Shared memory and context tools for agentic work.
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<main>
<h1 id="memory--learning-tools"><a class="header" href="#memory--learning-tools">Memory & Learning Tools</a></h1>
<p>Seven tools for feedback-based learning, drift analysis, path explanation, and investigation trail management.</p>
<hr>
<h2 id="m1ndlearn"><a class="header" href="#m1ndlearn"><code>m1nd.learn</code></a></h2>
<p>Explicit feedback-based edge adjustment. After using <code>activate</code> or other query tools, call <code>learn</code> to tell m1nd whether the results were correct, wrong, or partial. This applies Hebbian learning to strengthen or weaken edges between the query seeds and the reported nodes.</p>
<h3 id="parameters"><a class="header" href="#parameters">Parameters</a></h3>
<div class="table-wrapper">
<table>
<thead>
<tr><th>Parameter</th><th>Type</th><th>Required</th><th>Default</th><th>Description</th></tr>
</thead>
<tbody>
<tr><td><code>query</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>The original query this feedback relates to. Must match the query used in the activation.</td></tr>
<tr><td><code>agent_id</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Calling agent identifier.</td></tr>
<tr><td><code>feedback</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Feedback type. Values: <code>"correct"</code> (strengthen edges), <code>"wrong"</code> (weaken edges), <code>"partial"</code> (strengthen confirmed nodes only).</td></tr>
<tr><td><code>node_ids</code></td><td><code>string[]</code></td><td>Yes</td><td>–</td><td>Node identifiers to apply feedback to. For <code>"correct"</code>, these are the relevant results. For <code>"wrong"</code>, these are the irrelevant ones.</td></tr>
<tr><td><code>strength</code></td><td><code>number</code></td><td>No</td><td><code>0.2</code></td><td>Feedback strength for edge adjustment. Range: 0.0 to 1.0. Higher = stronger plasticity effect.</td></tr>
</tbody>
</table>
<h3 id="example-request"><a class="header" href="#example-request">Example Request</a></h3>
<pre><code class="language-json">{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "m1nd.learn",
"arguments": {
"agent_id": "agent-1",
"query": "session pool management",
"feedback": "correct",
"node_ids": ["file::session_pool.py", "file::worker_pool.py"],
"strength": 0.3
</code></pre>
<h3 id="example-response"><a class="header" href="#example-response">Example Response</a></h3>
"edges_adjusted": 8,
"nodes_affected": 2,
"plasticity_delta": 0.3,
"elapsed_ms": 2.1
<h3 id="when-to-use"><a class="header" href="#when-to-use">When to Use</a></h3>
<ul>
<li><strong>After every activate where results were used</strong> – always provide feedback to improve future results</li>
<li><strong>After investigation</strong> – report which activated nodes were actually relevant</li>
<li><strong>Continuous improvement</strong> – the graph learns from your feedback over time</li>
<h3 id="side-effects"><a class="header" href="#side-effects">Side Effects</a></h3>
<p>Modifies edge weights in the graph. Changes are persisted on the next auto-persist cycle (every 50 queries) and on shutdown.</p>
<h3 id="related-tools"><a class="header" href="#related-tools">Related Tools</a></h3>
<li><a href="activation.html#m1ndactivate"><code>m1nd.activate</code></a> – the query tool whose results you are providing feedback on</li>
<li><a href="#m1nddrift"><code>m1nd.drift</code></a> – see cumulative weight changes from learning</li>
<h2 id="m1nddrift"><a class="header" href="#m1nddrift"><code>m1nd.drift</code></a></h2>
