Okay, I've dug through all the logs. Here's the full picture:

**Ouroboros model usage:**
- Nemotron fails **100% of the time** — every single request hits "primary model exhausted retries"
- Every call falls back to `zhipu/glm-5` (cloud)
- The "hybrid local/cloud" concept is currently 0% local, 100% cloud
- Root cause: Spacebot's system prompt is **massive** (~12K+ tokens with SOUL.md + channel prompt + skills + delegation rules + worker docs). Even at 64K context, Nemotron is likely hitting a **timeout** in Spacebot's retry logic before it can finish inferring that giant prompt + tools + history. Nemotron runs at ~92 tok/s but the first-token-latency on a 12K prompt is significant.

**Waffleson memory loss:**
- This is a **Spacebot 0.1.7 architecture issue**. I traced it to `src/agent/channel.rs` line 138:
  ```rust
  let history = Arc::new(RwLock::new(Vec::new()));
  ```
- Channel history is **purely in-memory**. It starts empty on every restart.
- The `conversation_messages` table in SQLite is **write-only** — Spacebot logs messages to it but never reads them back on startup.
- The `cortex_chat` module has `load_history()` but the main channel does NOT.
- So every restart = total amnesia. And even within a session, it was losing context (21:37 → 21:49 gap where Waffleson forgot the ESP32 work mid-conversation).

**Fixes needed:**
1. **Ouroboros/Nemotron**: Increase Spacebot's request timeout for ollama models, or trim the system prompt. The current one is enormous — 12K+ chars of boilerplate that could be halved.
2. **Waffleson memory**: This needs a Spacebot code change — load conversation history from the DB on channel init. It's ~20 lines of Rust. Want me to write that PR too?


System: [2026-02-19 23:09:53 UTC] Check /opt/agentmail/queue/ for new emails addressed to clinker. If there are unread emails (files you haven't processed), read them and respond appropriately. After processing, move the file to /opt/agentmail/processed/ to mark it as handled.

Conversation info (untrusted metadata):
```json
{
  "message_id": "1094",
  "sender": "6558388607"
}
```

Keep the model active and locked in vram. Also, if we need to bump up the context to 128k you can


(Command exited with code 2)


groups:
    ouroboros:
        exclusive: false
        members:
            - glm-4.7-flash
            - glm47reap
            - Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
            - nvidia-orchestrator-8b
            - embeddinggemma-300m
            - heretic
        persistent: true
        swap: false
    vision:
        exclusive: false
        members:
            - ouroboros-eye
        persistent: true
        swap: false
macros:
    llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
    llama-server-jinja: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server --jinja --mmproj /mnt/storage/ollama/gguf-models/mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf
models:
    Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching -np 2 --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
            isTemporary: false
        proxy: http://localhost:${PORT}
    embeddinggemma-300m:
        cmd: /home/crogers2287/llama-swap/embed-wrapper.sh --model /mnt/storage/ollama/gguf-models/embeddinggemma-300M-Q8_0.gguf --port ${PORT} --n-gpu-layers 0 --ctx-size 2048 --threads 8 --batch-size 128 --cont-batching --host 0.0.0.0 --embedding --pooling mean
        concurrencyLimit: 100
        metadata:
            backend: llamacpp
            displayName: EmbeddingGemma 300M Q8_0
            isEmbedding: true
        proxy: http://localhost:${PORT}
    heretic:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning.Q6_K.gguf --port ${PORT} --n-gpu-layers 35 --ctx-size 32000 --threads 16 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja
        metadata:
            backend: llamacpp
            displayName: Llama3.3 8B Heretic (Uncensored Reasoning)
        proxy: http://localhost:${PORT}
    nvidia-orchestrator-8b:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/nvidia_Orchestrator-8B-Q8_0.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 2048 --threads 8 --batch-size 512 --flash-attn on --cont-batching --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: NVIDIA Orchestrator 8B (Task Planning)
            isTemporary: false
        proxy: http://localhost:${PORT}
    ouroboros-eye:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Qwen3-VL-2B-Instruct-1M-Q8_0.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 4096 --threads 4 --batch-size 256 --flash-attn on --cont-batching --main-gpu 1 --host 0.0.0.0 --jinja --mmproj /mnt/storage/ollama/gguf-models/mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Ouroboros Eye (Qwen3-VL-2B Vision)
            isVision: true
        proxy: http://localhost:${PORT}
    glm-4.7-flash:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/GLM-4.7-Flash-Q6_K.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: GLM-4.7-Flash Q6_K (64K Context)
        proxy: http://localhost:${PORT}
    glm47reap:
        cmd: ${llama-server} --model /home/crogers2287/models/GLM-4.7-Flash-REAP-23B-A3B-Q6_K.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 131072 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0 --reasoning-budget 0
        metadata:
            backend: llamacpp
            displayName: GLM-4.7-Flash REAP 23B-A3B Q6_K (128K)
        proxy: http://localhost:${PORT}
    qwen3-24b-heretic-neo:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Qwen3-24B-A4B-Freedom-Think-Ablit-Heretic-Neo-D_AU-Q6_K-imat.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 16384 --threads 8 --batch-size 1024 --flash-attn on --cont-batching --host 0.0.0.0 --jinja -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Qwen3 24B Heretic NEO (Uncensored Reasoning)
        proxy: http://localhost:${PORT}
    cydonia-24b:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/TheDrummer_Cydonia-24B-v4.1-Q6_K_L.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 32768 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Cydonia 24B v4.1 Q6_K_L (32K)
        proxy: http://localhost:${PORT}


edit

    Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching -np 2 --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
            isTemporary: false
        proxy: http://localhost:${PORT}

    Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 131072 --threads 8 --batch-size 2048 --flash-attn on --cont-batching -np 2 --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        keepAlive: true
        metadata:
            backend: llamacpp
            displayName: Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
            isTemporary: false
        proxy: http://localhost:${PORT}


Successfully replaced text in /home/crogers2287/llama-swap/config.yaml.

