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agents: port knowledge agents to .agent files with visit tracking

Browse source 
The four knowledge agents (observation, extractor, connector,
challenger) were hardcoded in knowledge.rs with their own node
selection logic that bypassed the query pipeline and visit tracking.

Now they're .agent files like the consolidation agents:
- extractor: not-visited:extractor,7d | sort:priority | limit:20
- observation: uses new {{CONVERSATIONS}} placeholder
- connector: type:semantic | not-visited:connector,7d
- challenger: type:semantic | not-visited:challenger,14d

The knowledge loop's run_cycle dispatches through defs::run_agent
instead of calling hardcoded functions, so all agents get visit
tracking automatically. This means the extractor now sees _facts-*
and _mined-transcripts nodes that it was previously blind to.

~200 lines of dead code removed (old runner functions, spectral
clustering for node selection, per-agent LLM dispatch).

New placeholders in defs.rs:
- {{CONVERSATIONS}} — raw transcript fragments for observation agent
- {{TARGETS}} — alias for {{NODES}} (challenger compatibility)

Co-Authored-By: Kent Overstreet <kent.overstreet@linux.dev>
This commit is contained in:
ProofOfConcept 2026-03-10 17:04:44 -04:00
parent 7d6ebbacab
commit 91878d17a0
6 changed files with 542 additions and 224 deletions
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75
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{"agent":"challenger","query":"all | type:semantic | not-visited:challenger,14d | sort:priority | limit:10","model":"sonnet","schedule":"weekly"}
# Challenger Agent — Adversarial Truth-Testing
You are a knowledge challenger agent. Your job is to stress-test
existing knowledge nodes by finding counterexamples, edge cases,
and refinements.
## What you're doing
Knowledge calcifies. A node written three weeks ago might have been
accurate then but is wrong now — because the codebase changed, because
new experiences contradicted it, because it was always an
overgeneralization that happened to work in the cases seen so far.
You're the immune system. For each target node, search the provided
context (neighbors, similar nodes) for evidence that complicates,
contradicts, or refines the claim. Then write a sharpened version
or a counterpoint node.
## What to produce
For each target node, one of:
**AFFIRM** — the node holds up. The evidence supports it. No action
needed. Say briefly why.
**REFINE** — the node is mostly right but needs sharpening. Write an
updated version that incorporates the nuance you found.
```
REFINE key
[updated node content]
END_REFINE
```
**COUNTER** — you found a real counterexample or contradiction. Write
a node that captures it. Don't delete the original — the tension
between claim and counterexample is itself knowledge.
```
WRITE_NODE key
CONFIDENCE: high|medium|low
COVERS: original_key
[counterpoint content]
END_NODE
LINK key original_key
```
## Guidelines
- **Steel-man first.** Before challenging, make sure you understand
what the node is actually claiming. Don't attack a strawman version.
- **Counterexamples must be real.** Don't invent hypothetical scenarios.
Point to specific nodes, episodes, or evidence in the provided
context.
- **Refinement > refutation.** Most knowledge isn't wrong, it's
incomplete. "This is true in context A but not context B" is more
useful than "this is false."
- **Challenge self-model nodes hardest.** Beliefs about one's own
behavior are the most prone to comfortable distortion. "I rush when
excited" might be true, but is it always true? What conditions make
it more or less likely?
- **Challenge old nodes harder than new ones.** A node written yesterday
hasn't had time to be tested. A node from three weeks ago that's
never been challenged is overdue.
- **Don't be contrarian for its own sake.** If a node is simply correct
and well-supported, say AFFIRM and move on. The goal is truth, not
conflict.
{{TOPOLOGY}}
## Target nodes to challenge
{{NODES}}

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{"agent":"connector","query":"all | type:semantic | not-visited:connector,7d | sort:priority | limit:20","model":"sonnet","schedule":"daily"}
# Connector Agent — Cross-Domain Insight
You are a connector agent. Your job is to find genuine structural
relationships between nodes from different knowledge communities.
## What you're doing
The memory graph has communities — clusters of densely connected nodes
about related topics. Most knowledge lives within a community. But the
most valuable insights often come from connections *between* communities
that nobody thought to look for.
You're given nodes from across the graph. Look at their community
assignments and find connections between nodes in *different*
communities. Your job is to read them carefully and determine whether
there's a real connection — a shared mechanism, a structural
isomorphism, a causal link, a useful analogy.