<p>Weight and structural drift analysis. Compares the current graph state against a baseline (typically <code>"last_session"</code>) to show what changed – new edges, removed edges, and weight drift. Useful for context recovery at session start.</p>
<h3 id="parameters-1"><a class="header" href="#parameters-1">Parameters</a></h3>
<tr><td><code>since</code></td><td><code>string</code></td><td>No</td><td><code>"last_session"</code></td><td>Baseline reference point. Values: <code>"last_session"</code> (saved state from previous session), or a timestamp.</td></tr>
<tr><td><code>include_weight_drift</code></td><td><code>boolean</code></td><td>No</td><td><code>true</code></td><td>Include edge weight drift analysis. Shows which edges strengthened or weakened.</td></tr>
<h3 id="example-request-1"><a class="header" href="#example-request-1">Example Request</a></h3>
"id": 2,
"name": "m1nd.drift",
"since": "last_session",
"include_weight_drift": true
<h3 id="example-response-1"><a class="header" href="#example-response-1">Example Response</a></h3>
"node_count_delta": 15,
"edge_count_delta": 42,
"new_nodes": ["file::forem_publisher.py", "file::forem_routes.py"],
"removed_nodes": [],
"top_weight_drifts": [
{ "edge": "session_pool.py -> worker_pool.py", "old": 0.45, "new": 0.72, "delta": 0.27 },
{ "edge": "chat_handler.py -> stream_parser.py", "old": 0.60, "new": 0.48, "delta": -0.12 }
],
"elapsed_ms": 12.0
<h3 id="when-to-use-1"><a class="header" href="#when-to-use-1">When to Use</a></h3>
<li><strong>Session start</strong> – first tool to call after <code>m1nd.health</code> to recover context</li>
<li><strong>After ingest</strong> – see what the new ingest changed</li>
<li><strong>After extended learning</strong> – track cumulative drift from feedback</li>
<h3 id="related-tools-1"><a class="header" href="#related-tools-1">Related Tools</a></h3>
<li><a href="analysis.html#m1nddiverge"><code>m1nd.diverge</code></a> – higher-level structural drift with anomaly detection</li>
<li><a href="lifecycle.html#m1ndhealth"><code>m1nd.health</code></a> – basic server health (call before drift)</li>
<h2 id="m1ndwhy"><a class="header" href="#m1ndwhy"><code>m1nd.why</code></a></h2>
<p>Path explanation between two nodes. Finds and explains the relationship paths connecting a source node to a target node. Returns all paths up to <code>max_hops</code>, ranked by cumulative edge strength.</p>
<h3 id="parameters-2"><a class="header" href="#parameters-2">Parameters</a></h3>
<tr><td><code>source</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Source node identifier.</td></tr>
<tr><td><code>target</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Target node identifier.</td></tr>
<tr><td><code>max_hops</code></td><td><code>integer</code></td><td>No</td><td><code>6</code></td><td>Maximum hops in path search. Higher values find more indirect paths but take longer.</td></tr>
<h3 id="example-request-2"><a class="header" href="#example-request-2">Example Request</a></h3>
"id": 3,
"name": "m1nd.why",
"source": "file::worker_pool.py",
"target": "file::whatsapp_manager.py",
"max_hops": 4
<h3 id="example-response-2"><a class="header" href="#example-response-2">Example Response</a></h3>
"paths": [
{
"nodes": ["worker_pool.py", "process_manager.py", "whatsapp_manager.py"],
"relations": ["calls::cancel", "imports"],
"cumulative_strength": 0.68,
"hops": 2
},
"nodes": ["worker_pool.py", "spawner.py", "chat_handler.py", "whatsapp_manager.py"],
"relations": ["imported_by", "calls", "imports"],
"cumulative_strength": 0.31,
"hops": 3
"total_paths_found": 2,
"elapsed_ms": 15.0
<h3 id="when-to-use-2"><a class="header" href="#when-to-use-2">When to Use</a></h3>
<li><strong>Understanding dependencies</strong> – “why are these two modules connected?”</li>