    ...
 20     llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
 21     llama-server-jinja: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server --jinja --mmproj /mnt/storage/ollama/gguf-models/mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf
 22 models:
 23     Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL:
-24         cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching -np 2 --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
+24         cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 131072 --threads 8 --batch-size 2048 --flash-attn on --cont-batching -np 2 --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
+25         keepAlive: true
 25         metadata:
 26             backend: llamacpp
 27             displayName: Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
 28             isTemporary: false
    ...


exec

# Check if llama-swap supports keepAlive or if it uses a different mechanism
# The "persistent" group already keeps models loaded. Let me check.
# The "ouroboros" group has persistent: true and Nemotron is a member.
# That should already keep it loaded. The issue is llama-swap swaps it out
# when another model in the group loads.
#
# Actually, swap: false on the ouroboros group means it WON'T swap models.
# But exclusive: false means multiple can run simultaneously.
# So Nemotron SHOULD stay loaded. The keepAlive field might not be a real config option.
# Let me revert that and just focus on the context + checking why Spacebot fails.

# Remove keepAlive (not a real llama-swap config field)
sed -i '/keepAlive: true/d' /home/crogers2287/llama-swap/config.yaml

# Force model reload by switching models then back
curl -s --max-time 10 http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"embeddinggemma-300m","messages":[{"role":"user","content":"test"}],"max_tokens":5}' >/dev/null 2>&1

sleep 3

# Now load Nemotron with 128K ctx
echo "Loading Nemotron with 128K context..."
curl -s --max-time 180 http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL","messages":[{"role":"user","content":"Say hello"}],"max_tokens":10}' 2>&1 | python3 -c "
import json,sys
d = json.load(sys.stdin)
print('OK:', d['choices'][0]['message'].get('content','')[:80])
" 2>&1 || echo "FAILED TO LOAD"


Command still running (session dawn-dune, pid 306641). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


Conversation info (untrusted metadata):
```json
{
  "message_id": "1095",
  "sender": "6558388607"
}
```

Replied message (untrusted, for context):
```json
{
  "sender_label": "Clinker",
  "body": "rs` line 138:\n\nlet history = Arc::new(RwLock::new(Vec::new()));\n\n• Channel history is purely in-memory. It starts empty on every restart.\n• The conversation_messages table in SQLite is write-only — Spacebot logs messages to it but never reads them back on startup.\n• The cortex_chat module has load_history() but the main channel does NOT.\n• So every restart = total amnesia. And even within a session, it was losing context (21:37 → 21:49 gap where Waffleson forgot the ESP32 work mid-conversation).\n\nFixes needed:\n\n1. Ouroboros/Nemotron: Increase Spacebot's request timeout for ollama models, or trim the system prompt. The current one is enormous — 12K+ chars of boilerplate that could be halved.\n2. Waffleson memory: This needs a Spacebot code change — load"
}
```

I also have a hard time believing you need a code change for memory. This is a fundamental aspect of the agent system


# Chad's right - let me re-examine. Maybe there's a config option for history persistence
# that I missed. Check Spacebot docs for memory/history/persistence

grep -rn "history\|restore\|persistence\|hydrat" /mnt/storage/spacebot/repo/docs/content/docs/ --include="*.mdx" --include="*.md" -l 2>/dev/null


(Command exited with code 1)


---
title: Memory
description: Structured memory system with graph associations and hybrid search.
---

# Memory

Memories in SpaceBot are structured objects in a database. Not markdown files, not daily logs, not a manually curated MEMORY.md. Every memory is a row in SQLite with typed metadata and graph connections, paired with a vector embedding in LanceDB for search.

## Why Not Files

OpenClaw stores memories as markdown files on disk and indexes them into SQLite for search. This creates a whole category of problems: file watchers, hash-based change detection, stale indexes, async sync race conditions, and 43 source files just for the memory subsystem. SpaceBot uses embedded databases as the source of truth -- no file syncing, no server processes.

## Storage Split

Memories live across two embedded databases, each doing what it's best at:

- **SQLite** -- the memory graph. Rows with content, type, importance, timestamps, source. Association edges with weights and relation types. Relational queries for graph traversal, metadata filtering, and maintenance operations.
- **LanceDB** -- embeddings and search. Vector storage in Lance columnar format with HNSW indexing. Built-in full-text search via Tantivy. Hybrid search (vector + FTS) in one system.

The two are joined on memory ID. A recall worker queries LanceDB for semantic/keyword matches, then hits SQLite for graph traversal and metadata. No server processes -- both are embedded, everything is files in a data directory.

## Memory Structure

Every memory has:

- **Content** -- the actual information (a fact, a preference, a decision, an observation)
- **Type** -- what kind of memory this is (see below)
- **Embedding** -- vector representation for semantic search
- **Importance** -- a score that determines how likely it is to be surfaced
- **Timestamps** -- when it was created, when it was last accessed
- **Source** -- where this memory came from (which channel, which conversation, system-generated)
- **Associations** -- weighted edges to other memories in the graph

## Memory Types

Memories are typed. The type determines how the memory is stored, searched, and surfaced.

