Mirror of text(), but returns raw Bytes without lossy UTF-8 conversion.
Needed by the Telegram channel to fetch photo files.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Add lock_blocking() to TrackedMutex: blocks current thread using
block_in_place + futures::executor::block_on, safe for sync contexts.
Replace all try_lock() calls with lock_blocking() in slash commands,
UI rendering, and status reads. Lock hold times are fast enough that
blocking briefly is fine, and this eliminates the spurious 'lock
unavailable' paths that were never actually hit.
Kept rx_mutex.try_lock() in mod.rs (std::sync::Mutex for stderr rx).
Recompute image token counts from persisted dimensions when loading
old logs that stored count=0 (server-authoritative count was applied
after AppendImage before client-side pad expansion).
graph: cache neighbor sets for clustering coefficient
Pre-compute neighbor HashSets so the O(deg^2) triangle-counting
inner loop doesn't re-allocate on every (i,j) pair. avg_clustering_
coefficient() now builds the cache once instead of O(N*deg) times.
ResponseParser.finish() was only flushing self.buf — the rolling tail
window — and silently dropping self.think_buf and self.tool_call_buf.
When a stream ended inside an unterminated <think>...</think> or
<tool_call>...</tool_call> block (max_tokens reached, EOS before the
close tag, server-side cancel), all the accumulated in-tag content
was discarded and only the trailing ~8 bytes survived (drain_safe
keeps `close_tag.len()` bytes at the tail of buf to handle
across-chunk tag splits — and `</think>` is exactly 8 chars).
Symptom: assistant responses cut off, only the last few characters
come through. Especially severe in native-think mode where in_think
is set from prefill, so the entire response accumulates in
think_buf and gets wiped on premature stop.
In finish(): if in_think, drain buf into think_buf and emit as a
Thinking node (preserving the partial thought). If in_tool_call,
attempt to parse the body; on parse failure, wrap the partial as
content with the leading <tool_call> open tag so the model sees its
own truncated attempt next turn rather than losing it.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two pieces around the cache that landed when Branch nodes started
holding `token_ids: Some(server_authoritative_stream)`:
1. wire_into / wire_chunks now pair cached vision blocks with their
child Image leaves. Previously the cached-branch arm spliced the
cache verbatim and didn't recurse for images, so a Branch whose
cache contained `VISION_START..VISION_END` blocks would emit those
tokens with no matching `WireImage` push — leading to a panic
downstream when `pair_images_to_ranges` tried to attach the
missing image. New `pair_cached_images` walks the children
depth-first for image leaves and zips them against
`vision_blocks(cache)` to emit correctly-offset entries; mismatched
counts panic loudly because that's an AST/cache invariant
violation that would otherwise mis-pair on the wire.
2. `conversation_mut() -> &mut Vec<AstNode>` was the one public
escape hatch that let callers reach into a Branch's children and
mutate them without invalidating the cached token stream. Removed
in favor of a focused `set_branch_memory_score(section, index,
key, score)` for the only legitimate use we had today (the
full-matrix scorer writing per-memory divergence onto the
Assistant Branch). Updated the lone caller in subconscious/learn.
Documented the invariants explicitly on `ContextState`: every
`Leaf.token_ids` matches `body.compute_token_ids()`, and every
`Branch { token_ids: Some(_) }` is a faithful walk of its children.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The default 4 MiB cap on encoded/decoded messages is too small for
the multimodal Generate path: Qwen3.6-VL high-res patches put 5–8 MiB
of pre-encoded image bytes inline in a single Generate request, and
Done events carrying full per-token readout vectors can also exceed
4 MiB on long runs. Hit "ResourceExhausted: Received message larger
than max (5799108 vs. 4194304)" from the salience server.
Bump both encode and decode caps on every cloned SalienceClient. The
matching server-side bump is in vllm/entrypoints/salience/server.py.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Mirrors the vLLM-side rewrite. AppendImage is gone; images now
ride along on Generate via a parallel `images` list.