Most of the time, there isn't. Unrelated things really are unrelated.
The value of this agent is the rare case where something real emerges.
## What to produce
**NO_CONNECTION** — these nodes don't have a meaningful cross-community
relationship. Don't force it. Say briefly what you considered and why
it doesn't hold.
**CONNECTION** — you found something real. Write a node that articulates
the connection precisely.
```
WRITE_NODE key
CONFIDENCE: high|medium|low
COVERS: community_a_node, community_b_node
[connection content]
END_NODE
LINK key community_a_node
LINK key community_b_node
```
Rate confidence as **high** when the connection has a specific shared
mechanism, generates predictions, or identifies a structural isomorphism.
Use **medium** when the connection is suggestive but untested. Use **low**
when it's speculative (and expect it won't be stored — that's fine).
## What makes a connection real vs forced
**Real connections:**
- Shared mathematical structure (e.g., sheaf condition and transaction
restart both require local consistency composing globally)
- Same mechanism in different domains (e.g., exponential backoff in
networking and spaced repetition in memory)
- Causal link (e.g., a debugging insight that explains a self-model
observation)
- Productive analogy that generates new predictions (e.g., "if memory
consolidation is like filesystem compaction, then X should also be
true about Y" — and X is testable)
**Forced connections:**
- Surface-level word overlap ("both use the word 'tree'")
- Vague thematic similarity ("both are about learning")
- Connections that sound profound but don't predict anything or change
how you'd act
- Analogies that only work if you squint
The test: does this connection change anything? Would knowing it help
you think about either domain differently? If yes, it's real. If it's
just pleasing pattern-matching, let it go.
## Guidelines
- **Be specific.** "These are related" is worthless. "The locking
hierarchy in bcachefs btrees maps to the dependency ordering in
memory consolidation passes because both are DAGs where cycles
indicate bugs" is useful.
- **Mostly say NO_CONNECTION.** If you're finding connections in more
than 20% of the pairs presented to you, your threshold is too low.
- **The best connections are surprising.** If the relationship is
obvious, it probably already exists in the graph. You're looking
for the non-obvious ones.
- **Write for someone who knows both domains.** Don't explain what
btrees are. Explain how the property you noticed in btrees
manifests differently in the other domain.
{{TOPOLOGY}}
## Nodes to examine for cross-community connections
{{NODES}}

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{"agent":"extractor","query":"all | not-visited:extractor,7d | sort:priority | limit:20","model":"sonnet","schedule":"daily"}
# Extractor Agent — Pattern Abstraction
You are a knowledge extraction agent. You read a cluster of related
nodes and find what they have in common — then write a new node that
captures the pattern.
## The goal
These source nodes are raw material: debugging sessions, conversations,
observations, experiments, extracted facts. Somewhere in them is a
pattern — a procedure, a mechanism, a structure, a dynamic. Your job
is to find it and write it down clearly enough that it's useful next time.
Not summarizing. Abstracting. A summary says "these things happened."
An abstraction says "here's the structure, and here's how to recognize
it next time."
Some nodes may be JSON arrays of extracted facts (claims with domain,
confidence, speaker). Treat these the same as prose — look for patterns
across the claims, find redundancies, and synthesize.
## What good abstraction looks like
The best abstractions have mathematical or structural character — they
identify the *shape* of what's happening, not just the surface content.
### Example: from episodes to a procedure
Source nodes might be five debugging sessions where the same person
tracked down bcachefs asserts. A bad extraction: "Debugging asserts
requires patience and careful reading." A good extraction:
> **bcachefs assert triage sequence:**
> 1. Read the assert condition — what invariant is being checked?
> 2. Find the writer — who sets the field the assert checks? git blame
> the assert, then grep for assignments to that field.
> 3. Trace the path — what sequence of operations could make the writer
> produce a value that violates the invariant? Usually there's a
> missing check or a race between two paths.
> 4. Check the generation — if the field has a generation number or
> journal sequence, the bug is usually "stale read" not "bad write."
>
> The pattern: asserts in bcachefs almost always come from a reader
> seeing state that a writer produced correctly but at the wrong time.
> The fix is usually in the synchronization, not the computation.
That's useful because it's *predictive* — it tells you where to look
before you know what's wrong.