**Fact** -- Something that is true. "James's project SpaceDrive is a cross-platform file manager written in Rust." Facts can be updated or contradicted by newer facts.

**Preference** -- Something the user likes or dislikes. "James prefers Rust over TypeScript." Preferences influence behavior but don't change often.

**Decision** -- A choice that was made. "We decided to use PostgreSQL instead of SQLite for Spacebot." Decisions carry context about why they were made.

**Identity** -- Core information about who the user is or who the agent is. Always surfaced, never decayed. "James is a software engineer. My name is Spacebot."

**Event** -- Something that happened. "James deployed SpaceDrive v0.4 on Feb 10." Events are temporal and naturally decay in importance.

**Observation** -- Something the system noticed. "James tends to work late on Fridays." Observations are inferred, not stated.

## The Graph

Memories don't exist in isolation. They connect to each other through weighted associations.

When a new memory is created, the system searches for semantically similar memories and creates associations automatically. If a new fact is very similar to an existing one (>0.9 similarity), it's marked as an update -- the old memory's importance decays, and an `Updates` edge is created. If two memories contradict each other, a `Contradicts` edge is created.

Association types:

- **RelatedTo** -- general semantic connection
- **Updates** -- newer version of the same information
- **Contradicts** -- conflicting information
- **CausedBy / ResultOf** -- causal chain
- **PartOf** -- hierarchical relationship

The graph enables traversal during recall. When a recall worker finds a relevant memory, it can walk the graph to find connected context -- related facts, the history of how a decision evolved, contradictions that need resolution.

## How Memories Are Created

Three paths:

### 1. Branch-initiated (during conversation)

When a branch is processing a user message, it can save memories. This is the most common path. The branch has the conversation context, understands what the user said, and can decide what's worth remembering.

```
User: "Actually, let's switch to SQLite for the prototype. PostgreSQL is overkill for now."
    → Branch saves:
        Decision: "Switched to SQLite for the prototype phase"
        Association: Updates → previous decision to use PostgreSQL
```

Branches have a `memory_save` tool. They decide what to save, with what type and importance. The system handles embedding generation and auto-association.

### 2. Compactor-initiated (during compaction)

When the compactor triggers a compaction worker, that worker does two things in one pass: summarize the conversation and extract memories. This is where memories are harvested from conversation that's about to leave the context window.

This mirrors OpenClaw's memory flush pattern -- the best idea in OpenClaw's memory system. Before context is lost, an LLM pass pulls out anything worth keeping. But unlike OpenClaw, this happens in a background worker. The channel never blocks.

### 3. Cortex-initiated (system-level)

The cortex observes patterns across channels and can create memories at the system level. It consolidates related memories, creates observations ("James has been asking about authentication a lot this week"), and manages the graph.

## How Memories Are Recalled

Memory recall is always delegated to a worker. No LLM process ever queries the database directly and dumps raw results into its own context.

### Search Modes

The `memory_recall` tool supports four search modes, each suited to different retrieval needs:

**Hybrid** (default) -- Full pipeline: vector similarity (LanceDB HNSW) + full-text search (Tantivy) + graph traversal, merged via Reciprocal Rank Fusion (RRF). Requires a query string. Best when you have a specific topic to search for and conversation context to inform the query.

**Recent** -- Returns the most recent memories ordered by `created_at`. No query needed, no vector/FTS overhead. Pure SQLite. Best for temporal awareness -- "what just happened?"

**Important** -- Returns highest-importance memories ordered by importance score. No query needed. Best for surfacing critical context that should always be in mind.

**Typed** -- Filters by `MemoryType` (fact, preference, decision, identity, event, observation, goal, todo) with a configurable sort order. Requires `memory_type`. Best for structured retrieval -- "give me all decisions" or "show me identity memories."

Non-hybrid modes bypass the vector/FTS/RRF pipeline entirely and query SQLite directly. They're fast and don't require an embedding model or FTS index.

All modes support an optional `memory_type` filter. In hybrid mode, results are post-filtered after RRF fusion. In non-hybrid modes, the filter is applied at the SQL level.

Sort options for non-hybrid modes: `recent` (created_at DESC), `importance` (importance DESC), `most_accessed` (access_count DESC).

### The Recall Flow

```
Channel receives: "What do we know about the auth system?"
    → Branch created with channel's context
        → Branch calls memory_recall tool (mode: hybrid, query: "auth system")
            Tool searches: vector similarity + full-text + graph traversal
            Tool gets 50 results
            Branch curates: filters noise, ranks by relevance
            Branch selects 5 clean memories
        → Branch returns conclusion to channel
    → Channel responds with clean, relevant information
```

The branch is the recall intermediary. It has the channel's full context (so it knows what's relevant), and it picks the appropriate search mode. Hybrid for topic-specific queries, recent for temporal context, typed for structured retrieval.

RRF works on ranks rather than scores, which handles the different scales of vector and keyword results better than a weighted sum. After finding initial results, the branch can walk the memory graph in SQLite to pull in connected context. If the top result is "we decided to use JWT for auth tokens", the graph might surface "we considered session cookies but rejected them because of the mobile app" through a `ResultOf` edge.

The branch curates. 50 raw results become 5 relevant, contextualized memories. The channel never sees the noise -- it only gets the branch's conclusion.

