Separate the scoring into two distinct functions:
- memory_score(key): scores one memory's importance by measuring
divergence in the 50 messages after it was surfaced. Two API calls
(baseline vs without that memory).
- finetune_score(count): scores recent messages with all memories
stripped to identify fine-tuning candidates. Responses with high
divergence depend on memories the model hasn't internalized yet.
The existing score_memories() with the full NxM matrix is preserved
for the debug screen.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
All process management now goes through active_tools:
- TUI reads metadata (name, elapsed time)
- Ctrl+K aborts handles (KillOnDrop sends SIGTERM)
- Running count from active_tools.len()
No more separate PID tracking, register/unregister, or
ProcessInfo. One data structure for everything.
Co-Developed-By: Kent Overstreet <kent.overstreet@linux.dev>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
tokio::spawn abort drops the future but leaves child processes
running as orphans. KillOnDrop sends SIGTERM to the process
group on drop, ensuring cleanup. Defused via mem::forget on
normal completion.
Co-Developed-By: Kent Overstreet <kent.overstreet@linux.dev>
One data structure for all in-flight tool calls — metadata for
TUI display + JoinHandle for result collection and cancellation.
Agent spawns tool calls via tokio::spawn, pushes to shared
Arc<Mutex<Vec<ActiveToolCall>>>. TUI reads metadata, can abort().
No separate inflight/background collections.
Non-background: awaited after stream ends.
Background: persists, drained at next turn start.
Co-Developed-By: Kent Overstreet <kent.overstreet@linux.dev>
Move active tool tracking from TUI message-passing to shared
Arc<RwLock> state. Agent pushes on dispatch, removes on
apply_tool_result. TUI reads during render. Background tasks
show as active until drained at next turn start.
Co-Developed-By: Kent Overstreet <kent.overstreet@linux.dev>
When </tool_call> is detected in the content stream, parse and
dispatch immediately via FuturesOrdered. Tool calls execute
concurrently while the stream continues. Results collected in
order after the stream ends.
Structured API path (ToolCallDelta) unchanged — still uses
post-stream parallel dispatch.
Co-Developed-By: Kent Overstreet <kent.overstreet@linux.dev>
Mechanical rename: src/agent/ -> src/user/, all crate::agent:: ->
crate::user:: references updated. Binary poc-agent renamed to
consciousness with CLI name and user-facing strings updated.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Replace prompt_logprobs-based scoring with the new vLLM /v1/score
endpoint. Much simpler: one API call per memory drop, returns
per-message total_logprob directly. No chunking needed, no OOM risk
— the endpoint only computes logits for scored tokens.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Images in the jsonl eat most of the byte budget. 64MB covers
any realistic conversation log; compact() trims to fit.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
After restore_from_log + compact, set last_prompt_tokens from
the budget's used() count instead of waiting for the first API call.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
age_out_images now keeps 1 existing image + 1 about to be added
= 2 live images for motion/comparison. Previously aged all to 1.
Reduces image bloat in conversation log and context.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Large tool results (memory renders, bash output) consume most of
the 2MB budget — only 37 entries loaded from a 527-line log.
8MB captures ~300 entries, giving compact() enough conversation
to work with.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Context was too aggressively trimmed — 80% free after compaction.
Budget was 60% of window minus 25% reserve = only 45% usable.
Now: 80% of window for total budget (20% output reserve built in),
no extra reserve subtraction. Journal budget 5% → 15% to carry
more context across compactions.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Check HTTP status from logprobs API (was silently ignoring 500s).
Call publish_context_state() after storing scores so F10 screen
updates. Add chunk size logging for OOM debugging.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Status bar shows "scoring 3/7..." during scoring. Debug pane logs
per-memory importance and top-5 response breakdowns. F10 context
screen shows which memories were important for each assistant
response as drilldown children (← memory_key (score)).
Added important_memories_for_entry() to look up the matrix by
conversation entry index.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
/score snapshots the context and client, releases the agent lock,
runs scoring in background. Only one score task at a time
(scoring_in_flight flag). Results stored on Agent and shown on
the F10 context debug screen with importance scores per memory.
ApiClient derives Clone. ContextState derives Clone.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
score_memories() drops each memory from the context one at a time,
runs prompt_logprobs against the full conversation, and builds a
divergence matrix: memories × responses.
Row sums = memory importance (for graph weight updates)
Column sums = response memory-dependence (training candidates)
Uses vLLM's prompt_logprobs to check "would the model have said
this without this memory?" — one forward pass per memory, all
responses scored at once. ~3s per memory on B200.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
User and assistant names now come from config.user_name and
config.assistant_name throughout: system prompt, DMN prompts,
debug screen, and all agent files. Agent templates use
{user_name} and {assistant_name} placeholders.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Show chunks received, SSE lines parsed, and the contents of
the line buffer (up to 500 bytes) on both stream errors and
timeouts. This tells us whether we got partial data, a non-SSE
response, or truly nothing from the server.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Stream chunk timeout is now api_stream_timeout_secs in config
(default 60s). Status bar shows total turn time and per-call
time with timeout: "thinking... 45s, 12/60s".
