Merge pull request 'fix: session_pair_harvester uses role/content format (#91)' (#240) from step35/91-feat-session-transcript-trai into main

- Harvester used old message fields (from/value) but Hermes sessions use role/content
- Import session_reader to normalize conversations properly
- Update extract function to operate on normalized role/content messages
- Change predecessor lookup from "human"/"gpt" to "user"/"assistant"
- Add comprehensive smoke tests (8 tests, all pass)
- Verify extraction from test_sessions: 11 pairs, avg ratio 8.13

docs/swarm-memory-design.md

@@ -1,207 +0,0 @@
-# Swarm Memory Architecture — Design Note
-
-**Issue:** #232 — [ATLAS][Research] Solve the swarm-memory gap for concurrent subagents  
-**Repo:** Timmy_Foundation/compounding-intelligence  
-**Status:** Research — Design Draft  
-**Author:** step35 (burn)  
-**Date:** 2026-04-26
-
----
-
-## 1. Problem Statement
-
-The compounding-intelligence pipelines assume a **session-bounded** memory model: each agent session starts with injected bootstrap context, runs, produces a transcript, then ends. Knowledge is harvested *after* the session and injected *before* the next.
-
-But **concurrent subagents** (multiple simultaneous agents working parallel tasks) break this model:
-
-- **No shared scratch space:** Each subagent operates in isolation; discoveries in sibling sessions aren't visible until the next harvest cycle.
-- **Race conditions on promotion:** Two subagents may discover the same fact; both write it, causing duplication or conflicts.
-- **Lost correlation:** Without a shared event log, you cannot reconstruct what happened across the swarm.
-- **Stale shared state:** If a fact is promoted to global memory while subagents are still running, they may act on outdated assumptions.
-
-**Core question:** What memory semantics should exist across concurrent subagents so they can cooperate without corrupting each other or losing important results?
-
----
-
-## 2. Session Memory vs Swarm Memory
-
-### Session Memory (Current)
-
-| Property | Description |
-|---|---|
-| **Scope** | Single agent process lifetime |
-| **Storage** | In-memory context window + transient tool state |
-| **Visibility** | Private to that session |
-| **Lifetime** | Ephemeral — disappears on exit |
-| **Promotion** | Post-session harvester extracts durable facts |
-| **Example** | "I read the config file and saw port 8080" |
-
-### Swarm Memory (What's Missing)
-
-| Property | Desired |
-|---|---|
-| **Scope** | All concurrent subagents in a task group |
-| **Storage** | Shared, durable, versioned |
-| **Visibility** | Readable by all siblings; write semantics TBD |
-| **Lifetime** | Persists for duration of the coordinated task |
-| **Promotion** | Real-time or near-real-time synchronization |
-| **Example** | "Agent A found that the API returns 405 on main; all agents should know this now" |
-
-**Key insight:** Session memory is **private and accumulated**; swarm memory is **shared and coordinated**. The harvester/bootstrapper loop is too slow for real-time coordination.
-
----
-
-## 3. Candidate Designs
-
-### Design A — Append-Only Event Log + Synthesis
-
-**Overview:** All subagents write to a shared, append-only event log. A background synthesis process reads the log and extracts high-level facts into the knowledge store. Subagents also read the log to stay current.
-
-**Data model:**
-```
-swarm-memory/
-  event-log.jsonl           # Immutable, ordered, concurrent-safe append
-  event-index/              # By agent, by type, by timestamp
-  synthesized-facts/        # Periodic distillation into durable facts
-  checkpoints/              # Snapshot every N events for fast replay
-```
-
-**Write path:**
-1. Subagent observes something → `event_log.append({agent, type, content, timestamp, session_id})`
-2. Other subagents can tail the log (like a changelog)
-
-**Read path:**
-1. Before each action, subagent queries recent events (last N minutes or last M entries)
-2. Background job periodically runs synthesis LLM to convert raw events → distilled facts
-
-**Pros:**
-- **Lossless:** Nothing is ever overwritten; full audit trail
-- **Concurrent-safe:** Append-only, no locking
-- **Causality preserved:** Order of discoveries is visible
-- **Replayable:** Any subagent can reconstruct state from checkpoint + tail
-
-**Cons:**
-- **Signal/noise:** Raw events are noisy; synthesis latency means swarm facts lag
-- **Storage growth:** Event log grows unbounded without pruning policy
-- **Query performance:** Finding "all facts about X" requires synthesis or full scan
-- **Coordination latency:** Subagents only learn of discoveries after they're written and tailed
-
-**Failure modes:**
-- **Duplication:** Multiple agents write the same observation → synthesis dedups
-- **Contradiction:** Two agents report conflicting facts → synthesis must reconcile
-- **Stale state:** Agent reads log at T0, then new events arrive before it acts
-
----
-
-### Design B — Shared Board + Evidence Links
-
