Shared memory and context tools for agentic work.
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<h1 class="menu-title">m1nd Documentation</h1>
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<main>
<h1 id="multi-agent-usage"><a class="header" href="#multi-agent-usage">Multi-Agent Usage</a></h1>
<p>m1nd is designed for multi-agent systems. One m1nd instance serves many agents simultaneously. This tutorial covers agent identity, concurrent access, perspective isolation, trail sharing, and a real-world example of how a production orchestration system uses m1nd with 19 agents.</p>
<h2 id="how-it-works"><a class="header" href="#how-it-works">How It Works</a></h2>
<p>m1nd runs as a single process with a single shared graph. Multiple MCP clients connect to the same instance (or multiple instances reading from the same persisted state). Every tool call includes an <code>agent_id</code> parameter that identifies the caller.</p>
<pre><code> Agent A (Claude Code) ----+
|
Agent B (Cursor) ----+----> m1nd-mcp (single graph)
Agent C (custom agent) ----+
</code></pre>
<p>The graph is shared. Learning by one agent benefits all agents. Perspectives are isolated per agent. Trails can be shared across agents.</p>
<h2 id="agent-id-conventions"><a class="header" href="#agent-id-conventions">Agent ID Conventions</a></h2>
<p>Every m1nd tool requires an <code>agent_id</code> parameter. This is a free-form string, but consistent naming matters:</p>
<pre><code>agent_id: "agent-1" -- orchestrator
agent_id: "hacker-auth" -- security hardening agent
agent_id: "forge-api" -- API building agent
agent_id: "analyst-perf" -- performance analysis agent
<p><strong>Recommended convention</strong>: <code>{archetype}-{task}</code> for short-lived task agents, simple names for persistent agents.</p>
<p>Rules:</p>
<ul>
<li>Agent IDs are case-sensitive</li>
<li>Use lowercase with hyphens</li>
<li>m1nd tracks all agent IDs it has seen (visible in <code>health</code> output)</li>
<li>Agent ID determines perspective ownership and trail ownership</li>
<h2 id="shared-graph-individual-learning"><a class="header" href="#shared-graph-individual-learning">Shared Graph, Individual Learning</a></h2>
<p>When Agent A calls <code>learn</code> with feedback, the edge weight changes are visible to all agents immediately:</p>
<pre><code class="language-jsonc">// Agent A: "session_pool.py was useful for my auth investigation"
{
"method": "tools/call",
"params": {
"name": "m1nd.learn",
"arguments": {
"query": "authentication flow",
"agent_id": "agent-a",
"feedback": "correct",
"node_ids": ["file::session_pool.py"]
// -> 740 edges strengthened
<pre><code class="language-jsonc">// Agent B: immediately benefits from stronger session_pool.py edges
"name": "m1nd.activate",
"query": "session management",
"agent_id": "agent-b",
"top_k": 5
// -> session_pool.py scores higher than it would have before Agent A's feedback
<p>This is by design. The graph represents collective intelligence about the codebase. Every agent’s learning contributes to the whole.</p>
<h2 id="perspective-isolation"><a class="header" href="#perspective-isolation">Perspective Isolation</a></h2>
<p>Perspectives are m1nd’s navigation system – a stateful exploration session anchored to a node, with a route surface, breadcrumb history, and focus tracking.</p>
<p>Perspectives are isolated per agent. Agent A’s perspectives are not visible to Agent B, and navigation in one perspective does not affect another.</p>
<h3 id="agent-a-start-a-perspective"><a class="header" href="#agent-a-start-a-perspective">Agent A: Start a Perspective</a></h3>
<pre><code class="language-jsonc">{
"name": "perspective_start",
"query": "authentication middleware",
"anchor_node": "file::middleware.py"
<p>Response:</p>
<pre><code class="language-json">{
"perspective_id": "persp-a1b2c3",
"focus": "file::middleware.py",
"routes": [
{"route_id": "r-001", "target": "file::auth.py", "label": "imports", "score": 0.92},
{"route_id": "r-002", "target": "file::session.py", "label": "calls", "score": 0.78}
