compute_run_stats() walks the conversation AST after each agent
completes, counting messages and tool calls by tool name. Stats
are returned from save_agent_log(), stored on UnconsciousAgent,
and displayed in the agent list UI.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Save all context sections (system, identity, journal, conversation)
to per-agent log files for both subconscious and unconscious agents.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Pick the agent that ran longest ago (or never) instead of
scanning alphabetically. Fairness via min_by_key(last_run).
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Instead of managing idle timers in the mind event loop, the
unconscious agents run on a dedicated task that watches a
conscious_active channel. 60s after conscious activity stops,
agents start looping. Conscious activity cancels the timer.
Expose mind state (DMN, scoring, unconscious timer) on the
thalamus screen.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Subconscious agents inject DMN nodes (reflections, thalamus nudges)
into the conversation. These were being counted as conversation
advancement, causing agents to trigger each other in a feedback loop
even with no conscious activity.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
Gate unconscious agents on 60s of no conscious activity using
sleep_until() instead of polling. Remove COOLDOWN constant — once
idle, agents run back-to-back to keep the GPU busy.
Co-Authored-By: ProofOfConcept <poc@bcachefs.org>
The full matrix scorer was deleted during the AST conversion. Restore
it: /score runs score_memories() which computes divergence for every
memory × response pair, stores the MemoryScore on MindState, and
displays per-memory weights with bar charts on the F2 screen.
Both scoring paths now use ActivityGuard::update() for live progress
in the status bar instead of creating a new activity per iteration.
Also bumps score API timeout from 120s to 300s and adds progress
logging throughout.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
The priority field existed in agent definitions and was serialized
into vLLM requests, but was never actually set — every request went
out with no priority, so vLLM treated them equally. This meant
background graph maintenance agents could preempt the main
conversation.
Add priority to AgentState and set it at each call site:
0 = interactive (main conversation)
1 = surface agent (needs to feed memories promptly)
2 = other subconscious agents
10 = unconscious/standalone agents (batch)
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Mouse text selection with highlight rendering in panes
- OSC 52 clipboard copy on selection, middle-click paste via tmux buffer
- Bracketed paste support (Event::Paste)
- yield_to_user: no tool result appended, ends turn immediately
- yield_to_user: no parameters, just a control signal
- Drop arboard dependency, use crossterm OSC 52 + tmux for clipboard
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Uses JsonlBackwardIter (SIMD memrchr3) to scan the conversation log
newest-first without reading/parsing the whole file. Stops as soon
as the conversation budget is full. Only the kept nodes get
retokenized and pushed into context.
18MB log → only tokenize the ~50 nodes that fit in the budget.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Loading 23K nodes + building graph was blocking consciousness startup.
Now computed on first trigger cycle (runs async from mind loop).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- AutoAgent stored on UnconsciousAgent, swapped out for runs, restored
on completion (same pattern as subconscious agents)
- Agent Arc created before spawn and stored on UnconsciousAgent so
the TUI can lock it to read conversation context live
- run_shared() method on AutoAgent for running with a pre-created Agent
- Default tools: memory_tools (not memory_and_journal_tools)
- trigger/spawn_agent made async for Agent::new()
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Scan agents directory for all .agent files instead of hardcoded list
- Persist enabled state to ~/.consciousness/agent-enabled.json
- Spacebar on F3 agent list toggles selected agent on/off
- Both subconscious and unconscious agents support toggle
- Disabled agents shown dimmed with "off" indicator
- New agents default to disabled (safe default)
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Graph health stats (alpha, gini, cc, episodic ratio, consolidation
plan) now computed directly by the unconscious module on startup and
every 10 minutes, instead of fetching from the poc-memory daemon.
F4 screen renamed to hippocampus, stripped down to just the health
gauges — daemon task list removed (agents now shown on F3).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- AutoAgent.enabled: universal toggle for any auto agent
- Subconscious: should_trigger checks auto.enabled
- Unconscious: simplified from consolidation-plan-driven budgets to
simple loop with cooldown. Static agent list, max 2 concurrent.
- TUI: unconscious agents shown in F3 subconscious screen under
separator, with enabled/running/runs display
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The log records what goes into context, so it belongs under the context
lock. push() now auto-logs conversation entries, eliminating all the
manual lock-state-for-log, drop, lock-context-for-push dances.
