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The default agent driver for users and maintainers choosing, configuring, or debugging how agents are created and how turns and steps run.	package-reference

@deepseek-ai/dsh-agent-loop

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Summary

dsh-agent-loop creates agents — fresh or resumed from persisted history — and runs the turn and step lifecycle that claims prompts, assembles requests, streams model responses, dispatches tool calls, and appends every result back to the session log. As the default driver it implements the Agent interface from dsh-agent and registers its factory there, so plugins create and drive agents through ctx.agents without depending on this package. Declarative config entries start agents automatically at boot, and maxParallelToolCalls caps how many parallel-safe tool calls run at once. It is the harness's only concrete loop — everything beyond "call the model, run the tools, repeat" belongs to plugins listening on the event taxonomy. Choose it as the driver for standard compositions; swap it by implementing Agent and registering through ctx.agents.

Use this package
Understand the implementation
Further Exploration
Model Experience
Known Limitations and Deferred Work
Dev Note

Use this package

Mount dsh-agent-loop in any composition that should run agents. It supplies the driver behind ctx.agents and starts any agents you declare in its config; the standard demo composition is examples/agent-spine-demo.

Configure declarative agents

Agents declared in the config start automatically when the plugin loads. Each entry needs an id label; a model call additionally requires both provider and model (agent/request may supply a missing pair before dispatch).

- name: '@deepseek-ai/dsh-agent-loop'
  config:
    maxParallelToolCalls: 10
    agents:
      - id: 'main'
        provider: deepseek
        model: deepseek-chat
        reasoningEffort: high
        cwd: /workspace

Field	Default	Meaning
`maxParallelToolCalls`	`10`	Parallel-safe tool calls in flight per step; `1` is serial
`agents[].id`	required	Stable label; a fresh session mints `${id}-session-<uuid>` unless `sessionId` is set
`agents[].provider` / `agents[].model`	—	Model route; both required before dispatch
`agents[].reasoningEffort`	—	Non-empty initial reasoning effort; `agent/request` may override it
`agents[].maxTokens`	—	Positive per-request output-token cap
`agents[].cwd`	—	Workspace directory for a fresh session
`agents[].sessionId`	—	Exact identity: first use creates, a remount resumes materialized history
`agents[].resumeSessionId`	—	Load this persisted session instead of creating one; mutually exclusive with `sessionId`

The generated configuration catalog is the exhaustive source for every accepted field. The adapter validates the effective reasoning effort and the loop records it in the request header. maxParallelToolCalls is also the whole agent-loop settings section, so a user layer over this entry caps the next tool group without a restart.

Create or resume agents programmatically

Plugins and hosts create agents through ctx.agents.create() and resume persisted sessions through ctx.agents.resume(); both return an AgentHandle whose dispose() owns exact teardown. The loop runs each created agent to completion — the handle is only needed when the caller must tear the agent down itself.

const handle = await ctx.agents.create({
  sessionId,
  agentOptions: { provider: 'deepseek', model: 'deepseek-chat' },
  setup: (agentCtx) => { /* scoped tools, prompt sections, listeners */ },
})

What a step does

Each step sends the agent's rendered system prompt, its visible tool schemas, and the session's derived history; the model's tool calls run through the guarded tool pipeline and every accepted fact is appended to the session log before the next step derives from it. Parallel-safe calls may overlap up to maxParallelToolCalls; exclusive calls run alone as ordering barriers. Cancellation is cooperative: agent.cancel() aborts the current activity and, unless keepInbox is set, clears pending work; a cancelled stream finalizes the text already delivered to the user.

Understand the implementation

Implementation internals — click to expand

This section explains how the package realizes the behavior above; the observable contract is covered in Use this package.

Design concept

The package is the one concrete implementation of the public Agent contract. It registers itself as the AgentFactory on ctx.agents, so consumers never import this package; ownership of each created agent lives with the caller fiber and the loop provider, converging on one memoized quiescence boundary. Every observable effect happens through session events and the agent/* taxonomy — package internals are never part of the public surface.

Request headers and adapter defaults

After agent/request, ctx.llm.prepareCall() validates adapter-owned fields and resolves reasoning-effort and output-token defaults under the active turn signal. The loop retains that exact adapter through resolution, request/header logging, and dispatch. It writes a full header for the first request, a changed envelope, an explicit message-series start, a request after surface replacement, and resume; unchanged steps, retries, and ordinary later turns in the same series inherit the latest header. Before the next waterfall, the loop removes adapter-default fields so the current route resolves them again, while explicit settings persist. An unhandled route still fails with NO_ADAPTER.