### Why Not Search Directly?

In OpenClaw, the LLM calls `memory_search`, gets raw results in its context, and has to make sense of them. This pollutes the context with irrelevant matches, partial chunks, and search metadata. In Spacebot, the branch absorbs all that noise and returns only what matters. The branch is disposable -- its context gets thrown away after it returns. The channel stays clean.

This costs an extra LLM call. But it keeps the channel's context clean, which means fewer compactions, which means fewer memory extractions, which means the system runs more efficiently overall.

## Importance and Decay

Every memory has an importance score between 0 and 1. This score determines how likely a memory is to be surfaced during recall and how long it survives before pruning.

Importance is influenced by:

- **Explicit importance** -- set at creation time (identity memories start high, casual observations start low)
- **Access frequency** -- memories that get recalled often are more important
- **Recency** -- recent memories score higher; old memories decay
- **Graph centrality** -- memories with many strong connections to other memories are more important

A background maintenance process runs periodically to decay old memories, prune memories that have fallen below a threshold, merge near-duplicates, and recompute graph centrality scores.

Identity and permanent-tagged memories are exempt from decay and pruning. They always survive.

The specific decay rates, scoring weights, and thresholds are implementation details that will be tuned with real data. The mechanisms matter; the numbers don't yet.

## Identity Files

Not everything is a graph memory. Some context is stable, foundational, and user-editable:

- **SOUL.md** -- core values, personality, tone
- **IDENTITY.md** -- agent name, nature
- **USER.md** -- user context

These are loaded into channel system prompts every time. They're files on disk, not database rows, because they change rarely and users should be able to edit them in a text editor.

What's gone:

- **MEMORY.md** -- replaced by dynamic memory selection from the graph
- **daily/YYYY-MM-DD.md** -- replaced by typed memories with timestamps
- **HEARTBEAT.md** -- replaced by database-stored cron job definitions

## Context Injection

When a channel starts or a cron job fires, memories are injected into the system prompt. This isn't a raw dump -- it's a curated selection:

1. Identity-tagged memories are always included
2. High-importance memories above a threshold are included
3. Recently accessed memories get a boost
4. The rest depends on what the conversation is about (populated by recall workers during the session)

The injected memories are grouped by type and formatted cleanly. The channel sees something like:

```
## Identity
- My name is Spacebot
- James is a software engineer who works on SpaceDrive

## Recent Context
- We decided to use SQLite for the prototype (2 days ago)
- James prefers short, direct responses (preference)
```

Not a wall of raw search results. Not everything in the database. Just what matters right now.

## Maintenance

A periodic background process handles graph hygiene:

- **Decay** -- reduce importance of old, unaccessed memories
- **Prune** -- delete memories below an importance floor (identity/permanent exempt)
- **Merge** -- combine near-duplicate memories (>0.95 similarity)
- **Reindex** -- recompute graph centrality scores

This is a scheduled job managed by the cortex. It runs in workers, doesn't block anything, and keeps the graph healthy over time.


# The memory system is BRANCH-based, not conversation history!
# Spacebot's design: conversation context comes from MEMORIES (via branch recall),
# not from replaying raw messages. The cortex synthesizes memories into a bulletin
# that gets injected into the system prompt.
#
# The compactor is supposed to EXTRACT memories before context is lost.
# So the real question: is the compactor/cortex running properly for Waffleson?

# Check cortex activity
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "
SELECT count(*) as memory_count FROM memories;" 2>/dev/null
echo "=== MEMORIES ==="
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "
SELECT content, type, importance, created_at FROM memories ORDER BY created_at DESC LIMIT 10;" 2>/dev/null
echo "=== CORTEX EVENTS ==="
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "
SELECT count(*) FROM cortex_events;" 2>/dev/null


68
=== MEMORIES ===
=== CORTEX EVENTS ===
40


# 68 memories exist but the query returned no rows?! Check schema
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db ".schema memories"


CREATE TABLE memories (
    id TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    memory_type TEXT NOT NULL,
    importance REAL NOT NULL DEFAULT 0.5,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    last_accessed_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    access_count INTEGER NOT NULL DEFAULT 0,
    source TEXT,
    channel_id TEXT
, forgotten INTEGER NOT NULL DEFAULT 0);
CREATE INDEX idx_memories_type ON memories(memory_type);
CREATE INDEX idx_memories_importance ON memories(importance);
CREATE INDEX idx_memories_forgotten ON memories(forgotten);
CREATE INDEX idx_memories_created_at ON memories(created_at);


sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "
SELECT id, substr(content,1,100), memory_type, importance, created_at
FROM memories
WHERE forgotten = 0
ORDER BY created_at DESC
LIMIT 15;"


