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Agents Remember

vmcp-v0.9.5 scope: mcp Feature

This release adds 3 notable features for engineering teams evaluating rollout.

✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

agent agent-memory ai-agent ai-memory coding-agents mcp-server

Summary

AI summary

Response token counter now reports accurate token counts instead of placeholder zeros.

Full changelog

Agents Remember 0.9.5

Makes the response token counter functional: every MCP tool response now reports an accurate token count instead of placeholder zeros.

Highlights

  • The token-accounting engine (models/tokens.py) is now wired into the single response choke point (_tool_payload), so every public tool response carries a real tokens, tokenizer, and tokenCountExact. Previously these shipped as Pydantic defaults (0 / "" / false).
  • Default tokenizer is tiktoken o200k_base (tokenCountExact: true), with a deterministic approximate counter retained as a fallback.
  • Widened response_payload / dump_with_token_count to accept any ResponseModel, so operation-less responses such as ping are counted too.
  • Added test_tokens.py — first direct coverage of the counters, serializers, and the count fixpoint.

Verification

Full quality wrapper green (ruff clean, Pyright 0 errors, 289 passed / 3 skipped). ping now reports tokens: 44, tokenizer: "tiktoken:o200k_base", tokenCountExact: true.

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Related context

Earlier breaking changes

  • vmcp-v2.9.0 Removed tools direct_closeout_preview and direct_closeout_apply.
  • vmcp-v2.0.0 `WorkflowKind` enum drops `heavy` and `heavy-task` values.
  • vmcp-v2.0.0 `skills_install` no longer accepts a `layout` input; installer performs flat copy.
  • vmcp-v2.0.0 Removed workflows W-01-heavy-task-workflow and W-03-chat-task-workflow.

Beta — feedback welcome: [email protected]