],
"route_set_version": 1
<h3 id="agent-b-has-its-own-perspectives"><a class="header" href="#agent-b-has-its-own-perspectives">Agent B: Has Its Own Perspectives</a></h3>
"query": "worker pool scaling"
<p>Agent B gets a completely independent perspective. Its routes, focus, and history are separate from Agent A’s.</p>
<h3 id="listing-perspectives"><a class="header" href="#listing-perspectives">Listing Perspectives</a></h3>
<p>Each agent only sees its own:</p>
<pre><code class="language-jsonc">// Agent A sees only its perspectives
"name": "perspective_list",
"arguments": {"agent_id": "agent-a"}
// -> [{"perspective_id": "persp-a1b2c3", "focus": "file::middleware.py", ...}]
// Agent B sees only its perspectives
"arguments": {"agent_id": "agent-b"}
// -> [{"perspective_id": "persp-d4e5f6", "focus": "file::worker_pool.py", ...}]
<h3 id="comparing-perspectives-across-agents"><a class="header" href="#comparing-perspectives-across-agents">Comparing Perspectives Across Agents</a></h3>
<p>You <em>can</em> explicitly compare two perspectives from different agents using <code>perspective.compare</code>. This is useful for discovering where two independent investigations overlap:</p>
"name": "perspective_compare",
"perspective_id_a": "persp-a1b2c3",
"perspective_id_b": "persp-d4e5f6"
"shared_nodes": ["file::process_manager.py", "file::config.py"],
"unique_to_a": ["file::auth.py", "file::middleware.py"],
"unique_to_b": ["file::worker_pool.py", "file::spawner.py"],
"dimension_deltas": {
"structural": 0.12,
"semantic": 0.34,
"temporal": 0.08
<h2 id="lock-system-for-concurrent-access"><a class="header" href="#lock-system-for-concurrent-access">Lock System for Concurrent Access</a></h2>
<p>When multiple agents might modify the same region of the codebase simultaneously, the lock system prevents conflicts.</p>
<h3 id="agent-a-lock-the-auth-region"><a class="header" href="#agent-a-lock-the-auth-region">Agent A: Lock the Auth Region</a></h3>
"name": "m1nd.lock.create",
"scope": "subgraph",
"root_nodes": ["file::auth.py"],
"radius": 2
"lock_id": "lock-xyz789",
"nodes_locked": 156,
"edges_locked": 423,
<h3 id="set-a-watch-strategy"><a class="header" href="#set-a-watch-strategy">Set a Watch Strategy</a></h3>
"name": "m1nd.lock.watch",
"strategy": "on_ingest"
<p>Now, whenever any agent triggers an ingest that touches the locked region, the lock records the changes.</p>
<h3 id="check-for-changes"><a class="header" href="#check-for-changes">Check for Changes</a></h3>
<p>After Agent B modifies some code and re-ingests:</p>
"name": "m1nd.lock.diff",
"lock_id": "lock-xyz789"
<p>Response (in 0.08 microseconds):</p>
"new_nodes": ["file::auth.py::fn::validate_token_v2"],
"removed_nodes": [],
"weight_changes": 3,
"structural_changes": true
<p>Agent A now knows exactly what changed in its locked region, without scanning the entire graph.</p>
<h3 id="rebase-or-release"><a class="header" href="#rebase-or-release">Rebase or Release</a></h3>
<pre><code class="language-jsonc">// Accept changes and update baseline
{"method":"tools/call","params":{"name":"m1nd.lock.rebase","arguments":{
"agent_id":"agent-a","lock_id":"lock-xyz789"
}}}
// Or release when done
{"method":"tools/call","params":{"name":"m1nd.lock.release","arguments":{
<h2 id="trail-sharing"><a class="header" href="#trail-sharing">Trail Sharing</a></h2>
<p>Trails are investigation snapshots: visited nodes, hypotheses, conclusions, open questions, and activation boosts. They persist across sessions and can be shared between agents.</p>
<h3 id="agent-a-save-an-investigation"><a class="header" href="#agent-a-save-an-investigation">Agent A: Save an Investigation</a></h3>
"name": "trail_save",
"label": "auth-token-leak-investigation",
"hypotheses": [
"statement": "Auth tokens leak through session pool",
"confidence": 0.7,
"status": "investigating"
},
"statement": "Rate limiter missing from auth chain",
"confidence": 0.9,
"status": "confirmed"
"open_questions": [
"Does the healing manager observe token lifecycle?",
"Is there a token rotation policy?"
