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    TPipe - LLM Pipeline Framework for AI Agents

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    TPipe is the Agent Operating Environment for engineering robust, deterministic AI systems. Reasoning Pipes, ContextBank, and headless orchestration.

    Overview

    TPipe is an Agent Operating Substrate - infrastructure that autonomous AI agents run inside, rather than a library they call. It's built for agents that operate for days, not minutes, and it addresses the two failure modes that most agent frameworks leave to the developer: memory loss across long sessions and silent failure when something goes wrong mid-task. Core capabilities:
    - Reasoning Pipes - structured JSON control over token prediction that forces any LLM to think in a defined pattern (Chain-of-Draft, Role Play, and 6 other modes), independent of whether the model has native reasoning. Reported 75% token reduction and 78% latency decrease with Chain-of-Draft.
    - ContextBank - thread-safe, persistent memory across distributed runs, with weighted lorebook-style retrieval, substring-triggered activation, and token-budget-aware selection. Designed to survive 120+ turn conversations without degradation.
    - Pipelines - sequential orchestration with declarative pause, resume, and jump points, plus 18 named hooks (DITL) across the pipe/pipeline lifecycle for validation, transformation, and human-in-the-loop review.
    - Manifold, Junction, and DistributionGrid - three distinct multi-agent patterns: manager-worker state machines, democratic voting/handoff between agents, and peer-to-peer cluster orchestration (8,773 LOC of distributed infrastructure).
    - PCP (Pipe Context Protocol) - secure, multi-language function/tool calling across Stdio, HTTP, Python, Kotlin, and JavaScript transports, with per-language access controls.
    - KillSwitch and loop limits - forced, non-catchable termination on token overrun or runaway loops, so failures stop cleanly instead of degrading silently. Deployment: TPipe is JVM-native (Kotlin, JVM 24) and headless-first. It also ships as a GraalVM Native Image - a ~50MB binary with no JVM at runtime, sub-128MB memory footprint, millisecond startup, and support for Linux, macOS, Windows, and ARM. It works with any LLM reachable through a standard transport executor, including AWS Bedrock, Ollama, and OpenRouter.
    Proof points from production use: Autogenesis, a headless game-master agent, runs 25 rounds and 120+ turns of inference without degradation. TPipeWriter maintains coherence across a 300-page manuscript through multi-stage refinement. TStep is a multi-agent debugger that tests, captures crashes, and steps through code via DAP/ADB. TPipe is a persistent substrate with topdown token governance and cross-session memory built in from the start.

    Highlights

    • Reasoning Pipes force structured, auditable thinking in any LLM via Chain-of-Draft and 7 other modes - <br>a reported 75% token reduction, independent of the model's native reasoning ability.
    • ContextBank gives agents persistent, thread-safe memory across distributed runs, with weighted retrieval<br>and token-budget-aware selection - built for 120+ turn tasks without context loss.
    • Three multi-agent patterns (Manifold, Junction, DistributionGrid) plus a GraalVM native binary - 50MB,<br>no JVM at runtime, millisecond startup, sub-128MB footprint.

    Details

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    Pricing

    TPipe - LLM Pipeline Framework for AI Agents

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    Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    12-month contract (1)

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    Dimension
    Description
    Cost/12 months
    Base Enterprise
    A fee for access to the platform and its core capabilities.
    $35,000.00

    Additional usage costs (1)

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    The following dimensions are not included in the contract terms, which will be charged based on your usage.

    Dimension
    Description
    Cost/unit
    usage_based
    Pricing based on the customer's actual usage of the platform.
    $0.01

    AI Insights

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    Dimensions summary

    This listing bills through a contract with two dimensions. Base Enterprise is a fixed fee for access to the platform and its core capabilities. Usage-based charges scale with your actual use of the platform. You combine both: the base fee covers access, while usage-based charges track activity on top of that. Pricing applies per organization rather than per user or per API call. Your separate LLM inference costs are billed by your model provider, not through this listing.

    Top-of-mind questions for buyers

    The Base Enterprise fee gives your organization access to the platform and its core capabilities. Usage-based charges track your actual activity on the platform and add to the base fee. Both appear on the same invoice. Your LLM inference costs are billed separately by your model provider, not through this listing.
    No. Charges apply per organization, not per user or per API call. The Base Enterprise fee is a flat per-organization access fee. Usage-based charges track platform activity. Your separate LLM inference costs are billed by your model provider based on your own consumption.
    The Base Enterprise fee applies regardless of activity, since it covers access to the platform. Usage-based charges scale with your actual use, so low activity means lower usage charges. The two combine on one invoice. Contact the vendor for the specific metric that meters usage.
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