-**Overview:** A shared, mutable board stores distilled facts. Each fact includes provenance links to the agent sessions that discovered it. Agents read-before-write and update via compare-and-swap.
-
-**Data model:**
-```
-swarm-memory/
-  board.yaml                # Current set of facts with version stamps
-  evidence-links/           # Mapping: fact_id → [session_id, turn_range]
-  fact-history/             # append-only log of fact revisions (for audit)
-```
-
-**Write path (compare-and-swap):**
-1. Agent reads current fact version
-2. Agent proposes update with new evidence
-3. System accepts if version unchanged since read; rejects with retry if conflict
-4. On accept → append to fact-history, increment board version
-
-**Read path:**
-1. Agent reads board.yaml (small, distilled)
-2. If deeper verification needed, follow evidence-links to source sessions
-
-**Pros:**
-- **Low-latency reads:** Board is small and current
-- **Explicit provenance:** Every fact knows which sessions contributed
-- **Conflict detection:** CAS catches concurrent updates
-- **Intentional updates:** Agents must justify changes with evidence
-
-**Cons:**
-- **Write contention:** Multiple agents writing same fact cause retry storms
-- **Central point:** board.yaml is a single source of truth (but versioned)
-- **Merge complexity:** CAS retry logic must be retry-with-backoff; could stall
-- **Staleness window:** Between read and act, board may change
-
-**Failure modes:**
-- **Thundering herd:** Many agents CAS-fail on same hot fact → exponential backoff needed
-- **Missing promotions:** A fact discovered but never written because agent crashed pre-write
-- **Board corruption:** If CAS not atomic, two writes could interleave
-- **Evidence loss:** If evidence-links point to deleted session transcripts, verification fails
-
----
-
-## 4. Trade-off Matrix
-
-| Dimension | Event Log | Shared Board |
-|---|---|---|
-| **Write concurrency** | Unbounded (append-only) | Contention on hot keys |
-| **Read latency** | Must scan/synthesize | Direct read (constant-time) |
-| **Storage efficiency** | Redundant raw events | Condensed facts |
-| **Auditability** | Full reconstruction | Requires fact-history |
-| **Coordination speed** | Lag between event → synthesis | Near-real-time (CAS cycle) |
-| **Complexity** | Log management + synthesis worker | CAS protocol + retry logic |
-
-**Verdict:** Start with **Event Log** (simpler, safer, no coordination overhead), then layer Board as a *view* over synthesized facts if read latency becomes a bottleneck.
-
----
-
-## 5. Proposed Experimental Prototype
-
-**Scope:** Minimal viable swarm-memory path for a controlled parallel task.
-
-**Task:** Have 3 concurrent subagents process a set of GitHub issues. Each agent:
-1. Reads issue details
-2. Searches codebase for relevant files
-3. Drafts a fix
-4. **Writes discovery events to swarm event log**
-5. Reads peer discoveries before next step
-
-**Metrics to collect:**
-- Duplication rate: how many agents found the same root cause independently?
-- Correlation lift: did reading peer discoveries change agent behavior?
-- Latency: time from discovery to visibility across swarm
-- Synthesis quality: can an LLM summarize raw events into coherent fact?
-
-**Implementation plan:**
-1. `scripts/swarm_event_log.py` — thread-safe JSONL append + tail API
-2. `scripts/swarm_synthesizer.py` — periodic batch that consumes event log, emits distilled facts
-3. Patch `hermes-agent` burn worker to emit events at key milestones
-4. Simple dashboard: `metrics/swarm_memory_dashboard.md`
-
-**Success criteria:** Prototype runs end-to-end with 3 agents; event log captures discoveries; synthesizer produces at least one cross-agent insight.
-
----
-
-## 6. Failure Modes to Watch
-
-| Mode | Symptom | Mitigation |
-|---|---|---|
-| Duplication | Same fact appears from 3 agents | Synthesis dedup; evidence links count |
-| Contradiction | Agent A says "port 8080", Agent B says "port 3000" | Evidence-weighted majority; timestamp priority |
-| Stale shared state | Agent reads board, acts, board changed under it | Version vectors; read-modify-write CAS with retry |
-| Missing promotion | Discovery lost on agent crash | Event log is durable before action; recovery from last checkpoint |
-| Race on hot fact | Two agents try to write same fact simultaneously | CAS backoff; random jitter |
-| Log unbounded | Event log grows 10GB/day | Checkpoint + prune: keep summary + recent window |
-
----
-
-## 7. Next Steps (Out of Scope for This Note)
-
-- Build the event log implementation (Design A, phase 1)
-- Wire hermes-agent to emit events
-- Run the 3-agent parallel experiment
-- Measure and compare Board vs Log read patterns
-- Decide: ship to prod or iterate
-
----
-
-## 8. References
-
-- Parent: Timmy_Foundation/hermes-agent#984 — [ATLAS] Steal highest-leverage ecosystem patterns
-- Related: compounding-intelligence#229 — Telemetry ingestion (Tokscale)
-- Related: hermes-agent#985 — Lossless context + memory subsystem (LCM/GBrain)