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Spawned streaming tasks were never cancelled when a turn ended or
retried, leaving zombie tasks blocked on dead vLLM connections.
AbortOnDrop wrapper aborts the task when it goes out of scope.
Chunk timeout reduced from 120s to 60s.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
"thinking..." was getting stuck in the status bar when a turn
ended with a stream error, context overflow, or model error —
only the success path cleared it. Now all error returns clear
the activity indicator.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
pub → pub(crate) for SseReader methods (used across child modules).
pub → pub(super) for openai::stream_events, tool definitions, store
helpers. pub → private for normalize_link and differentiate_hub_with_graph
(only used within their own files).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Journal entries are written to the memory graph via journal_new/
journal_update, not appended to a flat file. Remove thought/journal.rs
(67 lines), strip_ephemeral_tool_calls (55 lines), default_journal_path,
and all wiring. -141 lines.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Journal entries are loaded from the memory graph store, not from the
flat journal file. Remove build_context_window, plan_context,
render_journal_text, assemble_context, truncate_at_section,
find_journal_cutoff, parse_journal*, ContextPlan, and stale TODOs.
Keep JournalEntry, default_journal_path (write path), and the live
context management functions. -363 lines.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
trim_conversation moved to thought/context.rs where model_context_window,
msg_token_count, is_context_overflow, is_stream_error already lived.
Delete the duplicate agent/context.rs (94 lines).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
JournalEntry, parse_journal, parse_journal_text, parse_header_timestamp,
and default_journal_path consolidated into thought/context.rs. Delete
the duplicate agent/journal.rs (235 lines). Update all references.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Serialize request JSON before send_and_check so it's available
for both HTTP errors and stream errors. Extracted save logic
into save_failed_request helper on SseReader.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Memory tool results (memory_render) are now pushed as
ConversationEntry::Memory with the node key, instead of plain
Messages. Remove loaded_nodes from ContextState — the debug
screen reads memory info from Memory entries in the conversation.
Surfaced memories from surface-observe are pushed as separate
Memory entries, reflections as separate system-reminder messages.
User input is no longer polluted with hook output.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Log ConversationEntry (with Memory/Message typing) instead of
raw Message. restore_from_log reads typed entries directly,
preserving Memory vs Message distinction across restarts.
Remove current.json snapshot and save_session — the append-only
log is the single source of truth. Remove dead read_all and
message_count methods. Add push_entry for logging typed entries.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Delete anthropic.rs (713 lines) — we only use OpenAI-compatible
endpoints (vLLM, OpenRouter). Simplify ApiClient to store base_url
directly instead of Backend enum.
SseReader now stores the serialized request payload and saves it
to ~/.consciousness/logs/failed-request-{ts}.json on stream timeout,
so failed requests can be replayed with curl for debugging.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
model_context_window() now reads from config.api_context_window
instead of guessing from model name strings. is_anthropic_model()
replaced with backend == "anthropic" checks. Dead model field
removed from AgentDef/AgentHeader.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
build_context_window loaded journal from a stale flat file and
assembled the full context. Now journal comes from the memory graph
and context is assembled on the fly. All that's needed is trimming
the conversation to fit the budget.
trim_conversation accounts for identity, journal, and reserve
tokens, then drops oldest conversation messages until it fits.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The restore and compaction paths called build_context_window which
reads from the stale flat journal file, overwriting the journal we
loaded from the memory graph. Preserve the graph-loaded journal
across these operations.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Replace untyped message list with ConversationEntry enum:
- Message(Message) — regular conversation turn
- Memory { key, message } — memory content with preserved message
for KV cache round-tripping
Budget counts memory vs conversation by matching on enum variant.
Debug screen labels memory entries with [memory: key]. No heuristic
tool-name scanning.
Custom serde: Memory serializes with a memory_key field alongside
the message fields, deserializes by checking for the field.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Remove cached context_budget field and measure_budget(). Budget
is computed on demand via budget() which calls
ContextState::budget(). Each bucket counted from its typed source.
Memory split from conversation by identifying memory tool calls.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
context.messages is conversation-only now — remove conv_start
scanning. Memory counted from loaded_nodes (same as debug screen).
No subtraction heuristics.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
refresh_context_message was injecting personality into conversation
messages (assuming fixed positions that no longer exist). Replaced
with refresh_context_state which just re-measures and publishes.
conv_tokens now subtracts mem_tokens since memory tool results are
in the conversation message list.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
ContextState now owns everything in the context window:
system_prompt, personality, journal, working_stack, loaded_nodes,
and conversation messages. No duplication — each piece exists once
in its typed form.
assemble_api_messages() renders the full message list on the fly
from typed sources. measure_budget() counts each bucket from its
source directly. push_context() removed — identity/journal are
never pushed as messages.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Identity tokens from system_prompt + personality vec. Journal
from journal entries vec. Memory from loaded_nodes. Conversation
is the remainder. No string prefix matching.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