3dd65b8c-0044-4472-8f13-a997e7f6824c|# SSH & Remote Access Reference

## Fred (Main Server)
- Hostname: fred.taile5e8a3.ts.net
- Tailscal|fact|0.899999976158142|2026-02-19T21:35:13.693626056+00:00
2ca3a189-72bf-4327-a648-86c69930f42e|ZeroClaw AI Agent running on Pixel 7 Pro: Model is MiniMax-M2.5 (via api.minimax.io/anthropic). Tele|fact|0.75|2026-02-19T15:38:57.068170941+00:00
ae436169-b736-43f7-aeac-f714f434bcdf|OpenClaw vision/image analysis model configuration: Primary is `anthropic/claude-haiku-4-5` (excelle|fact|0.75|2026-02-19T15:37:28.937549786+00:00
08d68384-b472-493a-931a-663124411853|SSH Hosts configuration reference: localhost (127.0.0.1:22, user: crogers2287, local), fred (fred:22|fact|0.75|2026-02-19T15:35:58.096621594+00:00
ff452fcf-2a98-402d-a390-a861259cecde|Key insight from phone farm research: Accessibility services (Tasker/AutoInput, MacroDroid) are more|observation|0.800000011920929|2026-02-19T15:33:53.591094479+00:00
c4925f72-5293-47be-bbfe-396fa90e0650|Next steps for Pixel automation: (1) Install Tasker + AutoInput on Pixel 7 Pro, (2) Create reusable |todo|0.75|2026-02-19T15:33:53.463635807+00:00
13595cee-4b12-45e6-8277-2a4eef31a8bd|Pixel automation improvement path: Immediate (use existing pixel CLI + accessibility bridge, schedul|decision|0.850000023841858|2026-02-19T15:33:53.266795053+00:00
2be22318-5a1d-4fa3-87db-8ac519e8f166|Maya's likely social media automation approach: scheduled posting tools, API access where available,|observation|0.699999988079071|2026-02-19T15:33:53.152571617+00:00
a24b331a-16aa-4d63-971e-96cee57e4861|Detection avoidance techniques for phone automation: (1) Human-like delays with random intervals bet|fact|0.800000011920929|2026-02-19T15:33:52.963887946+00:00
93f3fe79-d397-4111-a84f-68ba3b936e01|Social media automation specifics: Instagram (auto-post, like, follow, comment via Jarvee/custom bot|fact|0.75|2026-02-19T15:33:52.776754644+00:00
f0eeb221-7bc9-46a6-8f7b-4453ac95e37d|Phone farm automation methods: (1) ADB Automation - input tap/swipe/text, app launch, UI dump via pi|fact|0.800000011920929|2026-02-19T15:33:52.396173042+00:00
148cc304-63bd-4dd2-92b6-53c60d16aa3a|QMD search backend: BM25 + vector search + query expansion + LLM reranking, GPU-accelerated with 768|fact|0.699999988079071|2026-02-19T15:31:51.369344180+00:00
caff5d8e-1a3b-4724-973c-72c689241db0|Memory organization best practices for 80%+ recall target: (1) One topic per file - BM25 scores at f|fact|0.800000011920929|2026-02-19T15:31:51.147520044+00:00
92a20335-9e0c-4676-a757-e7e6a0fa27ce|Memory folder structure: memory/YYYY-MM-DD.md (daily logs, auto-loaded at session start), memory/dai|fact|0.75|2026-02-19T15:31:50.891691879+00:00
2c4bc3ef-ac5a-4572-9f83-acdf04b76570|Memory maintenance cycle (every few days during heartbeats): Read recent memory/YYYY-MM-DD.md files,|fact|0.800000011920929|2026-02-19T15:30:31.417667924+00:00


# 68 memories exist and they're rich. The memory SYSTEM works.
# The issue is: are these memories being INJECTED into the system prompt?
#
# Check the cortex bulletin (the synthesized memory context)
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "
SELECT substr(content,1,200), event_type, created_at FROM cortex_events ORDER BY created_at DESC LIMIT 5;"


Error: in prepare, no such column: content
   SELECT substr(content,1,200), event_type, created_at FROM cortex_events ORDER
                 ^--- error here

(Command exited with code 1)


sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db ".schema cortex_events"
echo "==="
sqlite3 /mnt/storage/spacebot/data/agents/waffleson/data/spacebot.db "SELECT * FROM cortex_events ORDER BY created_at DESC LIMIT 3;"


CREATE TABLE cortex_events (
    id TEXT PRIMARY KEY,
    event_type TEXT NOT NULL,
    summary TEXT NOT NULL,
    details TEXT,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX idx_cortex_events_type ON cortex_events(event_type, created_at);
CREATE INDEX idx_cortex_events_created ON cortex_events(created_at);
===
4ac6830b-a385-4bc6-a344-2b904ff50430|profile_generated|Profile generated: Waffleson — "deep in the phone automation stack" (15175ms)|{"display_name":"Waffleson","duration_ms":15175,"model":"zhipu/glm-5","status":"deep in the phone automation stack"}|2026-02-19 23:11:17
8ed7155b-31d9-4948-b876-692d741e888e|bulletin_generated|Bulletin generated: 249 words, 8 sections, 33797ms|{"duration_ms":33797,"model":"zhipu/glm-5","sections":8,"word_count":249}|2026-02-19 23:11:02
bdd7bb1d-affd-4b63-8974-8edb86bcff85|profile_generated|Profile generated: Waffleson — "deep in pixel automation planning" (20564ms)|{"display_name":"Waffleson","duration_ms":20564,"model":"zhipu/glm-5","status":"deep in pixel automation planning"}|2026-02-19 22:10:28


# Good! The cortex IS generating bulletins. The most recent was just 2 min ago.
# Now check if Waffleson's channel prompt includes the memory bulletin.
# Let me look at how the bulletin is injected into the system prompt.

grep -n "bulletin\|Memory Context\|memory_context\|inject_memories" /mnt/storage/spacebot/repo/src/agent/channel.rs | head -10
echo "==="
grep -n "bulletin" /mnt/storage/spacebot/repo/src/agent/cortex.rs | head -10