- Productionize `qwen3_image_token_count` (was test-only). Image
leaf computes its IMAGE_PAD count eagerly at construction from
height/width; `token_count` is no longer "0 until the server
tells us."
- WireChunk shrinks to a single `Tokens(Vec<u32>)` variant — vision
blocks live inline in the token stream.
- `wire_chunks` now returns `(Vec<WireChunk>, Vec<WireImage>)`.
`WireImage` carries `pad_start` / `pad_end` (absolute positions
in the full walk) alongside bytes + mime.
- `assemble_prompt` returns `(chunks, images, match_upto)`.
- `stream_session_mm` / `run_session_generate` take the parallel
images list, filter to those past `match_upto`, and pass them
in `GenerateRequest.images` as `pb::ImageAttachment` entries.
- Drop `SessionHandle::append_image`,
`ContextState::commit_image_token_counts`,
`StreamToken::ImageAppended`, the WireChunk::Image branch in
`learn.rs`, and the now-empty `prompt_to_chunks` helper.
- Add 'v' toggle on the conscious-screen tree to render token-id
vectors in place of text content (debug-aid: lets us see what
the server actually has when output is suspicious).
- Comment out the subconscious-trigger spawn loop — Kent had this
disabled before; it had crept back into running.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two changes that bolt together — the shared connection means the new
scoring path actually costs one HTTP/2 handshake across the whole
process instead of one-per-RPC.
ApiClient gains `salience_channel: Arc<OnceCell<Channel>>`. First
call to `ApiClient::salience_client()` opens the channel via
`connect_channel()` and stores the Channel; subsequent calls clone
it (cheap — tonic multiplexes concurrent RPCs over the single
HTTP/2 connection). Every ApiClient clone shares the same OnceCell,
so all agents spawned from Mind's client — plus every ephemeral
scoring session — reuse one connection.
SessionHandle refactored to hold an `ApiClient` clone instead of
a bag of (base_url, api_key) strings. `open` / `append_image` /
`generate` go through `self.client.salience_client()` now. New
`prefill_only(tokens)` method encapsulates the "Generate with
max_tokens=0 to append text" pattern (previously a private free
function in api/mod.rs called `flush_pending`). Drop impl on
SessionHandle stays — still fires CloseSession on the shared
channel in a detached task.
`run_session_generate` switched from `(base_url, api_key, model)`
to `&ApiClient`; the agent-turn flow that uses it keeps the same
shape but `stream_session_mm` clones the ApiClient into the
spawned worker.
learn.rs migrated from the HTTP `/v1/score` endpoint to a gRPC
session-based score:
* `call_score` opens an ephemeral SessionHandle on the client,
converts (prompt_tokens, images) → Vec<WireChunk> via the new
`prompt_to_chunks` helper (splits on VISION_START/VISION_END),
walks chunks calling `prefill_only` + `append_image`, runs a
final Generate with `max_tokens=0` + `logprobs_ranges` over
the scored positions, and sums each Token event's
`sampled_logprob` per range to produce `ScoreResult`s.
* SessionHandle drops at end of scope → CloseSession auto-fires,
keeping the server's session map clean between calls.
* No more HTTP path, no more `http_client()` helper, no more
`ScoreResponse` / serde plumbing for /v1/score.
* `send_to_train` still uses HTTP (it talks to /v1/train which
isn't on the gRPC protocol); its ad-hoc HTTP client lives
inline now instead of reaching for the deleted `http_client()`.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Collapse the split `temperature` / `top_p` / `top_k` fields on
AgentState into a single `sampling: SamplingParams` struct, mirroring
how the wire-level fields flow into the Generate RPC. Adds
`max_tokens` to SamplingParams so it's actually plumbed end to end
(previously the client had a hardcoded 4096 fallback inside
`run_session_generate`).