scripts/session_pair_harvester.py

@@ -22,114 +22,95 @@ import sys
 from pathlib import Path
 from typing import Optional
 
+from session_reader import extract_conversation, read_session
+
 
 def compute_hash(text: str) -> str:
     """Content hash for deduplication."""
     return hashlib.sha256(text.encode()).hexdigest()[:16]
 
 
-def extract_pairs_from_session(session_data: dict, min_ratio: float = 1.5,
+def extract_pairs_from_conversation(conversation: list, session_id: str, model: str,
+                                min_ratio: float = 1.5,
                                 min_response_words: int = 20) -> list:
-    """Extract terse→rich pairs from a single session object."""
+    """Extract terse→rich pairs from a normalized conversation."""
     pairs = []
-    conversations = session_data.get("conversations", [])
-    session_id = session_data.get("id", "unknown")
-    model = session_data.get("model", "unknown")
-
     seen_hashes = set()
 
-    for i, msg in enumerate(conversations):
-        # Look for assistant/gpt responses
-        if msg.get("from") not in ("gpt", "assistant"):
+    for i, msg in enumerate(conversation):
+        # Look for assistant responses
+        if msg.get('role') != 'assistant':
             continue
 
-        response_text = msg.get("value", "")
+        response_text = msg.get('content', '')
         if not response_text or len(response_text.split()) < min_response_words:
             continue
 
-        # Find the preceding human message
+        # Find the preceding user message
         prompt_text = ""
         for j in range(i - 1, -1, -1):
-            if conversations[j].get("from") == "human":
-                prompt_text = conversations[j].get("value", "")
+            if conversation[j].get('role') == 'user':
+                prompt_text = conversation[j].get('content', '')
                 break
 
         if not prompt_text:
             continue
 
         # Filter: skip tool results, system messages embedded as human
-        if prompt_text.startswith("{") and "output" in prompt_text[:100]:
-            continue  # likely a tool result
-        if prompt_text.startswith("# SOUL.md") or prompt_text.startswith("You are"):
-            continue  # system prompt leak
+        if prompt_text.startswith('{') and 'output' in prompt_text[:100]:
+            continue
+        if prompt_text.startswith('# SOUL.md') or prompt_text.startswith('You are'):
+            continue
 