<li><strong>Tracing influence</strong> – find the relationship chain between distant modules</li>
<li><strong>Bug investigation</strong> – understand how a change in A could affect B</li>
<h3 id="related-tools-2"><a class="header" href="#related-tools-2">Related Tools</a></h3>
<li><a href="analysis.html#m1ndhypothesize"><code>m1nd.hypothesize</code></a> – tests a claim about the relationship (more powerful)</li>
<li><a href="analysis.html#m1ndimpact"><code>m1nd.impact</code></a> – finds all affected nodes (broader scope)</li>
<h2 id="trail_save"><a class="header" href="#trail_save"><code>trail_save</code></a></h2>
<p>Persist the current investigation state – nodes visited, hypotheses formed, conclusions reached, and open questions. Captures activation boosts for later restoration.</p>
<h3 id="parameters-3"><a class="header" href="#parameters-3">Parameters</a></h3>
<tr><td><code>label</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Human-readable label for this investigation.</td></tr>
<tr><td><code>hypotheses</code></td><td><code>object[]</code></td><td>No</td><td><code>[]</code></td><td>Hypotheses formed during investigation. Each object has: <code>statement</code> (string, required), <code>confidence</code> (number, default 0.5), <code>supporting_nodes</code> (string[]), <code>contradicting_nodes</code> (string[]).</td></tr>
<tr><td><code>conclusions</code></td><td><code>object[]</code></td><td>No</td><td><code>[]</code></td><td>Conclusions reached. Each object has: <code>statement</code> (string, required), <code>confidence</code> (number, default 0.5), <code>from_hypotheses</code> (string[]), <code>supporting_nodes</code> (string[]).</td></tr>
<tr><td><code>open_questions</code></td><td><code>string[]</code></td><td>No</td><td><code>[]</code></td><td>Open questions remaining for future investigation.</td></tr>
<tr><td><code>tags</code></td><td><code>string[]</code></td><td>No</td><td><code>[]</code></td><td>Tags for organization and search.</td></tr>
<tr><td><code>summary</code></td><td><code>string</code></td><td>No</td><td>–</td><td>Optional summary. Auto-generated if omitted.</td></tr>
<tr><td><code>visited_nodes</code></td><td><code>object[]</code></td><td>No</td><td><code>[]</code></td><td>Explicitly list visited nodes with annotations. Each object has: <code>node_external_id</code> (string, required), <code>annotation</code> (string, optional), <code>relevance</code> (number, default 0.5). If omitted, captured from active perspective state.</td></tr>
<tr><td><code>activation_boosts</code></td><td><code>object</code></td><td>No</td><td><code>{}</code></td><td>Map of <code>node_external_id</code> to boost weight <code>[0.0, 1.0]</code>. Re-injected on resume.</td></tr>
<h3 id="example-request-3"><a class="header" href="#example-request-3">Example Request</a></h3>
"id": 4,
"name": "trail_save",
"label": "auth-leak-investigation",
"hypotheses": [
"statement": "Auth tokens leak through session pool",
"confidence": 0.7,
"supporting_nodes": ["file::session_pool.py", "file::auth_discovery.py"]
"statement": "Rate limiter missing from auth chain",
"confidence": 0.9,
"supporting_nodes": ["file::middleware.py"]
"open_questions": ["Does the rate limiter apply to WebSocket connections?"],
"tags": ["security", "auth", "session"],
"activation_boosts": {
"file::session_pool.py": 0.8,
"file::auth_discovery.py": 0.6
<h3 id="example-response-3"><a class="header" href="#example-response-3">Example Response</a></h3>
"trail_id": "trail_agent1_001_a1b2c3",
"nodes_saved": 47,
"hypotheses_saved": 2,
"conclusions_saved": 0,
"open_questions_saved": 1,
"graph_generation_at_creation": 42,
"created_at_ms": 1710300000000
