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https://github.com/deepseek-ai/deepseek-harness
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7aabd2a3df
The second PR of the subagent seam: the two in-process backends that run a
child agent on the same cordis context, reusing the agent factory's quiescent
AgentHandle teardown. Both register on ctx.subagents (PR1's named-provider
registry) and share one run driver.
- dsh-subagent-spawn: a FRESH child via ctx.agents.create — own session, the
parent's model by default (overridable), zero inherited conversation. Also
exports the shared in-process run driver (startInProcessRun): mint ids, stamp
cwd/parentSession-lineage/depth, drive the one-shot (send → whenIdle), read
the last assistant/message + turn/end reason, dispose to quiescence.
- dsh-subagent-fork: a child SEEDED with the parent's balanced completed-turn
prefix (the log up to and including its last turn/end), so the child inherits
context. The in-flight unbalanced turn is excluded — a raw seed would fail the
invariants replay. Proven: a regression test goes red if the boundary seeds
the open turn.
- Seam extension: CreateAgentOptions.seed, threaded through AgentLoop.createAgent
→ ctx.sessions.prepare({ seed }) (the primitive resume already used). This is
the fork-lineage path the TODO(sub-agents) markers anticipated.
- Depth: a merge-extensible AgentOptions.subagentDepth (0 top-level, parent+1 for
a child); the depthLimit capability refuses a spawn past request.maxDepth.
Tests: real-loop unit tests for both backends (mock MODEL only, real loop +
invariants), a multi-subagent test (one parent drives a fork AND a spawn child
then keeps working), and a with-key e2e (a real parent delegates via the
`subagent` tool to a real child that writes a file on disk — world-verified).
100% per-file coverage. The coding-agent demo wires the spawn backend + tool.
Snapshot coverage of nested agents is deferred to a stacked follow-up
(TODO(subagent-snapshots)): dsh-llm-replay is a single global positional cursor
that cannot route calls to a parent vs. a child on one context. Recorded in the
RFC's deferrals and a new AGENTS.md rule: designing a subsystem must design its
test infrastructure END TO END up front, verifying the snapshot/e2e harness can
express the new shape — a gap this plan hit.
83 lines
3.5 KiB
YAML
83 lines
3.5 KiB
YAML
# The coding-agent plugin tree: the real coding agent. The two swappable
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# backends — the DeepSeek adapter and the local bash executor — plus `hmr` for
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# the dev/demo reload loop, then the stdio chat app (@deepseek-ai/dsh-stdio-
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# agent), which bundles the whole agent-core spine (timer, llm, sessions,
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# system-prompt, tools, agents, invariants, tool-bash, agent-loop), the console
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# logger, JSONL persistence, the readline UI, and a pre-created `main` agent.
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#
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# `hmr` is a leaf entry (not baked into dsh-stdio-agent): it is a Loader-only
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# dev plugin that needs `--expose-internals` — the `demo:coding` script passes
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# it. Requires DEEPSEEK_API_KEY (and optionally DEEPSEEK_BASE_URL) in the
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# environment — the dsh-stdio-agent bin loads the gitignored repo-root .env
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# first. cordis.yml reads them via the `!!js` tag.
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# Hot-module reload for the dev/demo loop (needs `node --expose-internals`).
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- id: hmr
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name: '@cordisjs/plugin-hmr'
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config:
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root: ['.']
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# The DeepSeek adapter. Swap to '@deepseek-ai/dsh-llm-pi-ai' for the pi-ai-backed
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# twin (same config shape; `reasoning: high` replaces thinking/reasoningEffort).
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- id: llm-deepseek
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name: '@deepseek-ai/dsh-llm-deepseek'
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config:
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apiKey: !!js process.env.DEEPSEEK_API_KEY
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baseURL: !!js process.env.DEEPSEEK_BASE_URL
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models:
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- deepseek-v4-flash
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- deepseek-v4-pro
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# Local bash executor (the model's only tool, via agent-core's tool-bash schema).
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- id: bash
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name: '@deepseek-ai/dsh-bash-local'
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config:
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timeoutMs: 60000
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# The stdio chat app: the whole spine + front-door cluster, configured for a
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# real coding agent driving a pre-created `main` agent.
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- id: stdio-agent
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name: '@deepseek-ai/dsh-stdio-agent'
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config:
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model: deepseek-v4-flash
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# Set RESUME_SESSION_ID to continue a prior persisted session (the ids live
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# under ./.sessions); unset starts a fresh session each run.
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resumeSessionId: !!js process.env.RESUME_SESSION_ID
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persistenceRoot: './.sessions'
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welcome: 'coding-agent ready. Give it a coding task (bash is its only tool).'
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systemPrompt: |
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You are coding-agent, a CLI coding assistant.
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Your tools are bash (plus bash_output/bash_kill for background
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tasks) and subagent. Do ALL file operations through bash: read with
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cat/sed/head, search with grep, write with heredocs (cat <<'EOF' >
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file), edit with sed or a rewrite. Each bash call runs in a fresh
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shell — pass workdir instead of cd, and never rely on shell state
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between calls.
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Use the subagent tool to delegate a focused, self-contained subtask
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to a fresh child agent (it works in its own context and returns only
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its final result) — give it a complete, standalone instruction.
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Check the [exit code: N] marker on every command; investigate
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failures before moving on. Verify your work by running the code or
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tests. Keep answers brief and factual.
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# The subagent seam + an in-process spawn backend + the model-facing `subagent`
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# tool, as leaf entries after the app (which provides ctx.agents/ctx.tools). The
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# tool is bound to the `spawn` backend: a delegated task runs as a fresh child
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# agent on this same process. (fork is available too — load dsh-subagent-fork
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# and a second dsh-tool-subagent bound to it with a distinct toolName.)
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- id: subagent
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name: '@deepseek-ai/dsh-subagent'
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- id: subagent-spawn
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name: '@deepseek-ai/dsh-subagent-spawn'
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config:
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providerName: spawn
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- id: tool-subagent
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name: '@deepseek-ai/dsh-tool-subagent'
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config:
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provider: spawn
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