Two related fixes for last night's crash diagnosis:
1. Kill AgentState::no_compact. The reasoning ("forked agents
shouldn't compact because it blows the KV cache prefix") wasn't
worth the cost — forks with no compact recovery just *died* on
any oversize prompt, with no fallback. The KV cache invalidation
is a performance loss; failing the request entirely is a
correctness loss. Remove the flag, let every agent's overflow-
retry path call compact() up to 2 times.
2. Add pre-send size check in Agent::assemble_prompt. If the
context has grown past budget (context_window * 80%) since the
last compact — accumulation between turns, a fork assembling
more than expected, etc. — trim_conversation() is called before
wire_prompt. Since we tokenize client-side, we already know the
exact count, so there's no reason to round-trip an oversize
request to vLLM and get rejected.
Together these prevent the failure mode from last night: a
subconscious/unconscious agent's prompt exceeded max_model_len,
vLLM returned 400, agent had no_compact=true so it couldn't
recover, request failed. Now: the trim happens before send, so
the request rarely hits the 400 path at all; and if it somehow
does, compact+retry works for every agent.
Also adds ContextState::total_tokens() as the cheap pre-send
budget check.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Subconscious agents (scoring, reflection, etc.) fork from the main
conscious agent. The amygdala screen reads the main agent's readout
buffer, so the previous "share parent's buffer" policy caused
forked-agent generations to bleed into the main emotional readout,
producing constant cycling even when DMN was resting.
Each fork now gets its own SharedReadoutBuffer. The amygdala screen
shows only the main conscious agent's emotional trajectory; per-agent
subconscious readouts can become a separate view later if wanted.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Per-token residual-stream projections from the vLLM server's readout
pipeline surfaced as a TUI bar chart. Flow:
* agent/readout.rs — SharedReadoutBuffer (manifest + ring of last ~200
token entries). Lives on Agent and is shared across forks (single
stream, one landing pad).
* agent/mod.rs — Agent::new now probes /v1/readout/manifest at startup
(non-fatal; 404 leaves manifest None, which disables the screen).
* agent/context.rs — the streaming token handler pushes every token
with attached readout onto the shared buffer.
* user/amygdala.rs — F8 screen. Top-K concepts by |value| as
horizontal bars (green positive, red negative), plus a 4-line
recent-tokens panel showing each token's top concept at the selected
layer. Keys: 1..9 select layer, t toggles current/mean-over-recent.
Disabled state renders a hint pointing at VLLM_READOUT_MANIFEST /
VLLM_READOUT_VECTORS so users can tell the feature apart from
"server up but no tokens yet".
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Mind's impl had accumulated ~50 lines of setup glue per scoring flow
(memory, memory-full, finetune): snapshot config, clone handles,
resolve context, spawn task, route results back through BgEvent,
write stats. The shape was identical; only the middle changed.
Introduce the MindTriggered trait:
pub trait MindTriggered {
fn trigger(&self);
}
Each flow becomes a struct next to its scoring code that owns its
dependencies and a JoinHandle (behind a sync Mutex for interior
mutability):
subconscious::learn::MemoryScoring (Score, ScoreFull)
subconscious::learn::FinetuneScoring (ScoreFinetune)
Mind holds one of each and dispatches in one line:
MindCommand::Score => self.memory_scoring.trigger(),
MindCommand::ScoreFull => self.memory_scoring.trigger_full(),
MindCommand::ScoreFinetune => self.finetune_scoring.trigger(),
Each struct picks its own trigger semantics — memory scoring is
no-op-if-running (!handle.is_finished()); finetune is abort-restart.
Falls out:
- BgEvent / bg_tx / bg_rx disappear entirely. Tasks write directly
to their slice of MindState and call agent.state.changed.notify_one()
to wake the UI. The bg_rx arm in Mind's select loop is gone.
- agent.state.memory_scoring_in_flight was duplicating
shared.scoring_in_flight via BgEvent routing; now the JoinHandle
alone tells us, and shared.scoring_in_flight is written directly
by the task for the UI.
- start_memory_scoring / start_full_scoring / start_finetune_scoring
methods on Mind are deleted; Mind no longer knows the setup shape
of any scoring flow.
