Troubleshooting: \"My Agent Feels Dumber\"

Sometimes Hermes seems less sharp than it was yesterday, or forgets something you told it twenty minutes ago. This is almost never mysterious — there's usually one specific, checkable cause. Work through this checklist in order: the steps are sorted by how often each one turns out to be the answer.

Symptom: Answers are shallower, code quality dropped, reasoning feels off — across the board.

Check: Run /model with no arguments to show the current model, or /status to see the session's model, provider, and profile in one view.

What it means: A model switch is a capability change, and it's easy to end up on a different model than you think:

• A plain /model <name> switch is session-only by default (unless model.persist_switch_by_default: true is set), so the model you're on may not match what's in config.yaml.
• Changing the main model from the dashboard's Models page applies to new sessions only — an already-open chat keeps running whatever model it started with.
• If you switched to a faster model for a simple task (a pattern Tips & Best Practices recommends), remember to switch back for complex reasoning work.

If the model is wrong, /model <name> fixes it for this session; add --global to persist the change to config.yaml. Note that a mid-session switch resets the prompt cache, so the next turn re-reads the conversation at full input price — on a long session it can be cheaper to start fresh on the right model.

Symptom: The session started strong but responses are slowing down, getting truncated, or losing track of earlier details.

Check: Run /usage to see token usage and context window state, or /context for a visual breakdown of what's occupying the window (system prompt, tool definitions, skills, memory, conversation) versus free space.

What it means: Extended conversations accumulate messages and tool outputs, approaching context limits. When you notice degradation in a long session:

# Compress the conversation (summarizes history, preserves key context)
/compress

# Or start a fresh session
/new

/compress summarizes the conversation history, dramatically reducing token count while preserving key context. /compress here [N] keeps the most recent N exchanges verbatim and summarizes the rest, and a focus topic (/compress focus <topic>) narrows what a full summary preserves.

Symptom: Context problems appear surprisingly early — the first long conversation already hits limits, or compression fires far sooner than the model's advertised window should allow.

Check: Look at the CLI startup line — it shows the detected context length (e.g., 📊 Context limit: 128000 tokens). You can also check with /usage during a session.

What it means: Hermes may have auto-detected the wrong context length for your model. Set it explicitly:

# In ~/.hermes/config.yaml
model:
  default: your-model-name
  context_length: 131072  # your model's actual context window

Or for custom endpoints, per-model on the provider entry:

providers:
  my-server:
    api: "http://localhost:11434/v1"
    models:
      qwen3.5:27b:
        context_length: 64000

Ollama users: if you set a custom num_ctx, set the matching context length in Hermes — Ollama's /api/show reports the model's maximum context, not the effective num_ctx you configured. On a running gateway, edits to model.context_length or any compression.* key take effect on the next message — no restart needed.

See Context Length Detection for how auto-detection works and all override options.

Symptom: You asked Hermes to remember something during this session, it confirmed the save, but later in the same session it doesn't seem to know it.

Check: Nothing is broken — check the timing. Memory saved mid-session is written to disk immediately, but the system prompt won't reflect it until the next session.

What it means: This is documented, intentional behavior. Memory is injected into the system prompt as a frozen snapshot at session start, and that injection never changes mid-session — it preserves the LLM's prefix cache for performance. When the agent adds or removes memory entries during a session, the changes persist to disk right away but appear in the system prompt only when the next session starts. Tool responses always show the live state, so the save itself is confirmed and real.

See Persistent Memory for the full mechanics.

Symptom: Hermes doesn't recall a detail from a session last week, even though you discussed it at length.

Check: Memory capacity and contents. The system prompt memory header shows usage (e.g., [67% — 1,474/2,200 chars]), and hermes journey list shows every saved memory entry and skill.

What it means: Persistent memory is intentionally bounded — 2,200 chars (~800 tokens) for MEMORY.md and 1,375 chars (~500 tokens) for USER.md. It holds curated key facts, not conversation transcripts. Things worth saving are preferences, environment facts, conventions, and corrections; raw discussion detail is not stored there by design.

For "did we discuss X last week?" recall, the agent has a separate mechanism: session_search queries all past sessions (stored in SQLite with full-text search) and can find things discussed weeks ago even when they're not in active memory. Just ask — "search our past sessions for the deploy discussion."

You can also help directly: say "remember this for next time" after a productive session, or "clean up your memory" when it's near capacity so the agent consolidates entries. See Memory & Skills tips and Capacity Management.

Symptom: Hermes used to handle a specific workflow expertly and now approaches it naively, or says it can't do something it did before.

Check:

• /skills — browse installed skills (a skill the agent relied on may have been removed).
• /reload-skills — re-scan ~/.hermes/skills/ for newly installed or removed skills.
• /tools list — see available tools; a tool disabled earlier with /tools disable stays out of the agent's toolset for the session.
• /context all — per-skill and per-toolset cost listing, which doubles as an inventory of what's actually loaded.

What it means: Skills are the agent's procedural knowledge — multi-step workflows and tool-specific instructions. If a skill is missing or a toolset was trimmed (e.g., a session started with hermes chat -t "terminal" to reduce prompt weight), the agent genuinely has less to work with in that session. Re-enable tools with /tools enable, or invoke the skill explicitly by name (/github-pr-workflow) to confirm it loads.

Symptom: After a long session (or right after running /compress), Hermes remembers the broad strokes but has lost fine detail from earlier in the conversation.

Check: Whether compression has fired — /usage and /context show compression stats and context state on messaging platforms, and manual /compress always reports its result.

What it means: Compression replaces older conversation history with a summary — that's the point, and it necessarily trades detail for headroom. Know the shape of it:

• Recent messages are protected: by default the last 20 messages stay uncompressed (protect_last_n) and the opening exchange is pinned (protect_first_n: 3) so the original goal stays visible.
• Compaction is non-destructive: with the default compression.in_place: true, the session keeps one durable id and pre-compaction turns are soft-archived — still searchable via session_search and recoverable, not deleted.
• With in_place: false (legacy behavior), each compaction rotates to a new session linked to the old one — a titled session becomes "my project" → "my project #2" → "my project #3". If you resume by title, hermes -c "my project" automatically picks the most recent variant.
• A focus topic narrows what a full summary preserves: /compress focus auth-refactor keeps that thread's detail at the expense of the rest.

If a compressed-away detail matters, ask the agent to search for it (session_search reaches the archived turns), or re-paste the key facts into the conversation.

See Context Compression for the full settings reference and Auto-Lineage on Compression for how titled sessions chain.

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