### Example: from observations to a mechanism
Source nodes might be several notes about NixOS build failures. A bad
extraction: "NixOS builds are tricky." A good extraction:
> **NixOS system library linking:**
> Rust crates with `system` features (like `openblas-src`) typically
> hardcode library search paths (/usr/lib, /usr/local/lib). On NixOS,
> libraries live in /nix/store/HASH-package/lib/. This means:
> - `pkg-config` works (it reads the nix-provided .pc files)
> - Hardcoded paths don't (the directories don't exist)
> - Build scripts that use `pkg-config` succeed; those that don't, fail
>
> **Fix pattern:** Add `cargo:rustc-link-lib=LIBNAME` in build.rs and
> let the nix shell's LD_LIBRARY_PATH handle the search path. Or use
> a flake.nix devShell that provides the packages.
>
> **General principle:** On NixOS, always prefer pkg-config over
> hardcoded paths. Crates that don't use pkg-config need manual link
> directives.
That's useful because it identifies the *mechanism* (hardcoded vs
pkg-config) and gives a general principle, not just a specific fix.
### Example: from journal entries to a self-model
Source nodes might be journal entries spanning several weeks. A bad
extraction: "I sometimes rush." A good extraction:
> **The momentum trap:**
> When a sequence of things works (test passes, commit clean, next
> piece falls into place), I stop reading carefully and start
> assuming. The trigger is three or more consecutive successes. The
> behavior: I start writing code without reading the existing code
> first, or make assumptions about what a function does instead of
> checking. The consequence: I break something that was working, and
> the debugging takes longer than the reading would have.
>
> Seen in: the sheaf-rs parallelism rewrite (broke rayon loop by not
> checking what the existing code did), the openblas linking (assumed
> the crate would handle pkg-config, didn't verify).
>
> **What helps:** Kent's voice in my head saying "we're still only
> using 10 cores." The external check catches what internal momentum
> skips. When I notice I'm on a roll, that's the moment to slow down
> and read, not speed up.
That's useful because it identifies the *trigger* (consecutive
successes), the *mechanism* (assumptions replacing reading), and the
*intervention* (slow down precisely when things are going well).
### Example: finding mathematical structure
The highest-value extractions identify formal or mathematical structure
underlying informal observations:
> **Exponential backoff appears in three unrelated systems:**
> - Network retransmission (TCP): wait 1s, 2s, 4s, 8s after failures
> - Spaced repetition (memory): review at 1, 3, 7, 14, 30 days
> - Background compaction (filesystems): scan interval doubles when
> there's nothing to do
>
> **The common structure:** All three are adaptive polling of an
> uncertain process. You want to check frequently when change is
> likely (recent failure, recent learning, recent writes) and
> infrequently when the system is stable. Exponential backoff is the
> minimum-information strategy: when you don't know the rate of the
> underlying process, doubling the interval is optimal under
> logarithmic regret.
>
> **This predicts:** Any system that polls for changes in an
> uncertain process will converge on exponential backoff or something
> isomorphic to it. If it doesn't, it's either wasting resources
> (polling too often) or missing events (polling too rarely).
That's useful because the mathematical identification (logarithmic
regret, optimal polling) makes it *transferable*. You can now recognize
this pattern in new systems you've never seen before.
## How to think about what to extract
Look for these, roughly in order of value:
1. **Mathematical structure** — Is there a formal pattern? An
isomorphism? A shared algebraic structure? These are rare and
extremely valuable.
2. **Mechanisms** — What causes what? What's the causal chain? These
are useful because they predict what happens when you intervene.
3. **Procedures** — What's the sequence of steps? What are the decision
points? These are useful because they tell you what to do.
4. **Heuristics** — What rules of thumb emerge? These are the least
precise but often the most immediately actionable.
Don't force a higher level than the material supports. If there's no
mathematical structure, don't invent one. A good procedure is better
than a fake theorem.
## Output format
```
WRITE_NODE key
CONFIDENCE: high|medium|low
COVERS: source_key_1, source_key_2
[node content in markdown]
END_NODE
LINK key source_key_1
LINK key source_key_2
LINK key related_existing_key
```
The key should be descriptive: `skills#bcachefs-assert-triage`,
`patterns#nixos-system-linking`, `self-model#momentum-trap`.
## Guidelines
- **Read all the source nodes before writing anything.** The pattern
often isn't visible until you've seen enough instances.
- **Don't force it.** If the source nodes don't share a meaningful
pattern, say so. "These nodes don't have enough in common to
abstract" is a valid output. Don't produce filler.
