs at 24.7M tokens.
Models narrate instead of act
The model says “I'll run the command…” and never emits the tool call. You pay for the prose; the work never happens.
Tool calls leak as text
A real tool call comes back as <tool_call>… text and never executes — the loop silently stalls.
You're flying blind
No provider or router tells you your action rate, your tool-call success, or which agent and file are bloating your context.
Four things we manage so your runs finish.
Faithful tool-calling — zero degradation
We preserve the tool-use contract end-to-end and recover tool calls that backends leak as text. Verified: the same model tool-calls identically through us as hitting it directly — a 0-point gap across 150+ head-to-head trials.
Routing that optimizes for completion
Each turn goes to the cheapest model that actually finishes the task. When a model analyzes-but-doesn't-act, we escalate it to one that does — not just the cheapest token.
Managed context — runs stop failing from size
Caching, monster-result caps, overflow protection, and tiered compaction with external memory keep runs inside the window. A too-big run completes (minus re-fetchable detail) instead of failing.
Diagnosis you can act on
Per-run Context Health: tokens used, % of the window, cache hits, what's bloating, and a plain-language reason on every failure — plus how to fix it on your own harness.
Each harness is strong — and shares the same blind spots.
Each handles context differently (or not at all). We fit all four — the connective layer, not a favorite.
Paperclip
orchestrator, no compaction of its own
It delegates to sub-agents and their tool traces flood back into its context, with no native way to compact — so its only lever is to stop the run. We're the only context management Paperclip gets, plus the diagnosis it can't build itself.
Hermes
great internal compaction, tuned to its own model
When a request routes to a different or smaller backend, Hermes compacted for the wrong window. We adapt to the real backend window and add the cross-model visibility Hermes doesn't surface.
OpenClaw
/compact is whole-transcript and coarse
10 file reads become 10 separate result messages. We're surgical — we compress only old tool results, and we dedup and batch parallel reads (the exact thing its community has asked for).
Kilo Code
/condense nukes the whole history
It replaces the entire history with one lossy summary and costs an extra API call. We keep your conversation structure intact, compress only the cold tool results, and need no extra model call — higher fidelity, lower cost.
Three of them compact coarsely or not at all; one nukes the whole history. We do it surgically, adapt to the real backend, and show you exactly what happened.
Price-optimizers stop at price. We optimize for a finished task.
| Direct provider API | Routing service | Flow AI (managed) | |
|---|---|---|---|
| Model choice | the one you call | cheapest/fastest by price | cheapest that completes the task |
| Tool-calling | native | passthrough — can degrade | recovered + verified zero-degradation |
| Model narrates instead of acting | you eat it | you eat it | escalated to a model that acts |
| Context overflow / giant runs | you handle it, or it fails | you handle it, or it fails | managed: caps, caching, tiered compaction, external memory |
| Prompt caching | you wire it per provider | varies | honored + surfaced |
| Visibility | tokens + latency | tokens + price | action rate, tool-call success, context health, what's bloating |
| Fix-it guidance | none | none | per-harness optimization feedback |
See the general breakdown on Flow vs OpenRouter vs direct APIs →
Trade the failure modes for outcomes.
Verified, unchanged stack, nothing silent.
Zero degradation, proven
Same model, same request — head-to-head direct vs through us: a 0-point gap across every model we can measure.
Works with your stack unchanged
OpenAI, Anthropic Messages, and Responses protocols all supported; verified end-to-end with Hermes, OpenClaw, and Kilo Code executing real tool-using tasks through us.
Transparent by default
Every response tells you what we did — model used, context used, cache hits, any compaction. Nothing silent.
Keep your workflow. Lose the failures.
Point your harness at Flow AI and see your fleet's action rate, tool-call success, and context health.