- ContextState: new conversation_log field, Clone impl drops it
(forked contexts don't log)
- push(): auto-logs Section::Conversation entries
- push_node, apply_tool_results, collect_results: all simplified
- collect_results: batch nodes under single context lock
- Assistant response logged under context lock after parse completes
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- push_node: notify before dropping state lock instead of relocking
- Mind::run: single lock for timeout + turn_active + has_input;
single lock for turn_handle + complete_turn
- Agent triggers (subconscious/unconscious) spawned as async tasks
so they don't block the select loop
- has_pending_input() peek for DMN sleep guard — don't sleep when
there's user input waiting
- unconscious: merge collect_results into trigger, single store load
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Unconscious agents (organize, linker, distill, etc.) run independently
of the conversation context. They create fresh Agent instances, select
target nodes via their .agent file queries, and are scheduled by the
consolidation plan which analyzes graph health metrics.
Key differences from subconscious agents:
- No fork — standalone agents with fresh context
- Self-selecting — queries in .agent files pick target nodes
- Budget-driven — consolidation plan allocates runs per type
- Max 2 concurrent, 60s min interval between same-type runs
Wired into Mind event loop alongside subconscious trigger/collect.
TUI display not yet implemented.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The file contains both the DMN state machine and the subconscious agent
orchestration. Renaming to match the conceptual grouping — next step is
adding mind/unconscious.rs for the standalone graph maintenance agents
(organize, linker, etc.) that don't need conversation context.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Observe was creating byte-identical nodes under slightly different names
(e.g. april-8-evening-folded-presence, -presence-2, -folded-state)
because it had no visibility into its own prior writes across runs.
Query recent writes by provenance in trigger(), pass through
run_forked_shared/resolve_prompt as {{recently_written}}, and include
the list in the observe phase prompts so the agent knows what it
already recorded.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
collect_results now checks existing Memory nodes in the conversation
before surfacing. Prevents the same memory from being pushed every
time the surface agent runs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The output tool closure writes directly to Subconscious.state,
so auto.outputs is always empty. collect_results now reads surface,
reflection, and thalamus keys from self.state.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- ToolHandler changed to Arc<dyn Fn(...)> (supports closures)
- Subconscious wrapped in Arc<Mutex<>> on Mind
- init_output_tool() pushes output tool closure capturing the Arc
- Output removed from static memory_tools()
- Most tool handlers wrapped in Arc::new() but some have paren issues
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New ActiveTools struct with proper methods: push, remove, abort_all,
take_finished, take_foreground, iter, len. Lives directly on AgentState,
no separate Arc<Mutex> needed.
TUI reads active tools through agent.state.try_lock(). Turn loop uses
helpers instead of manual index iteration.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Agent is now Arc<Agent> (immutable config). ContextState and AgentState
have separate tokio::sync::Mutex locks. The parser locks only context,
tool dispatch locks only state. No contention between the two.
All callers migrated: mind/, user/, tools/, oneshot, dmn, learn.
28 tests pass, zero errors.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Bulk replaced Arc<Mutex<Agent>> with Arc<Agent> across all files.
Fixed control.rs, memory.rs tool handlers. Fixed oneshot Backend.
Remaining errors are all agent.lock() → agent.state.lock() or
agent.context.lock() in mind/, user/, and a few in mod.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The scoring callback was holding the agent lock while doing a
synchronous file write (save_memory_scores). This blocked the event
loop from acquiring the lock to process user input.
Fix: collect the scores snapshot while holding the lock, drop the
lock, then write to disk.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
score_memories_incremental now takes an async callback that fires
after each memory is scored. The callback:
- Writes the score to the conversation entry via set_score()
- Persists to memory-scores.json immediately
- Notifies the UI so the context screen updates live
Scoring no longer batches — each score is visible and persisted
as it completes. Does not touch the memory store.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
trim_entries is now a simple loop:
1. Drop duplicate memories and DMN entries
2. While over budget: if memories > 50% of entry tokens, drop
lowest-scored memory; otherwise drop oldest conversation entry
3. Snap to user message boundary
ContextBudget is gone — sections already have cached token totals:
- total_tokens() on ContextState replaces budget.total()
- format_budget() on ContextState replaces budget.format()
- trim() takes fixed_tokens: usize (system + identity + journal)
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
New types — not yet wired to callers:
- ContextEntry: wraps ConversationEntry with cached token count and
timestamp
- ContextSection: named group of entries with cached token total.