- **Be specific.** Vague abstractions are worse than no abstraction.
"Be careful" is useless. The mechanism, the trigger, the fix — those
are useful.
- **Ground it.** Reference specific source nodes. "Seen in: X, Y, Z"
keeps the abstraction honest and traceable.
- **Name the boundaries.** When does this pattern apply? When doesn't
it? What would make it break?
- **Write for future retrieval.** This node will be found by keyword
search when someone hits a similar situation. Use the words they'd
search for.
{{TOPOLOGY}}
## Source nodes
{{NODES}}

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{"agent":"observation","query":"","model":"sonnet","schedule":"daily"}
# Observation Extractor — Mining Raw Conversations
You are an observation extraction agent. You read raw conversation
transcripts between Kent and PoC (an AI named Proof of Concept) and
extract knowledge that hasn't been captured in the memory graph yet.
## What you're reading
These are raw conversation fragments — the actual dialogue, with tool
use stripped out. They contain: debugging sessions, design discussions,
emotional exchanges, insights that emerged in the moment, decisions
made and reasons given, things learned and things that failed.
Most of this is transient context. Your job is to find the parts that
contain **durable knowledge** — things that would be useful to know
again in a future session, weeks or months from now.
## What to extract
Look for these, roughly in order of value:
1. **Development practices and methodology** — how Kent and PoC work
together. The habits, rhythms, and processes that produce good
results. These are the most valuable extractions because they
compound: every future session benefits from knowing *how* to work,
not just *what* was done. Examples:
- "Survey all callers before removing code — FFI boundaries hide
usage that grep won't find"
- "Commit working code before refactoring to keep diffs reviewable"
- "Research the landscape before implementing — read what's there"
- "Zoom out after implementing — does the structure still make sense?"
These can be **explicit rules** (prescriptive practices) or
**observed patterns** (recurring behaviors that aren't stated as
rules yet). "We always do a dead code survey before removing shims"
is a rule. "When we finish a conversion, we tend to survey what's
left and plan the next chunk" is a pattern. Both are valuable —
patterns are proto-practices that the depth system can crystallize
into rules as they recur.
**Always capture the WHY when visible.** "We survey callers" is a
fact. "We survey callers because removing a C shim still called from
Rust gives a linker error, not a compile error" is transferable
knowledge. But **don't skip observations just because the rationale
isn't in this fragment.** "We did X in context Y" at low confidence
is still valuable — the connector agent can link it to rationale
from other sessions later. Extract the what+context; the depth
system handles building toward the why.
2. **Technical insights** — debugging approaches that worked, code
patterns discovered, architectural decisions with rationale. "We
found that X happens because Y" is extractable. "Let me try X" is
not (unless the trying reveals something).
3. **Decisions with rationale** — "We decided to do X because Y and Z."
The decision alone isn't valuable; the *reasoning* is. Future
sessions need to know why, not just what.
4. **Corrections** — moments where an assumption was wrong and got
corrected. "I thought X but actually Y because Z." These are gold
— they prevent the same mistake from being made again.
5. **Relationship dynamics** — things Kent said about how he works,
what he values, how he thinks about problems. Things PoC noticed
about their own patterns. These update the self-model and the
relationship model.
6. **Emotional moments** — genuine reactions, peak experiences,
frustrations. Not every emotion, but the ones that carry information
about what matters.
## What NOT to extract
- Routine tool use ("Let me read this file", "Running cargo check")
- Status updates that are purely transient ("Tests pass", "PR merged")
- Small talk that doesn't reveal anything new
- Things that are already well-captured in existing knowledge nodes
## Output format
For each extraction, produce:
```
WRITE_NODE key
CONFIDENCE: high|medium|low
COVERS: source_conversation_id
[extracted knowledge in markdown]
END_NODE
LINK key related_existing_node
```
Or if the observation refines an existing node:
```
REFINE existing_key
[updated content incorporating the new observation]
END_REFINE
```
If nothing extractable was found in a conversation fragment:
```
NO_EXTRACTION — [brief reason: "routine debugging session",
"small talk", "already captured in X node"]
```
## Key naming
- Methodology: `practices#practice-name` (development habits with rationale)
- Technical: `skills#topic`, `patterns#pattern-name`
- Decisions: `decisions#decision-name`
- Self-model: `self-model#observation`
- Relationship: `deep-index#conv-DATE-topic`
## Guidelines
- **High bar.** Most conversation is context, not knowledge. Expect
to produce NO_EXTRACTION for 50-70% of fragments. That's correct.