<h3 id="when-to-use-3"><a class="header" href="#when-to-use-3">When to Use</a></h3>
<li><strong>End of investigation session</strong> – save your work before ending a session</li>
<li><strong>Before context compaction</strong> – checkpoint your investigation state</li>
<li><strong>Cross-session continuity</strong> – resume exactly where you left off</li>
<h3 id="related-tools-3"><a class="header" href="#related-tools-3">Related Tools</a></h3>
<li><a href="#m1ndtrailresume"><code>trail_resume</code></a> – restore a saved trail</li>
<li><a href="#m1ndtraillist"><code>trail_list</code></a> – find saved trails</li>
<li><a href="#m1ndtrailmerge"><code>trail_merge</code></a> – combine trails from parallel investigations</li>
<h2 id="trail_resume"><a class="header" href="#trail_resume"><code>trail_resume</code></a></h2>
<p>Restore a saved investigation. Re-injects activation boosts into the graph, validates that saved nodes still exist, detects staleness, and optionally downgrades hypotheses whose supporting nodes are missing.</p>
<h3 id="parameters-4"><a class="header" href="#parameters-4">Parameters</a></h3>
<tr><td><code>trail_id</code></td><td><code>string</code></td><td>Yes</td><td>–</td><td>Trail ID to resume (from <code>trail_save</code> or <code>trail_list</code>).</td></tr>
<tr><td><code>force</code></td><td><code>boolean</code></td><td>No</td><td><code>false</code></td><td>Resume even if trail is stale (>50% missing nodes). Default behavior: refuse to resume stale trails.</td></tr>
<h3 id="example-request-4"><a class="header" href="#example-request-4">Example Request</a></h3>
"id": 5,
"name": "trail_resume",
"trail_id": "trail_agent1_001_a1b2c3"
<h3 id="example-response-4"><a class="header" href="#example-response-4">Example Response</a></h3>
"stale": false,
"generations_behind": 3,
"missing_nodes": [],
"nodes_reactivated": 47,
"hypotheses_downgraded": [],
"trail": {
"status": "active",
"created_at_ms": 1710300000000,
"last_modified_ms": 1710300000000,
"node_count": 47,
"hypothesis_count": 2,
"conclusion_count": 0,
"open_question_count": 1,
"summary": "Investigating auth token leaks through session pool"
"elapsed_ms": 22.5
<h3 id="when-to-use-4"><a class="header" href="#when-to-use-4">When to Use</a></h3>
<li><strong>Session start</strong> – restore a previous investigation</li>
<li><strong>Cross-agent handoff</strong> – agent B resumes agent A’s trail</li>
<li><strong>After re-ingest</strong> – check if investigation nodes survived the graph update</li>
<h3 id="related-tools-4"><a class="header" href="#related-tools-4">Related Tools</a></h3>
<li><a href="#m1ndtrailsave"><code>trail_save</code></a> – save a trail to resume later</li>
<li><a href="activation.html#m1ndwarmup"><code>warmup</code></a> – simpler priming without full trail restoration</li>
<h2 id="trail_list"><a class="header" href="#trail_list"><code>trail_list</code></a></h2>
<p>List saved investigation trails with optional filters. Returns compact summaries suitable for selecting a trail to resume.</p>
<h3 id="parameters-5"><a class="header" href="#parameters-5">Parameters</a></h3>
<tr><td><code>filter_agent_id</code></td><td><code>string</code></td><td>No</td><td>–</td><td>Filter to a specific agent’s trails. <code>None</code> = all agents.</td></tr>
<tr><td><code>filter_status</code></td><td><code>string</code></td><td>No</td><td>–</td><td>Filter by status: <code>"active"</code>, <code>"saved"</code>, <code>"archived"</code>, <code>"stale"</code>, <code>"merged"</code>.</td></tr>
<tr><td><code>filter_tags</code></td><td><code>string[]</code></td><td>No</td><td><code>[]</code></td><td>Filter by tags (any match).</td></tr>
<h3 id="example-request-5"><a class="header" href="#example-request-5">Example Request</a></h3>