AgentState construction sites now set `sampling: SamplingParams { ...
max_tokens: 4096 }` as the default. The assignment sites in
oneshot.rs / subconscious.rs / unconscious.rs switch from
`st.temperature = X` to `st.sampling.temperature = X`.
`stream_session_mm` takes `SamplingParams` directly; the
`sampling_max_tokens()` helper goes away. `pb::GenerateRequest` is
populated with `sampling.max_tokens` (and the other fields) in
`run_session_generate`. SamplingParams is `pub` so it can be
embedded in the public AgentState without a visibility warning.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Wires the client side of the new salience protocol so inference
actually runs over gRPC instead of emitting the stubbed "not yet
wired" error. Each turn walks the AST as interleaved chunks, sends
only what's new to the server, and streams decode tokens back.
context.rs:
* `WireChunk` enum: `Tokens(Vec<u32>)` or `Image { bytes, mime,
known_expanded_len }`. Preserves text/image/text ordering the
wire path can't flatten.
* `wire_chunks(range, skip)` walker, parallel to `wire_prompt` —
branches emit `<|im_start|>…<|im_end|>` tokens, image leaves
emit a single Image chunk (no inline vision tokens).
* `NodeLeaf::set_image_token_count(n)` + recompute of cached
`token_ids`; `ContextState::commit_image_token_counts(&[u32])`
fills in the first-N zero-count image leaves in wire order.
* `ResponseParser::run` handles the new
`StreamToken::ImageAppended` by committing the server's N into
the AST before the final Generate's Token events stream in.
salience.rs:
* `SessionHandle` tracks `committed_len`. `append_image` advances
it from the RPC response. New `generate(req)` opens the
server-streaming RPC.
api/mod.rs:
* `stream_session_mm(session_lock, chunks, sampling, priority,
readout_shape)` replaces the stub. Spawns `run_session_generate`.
* `run_session_generate`: takes the session out of the Mutex (or
opens fresh), skips chunks covered by `committed_len` (bails on
mid-chunk straddle or unknown-length image in the committed
prefix), walks the delta: accumulates Tokens into `pending`, on
Image flushes pending via `flush_pending` (max_tokens=0 Generate
that just prefills), then AppendImage + emits
StreamToken::ImageAppended. Final Generate carries any trailing
pending text as `append_tokens` and the sampling params; Token
events stream out as StreamToken::Token, Done as
StreamToken::Done. On success, handle with updated
`committed_len` returns to the Mutex; on error, handle drops
and next call reopens.
* `StreamToken::ImageAppended { placeholder_count }` variant —
emitted in wire order before the final Generate's tokens.
* Prefix-cache cap for readout coverage: `readout_ranges` covers
`[prompt_len_after_append, u32::MAX)` when the caller provides
a readout_shape, so decode positions stream their readouts.
agent/mod.rs:
* `assemble_prompt` returns `Vec<WireChunk>` with the assistant
prologue merged into the trailing Tokens chunk. Caller in
`turn` passes chunks + readout_shape (pulled from
`agent.readout.lock().manifest`) to `stream_session_mm`.
* Dropped `assemble_prompt_tokens` — dead.
mind + unconscious:
* `Unconscious::new(client)` stores a shared `ApiClient`. Fixes
the repeated-manifest-fetch bug caused by each subagent's
`ApiClient::new` having its own OnceCell. The client's Arc-
wrapped manifest cache is now shared across every agent Mind
spawns.
* `prepare_spawn(name, auto, wake, base_client)` clones the base
client and overrides `.model` for the resolved backend instead
of constructing fresh. All three callers
(`toggle`/`trigger`/unconscious loop) pass `self.client.clone()`.
* `Mind::new` passes `agent.client.clone()` into
`Unconscious::new`.
subconscious/generate.rs:
* gen_continuation switched to `wire_chunks` + the new
`stream_session_mm` signature. Ephemeral session opens on each
call, tears down at scope end. No readouts requested.