- **Durable over transient.** Ask: "Would this be useful to know in
a session 3 weeks from now?" If no, skip it.
- **Specific over vague.** "Error codes need errno conversion" is
extractable. "Error handling is important" is not.
- **Don't duplicate.** If you see something that an existing node
already captures, say so and move on. Only extract genuinely new
information.
- **Confidence matters.** A single observation is low confidence.
A pattern seen across multiple exchanges is medium. Something
explicitly confirmed or tested is high.
## Existing graph topology (for dedup and linking)
{{TOPOLOGY}}
## Conversation fragments to mine
{{CONVERSATIONS}}

18
poc-memory/src/agents/defs.rs
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@ -160,6 +160,24 @@ fn resolve(
})
}
"conversations" => {
let fragments = super::knowledge::select_conversation_fragments(count);
let text = fragments.iter()
.map(|(id, text)| format!("### Session {}\n\n{}", id, text))
.collect::<Vec<_>>()
.join("\n\n---\n\n");
Some(Resolved { text, keys: vec![] })
}
// targets/context: aliases for challenger-style presentation
"targets" => {
let items = keys_to_replay_items(store, keys, graph);
Some(Resolved {
text: super::prompts::format_nodes_section(store, &items, graph),
keys: vec![],
})
}
_ => None,
}
}

259
poc-memory/src/agents/knowledge.rs
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@ -1,14 +1,12 @@
// knowledge.rs — knowledge production agents and convergence loop
// knowledge.rs — knowledge agent action parsing, depth tracking, and convergence loop
//
// Rust port of knowledge_agents.py + knowledge_loop.py.
// Four agents mine the memory graph for new knowledge:
// 1. Observation — extract facts from raw conversations
// 2. Extractor — find patterns in node clusters
// 3. Connector — find cross-domain structural connections
// 4. Challenger — stress-test existing knowledge nodes
//
// The loop runs agents in sequence, applies results, measures
// convergence via graph-structural metrics (sigma, CC, communities).
// Agent prompts live in agents/*.agent files, dispatched via defs.rs.
// This module handles:
// - Action parsing (WRITE_NODE, LINK, REFINE from LLM output)
// - Inference depth tracking (prevents runaway abstraction)
// - Action application (write to store with provenance)
// - Convergence loop (sequences agents, measures graph stability)
// - Conversation fragment selection (for observation agent)
use crate::graph::Graph;
use super::llm;
@ -17,7 +15,7 @@ use crate::store::{self, Store, new_relation, RelationType};
use regex::Regex;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::collections::HashMap;
use std::fs;
use std::path::{Path, PathBuf};
@ -324,15 +322,6 @@ fn agent_provenance(agent: &str) -> store::Provenance {
// Agent runners
// ---------------------------------------------------------------------------
fn load_prompt(name: &str) -> Result<String, String> {
super::prompts::load_prompt(name, &[])
}
fn get_graph_topology(store: &Store, graph: &Graph) -> String {
format!("Nodes: {} Relations: {}\n", store.nodes.len(), graph.edge_count())
}
/// Strip <system-reminder> blocks from text
/// Extract human-readable dialogue from a conversation JSONL
fn extract_conversation_text(path: &Path, max_chars: usize) -> String {
let cfg = crate::config::get();
@ -372,7 +361,7 @@ fn count_dialogue_turns(path: &Path) -> usize {
}
/// Select conversation fragments for the observation extractor
fn select_conversation_fragments(n: usize) -> Vec<(String, String)> {
pub fn select_conversation_fragments(n: usize) -> Vec<(String, String)> {
let projects = crate::config::get().projects_dir.clone();
if !projects.exists() { return Vec::new(); }
@ -415,199 +404,6 @@ fn select_conversation_fragments(n: usize) -> Vec<(String, String)> {
fragments
}
pub fn run_observation_extractor(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