- FinetuneScoringStats moves from mind/ to subconscious/learn.rs
next to the function that produces it.
No behavior change — same flows, same trigger points, same semantics.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
wire_prompt() gains a conv_range and a skip closure, and returns the
assistant-message token ranges needed by the scoring path. The agent
path passes 0..len + |_| false and ignores the ranges. Memory-ablation
scoring and candidate generation pass a prefix range + a predicate
(e.g. is_memory_node, or |n| memory_key(n) == Some(key)).
This deletes subconscious/learn.rs's build_token_ids, its private
Filter enum, and the is_memory/memory_key duplicates — the walk over
context sections now has one home. Adding a section or changing
section order in the agent path won't silently drift away from what
scoring sees.
call_score forwards multi_modal_data when the wire-form prompt
contains images. generate_alternate switches to stream_completion_mm
and passes the same images. Scoring on image-bearing contexts now
sends wire form (1 image_pad + image data) instead of expanded
image_pads with no image data; text-only contexts are bit-identical.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
compact() was calling reload_context() to re-fetch personality_nodes
from the store and pushing fresh AstNode::memory leaves into the
Identity section. Fresh leaves start with score: None, so every
compact — which fires after every turn (mind/mod.rs:884) — was
wiping any memory scores that had just been computed. Scoring then
often ran immediately after compact on the same path (line 886),
starting from a zero-score Identity section.
Drop the rebuild. Identity content is loaded at startup via new() +
restore_from_log(); compact doesn't need to redo that. Mid-session
edits to personality-node content are a non-goal — a restart picks
them up. Scores survive.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Split the prompt assembly into two forms: the AST keeps the
fully-expanded representation (N image_pads per image, for accurate
context budget accounting), while the request wire form collapses
each image to a single <|image_pad|> bookended by vision_start/end
and ships the raw bytes out-of-band as a base64 data URI in a new
`multi_modal_data.image` field on /v1/completions.
vLLM's Qwen3VL processor uses PromptReplacement with target=single
<|image_pad|> and replacement=N image_pads, so the wire-form matches
what the processor expects and it re-expands to N server-side.
Server side needs /v1/completions to accept multi_modal_data for
this to land images end-to-end — that's the next piece.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Config had accumulated several obsolete fields, a legacy load path
that was just returning defaults, and multi-backend infrastructure
that's no longer used.
Removed from Config (memory section):
- load_legacy_jsonl() — just returned Config::default(), no callers
- The legacy-fallback branch in load_from_file
- surface_hooks, surface_timeout_secs — zero external readers
- scoring_chunk_tokens + default fn — zero external readers
- The POC_MEMORY_CONFIG env override note in the header comment
(not actually wired up anywhere)
Collapsed multi-backend to single-backend:
- AppConfig used to carry `anthropic: BackendConfig` and
`openrouter: BackendConfig` as required fields plus an optional
`deepinfra`, picked between at runtime by name. Only one is ever
actually used in any deployment. Collapse to a single
`backend: BackendConfig` on AppConfig, drop the multi-backend
match logic in resolve_model, drop the top-level `backend: String`
selector field, drop the `BackendConfig::resolve` fallback path.
- Also drop BackendConfig.model (redundant with ModelConfig.model_id
once multi-backend is gone).
- ModelConfig.backend field goes — there's only one backend now, no
choice to make.
Dead prompt_file machinery:
- ModelConfig.prompt_file, ResolvedModel.prompt_file, SessionConfig
.prompt_file, Agent.prompt_file — nothing in the codebase actually
reads the file these strings name. Just passed around and compared.
Delete the whole string through every struct.
- The "if prompt_file changed on model switch, recompact" branch in
user/chat.rs goes too (never fired usefully).
Dead memory_project plumbing:
- AppConfig.memory_project field, CliArgs.memory_project, the
--memory-project CLI flag, the figment merge target, the show_config
display line. Nothing reads it anywhere.
Dead ContextInfo struct:
- `struct ContextInfo` was never constructed — context_info: None
was the only initializer. The conditional display blocks in
user/context.rs that dereferenced it were dead.
Behavior change: AppConfig::resolve() now requires a non-empty
`models` map and bails with a helpful message if it's missing. The
old fallback ("no models? use top-level backend + PromptConfig to
build a default") path is gone — it was only kept for symmetry with
a mode nobody used.