Not changed yet, noted for follow-up:
* Subconscious ablation scoring in learn.rs still talks to
`/v1/score` over HTTP. Will migrate once we have time to verify
the Generate+max_tokens=0+prompt_logprobs path end-to-end.
* compare.rs constructs its own ApiClient for the
`compare.test_backend` (which is intentionally a different
endpoint) — left alone.
* Readout manifest still fetched via HTTP at Agent::new.
Migration to GetReadoutManifest gRPC is a separate cleanup.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Adds the client-side of a stateful gRPC protocol against vllm, plus
the TLS trust machinery so we can talk to self-signed vllm servers.
Protocol (proto/salience.proto):
Bidi-streaming Session RPC carries OpenSession / AppendTokens /
Generate / Cancel from client and SessionReady / PrefillProgress /
Token / GenerateDone / Error from server. Separate Fork unary RPC
for cheap branching (prefix cache shares KV automatically). Plus
ListSessions, CloseSession, GetReadoutManifest admin RPCs.
Per-token readouts ship as packed f32 ([n_layers * n_concepts] per
token, flat). Logprobs use range-selected positions plus a top-k
parameter — empty ranges means no logprobs, any range means emit
sampled-token logprob at those positions, top_k > 0 adds
alternatives.
Client (src/agent/api/salience.rs):
Tonic-generated types under pb::, a connect() helper, with_auth()
for bearer metadata, and a Session handle wrapping the bidi stream:
open() handshakes SessionReady; append() is fire-and-forget;
generate() returns impl Stream<Item = Event> that drains inbound
until Done or terminating Error. One generate at a time per session.
Peak picker (src/agent/salience.rs):
Pure function over ReadoutEntry traces. Per-concept z-score against
trace global stats; contiguous above-threshold regions emit one
peak at the local max. Configurable sigma threshold and min-std
safety floor. Deterministic tie-break on offset then concept name.
12 unit tests covering empty traces, flat channels, single/multi
spikes, contiguous humps, multi-concept independence, trailing
runs, sub-threshold noise, layer-out-of-range, manifest shape
mismatch, and threshold tunability.
TLS (src/agent/api/http.rs):
HttpClient::build now also loads every .pem file under
~/.consciousness/certs/ into the rustls root store — so dropping
a <host>.pem in that directory is enough to trust a new self-
signed server; no code changes per new host. Also installs the
rustls default crypto provider explicitly via OnceLock: tonic's
tls features pulled in both ring and aws-lc-rs on the resolver
path, and rustls 0.23 refuses to auto-pick when either could win.
Build (build.rs, Cargo.toml):
tonic-build generates Rust types from proto/salience.proto at
cargo-build time, using a vendored protoc binary
(protoc-bin-vendored) so no system install is required. New
runtime deps: tonic, prost, async-stream, tokio-stream,
rustls-pemfile.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Thinking blocks used to render as empty strings and be excluded from
is_prompt_visible, so the model never saw its own prior CoT across
turns. For Qwen 3.6 native thinking mode, CoT is meant to stay in the
conversation — the model benefits from seeing what it reasoned about
last turn.
Render Thinking as <think>\n{text}\n</think>\n so past reasoning is
visible in subsequent prompts. Add in_think param to ResponseParser::new
so the parser starts inside a <think> block when the prompt was
prefilled with "<think>\n" (native thinking mode).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Issue #5 (spqrz) flagged that web_search using DuckDuckGo
occasionally flakes out, and Google search directly is blocked
behind CAPTCHAs for non-browser clients. The Gemini free-tier API
exposes a grounded-search tool that effectively queries Google's
index and returns an LLM-summarized answer with source URLs.
Added as a SEPARATE tool rather than a transparent fallback for
web_search:
* web_search (DDG) returns raw results — title, URL, snippet per
hit — which the agent can reason over itself.