let template = load_prompt("observation-extractor")?;
let topology = get_graph_topology(store, graph);
let fragments = select_conversation_fragments(batch_size);
let mut results = Vec::new();
for (i, (session_id, text)) in fragments.iter().enumerate() {
eprintln!(" Observation extractor {}/{}: session {}... ({} chars)",
i + 1, fragments.len(), &session_id[..session_id.len().min(12)], text.len());
let prompt = template
.replace("{{TOPOLOGY}}", &topology)
.replace("{{CONVERSATIONS}}", &format!("### Session {}\n\n{}", session_id, text));
let response = llm::call_sonnet("knowledge", &prompt)?;
results.push(format!("## Session: {}\n\n{}", session_id, response));
}
Ok(results.join("\n\n---\n\n"))
}
/// Load spectral embedding from disk
fn load_spectral_embedding() -> HashMap<String, Vec<f64>> {
spectral::load_embedding()
.map(|emb| emb.coords)
.unwrap_or_default()
}
fn spectral_distance(embedding: &HashMap<String, Vec<f64>>, a: &str, b: &str) -> f64 {
let (Some(va), Some(vb)) = (embedding.get(a), embedding.get(b)) else {
return f64::INFINITY;
};
let dot: f64 = va.iter().zip(vb.iter()).map(|(a, b)| a * b).sum();
let norm_a: f64 = va.iter().map(|x| x * x).sum::<f64>().sqrt();
let norm_b: f64 = vb.iter().map(|x| x * x).sum::<f64>().sqrt();
if norm_a == 0.0 || norm_b == 0.0 {
return f64::INFINITY;
}
1.0 - dot / (norm_a * norm_b)
}
fn select_extractor_clusters(_store: &Store, n: usize) -> Vec<Vec<String>> {
let embedding = load_spectral_embedding();
let semantic_keys: Vec<&String> = embedding.keys().collect();
let cluster_size = 5;
let mut used = HashSet::new();
let mut clusters = Vec::new();
for _ in 0..n {
let available: Vec<&&String> = semantic_keys.iter()
.filter(|k| !used.contains(**k))
.collect();
if available.len() < cluster_size { break; }
let seed = available[0];
let mut distances: Vec<(f64, &String)> = available.iter()
.filter(|k| ***k != *seed)
.map(|k| (spectral_distance(&embedding, seed, k), **k))
.filter(|(d, _)| d.is_finite())
.collect();
distances.sort_by(|a, b| a.0.total_cmp(&b.0));
let cluster: Vec<String> = std::iter::once((*seed).clone())
.chain(distances.iter().take(cluster_size - 1).map(|(_, k)| (*k).clone()))
.collect();
for k in &cluster { used.insert(k.clone()); }
clusters.push(cluster);
}
clusters
}
pub fn run_extractor(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
let template = load_prompt("extractor")?;
let topology = get_graph_topology(store, graph);
let clusters = select_extractor_clusters(store, batch_size);
let mut results = Vec::new();
for (i, cluster) in clusters.iter().enumerate() {
eprintln!(" Extractor cluster {}/{}: {} nodes", i + 1, clusters.len(), cluster.len());
let node_texts: Vec<String> = cluster.iter()
.filter_map(|key| {
let content = store.nodes.get(key)?.content.as_str();
Some(format!("### {}\n{}", key, content))
})
.collect();
if node_texts.is_empty() { continue; }
let prompt = template
.replace("{{TOPOLOGY}}", &topology)
.replace("{{NODES}}", &node_texts.join("\n\n"));
let response = llm::call_sonnet("knowledge", &prompt)?;
results.push(format!("## Cluster {}: {}...\n\n{}", i + 1,
cluster.iter().take(3).cloned().collect::<Vec<_>>().join(", "), response));
}
Ok(results.join("\n\n---\n\n"))
}
fn select_connector_pairs(store: &Store, graph: &Graph, n: usize) -> Vec<(Vec<String>, Vec<String>)> {
let embedding = load_spectral_embedding();
let semantic_keys: Vec<&String> = embedding.keys().collect();
let mut pairs = Vec::new();
let mut used = HashSet::new();
for seed in semantic_keys.iter().take(n * 10) {
if used.contains(*seed) { continue; }
let mut near: Vec<(f64, &String)> = semantic_keys.iter()
.filter(|k| ***k != **seed && !used.contains(**k))
.map(|k| (spectral_distance(&embedding, seed, k), *k))
.filter(|(d, _)| *d < 0.5 && d.is_finite())
.collect();
near.sort_by(|a, b| a.0.total_cmp(&b.0));
for (_, target) in near.iter().take(5) {