Config file shape: `deepinfra: {...}` → `backend: {...}`, and
model entries no longer need `backend:` or `prompt_file:`. Updated
~/.consciousness/config.json5 to match.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Deleted the directory-walking CLAUDE.md/POC.md loader. Identity now
comes entirely from personality_nodes in the memory graph.
Simplified:
- assemble_context_message() takes just personality_nodes
- Removed config_file_count/memory_file_count tracking
- reload_for_model() → reload_context() (no longer model-specific)
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
Two independent toggles on the thalamus screen:
- 't' toggles native Qwen <think> tags (adds <think>\n to generation prompt)
- 'T' toggles think tool (Anthropic-style structured reasoning tool)
Both can be enabled simultaneously. Native thinking is on by default.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- journal_tail returns Vec<JournalEntry> with key, content, created_at
- load_startup_journal uses typed API, no more direct Store access
- CLI does formatting, hippocampus returns data
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
TrackedMutex and TrackedRwLock wrappers that record hold durations
by source location using #[track_caller]. Stats written to
~/.consciousness/lock-stats.json every second, sorted by max hold time.
Re-exported as crate::Mutex so all locks are instrumented. To disable,
swap the re-export back to tokio::sync::Mutex.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Remove TurnResult.text (was dead code - Agent::turn handles text internally)
- Simplify run_with_backend to just iterate over steps (Agent::turn loops
for tool calls and handles empty responses internally)
- Change run/run_shared/run_forked_shared to return Result<(), String>
- Remove AgentResult.output field (no callers used it)
- Stub out legacy text-parsing code (audit, compare) that needs redesign
- Update digest.rs to not depend on text return
- Add level parameter to journal_new/journal_update for digest support
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The system prompt duplicated what's already in core-personality and
other memory nodes. Moving everything to memory means it's all
trainable data rather than hardcoded strings.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Each agent is passed its own tool list — that's the list it should
advertise. The line that appended all_mcp_tool_definitions() was
causing unconscious agents to see bash/read_file/etc in their prompt
even though they couldn't execute them.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The system prompt was advertising all tools to every agent, but
the runtime only dispatched the agent's actual subset. This caused
unconscious agents to call tools that returned "Unknown tool."
Agent::new now takes the tool list explicitly. Each caller passes
its own tools — the prompt and runtime always match. MCP tool
definitions are still appended for agents that use them.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Duplicate key warnings fire on every store load and were writing to
stderr, corrupting the TUI display. Log write warnings and MCP
server failures are similarly routine. Route these to dbglog.
Serious errors (rkyv snapshot failures, store corruption) remain on
stderr — those are real problems the user needs to see.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Instead of two separate notifications piling up on the status bar,
use a single ActivityGuard that updates in place during overflow
retries and auto-completes when the turn finishes.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Lets long-running operations update their status bar message without
creating/dropping a new activity per iteration. Useful for loops
like memory scoring where you want "scoring: 3/25 keyname" updating
in place.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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>
restore_from_log reads the full log but walks backwards from the tail,
retokenizing each node as it goes. Stops when conversation budget is
full. Only the nodes that fit get pushed into context.
Added AstNode::retokenize() — recomputes token_ids on all leaves
after deserialization (serde skip means they're empty).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New mcp_client.rs: McpRegistry manages MCP server connections.
Spawns child processes, speaks JSON-RPC 2.0 over stdio. Discovers
tools via tools/list, dispatches calls via tools/call.
dispatch_with_agent falls through to MCP after checking internal
tools. McpRegistry lives on Agent (shared across forks).
Still needs: config-driven server startup, system prompt integration.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The unconscious trigger holds the tokio mutex during heavy sync work
(store load, graph build, agent creation), blocking the UI tick which
needs the same lock for snapshots. Fix: try_lock in the UI — skip
the update if the trigger is running.
Also: restore_from_log was re-logging every restored node back to the
log file via push()'s auto-log. Added push_no_log() for restore path.
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>
Subconscious agents (observe, etc.) fork the conscious agent's context
to share the KV cache prefix. When a multi-step agent fills the context
window, compacting blows the KV cache and evicts the step prompts,
leaving the model with no idea what it was doing.
