acorn – LLM framework for long running agents
Yet another LLM orchestration layer over LiteLLM + Pydantic when DSPy and LangChain dominate.
Interbase is an open-source agent CLI for work on your computer and everywhere else
Model abstraction across 4,800+ models but agent CLI space is already crowded.
Developers using AI agents for computer tasks
Continue · Cursor · Aider
I've been working on an open-source CLI agent called Interbase:
https://github.com/agentsorchestrationcompany/interbase
Two ideas motivated a lot of the project.
The first is that long-running agent workflows shouldn't be restricted to a small number of frontier models.
Many recent agent products are beginning to support persistent tasks, background work, and goal-oriented workflows. I think those capabilities are useful abstractions independent of the underlying model.
Interbase includes a `/goal` command that allows work to be organized around long-running objectives and supports more than 135 providers and 4,800+ models. The goal is to let users choose the model that works best for them rather than forcing a specific provider because a particular workflow feature only exists there.
The second idea is that AI workflows should be reusable in the same way shell workflows are.
Interbase includes `/aliases`, which allows users to create shortcuts for workflows they run frequently. For example, a user might create aliases such as:
`gcm` → preferred git commit workflow
`review` → code review workflow
`ship` → release readiness workflow
After a while these become muscle memory in much the same way traditional shell aliases do.
The project also includes encrypted remote access, and one of the next areas I'm exploring is computer use capabilities that can work across a broad range of models rather than a handful of specialized offerings.
I'm curious whether others think long-running goals and reusable workflows should live above the model layer, or whether they belong as model-specific capabilities.
Happy to answer questions about the implementation or design decisions.
Yet another LLM orchestration layer over LiteLLM + Pydantic when DSPy and LangChain dominate.
Reference docs for AI agents so they stop hallucinating OpenRouter code, but it's a structured prompt.
Active memory extraction with GRPO beats passive transcription on LOCOMO benchmarks.
Models projects as interconnected realities, not just task lists — fresh take on PM tools.
MemOS turns long-running conversations into reusable, load-on-demand 'Skills' that agents can call during task execution — a clear attempt to move beyond one-shot context into durable task logic. It’s an interesting engineering abstraction and nice to see an API-first demo, but the landing material glosses over crucial details like skill validation, deduplication, and safeguards against propagating bad agent behavior; show me metrics or human-in-the-loop tooling and this gets a lot more compelling.
95% cost savings via dual-model optimization, but unproven and only 11 installs.