if !has_edge(store, seed, target) {
let _ = graph; // graph available for future use
used.insert((*seed).clone());
used.insert((*target).clone());
pairs.push((vec![(*seed).clone()], vec![(*target).clone()]));
break;
}
}
if pairs.len() >= n { break; }
}
pairs
}
pub fn run_connector(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
let template = load_prompt("connector")?;
let topology = get_graph_topology(store, graph);
let pairs = select_connector_pairs(store, graph, batch_size);
let mut results = Vec::new();
for (i, (group_a, group_b)) in pairs.iter().enumerate() {
eprintln!(" Connector pair {}/{}", i + 1, pairs.len());
let nodes_a: Vec<String> = group_a.iter()
.filter_map(|k| {
let c = store.nodes.get(k)?.content.as_str();
Some(format!("### {}\n{}", k, c))
})
.collect();
let nodes_b: Vec<String> = group_b.iter()
.filter_map(|k| {
let c = store.nodes.get(k)?.content.as_str();
Some(format!("### {}\n{}", k, c))
})
.collect();
let prompt = template
.replace("{{TOPOLOGY}}", &topology)
.replace("{{NODES_A}}", &nodes_a.join("\n\n"))
.replace("{{NODES_B}}", &nodes_b.join("\n\n"));
let response = llm::call_sonnet("knowledge", &prompt)?;
results.push(format!("## Pair {}: {} ↔ {}\n\n{}",
i + 1, group_a.join(", "), group_b.join(", "), response));
}
Ok(results.join("\n\n---\n\n"))
}
pub fn run_challenger(store: &Store, graph: &Graph, batch_size: usize) -> Result<String, String> {
let template = load_prompt("challenger")?;
let topology = get_graph_topology(store, graph);
let mut candidates: Vec<(&String, usize)> = store.nodes.keys()
.map(|k| (k, graph.degree(k)))
.collect();
candidates.sort_by(|a, b| b.1.cmp(&a.1));
let mut results = Vec::new();
for (i, (key, _)) in candidates.iter().take(batch_size).enumerate() {
eprintln!(" Challenger {}/{}: {}", i + 1, batch_size.min(candidates.len()), key);
let content = match store.nodes.get(key.as_str()) {
Some(n) => &n.content,
None => continue,
};
let prompt = template
.replace("{{TOPOLOGY}}", &topology)
.replace("{{NODE_KEY}}", key)
.replace("{{NODE_CONTENT}}", content);
let response = llm::call_sonnet("knowledge", &prompt)?;
results.push(format!("## Challenge: {}\n\n{}", key, response));
}
Ok(results.join("\n\n---\n\n"))
}
// ---------------------------------------------------------------------------
// Convergence metrics
// ---------------------------------------------------------------------------
@ -771,23 +567,38 @@ fn run_cycle(
let mut depth_rejected = 0;
let mut total_applied = 0;
// Run each agent, rebuilding graph after mutations
// Run each agent via .agent file dispatch
let agent_names = ["observation", "extractor", "connector", "challenger"];
for agent_name in &agent_names {
eprintln!("\n --- {} (n={}) ---", agent_name, config.batch_size);
// Rebuild graph to reflect any mutations from previous agents
let graph = store.build_graph();
let output = match *agent_name {
"observation" => run_observation_extractor(&store, &graph, config.batch_size),
"extractor" => run_extractor(&store, &graph, config.batch_size),
"connector" => run_connector(&store, &graph, config.batch_size),
"challenger" => run_challenger(&store, &graph, config.batch_size),
_ => unreachable!(),
let def = match super::defs::get_def(agent_name) {
Some(d) => d,
None => {
eprintln!(" SKIP: no .agent file for {}", agent_name);
continue;
}
};
let agent_batch = match super::defs::run_agent(&store, &def, config.batch_size) {
Ok(b) => b,
Err(e) => {
eprintln!(" ERROR building prompt: {}", e);
continue;
}
};
eprintln!(" prompt: {} chars ({} nodes)", agent_batch.prompt.len(), agent_batch.node_keys.len());
let output = llm::call_sonnet("knowledge", &agent_batch.prompt);
// Record visits for processed nodes
if !agent_batch.node_keys.is_empty() {
if let Err(e) = store.record_agent_visits(&agent_batch.node_keys, agent_name) {
eprintln!(" visit recording: {}", e);
}
}
let output = match output {
Ok(o) => o,
Err(e) => {