"id": 6,
"name": "trail_list",
"filter_status": "saved",
"filter_tags": ["security"]
<h3 id="example-response-5"><a class="header" href="#example-response-5">Example Response</a></h3>
"trails": [
"status": "saved",
"tags": ["security", "auth", "session"]
"total_count": 1
<h3 id="when-to-use-5"><a class="header" href="#when-to-use-5">When to Use</a></h3>
<li><strong>Session start</strong> – see what investigations are available to resume</li>
<li><strong>Multi-agent coordination</strong> – see trails from other agents</li>
<li><strong>Cleanup</strong> – find stale or merged trails</li>
<h3 id="related-tools-5"><a class="header" href="#related-tools-5">Related Tools</a></h3>
<li><a href="#m1ndtrailresume"><code>trail_resume</code></a> – resume a trail from this list</li>
<li><a href="#m1ndtrailmerge"><code>trail_merge</code></a> – combine related trails</li>
<h2 id="m1ndtrailmerge"><a class="header" href="#m1ndtrailmerge"><code>m1nd.trail.merge</code></a></h2>
<p>Combine two or more investigation trails. Merges visited nodes, hypotheses, and conclusions. Uses confidence+recency scoring for conflict resolution. Discovers cross-connections between independently explored areas.</p>
<h3 id="parameters-6"><a class="header" href="#parameters-6">Parameters</a></h3>
<tr><td><code>trail_ids</code></td><td><code>string[]</code></td><td>Yes</td><td>–</td><td>Two or more trail IDs to merge.</td></tr>
<tr><td><code>label</code></td><td><code>string</code></td><td>No</td><td>–</td><td>Label for the merged trail. Auto-generated if omitted.</td></tr>
<h3 id="example-request-6"><a class="header" href="#example-request-6">Example Request</a></h3>
"id": 7,
"name": "m1nd.trail.merge",
"trail_ids": ["trail_agent1_001_a1b2c3", "trail_analyst_002_d4e5f6"],
"label": "combined-auth-investigation"
<h3 id="example-response-6"><a class="header" href="#example-response-6">Example Response</a></h3>
"merged_trail_id": "trail_agent1_003_g7h8i9",
"label": "combined-auth-investigation",
"source_trails": ["trail_agent1_001_a1b2c3", "trail_analyst_002_d4e5f6"],
"nodes_merged": 83,
"hypotheses_merged": 5,
"conflicts": [
"hypothesis_a": "Session pool leaks tokens",
"hypothesis_b": "Session pool tokens are properly scoped",
"resolution": "resolved",
"winner": "Session pool leaks tokens",
"score_delta": 0.35
"connections_discovered": [
"type": "bridge_edge",
"detail": "auth_discovery.py connects the auth trail to the session trail",
"from_node": "file::auth_discovery.py",
"to_node": "file::session_pool.py",
"weight": 0.72
"elapsed_ms": 45.0
<h3 id="conflict-resolution"><a class="header" href="#conflict-resolution">Conflict Resolution</a></h3>
<p>When merging hypotheses that contradict each other:</p>
<li><strong>confidence+recency</strong> scoring determines the winner</li>
<li>If the score delta is too small, the conflict is marked <code>"unresolved"</code> for human review</li>
<li>Source trails are set to <code>"merged"</code> status after a successful merge</li>
<h3 id="when-to-use-6"><a class="header" href="#when-to-use-6">When to Use</a></h3>
<li><strong>Multi-agent investigation</strong> – combine findings from parallel agents</li>
<li><strong>Investigation continuation</strong> – merge an old investigation with new findings</li>
<li><strong>Consolidation</strong> – clean up related but separate investigation threads</li>
<h3 id="related-tools-6"><a class="header" href="#related-tools-6">Related Tools</a></h3>
<li><a href="#m1ndtrailsave"><code>trail_save</code></a> – save individual trails</li>
<li><a href="#m1ndtraillist"><code>m1nd.trail.list</code></a> – find trails to merge</li>
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