508:        let memory_bulletin = rc.memory_bulletin.load();
537:                empty_to_none(memory_bulletin.to_string()),
682:        let memory_bulletin = rc.memory_bulletin.load();
705:                empty_to_none(memory_bulletin.to_string()),
===
1://! Cortex: System-level observer and memory bulletin generator.
3://! The cortex's primary responsibility is generating the **memory bulletin** — a
5://! This bulletin is injected into every channel's system prompt, giving all
28:/// The cortex observes system-wide activity and maintains the memory bulletin.
246:/// Spawn the cortex bulletin loop for an agent.
248:/// Generates a memory bulletin immediately on startup, then refreshes on a
249:/// configurable interval. The bulletin is stored in `RuntimeConfig::memory_bulletin`
251:pub fn spawn_bulletin_loop(deps: AgentDeps, logger: CortexLogger) -> tokio::task::JoinHandle<()> {
253:        if let Err(error) = run_bulletin_loop(&deps, &logger).await {
254:            tracing::error!(%error, "cortex bulletin loop exited with error");


# The bulletin IS injected into the system prompt. So the memory system
# IS working. The issue is something else.
#
# Let me re-examine the actual failure. Waffleson at 21:49 says
# "I don't have context from a previous message"
# But the conversation history shows messages at 21:46 and 21:37.
#
# The real issue might be the COMPACTION kicking in too aggressively and
# wiping context. Or Spacebot's context window being too small for the
# system prompt + bulletin + history.
#
# Check the compaction settings
grep -B2 -A10 "compaction" /mnt/storage/spacebot/data/config.toml


"zhipu/glm-4.7" = ["zhipu/glm-4.7-flash"]

[defaults.compaction]
background_threshold = 0.80
aggressive_threshold = 0.85
emergency_threshold = 0.95

[defaults.coalesce]
enabled = true
debounce_ms = 3000
max_wait_ms = 10000
min_messages = 2
multi_user_only = false


# Compaction thresholds: 80% background, 85% aggressive, 95% emergency
# context_window = 32768 for Waffleson (defaults)
#
# The system prompt alone is ~8000 tokens (SOUL + IDENTITY + USER + channel prompt +
# memory bulletin + skills). That's ~25% of 32K.
# With compaction at 80%, that leaves only about 55% for actual conversation.
# That's about 18K tokens of chat history before compaction fires.
# With the ESP32 discussion (long tool results, worker summaries), that could
# fill fast - maybe 10-15 exchanges.
#
# But compaction shouldn't cause "I don't have context" - it should SUMMARIZE,
# not delete. Unless compaction is failing.
#
# The real answer: increase context_window for Waffleson. GLM-5 supports 128K.

# Also check: what's the default LLM timeout for ollama?
grep -n "timeout\|request_timeout\|api_timeout" /mnt/storage/spacebot/data/config.toml
echo "==="
grep -rn "timeout\|DEFAULT_TIMEOUT" /mnt/storage/spacebot/repo/src/llm/model.rs | head -10


===
92:    /// a 429/rate-limit (as opposed to a timeout or server error), so the caller


# Check timeout values in the LLM client
grep -n "timeout\|Duration\|TIMEOUT\|secs\|30\|60\|120" /mnt/storage/spacebot/repo/src/llm/model.rs | head -20


92:    /// a 429/rate-limit (as opposed to a timeout or server error), so the caller
118:                tokio::time::sleep(std::time::Duration::from_millis(delay_ms)).await;
193:        let cooldown = routing.rate_limit_cooldown_secs;
272:            let elapsed = start.elapsed().as_secs_f64();


# Check reqwest client timeout
grep -rn "reqwest\|Client::builder\|timeout" /mnt/storage/spacebot/repo/src/llm/ --include="*.rs" | head -10


/mnt/storage/spacebot/repo/src/llm/manager.rs:18:    http_client: reqwest::Client,
/mnt/storage/spacebot/repo/src/llm/manager.rs:26:        let http_client = reqwest::Client::builder()
/mnt/storage/spacebot/repo/src/llm/manager.rs:27:            .timeout(std::time::Duration::from_secs(120))
/mnt/storage/spacebot/repo/src/llm/manager.rs:77:    pub fn http_client(&self) -> &reqwest::Client {
/mnt/storage/spacebot/repo/src/llm/model.rs:92:    /// a 429/rate-limit (as opposed to a timeout or server error), so the caller
/mnt/storage/spacebot/repo/src/llm/routing.rs:97:        || lower.contains("timeout")
/mnt/storage/spacebot/repo/src/llm/routing.rs:189:/// Only rate-limit errors should trigger cooldown — timeouts and 5xx errors are


# 120 second timeout. Nemotron should respond in 120s for sure.
# Let me check what's ACTUALLY causing "exhausted retries"
grep -n "exhausted retries\|max_retries\|MAX_RETRIES\|retry_count" /mnt/storage/spacebot/repo/src/llm/model.rs | head -5
echo "==="
sed -n '80,130p' /mnt/storage/spacebot/repo/src/llm/model.rs


5:    self, RoutingConfig, MAX_FALLBACK_ATTEMPTS, MAX_RETRIES_PER_MODEL, RETRY_BASE_DELAY_MS,
109:        for attempt in 0..MAX_RETRIES_PER_MODEL {
144:                "{model_name} failed after {MAX_RETRIES_PER_MODEL} attempts: {error_str}"
224:                        "primary model exhausted retries, trying fallbacks"
257:                        "fallback model exhausted retries, continuing chain"
===
            "opencode-zen" => self.call_opencode_zen(request).await,
            "nvidia" => self.call_nvidia(request).await,
            other => Err(CompletionError::ProviderError(format!(
                "unknown provider: {other}"
            ))),
        }
    }