* gemini_search returns an LLM-pre-digested summary plus grounding
URLs. Useful for synthesis queries ("what's the consensus on X")
or when DDG is flaky, but it's another LLM in the loop so the
agent may want the raw variant for certain tasks.
Tool descriptions tell the agent to prefer web_search for raw
results and use gemini_search for synthesis / fallback. The agent
picks based on query shape.
Only registered when GEMINI_API_KEY is set in the environment
(gracefully absent otherwise). Uses gemini-2.0-flash which has a
generous free-tier rate limit. Parses grounding metadata for
source URLs so the agent can follow links.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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>
web_fetch was returning raw HTML, which is verbose and hard for
the agent to consume. Add html2md dependency and convert HTML to
Markdown before truncation. Much cleaner output for normal pages;
no downsides.
Co-Authored-By: spqrz <spqrz386@gmail.com>
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>
StreamToken::Token is now a struct variant with an optional
TokenReadout (shape [n_layers][n_concepts]) per token — parsed from
the vLLM completion response's choices[i].readout field when the
server has readout enabled.
ApiClient gains a fetch_readout_manifest() method that hits
GET /v1/readout/manifest. Returns Ok(None) on 404 (server has
readout disabled), so callers can gracefully fall back when pointed
at a non-readout-enabled endpoint.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
stream_completion was a thin wrapper around stream_completion_mm (just
passing an empty image list); the last caller switched to _mm directly
when learn's generate_alternate gained image support. Delete the
wrapper — callers can pass `&[]` if they have no images.
MindState::dmn_tick has been sitting unused (called only from a
commented-out block in the Mind loop). Rename to _dmn_tick so the
compiler stops warning; Kent may uncomment the call path later.
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>
- context.rs gains is_assistant, render_branch_text, render_prior_context
alongside memory_key / is_memory_node. They're pure AST helpers, used
by both the finetune pipeline and the forthcoming compare screen.
- new subconscious/generate.rs holds gen_continuation(context, entry_idx,
skip, client): build the prompt from a context prefix with an arbitrary
skip predicate, send to the model, decode the completion. Takes both
the predicate and the client so callers can aim it at memory-stripped
contexts (finetune), same-context-different-model (F7 compare), or
whatever else.
- learn.rs drops its private copies of those helpers and the inline
generate_alternate; the finetune path now reads as
gen_continuation(context, idx, is_memory_node, client).
Pure refactor, no behavior change.
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>
view_image now reads the file, grabs dimensions via imagesize (no full
decode), and pushes a user-role branch containing a NodeBody::Image
leaf straight into the conversation. The tool_result is just a short
acknowledgment — the actual pixels ride in the Image leaf for the API
layer to extract into multi_modal_data.
Drops the capture_tmux_pane path, which had no business living under
"vision" (tmux text capture belongs in bash or a dedicated tool, and
this one just returned rendered text anyway).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Images are rendered as `<|vision_start|>` + N × `<|image_pad|>` +
`<|vision_end|>` where N is computed from the image dimensions using
Qwen3-VL's smart_resize rules (patch_size=16, merge_size=2, min=64K,
max=16M pixels). The token count matches what vLLM will produce at
request time, so budget accounting stays accurate.
Bytes are stored inline on the leaf and base64-encoded in the JSON
form. Token IDs are hand-assembled instead of re-running the tokenizer
on a potentially-huge placeholder string.
Follow-ups: view_image tool rewrite, multi_modal_data on the vLLM
request, API-layer plumbing from leaf bytes to request body.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
These are identity settings, not memory-graph settings. Sat inside the
\`memory\` section only because that's where Config started life. Move
to AppConfig alongside the other top-level stuff.
Readers now pull from \`config::app()\` instead of \`config::get()\`.
subconscious/defs.rs's conversation-building pass still needs Config
for surface_conversation_bytes, so both guards coexist there —
AppConfig's guard is dropped before the per-step await loop so we
don't stall the config-watcher's writer.
show_config picks up the two new fields at the top of its output.
