Recursive Language Model (RLM) runtime
Prime Agent treats context as a variable and sub-agent delegation as function calls inside a persistent REPL, so the model can keep working across long sessions without losing access to earlier state.
Prime Agent is an open-source coding harness from Prime Intellect that uses a persistent REPL, sub-agent orchestration, and session recovery to support long-running coding and research workflows. It is aimed at users who need an agent runtime for extended tasks rather than a basic chat interface.
Prime Agent is an open-source coding harness from Prime Intellect built around two abstractions: the Recursive Language Model (RLM) and the Continual Harness. The product is presented as a way to move beyond fixed tool schemas and static prompts by letting the agent operate over its own context, subagents, and session state.
In practical terms, Prime Agent combines a persistent IPython REPL, programmatic tool calling, background session management, and agent-to-agent messaging. Prime Intellect positions it as a runtime for general coding assistance, long-horizon autonomous evaluation, research, and autoresearch, and says it can be installed directly from a shell command.
Prime Agent treats context as a variable and sub-agent delegation as function calls inside a persistent REPL, so the model can keep working across long sessions without losing access to earlier state.
The Continual Harness abstraction lets the agent create, read, update, and delete its own prompts, skills, memory, and subagents as it runs, rather than relying on static scaffolding.
Subagents can be spawned, messaged later, and coordinated across sessions, which supports parallel task execution and agent-to-agent communication.
The harness uses a persistent IPython kernel as its primary tool layer, with standard harness actions exposed as functions inside the kernel.
A background daemon owns live sessions, supports attach/detach without stopping the agent loop, and can recover worker processes from session JSONL and kernel snapshots.
The Agents View exposes running, idle, and inactive sessions and lets users navigate into live chats and nested subagents from the same interface.
Use Prime Agent as a coding assistant when you want the model to keep state across a long development session, refer back to earlier work, and manage sub-tasks without losing context.
Use it for autonomous or semi-autonomous benchmark runs where the agent needs a durable runtime, recoverable sessions, and structured tool execution over many steps.
Use it in research settings where agents need to spawn subagents, exchange messages, and coordinate work across different branches of a session tree.
Use it when you want to fan out analysis into multiple sub-agents in parallel and collect their replies back through the main session.
Use the Agents View and session recovery features to inspect live, idle, and inactive chats, attach and detach from running work, and resume sessions after a worker crash.
Prime Agent is designed around a persistent IPython REPL, programmatic tool calling, and sub-agent orchestration. The source says it can be used as a general coding assistant, a runtime for long-horizon autonomous evaluation, and a collaborator for research and autoresearch.
The source describes Prime Agent as fully open-source and installable with a one-line shell command: `curl -fsSL https://app.primeintellect.ai/prime-agent/install.sh | sh`.
Prime Agent is built to work with modern open and closed frontier models. The source specifically notes that its performance depends on both the harness design and the model trained around it.
The public source does not list a separate pricing page for Prime Agent itself. The Prime Intellect site shows platform offerings such as training, inference, compute, and sandboxes, but the pricing URL currently returns a 404 page.
Prime Agent uses a background daemon, a persistent IPython kernel, JSONL session history, and an Agents View for navigating live sessions and subagents. Those details are presented as part of its runtime and session management model.
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