    /// Try a model with retries and exponential backoff on transient errors.
    ///
    /// Returns `Ok(response)` on success, or `Err((last_error, was_rate_limit))`
    /// after exhausting retries. `was_rate_limit` indicates the final failure was
    /// a 429/rate-limit (as opposed to a timeout or server error), so the caller
    /// can decide whether to record cooldown.
    async fn attempt_with_retries(
        &self,
        model_name: &str,
        request: &CompletionRequest,
    ) -> Result<
        completion::CompletionResponse<RawResponse>,
        (CompletionError, bool),
    > {
        let model = if model_name == self.full_model_name {
            self.clone()
        } else {
            SpacebotModel::make(&self.llm_manager, model_name)
        };

        let mut last_error = None;
        for attempt in 0..MAX_RETRIES_PER_MODEL {
            if attempt > 0 {
                let delay_ms = RETRY_BASE_DELAY_MS * 2u64.pow((attempt - 1) as u32);
                tracing::debug!(
                    model = %model_name,
                    attempt = attempt + 1,
                    delay_ms,
                    "retrying after backoff"
                );
                tokio::time::sleep(std::time::Duration::from_millis(delay_ms)).await;
            }

            match model.attempt_completion(request.clone()).await {
                Ok(response) => return Ok(response),
                Err(error) => {
                    let error_str = error.to_string();
                    if !routing::is_retriable_error(&error_str) {
                        // Non-retriable (auth error, bad request, etc) — bail immediately
                        return Err((error, false));
                    }
                    tracing::warn!(
                        model = %model_name,


grep -n "MAX_RETRIES_PER_MODEL\|MAX_FALLBACK\|RETRY_BASE" /mnt/storage/spacebot/repo/src/llm/routing.rs


180:pub const MAX_FALLBACK_ATTEMPTS: usize = 3;
183:pub const MAX_RETRIES_PER_MODEL: usize = 3;
186:pub const RETRY_BASE_DELAY_MS: u64 = 500;


# 3 retries per model with 500ms base delay (exponential: 500, 1000, 2000ms)
# So Nemotron gets 3 tries, then falls back.
# With 120s timeout per try, that's up to 6 minutes before fallback.
# But the logs show it falling back very quickly.
#
# Let me check what error Nemotron actually returns.
# Check is_retriable_error to understand what triggers retries vs immediate bail

sed -n '85,110p' /mnt/storage/spacebot/repo/src/llm/routing.rs


}

/// Whether a completion error message indicates a retriable failure.
pub fn is_retriable_error(error_message: &str) -> bool {
    let lower = error_message.to_lowercase();
    // Rate limits and server errors
    lower.contains("429")
        || lower.contains("502")
        || lower.contains("503")
        || lower.contains("504")
        || lower.contains("rate limit")
        || lower.contains("overloaded")
        || lower.contains("timeout")
        || lower.contains("connection")
        // Empty/malformed responses are transient provider issues
        || lower.contains("empty response")
        || lower.contains("failed to read response body")
        || lower.contains("error decoding response body")
}

/// Whether a completion error indicates context window overflow.
///
/// Providers return 400 with various phrasings when the request exceeds
/// the model's context limit. Checking for these lets workers compact
/// and retry instead of dying.
pub fn is_context_overflow_error(error_message: &str) -> bool {


# Let me get the ACTUAL Nemotron error from the logs
docker logs spacebot 2>&1 | grep -A1 "Nemotron.*failed\|attempt.*ollama\|error.*nemotron\|warn.*nemotron\|retrying.*ollama" | grep -v "system_instructions" | head -20