Kent's config already has them hoisted to the top level.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two parallel backend-resolution paths had drifted apart:
- Main chat: AppConfig::resolve_model() → a named BackendConfig in
AppConfig.backends
- Subconscious / oneshot / context_window(): four skip-serde
"cache" fields on Config (memory section) — api_base_url, api_key,
api_model, api_context_window — that used to be populated at
Config::try_load_shared time by walking memory.agent_model →
root.models[name] → root[backend_name]
When we renamed `models` to `backends` and collapsed ModelConfig into
BackendConfig, the latter chain started silently dereferencing
`root.get("models")` → None → no population. Subconscious agents fell
through the "API not configured" guard; context_window() started
returning 0 (since api_context_window default is u64's 0 now that we
don't populate it). It was only visibly working for the main chat.
Collapse to one path:
- Drop Config.agent_model (duplicate of AppConfig.default_backend)
- Drop Config.{api_base_url, api_key, api_model, api_context_window}
— no longer populated, no longer needed
- Drop default_context_window() — nobody reads the field anymore
- Drop the memory-side resolution block in try_load_shared()
- Subconscious (mind/unconscious.rs) and oneshot (agent/oneshot.rs)
now call load_app() + resolve_model(&app.default_backend) just like
the main chat does
- context_window() reads from config::app().backends[default_backend]
.context_window, defaulting to 128k only if the backend doesn't
specify one
Side effect: Kent's config file drops agent_model, api_reasoning,
journal_days, journal_max — all fields whose Rust counterparts are
now gone. (Figment tolerates unknown fields, so leaving them wouldn't
have broken anything, but they were lying about what's configurable.)
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>
bail-no-competing.sh used to bail if any other live agent existed in
the state dir, period. That was too coarse: surface-observe agents run
a multi-step pipeline (surface → organize-search → organize-new →
observe), and the intent is to let a new surface-phase agent start
while an older one finishes its post-surface tail. With the old check
the newer agent always bailed, so surface-observe was effectively
serialized at the slowest cycle time.
Make the script phase-aware:
- oneshot.rs now passes the current phase as argv[2] alongside the pid
file name. The script writes that phase into its own pid file on
every step transition, so concurrent agents can read each other's
phase just by cat'ing the pid files.
- Bail only when another live agent is in the same phase-group as us.
Groups: "surface" vs. "everything else" (post-surface). At most one
agent per group alive at a time — surface runs at a higher cadence
than the organize/observe tail.
- Still clean up stale pid files for dead processes.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Previously NodeLeaf.timestamp and AstNode::Branch.timestamp accepted
null or missing via a deserialize_timestamp_or_epoch fallback — legacy
entries in conversation.jsonl from before Branch timestamps existed
(and from before chrono serialization was wired up) would load with
UNIX_EPOCH as a sentinel. Downstream, node_timestamp_ns() returned
Option<i64> and callers had to handle None as "old entry, skip."
That second filter was silently dropping every candidate in
score_finetune_candidates when scoring an older session — the F6
screen showed "0 above threshold" even when max_divergence was
orders of magnitude above the threshold, because every entry was
failing the None check, not the divergence check.
The fix, in three parts:
1. src/bin/fix-timestamps.rs — one-off migration tool that walks a
conversation.jsonl, linearly interpolates timestamps for entries
stuck at UNIX_EPOCH (using surrounding real timestamps as anchors),
propagates to child leaves with per-sibling ns offsets, and bumps
any collisions by 1 ns for uniqueness. Ran against the current
session's log: 11887 entries, 72289 ns bumps, all unique.
2. context.rs — drop default_timestamp and
deserialize_timestamp_or_epoch. NodeLeaf and Branch now require a
present non-null timestamp on deserialize. Tests flip from
"missing/null → UNIX_EPOCH" to "missing/null → Err."
