mirror of
https://github.com/deepseek-ai/deepseek-harness
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5252477bc9
Address @tianyicui's minor-revision review on PR #110: - Make every BasicCompactConfig knob required except `auto` (defaults true): there is no data yet to justify default thresholds/budgets, so a consumer states each value explicitly. Drop the DEFAULTS export and the constructor's `= {}` default; example cordis.yml, the compaction e2e, the README, and every test construction site now pass a complete config (tests route through a `cfg()` helper). - Add a TODO on estimateContentTokens: char/4 is coarse; replace with a real tokenizer or post-response usage feedback in a follow-up. - Add a TODO on the agent/pre-step `fullSystemPrompt` param flagging it as a smell on a generic per-step seam (compaction is its sole consumer); a `//` line comment so it stays out of the generated catalog.
126 lines
5.1 KiB
YAML
126 lines
5.1 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 for agent-core's tool-bash schema.
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# FIXME(config-comments): keep this executor note from implying bash is the
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# whole tool set; subagent and todo_write are loaded below.
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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 (its tools are bash, subagent, and todo_write).'
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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. Use
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subagent_fork instead when the subtask needs THIS conversation's
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context: the child inherits the log so far.
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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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For multi-step work, use the todo_write tool to track a task list:
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send the WHOLE list each call (it replaces the previous one), keep at
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most one task in_progress (exactly one while work remains), and mark a
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task completed as soon as it is done. Skip it for trivial single-step
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tasks.
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# Automatic context compaction: when the derived history approaches the model's
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# context window, summarize an older range into a checkpoint so a long-running
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# or tool-heavy session keeps fitting. A leaf entry (needs ctx.llm + the
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# agent-loop's `agent/pre-step` seam from the app above).
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- id: compact-basic
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name: '@deepseek-ai/dsh-compact-basic'
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config:
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contextWindow: 128000
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thresholdRatio: 0.8
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retainTokens: 20480
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summarizationModel: ''
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maxTokens: 8192
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compactionRetries: 1
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# The subagent seam + BOTH in-process backends + two model-facing tools, as leaf
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# entries after the app (which provides ctx.agents/ctx.tools). spawn (a fresh
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# child) and fork (a child seeded with the parent's completed-turn prefix) are
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# independent backends over the shared dsh-subagent-inprocess driver. Exposing
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# both transports is pure config: load each backend, then load dsh-tool-subagent
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# once per backend with a distinct toolName (the tool registry rejects a
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# duplicate name) — no code change.
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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: subagent-fork
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name: '@deepseek-ai/dsh-subagent-fork'
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config:
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providerName: fork
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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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toolName: subagent
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- id: tool-subagent-fork
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name: '@deepseek-ai/dsh-tool-subagent'
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config:
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provider: fork
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toolName: subagent_fork
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# The model-facing todo_write tool: whole-list task tracking written to the
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# session log (todo/write), rendered as a stdio checklist / ACP plan.
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- id: tool-todo
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name: '@deepseek-ai/dsh-tool-todo'
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