Source map

File	Role
`src/index.ts`	Plugin entry: `AgentLoop` service, config schema, declarative agent startup, factory registration
`src/agent.ts`	The concrete `ReactLoopAgent` driver: inbox, turn/step machine, cancellation
`src/tool-calls.ts`	Tool scheduling: exclusive barriers and the bounded parallel pool
`src/runtime-context.ts`	Per-step runtime-context snapshot handling
`src/constants.ts`	`DEFAULT_MAX_PARALLEL_TOOL_CALLS`
`src/invariant.ts`	Invariant companion: request reconstruction from the session log

Creation and teardown

Creation is one rollback-covered transaction: construct a private session, concrete agent, and scoped context; await optional setup; enter both registries; announce session/created then agent/created; emit agent/session-start; only then start the driver. A setup throw, commit failure, or owner disposal rolls the transaction back without publishing either id. Teardown runs stop-and-drain, unwind the scope, detach the agent, then detach the session, and every detach is bound to the exact entered object so a stale disposer cannot remove a later same-id replacement.

Turn and step flow

The driver owns one agent for its lifetime and runs inside ctx.agents.withInitiator(agent, ...). At a turn boundary it opens the durable turn, then atomically claims pending next-step input plus one queued prompt; between steps it claims only next-step input. agent/pre-step decides what enters the step; each successful model call appends one assistant/message anchor citing its chunk seqs, and a cancelled stream appends an interrupted: true anchor with the delivered prefix so the next request contains what the user saw. Within a step, exclusive calls form barriers and parallel-safe calls use the bounded rolling pool; policy, durable results, and result context remain model-ordered.

Failure and cancellation

Final adapter selection, dispatch, and iteration failures arrive as terminal finishes and enter agent/request-error; a handling listener returns { kind: 'retry' } without calling next(), while an unhandled failure is terminal. Middleware, result-processing, tool, and other extension failures remain thrown and close the turn directly — plugin failure ends the turn, not the loop. Undispatched model tool calls after cancellation receive synthetic tool/call plus ABORTED_BEFORE_DISPATCH result pairs. The explicit-cancellation decision owns the signal lifecycle.

Further Exploration

The package-level contract is enough for most consumers; read these when you need the surrounding domain and the design rationale.

agent package — the Agent handle, registry, and agent/* events this loop implements.
Core subsystem — the turn flow and interception decisions.
Session subsystem — the durable log the loop writes and derives from.
Tools subsystem — the pipeline the loop dispatches through.
Explicit-cancellation Agent Note — signal lifetime and cancellation races.
Core group map — how the core packages compose.

Model Experience

Complete conversation request

What the model sees

For each step, the loop sends the rendered per-agent system prompt, the visible tool schemas, and the session's derived messages. It supplies provider, model, and cwd variable values but no additional fixed prose.

Token effect

System text and schemas are paid again on every step. Per-agent scoping chooses the contributions, while the authoritative assembly waterfall can alter the final request and makes its listener responsible for protocol coherence.

KV Cache effect

Append-only only while system text, schemas, and earlier history remain byte-identical under the same provider and model route. A token-bearing assembly rewrite or composition change may invalidate reuse from the first altered request token.

Retained message history

What the model sees

Accepted user messages, assistant messages, tool calls and results, injected context, and steering are logged and sent on later steps. Raw stream chunks, lifecycle boundaries, and other log-only events are excluded.

Token effect

Input grows with every surface message until a compaction replacement shadows older nodes; a multi-step tool turn resends the accumulated history each step.

KV Cache effect

Ordinary history growth is append-only and preserves reusable entries. A surface replacement or compaction invalidates reuse from the first shadowed history token.

Undispatched calls after cancellation

What the model sees

If a later request replays an aborted step, each tool call that cancellation prevented from dispatching has error code ABORTED_BEFORE_DISPATCH and result text Error: tool call aborted before dispatch.

Token effect

One fixed error result per skipped call remains in history until compaction shadows it.

KV Cache effect

Append-only; each synthetic result follows the reusable request prefix and does not invalidate existing KV-cache entries.

Known Limitations and Deferred Work

These limits define when the loop needs special care. They are current package constraints, not a task backlog.

Classification is unary — calls whose safety depends on comparing siblings or resources must remain exclusive (rationale).
Config labels are fresh by default — omitting sessionId creates a fresh ${id}-session-<uuid> on every startup; exact resume-or-create behavior requires an explicit stable sessionId, while resumeSessionId requires existing persisted history.
Config agents have no per-agent persona field or setup hook — they use the deployment persona; scoped persona and tool composition are available only through the programmatic ctx.agents.create() / resume() factory options.
No built-in turn budget — tool calls or steering continue the current turn; a policy that bounds runaway turns must cancel from an existing lifecycle extension point such as agent/turn-stopping.

Dev Note

Working context for maintainers — click to expand

None.

README.md

@deepseek-ai/dsh-agent-loop

Summary

Table of Contents

Use this package

Configure declarative agents

Create or resume agents programmatically

What a step does

Understand the implementation

Design concept

Request headers and adapter defaults

Source map

Creation and teardown

Turn and step flow

Failure and cancellation

Further Exploration

Model Experience

Complete conversation request

What the model sees

Token effect

KV Cache effect

Retained message history

What the model sees

Token effect

KV Cache effect

Undispatched calls after cancellation

What the model sees

Token effect

KV Cache effect

Known Limitations and Deferred Work

Dev Note