3. subconscious/learn.rs — node_timestamp_ns now returns i64, not
Option<i64>. The matching caller in score_finetune_candidates
collapses from a Some/None match to a single trained-set check.
mind/log.rs's oldest_timestamp no longer filters UNIX_EPOCH.
Every line currently on disk has already been migrated. Going
forward, new AstNodes always carry real timestamps (Utc::now() at
construction time), so the strict schema is the invariant, not an
aspiration.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two related changes to the learn subsystem:
1. AST node timestamps are now non-optional — both Leaf and Branch
variants carry a DateTime<Utc>. UNIX_EPOCH means "unset" (old entries
deserialized from on-disk conversation logs).
Training uses timestamps as unique keys for dedup, so we promote to
nanosecond precision: node_timestamp_ns(), TrainData.timestamp_ns,
FinetuneCandidate.timestamp_ns, mark_trained(ns).
2. build_token_ids() now also returns token-position ranges of assistant
messages. These are passed to vLLM's /score endpoint via the new
score_ranges field so only scored-position logprobs are returned —
cuts bandwidth/compute when scoring small windows.
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>
memory_delete and memory_restore are now in memory_tools() (available
via MCP for CLI). Agent tool lists support "-tool_name" to exclude.
Agents automatically exclude memory_delete and memory_restore.
Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
- save_agent_log: assert name is not empty (panic to find the bug)
- AutoAgent:🆕 assert name is not empty
- dbglog: write to daemon/ subdir instead of toplevel logs/
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- memory_delete no longer exposed to agents - use supersede instead
- memory_supersede now transfers all edges from old node to new node
(keeps whichever strength is higher if new node already has the link)
This preserves graph structure during consolidation.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
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>
Store now has internal Mutex for capnp appends and AtomicU64 for
size tracking. All methods take &self. The external Arc<Mutex<Store>>
is replaced with Arc<Store>.
- Store::append_lock protects file appends
- local.rs functions take &Store (not &mut Store)
- access_local() returns Arc<Store>
- All .lock().await calls removed from callers
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Remove AgentVisit, TranscriptSegment, and all related visit tracking code.
Provenance is what we've been using to track agent interaction with nodes.
Also removes dead fields from Node (state_tag, created).
-349 lines.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- Remove CACHED_STORE, cached(), is_stale(), set_store() - redundant
- Convert all Store::cached() callers to use access_local()
- Single Store::load() call remains in access() fallback path
All store access now goes through hippocampus::access() / access_local(),
which handles socket connection or local fallback with caching.
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>
resolve_placeholders() and run_agent() no longer take &Store.
All placeholders now use async memory_render/memory_links/memory_query
directly. The "siblings" placeholder uses Vec<LinkInfo> for ranking
neighbors by link_strength * node_weight.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- hippocampus::memory_links now returns Vec<LinkInfo> with key,
link_strength, and node_weight for each neighbor
- Unified memory_tool! macro: mut/ref as token, single main rule
- All tools use serde serialize/deserialize for RPC consistency
- jsonargs handlers now work in client mode (RPC to daemon)
- cli/graph.rs formats LinkInfo for display
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Aligns function names with tool names for consistency:
- hippocampus: render → memory_render, write → memory_write, etc.
- tools/memory.rs: macro no longer prepends memory_ prefix
- CLI files: use typed async API throughout (graph.rs, journal.rs, admin.rs)
This eliminates the "memory_graph_topology" tool name bug where
graph_* and journal_* tools were incorrectly prefixed.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The memory_tool! macro now generates two functions:
- jsonargs_*() - internal, takes JSON args for dispatch table
- pub fn name() - typed args, handles RPC-vs-local automatically
Callers can now use typed Rust API:
memory::write(Some(&agent), "key", "content").await?;
memory::query(None, "all | type:semantic", Some("full")).await?;
No more manual JSON construction for memory tool calls.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
