TPipe offers an infrastructure layer for long-running AI agents, providing structured JSON “Reasoning Pipes” that enforce token‑level control and persistent ContextBank memory across distributed executions. The platform supports pipeline orchestration with pause, resume, and jump capabilities, enabling peer‑to‑peer agent coordination without central dispatchers, and includes SDKs for integrating with services like Amazon Bedrock.
Funding
Funding not disclosed
Founders
Product
Problem
Developers of autonomous AI agents often struggle to run long‑running tasks reliably because existing frameworks provide only short‑lived, stateless pipelines, limited memory persistence, and no built‑in token budgeting or distributed coordination. This leads to memory loss, cost overruns, and frequent failures when agents need to operate over days or handle complex multi‑agent workflows.
Solution
TPipe delivers an Agent Operating Substrate that turns large language models into deterministic, production‑grade agents. It introduces structured JSON Reasoning Pipes that force any LLM to follow explicit reasoning steps, while a thread‑safe ContextBank supplies persistent, weighted memory that survives distributed runs. Pipelines orchestrate sequences of pipes with pause, resume, and jump controls, enabling developer‑in‑the‑loop validation. A built‑in P2P discovery layer (Pipe‑to‑Pipe) removes central dispatcher bottlenecks, allowing scalable multi‑agent coordination. TokenBudget settings and a KillSwitch enforce cost and safety limits, making long‑horizon tasks such as code analysis, research, debugging, or manuscript generation reliable and auditable.
Target Audience
Primary customers are AI developers and engineering teams building autonomous, long‑running agents—such as enterprise R&D groups, SaaS platforms, and research labs—that require reliable orchestration, persistent memory, and cost‑controlled token usage.
Features
- Reasoning Pipes: eight built‑in reasoning modes (e.g., Chain‑of‑Draft, Role‑Play) that structure LLM output via JSON control, bypassing native prompt heuristics.
- ContextBank: persistent, global memory with mutex‑protected writes, weighted lorebook injection, substring‑triggered activation, and token‑budget‑aware retrieval.
- Pipelines: declarative sequential orchestration with pause, resume, and jump points for fine‑grained workflow control and human validation.
- Manifold & Junction: stateful multi‑agent hierarchy and consensus voting mechanisms for complex collaborative tasks.
- PCP (Polyglot Call Protocol): secure multi‑language function calling across Stdio, HTTP, Python, Kotlin, and JavaScript executors.
- DistributionGrid & P2P (Pipe‑to‑Pipe): 8,773 lines of distributed infrastructure for node routing, discovery, and secure cross‑pipe calls without a central dispatcher.
- Token Governance: per‑model token counting, truncation, and configurable budgets; KillSwitch propagates termination across all containers.
- Integrated tooling: TStep debugger, Autogenesis headless game master, and TPipeWriter for long‑form manuscript orchestration.