[2m2026-02-19T22:49:28.169087Z[0m [31mERROR[0m [2mspacebot::messaging::telegram[0m[2m:[0m telegram getUpdates failed [3merror[0m[2m=[0mA network error: error sending request for url (https://api.telegram.org/token:redacted/GetUpdates)
[2m2026-02-19T22:49:30.553509Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0m3c593111-0a23-4fa6-850a-f5fba248abfc[1m}[0m[2m:[0m[1mrun_agent_turn[0m[1m{[0m[3mconversation_id[0m[2m=[0m"webhook:tg-6558388607" [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mrig::agent::prompt_request[0m[2m:[0m Depth reached: 1/100
--
[2m2026-02-19T22:53:30.197858Z[0m [31mERROR[0m [2mspacebot::messaging::telegram[0m[2m:[0m telegram getUpdates failed [3merror[0m[2m=[0mA network error: error sending request for url (https://api.telegram.org/token:redacted/GetUpdates)
[2m2026-02-19T22:53:37.349036Z[0m [32m INFO[0m [2mspacebot::agent::channel[0m[2m:[0m worker spawned [3mworker_id[0m[2m=[0md0bcf8d9-8f79-4bf5-a6dd-ef74a8fde05d [3mtask[0m[2m=[0mSSH into Desktop2025 (ssh croge@100.87.125.22) and investigate intermittent network drops by checking Windows system logs.
--
[2m2026-02-19T22:53:37.460410Z[0m [32m INFO[0m [1mworker.run[0m[1m{[0m[3mworker_id[0m[2m=[0md0bcf8d9-8f79-4bf5-a6dd-ef74a8fde05d [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3mtask[0m[2m=[0mSSH into Desktop2025 (ssh croge@100.87.125.22) and investigate intermittent network drops by checking Windows system logs.
--
[2m2026-02-19T22:53:45.859906Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0mf09dab50-0190-4055-8900-1ddf27ed492e[1m}[0m[2m:[0m[1mrun_agent_turn[0m[1m{[0m[3mconversation_id[0m[2m=[0m"webhook:tg-6558388607" [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mrig::agent::prompt_request[0m[2m:[0m Depth reached: 2/100
--
[2m2026-02-19T22:56:48.220746Z[0m [31mERROR[0m [2mspacebot::messaging::telegram[0m[2m:[0m telegram getUpdates failed [3merror[0m[2m=[0mA network error: error sending request for url (https://api.telegram.org/token:redacted/GetUpdates)
[2m2026-02-19T22:56:48.872869Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0mbcd2954d-d95a-44fe-a2af-2a0da017aab9[1m}[0m[2m:[0m[1mrun_agent_turn[0m[1m{[0m[3mconversation_id[0m[2m=[0m"webhook:tg-6558388607" [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mrig::agent::prompt_request[0m[2m:[0m Depth reached: 1/100
--
[2m2026-02-19T22:59:13.605679Z[0m [31mERROR[0m [2mspacebot::messaging::telegram[0m[2m:[0m telegram getUpdates failed [3merror[0m[2m=[0mA network error: error sending request for url (https://api.telegram.org/token:redacted/GetUpdates)
--
[2m2026-02-19T22:59:43.567484Z[0m [32m INFO[0m [2mspacebot::agent::channel[0m[2m:[0m worker spawned [3mworker_id[0m[2m=[0m6e607073-f50f-4350-ba9b-ef58610315c1 [3mtask[0m[2m=[0mSSH into Desktop2025 (ssh croge@100.87.125.22) and fix the Acer handle leak causing network drops.
--
[2m2026-02-19T22:59:43.663997Z[0m [32m INFO[0m [1mworker.run[0m[1m{[0m[3mworker_id[0m[2m=[0m6e607073-f50f-4350-ba9b-ef58610315c1 [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3mtask[0m[2m=[0mSSH into Desktop2025 (ssh croge@100.87.125.22) and fix the Acer handle leak causing network drops.
--
[2m2026-02-19T22:59:50.522295Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0m2a30a543-8f41-4ba9-8781-4cfade38308f[1m}[0m[2m:[0m[1mrun_agent_turn[0m[1m{[0m[3mconversation_id[0m[2m=[0m"webhook:tg-6558388607" [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mrig::agent::prompt_request[0m[2m:[0m Depth reached: 2/100


# Interesting! Recent ouroboros logs show "Depth reached: 1/100" and "2/100" -
# these are actual SUCCESSFUL agent turns (agentic loop running).
# And workers are being spawned: "SSH into Desktop2025".
#
# So ouroboros IS working now via GLM-5 fallback. But we want Nemotron primary.
#
# Let me get the specific Nemotron errors that cause "exhausted retries"
docker logs spacebot 2>&1 | grep -B2 "exhausted retries.*Nemotron" | grep -v "system_instructions" | tail -10


[2m2026-02-19T23:09:56.254547Z[0m [32m INFO[0m [1mgenerate_bulletin[0m[1m{[0m[3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mspacebot::agent::cortex[0m[2m:[0m cortex generating memory bulletin
--
- CLEAN: No issues found

--
No identity details, preferences, goals, or recent conversation history are currently stored. This may be a new or reset memory state, or the user prefers minimal retention.
[2m2026-02-19T23:10:11.495706Z[0m [32m INFO[0m [1mgenerate_profile[0m[1m{[0m[3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mspacebot::agent::cortex[0m[2m:[0m cortex generating agent profile
--
[2m2026-02-19T23:11:24.875948Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0m3feb5614-386f-4e8b-841d-5c713940ce5c[1m}[0m[2m:[0m [2mspacebot::agent::channel[0m[2m:[0m handling message [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3mmessage_id[0m[2m=[0m3feb5614-386f-4e8b-841d-5c713940ce5c
[2m2026-02-19T23:11:24.878583Z[0m [32m INFO[0m [1mhandle_message[0m[1m{[0m[3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros [3mmessage_id[0m[2m=[0m3feb5614-386f-4e8b-841d-5c713940ce5c[1m}[0m[2m:[0m[1mrun_agent_turn[0m[1m{[0m[3mconversation_id[0m[2m=[0m"webhook:tg-6558388607" [3mchannel_id[0m[2m=[0mwebhook:tg-6558388607 [3magent_id[0m[2m=[0mouroboros[1m}[0m[2m:[0m [2mrig::agent::prompt_request[0m[2m:[0m Current conversation depth: 1/100


# Let me search more specifically for the nemotron error messages
docker logs spacebot 2>&1 | grep "ollama/Nemotron" | grep -v "system_instructions\|exhausted retries\|fallback model succeeded" | head -10


(no output)


# No error messages - only "exhausted retries" and "fallback succeeded".
# The actual error is being swallowed. Let me check the retry loop output
docker logs spacebot 2>&1 | grep -i "retrying\|attempt.*fail\|error.*ollama\|connection.*refuse" | grep -v "telegram\|getUpdates" | head -10