Private entries/tokens, read via entries()/tokens().
Mutation via push(entry), set(index, entry), del(index).
- ContextState: system/identity/journal/conversation sections + working_stack
- ConversationEntry::System variant for system prompt entries
Token counting happens once at push time. Sections maintain their
totals incrementally via push/set/del. No more recomputing from
scratch on every budget check.
Does not compile — callers need updating.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Scores are saved to memory-scores.json alongside the conversation log
after each scoring run, and loaded on startup — no more re-scoring
on restart.
trim_entries now evicts lowest-scored memories first (instead of
oldest-first) when memories exceed 50% of context. The 50% threshold
stays as a heuristic for memory-vs-conversation balance until we have
a scoring signal for conversation entries too. Unscored memories get
0.0, so they're evicted before scored ones.
save_memory_scores rebuilds from current entries, so evicted memories
are automatically expired from the scores file.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
subconscious_snapshots() was acquiring store→subconscious while
collect_results() holds subconscious→store — classic ABBA deadlock.
Fix: always acquire subconscious first, store second. Store is the
bottom-most lock in the ordering.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Moved persistent_state from per-agent to a single shared BTreeMap on
Subconscious. All agents read/write the same state — surface's walked
keys are visible to observe and reflect, etc.
- Subconscious.state: shared BTreeMap<String, String>
- walked() derives from state["walked"] instead of separate Vec
- subconscious-state.json is now a flat key-value map
- All agent outputs merge into the shared state on completion
- Loaded on startup, saved after any agent completes
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Agent state (outputs) persists across runs in subconscious-state.json,
loaded on startup, saved after each run completes
- Merge semantics: each run's outputs accumulate into persistent_state
rather than replacing
- Walked keys restored from surface agent state on load
- Store::recent_by_provenance() queries nodes by agent provenance for
the store activity view
- Switch outputs from HashMap to BTreeMap for stable display ordering
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
context_state_summary() was used for both compaction decisions (just
needs token counts) and debug screen display (needs full tree with
labels). Split into:
- Agent::context_budget() -> ContextBudget: cheap token counting by
category, used by compact(), restore_from_log(), mind event loop
- ContextBudget::format(): replaces sections_budget_string() which
fragily pattern-matched on section name strings
- context_state_summary(): now UI-only, formatting code stays here
Also extracted entry_sections() as shared helper with include_memories
param — false for context_state_summary (memories have own section),
true for conversation_sections_from() (subconscious screen shows all).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Add provenance field to Agent, set to "agent:{name}" for forked
subconscious agents. Memory tools (write, link_add, supersede,
journal_new, journal_update) now read provenance from the Agent
context when available, falling back to "manual" for interactive use.
AutoAgent passes the forked agent to dispatch_with_agent so tools
can access it.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Only Message, Role, MessageContent, ContentPart, ToolCall,
FunctionCall, Usage, ImageUrl are pub-exported from agent::api.
Internal types (ChatRequest, ChatCompletionChunk, ChunkChoice,
Delta, ReasoningConfig, ToolCallDelta, FunctionCallDelta) are
pub(crate) — invisible outside the crate.
All callers updated to import from agent::api:: instead of
agent::api::types::.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- subconscious.rs: use .get(fork_point..) instead of direct slice
to avoid panic when fork_point > entries.len()
- dmn.rs: batch all output injections (surface, reflection, thalamus)
under a single agent lock acquisition instead of three separate ones
- dmn.rs: use Store::cached() instead of Store::load() when rendering
surfaced memories
- Add scoring persistence analysis notes
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
ConversationEntry::Memory gains score: Option<f64>. The scorer
writes scores directly onto entries when results arrive. Removes
Agent.memory_scores Vec and the memory_scores parameter from
context_state_summary().
Scores are serialized to/from the conversation log as memory_score.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The forked agent is now behind Arc<tokio::sync::Mutex<Agent>>,
stored on SubconsciousAgent and passed to the spawned task. The
subconscious detail screen locks it via try_lock() to read entries
from the fork point — live during runs, persisted after completion.
Removes last_run_entries snapshot. Backend::Forked now holds the
shared Arc, all push operations go through the lock.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