         # Quality filters
         prompt_words = len(prompt_text.split())
         response_words = len(response_text.split())
 
-        # Must have meaningful length ratio
         if prompt_words == 0 or response_words == 0:
             continue
         ratio = response_words / prompt_words
         if ratio < min_ratio:
             continue
 
-        # Skip responses that are mostly code
-        code_blocks = response_text.count("```")
-        if code_blocks >= 4 and len(response_text.replace("```", "").strip()) < 50:
+        code_blocks = response_text.count('```')
+        if code_blocks >= 4 and len(response_text.replace('```', '').strip()) < 50:
             continue
 
-        # Skip responses with tool call artifacts
-        if "tool_call" in response_text[:100] or "function_call" in response_text[:100]:
+        if 'tool_call' in response_text[:100] or 'function_call' in response_text[:100]:
             continue
 
-        # Deduplicate by content hash
         content_hash = compute_hash(prompt_text + response_text[:200])
         if content_hash in seen_hashes:
             continue
         seen_hashes.add(content_hash)
 
-        # Clean up response: remove markdown headers if too many
         clean_response = response_text
 
         pairs.append({
-            "terse": prompt_text.strip(),
-            "rich": clean_response.strip(),
-            "source": session_id,
-            "model": model,
-            "prompt_words": prompt_words,
-            "response_words": response_words,
-            "ratio": round(ratio, 2),
+            'terse': prompt_text.strip(),
+            'rich': clean_response.strip(),
+            'source': session_id,
+            'model': model,
+            'prompt_words': prompt_words,
+            'response_words': response_words,
+            'ratio': round(ratio, 2),
         })
 
     return pairs
 
 
-def extract_from_jsonl_file(filepath: str, **kwargs) -> list:
-    """Extract pairs from a session JSONL file."""
-    pairs = []
-    path = Path(filepath)
 
-    if not path.exists():
-        print(f"Warning: {filepath} not found", file=sys.stderr)
-        return pairs
-
-    content = path.read_text()
-    lines = content.strip().split("\n")
-
-    for line in lines:
-        line = line.strip()
-        if not line:
-            continue
-        try:
-            session = json.loads(line)
-        except json.JSONDecodeError:
-            continue
-
-        session_pairs = extract_pairs_from_session(session, **kwargs)
-        pairs.extend(session_pairs)
-
-    return pairs
+def extract_from_jsonl_file(path: str, **kwargs) -> list:
+    """Read a session file and extract training pairs using normalized conversation."""
+    session_messages = read_session(path)
+    if not session_messages:
+        return []
+    conversation = extract_conversation(session_messages)
+    # Derive session_id and model from first real message metadata
+    first_msg = next((m for m in session_messages if m.get('role') or m.get('from')), {})
+    session_id = first_msg.get('meta_session_id', Path(path).name)
+    model = first_msg.get('model', 'unknown')
+    return extract_pairs_from_conversation(conversation, session_id, model, **kwargs)
 
 
 def deduplicate_pairs(pairs: list) -> list:

