dongarandClaude Opus 5.5
494715efa9
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Every turn re-sent the whole transcript with nothing checking it against the model's window. On a local model that fails silently: Ollama drops the start of an over-long prompt, which is where the system prompt and the plan are. Measured on the Jetson's llama3.2, cut at 4096 tokens with no error. - The model catalog reads max_input_tokens and max_output_tokens from LiteLLM's model_info, and startup refuses a role on a model with no declared window - BudgetedClient wraps both routes. It fits the canonical events before rendering: older tool results cut to head and tail, then the oldest turns dropped with a note, keeping calls with their results; a request that still cannot fit fails with ContextOverflow and is never sent. The output ceiling is the smaller of config and the model's limit - Tokens are estimated from characters, pessimistically, and the estimate is corrected from reported prompt sizes, only ever upward - The executor takes its scope from the log: the latest plan, which is the approved milestone plan, goes in its system prompt, and it sees only events after it - Tool output keeps its head and, mostly, its tail, so a test runner's failure summary survives truncation - tuning.context.keep_recent_turns and safety_margin are new knobs - scripts/probe_context.py checks a declared window against what the server does; it confirmed llama3.2 cuts at the declared 4096 - The tuning guide covers the budget, declaring a local model's limits, and the probe Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
61 lines
2.4 KiB
YAML
61 lines
2.4 KiB
YAML
# Example configuration for nisse-dev. Copy to nisse-dev.yaml (or point
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# NISSE_CONFIG at your copy) and edit.
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#
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# - Structure lives here. Secrets do not: write them as ${VARIABLE} references,
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# resolved from the environment (or .env, see .env.example) at startup.
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# - Keep this file outside the workspace clone. Startup refuses otherwise,
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# because the agent commits and pushes everything in the clone.
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# - Unknown keys are errors, so a misspelt knob fails at startup instead of
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# silently doing nothing.
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state_db: ~/nisse-demo-workspace/state.db
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workspace: ~/nisse-demo-workspace/project # the clone
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project: demo
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llm: # A5: LiteLLM is the only LLM destination
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base_url: http://litellm.example:4000
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token: ${NISSE_LLM_TOKEN}
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# Ceiling on each response. The model's own max_output_tokens in LiteLLM
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# caps it further, which is where to hold a local model to less.
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max_output_tokens: 16384
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roles: # A11: role -> LiteLLM model alias
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executor: local-fast
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planner: claude-opus-5
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delegate: claude-opus-5
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consultant: claude-opus-5
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matrix: # A1
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homeserver: https://matrix.example
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user_id: "@nisse-dev:example"
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token: ${NISSE_MATRIX_TOKEN}
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room_id: "!roomid:example"
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operators: ["@you:example"]
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gitea: # B2
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url: https://gitea.example
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token: ${NISSE_GITEA_TOKEN}
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repo: owner/demo-repo
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operators: [your-gitea-login]
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base_branch: main
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# What each knob does, and how to choose a value: docs/Tuning-Guide.md.
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# These are the defaults; the section can be left out entirely. The file is
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# read at startup, so a change takes effect on the next restart.
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tuning:
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executor:
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max_turns: 24 # model calls per milestone before it stops as capped
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failure_limit: 2 # consecutive failed commands before escalating (A12)
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expert:
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consult_budget: 3 # ask_expert calls per milestone (A14)
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delegation: # hard caps on one delegation (A13)
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max_turns: 30
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max_tokens: 200000
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wall_clock_s: 900
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tools:
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command_timeout_s: 120 # one run_command before it is killed
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max_output_chars: 4000 # tool output kept for the model, head and tail
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context: # fitting each request into the model's window
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keep_recent_turns: 6 # turns never cut or dropped
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safety_margin: 0.1 # fraction of the window held back
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