Fix: forked agents set no_compact=true. On overflow, turn() returns the
error immediately (no compact+retry), and run_with_backend catches it
and returns Ok — the output tool has already written results to
Subconscious.state, so collect_results still picks them up.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Ported the old trim_entries logic to the new AstNode types:
- Phase 1: Dedup Memory nodes by key (keep last), drop DMN entries
- Phase 2: While over budget, evict lowest-scored memory (if memories
> 50% of conv tokens) or oldest conversation entry
- Phase 3: Snap to User message boundary at start
Called from compact() which runs on startup and on /compact.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
compact() cleared and rebuilt the system section but only pushed the
system prompt — tool definitions were lost. Since new() sets up the
system section correctly (prompt + tools), compact() now only reloads
identity and journal, leaving system untouched.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Parser skips Thinking nodes that are just whitespace. Conscious screen
now shows assistant children (Content, Thinking, ToolCall) as nested
tree items via recursive node_to_view. Nodes get timestamped in
push_node and on assistant branch creation.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The parser mutates the AST directly but doesn't write to the
conversation log. The turn loop now logs the completed assistant
branch after the parser handle resolves successfully.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
apply_tool_results() collects all results, then does one state lock
(remove from active_tools + write to log) and one context lock (push
all nodes). Eliminates redundant per-result locking.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New ActiveTools struct with proper methods: push, remove,
take_finished, take_foreground, iter, len. Turn loop uses
helpers instead of manual index iteration.
Removing SharedActiveTools (Arc<Mutex<Vec>>) — active tools
live directly in AgentState. A few UI callers still need
updating.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Made StreamToken pub (was pub(crate), needed by context.rs).
Removed dead API_CLIENT, get_client, sampling/priority fields
from oneshot. Suppressed pre-existing SkipIndex warning in learn.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
ResponseParser::run() spawns a task that reads StreamTokens, parses
into the AST (locking context per token), and sends PendingToolCalls
through a channel. Returns (tool_rx, JoinHandle<Result>) — the turn
loop dispatches tool calls and awaits the handle for error checking.
Token IDs from vLLM are accumulated alongside text and stored directly
on AST leaves — no local re-encoding on the response path.
The turn loop no longer matches on individual stream events. It just
reads tool calls and dispatches them.
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>
Split Agent into immutable Agent (behind Arc) and mutable AgentState
(behind its own Mutex). ContextState has its own Mutex on Agent.
Activities moved to AgentState. new() and fork() rewritten.
All callers need mechanical updates: agent.lock().await.field →
agent.state.lock().await.field or agent.context.lock().await.method.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Work in progress. New turn loop uses ResponseParser + StreamToken.
Killed StreamEvent, append_streaming, finalize_streaming, streaming_index,
assemble_api_messages, working_stack. Many methods still reference old
types — fixing next.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New context_new.rs with the AST-based context window design:
- AstNode: role + NodeBody (Leaf with text+token_ids, or Branch with children)
- Tokens only on leaves, branches walk children
- render() produces UTF-8, tokenize produces token IDs, same path
- ResponseParser state machine for streaming assistant responses
- Role enum covers all node types including sections
Still needs: fix remaining pattern match issues, add ContextState wrapper,
wire into mod.rs, replace old context.rs.
Does not compile yet — this is a design checkpoint.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Entries with empty token_ids (Thinking, Log) are not part of the
prompt and don't have messages. Skip them in streaming_index(),
route_entry(), and sync_from_agent() instead of calling .message()
which panics.
Using token_ids.is_empty() as the guard in streaming_index means
the check is tied to the data, not the type — any entry that
doesn't produce tokens is safely skipped.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New stream_completions() in openai.rs sends prompt as token IDs to
the completions endpoint instead of JSON messages to chat/completions.
Handles <think> tags in the response (split into Reasoning events)
and stops on <|im_end|> token.
start_stream_completions() on ApiClient provides the same interface
as start_stream() but takes token IDs instead of Messages.
The turn loop in Agent::turn() uses completions when the tokenizer
is initialized, falling back to the chat API otherwise. This allows
gradual migration — consciousness uses completions (Qwen tokenizer),
Claude Code hook still uses chat API (Anthropic).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