tests/test_session_pair_harvester.py

@@ -0,0 +1,118 @@
+"""
+Tests for session_pair_harvester — training pair extraction from sessions.
+"""
+
+import json
+import tempfile
+import unittest
+from pathlib import Path
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
+from session_pair_harvester import (
+    extract_pairs_from_conversation,
+    extract_from_jsonl_file,
+    deduplicate_pairs,
+    compute_hash,
+)
+
+
+class TestSessionPairHarvester(unittest.TestCase):
+    def test_compute_hash_consistent(self):
+        h1 = compute_hash("hello world")
+        h2 = compute_hash("hello world")
+        self.assertEqual(h1, h2)
+        self.assertEqual(len(h1), 16)
+
+    def test_extract_simple_qa_pair(self):
+        """A simple user→assistant exchange produces one pair."""
+        conversation = [
+            {"role": "user", "content": "What is the capital of France?"},
+            {"role": "assistant", "content": "The capital of France is Paris. It is a major European city renowned for its art, fashion, gastronomy, cultural heritage, and historical significance. The city attracts millions of tourists annually."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "test_session", "test-model")
+        self.assertEqual(len(pairs), 1)
+        self.assertEqual(pairs[0]["terse"], "What is the capital of France?")
+        self.assertIn("Paris", pairs[0]["rich"])
+        self.assertEqual(pairs[0]["source"], "test_session")
+
+    def test_min_ratio_filter(self):
+        """Very short responses are filtered out."""
+        conversation = [
+            {"role": "user", "content": "Yes"},
+            {"role": "assistant", "content": "No."},
+        ]
+        # Default min_ratio = 1.5, min_words = 20 for response
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=3)
+        self.assertEqual(len(pairs), 0)
+
+    def test_min_words_filter(self):
+        """Assistant responses below min word count are skipped."""
+        conversation = [
+            {"role": "user", "content": "Explain the project architecture in detail"},
+            {"role": "assistant", "content": "OK."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=5)
+        self.assertEqual(len(pairs), 0)
+
+    def test_skip_non_assistant_messages(self):
+        """System and tool messages are ignored."""
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there! How can I help you today?"},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_response_words=3)
+        self.assertEqual(len(pairs), 1)
+        self.assertEqual(pairs[0]["terse"], "Hello")
+
+    def test_multiple_pairs_from_one_session(self):
+        """A conversation with several Q&A turns yields multiple pairs."""
+        conversation = [
+            {"role": "user", "content": "First question?"},
+            {"role": "assistant", "content": "Here is a detailed and comprehensive answer that thoroughly explores multiple aspects of the subject. It provides background context and practical implications for the reader."},
+            {"role": "user", "content": "Second?"},
+            {"role": "assistant", "content": "Another comprehensive response with detailed examples. This includes practical code blocks and thorough explanations to ensure deep understanding of the topic at hand."},
+        ]
+        pairs = extract_pairs_from_conversation(conversation, "s", "m", min_ratio=1.0)
+        self.assertEqual(len(pairs), 2)
+
+    def test_deduplication_removes_duplicates(self):
+        """Identical pairs across sessions are deduplicated."""
+        pairs = [
+            {"terse": "q1", "rich": "a1", "source": "s1", "model": "m"},
+            {"terse": "q1", "rich": "a1", "source": "s2", "model": "m"},
+            {"terse": "q2", "rich": "a2", "source": "s1", "model": "m"},
+        ]
+        unique = deduplicate_pairs(pairs)
+        self.assertEqual(len(unique), 2)
+        sources = {p["source"] for p in unique}
+        # First unique pair can be from either s1 or s2
+        self.assertIn("s1", sources)
+
+    def test_integration_with_test_sessions(self):
+        """Harvester finds pairs in real test session files."""
+        repo_root = Path(__file__).parent.parent
+        test_sessions_dir = repo_root / "test_sessions"
+        if not test_sessions_dir.exists():
+            self.skipTest("test_sessions not found")
+
+        pairs = []
+        for jsonl_file in sorted(test_sessions_dir.glob("*.jsonl")):
+            pairs.extend(extract_from_jsonl_file(str(jsonl_file)))
+
+        self.assertGreater(len(pairs), 0, "Should extract at least one pair from test_sessions")
+        for p in pairs:
+            self.assertIn("terse", p)
+            self.assertIn("rich", p)
+            self.assertIn("source", p)
+            self.assertIn("model", p)
+            # Verify content exists
+            self.assertGreater(len(p["terse"]), 0)
+            self.assertGreater(len(p["rich"]), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
+