"tags": ["security", "auth", "session"]
"trail_id": "trail-abc123",
"nodes_captured": 47,
"hypotheses_saved": 2,
"activation_boosts_saved": 12
<h3 id="agent-b-resume-agent-as-trail"><a class="header" href="#agent-b-resume-agent-as-trail">Agent B: Resume Agent A’s Trail</a></h3>
"name": "m1nd.trail.resume",
"trail_id": "trail-abc123"
"nodes_reactivated": 47,
"stale_nodes": 2,
"hypotheses_restored": 2,
"hypotheses_downgraded": 0
<p>Agent B now has Agent A’s exact cognitive context: the same nodes are activated, the same hypotheses are loaded, and any stale nodes (changed since the trail was saved) are flagged.</p>
<h3 id="merging-trails-from-multiple-agents"><a class="header" href="#merging-trails-from-multiple-agents">Merging Trails from Multiple Agents</a></h3>
<p>When two agents investigate independently and you want to combine findings:</p>
"name": "m1nd.trail.merge",
"agent_id": "orchestrator",
"trail_ids": ["trail-abc123", "trail-def456"],
"label": "combined-auth-investigation"
"merged_trail_id": "trail-merged-789",
"total_nodes": 83,
"shared_nodes": 12,
"conflicts": [
"node": "file::auth.py",
"trail_a_hypothesis": "token leak source",
"trail_b_hypothesis": "not involved"
"conflict_count": 3
<p>The merge automatically detects where independent investigations converged (12 shared nodes) and where they conflict (3 disagreements). This is essential for synthesizing multi-agent research.</p>
<h3 id="browsing-trails"><a class="header" href="#browsing-trails">Browsing Trails</a></h3>
"name": "m1nd.trail.list",
"filter_tags": ["security"]
<h2 id="real-world-example-project-f-os"><a class="header" href="#real-world-example-project-f-os">Real-World Example: a production orchestration system</a></h2>
<p>a production orchestration system is a multi-agent orchestration system that uses m1nd as its shared code intelligence layer. Here is how it works in production:</p>
<h3 id="architecture"><a class="header" href="#architecture">Architecture</a></h3>
<pre><code>the orchestrator (orchestrator)
+-- hacker-auth (security agent) --+
+-- forge-api (API builder) --+-- All share one m1nd instance
+-- analyst-perf (performance) --+
+-- sentinel-files (file watcher) --+
+-- critic-quality (code reviewer) --+
+-- ... 14 more agents --+
<p>One m1nd instance serves 19 agents. The graph covers a 335-file Python backend (~52K lines), a React frontend, and MCP server infrastructure.</p>
<h3 id="orchestrator-boot-sequence"><a class="header" href="#orchestrator-boot-sequence">Orchestrator Boot Sequence</a></h3>
<p>When the orchestrator (the orchestrator) starts a session:</p>
<pre><code class="language-jsonc">// Step 1: Check m1nd health
{"method":"tools/call","params":{"name":"m1nd.health","arguments":{"agent_id":"agent-1"}}}
// Step 2: Check what changed since last session
{"method":"tools/call","params":{"name":"m1nd.drift","arguments":{"agent_id":"agent-1","since":"last_session"}}}
// Step 3: Re-ingest if the graph is stale
{"method":"tools/call","params":{"name":"m1nd.ingest","arguments":{
"path":"/project/backend","agent_id":"agent-1","incremental":true
<h3 id="task-delegation-with-graph-context"><a class="header" href="#task-delegation-with-graph-context">Task Delegation with Graph Context</a></h3>
<p>When the orchestrator delegates a security hardening task:</p>
<pre><code class="language-jsonc">// Before spawning the security agent, get blast radius context
{"method":"tools/call","params":{"name":"m1nd.impact","arguments":{
"node_id":"file::auth.py","agent_id":"agent-1"
// Warm up the graph for the security task
{"method":"tools/call","params":{"name":"m1nd.warmup","arguments":{
"task_description":"harden authentication token validation","agent_id":"agent-1"
// The security agent then uses the primed graph
{"method":"tools/call","params":{"name":"m1nd.activate","arguments":{
"query":"token validation vulnerabilities","agent_id":"hacker-auth"
<h3 id="collective-learning"><a class="header" href="#collective-learning">Collective Learning</a></h3>
<p>After each agent completes its task, it provides feedback:</p>
<pre><code class="language-jsonc">// Security agent found useful results
{"method":"tools/call","params":{"name":"m1nd.learn","arguments":{
"query":"token validation vulnerabilities",
"agent_id":"hacker-auth",
"feedback":"correct",
"node_ids":["file::auth.py","file::middleware.py","file::session_pool.py"]
// Performance agent found different useful results
"query":"connection pool bottleneck",
"agent_id":"analyst-perf",
"node_ids":["file::worker_pool.py","file::process_manager.py"]
<p>Over a session with 19 agents, the graph accumulates thousands of learning signals. Each agent benefits from every other agent’s discoveries.</p>
<h3 id="investigation-handoff"><a class="header" href="#investigation-handoff">Investigation Handoff</a></h3>
<p>When Agent A finds something that Agent B needs to investigate:</p>
<pre><code class="language-jsonc">// Agent A saves its investigation
{"method":"tools/call","params":{"name":"trail_save","arguments":{
"label":"session-hijack-vector",
"tags":["security","critical"]
// Orchestrator merges with Agent B's independent findings
{"method":"tools/call","params":{"name":"m1nd.trail.merge","arguments":{
"agent_id":"agent-1",
"trail_ids":["trail-hacker-001","trail-analyst-002"]
<h2 id="best-practices"><a class="header" href="#best-practices">Best Practices</a></h2>
<ol>
<li>
<p><strong>Use consistent agent IDs.</strong> The same agent should always use the same ID across sessions. This enables drift detection and trail continuity.</p>
</li>
<p><strong>Learn after every useful activation.</strong> The more feedback the graph gets, the smarter it becomes. Make <code>learn</code> calls automatic in your agent loop.</p>
<p><strong>Use locks for overlapping work.</strong> If two agents might modify the same code region, lock it first. Lock diffs are essentially free (0.08 microseconds).</p>
<p><strong>Save trails at investigation checkpoints.</strong> Trails are cheap to save and invaluable for handoff, resume, and post-mortem analysis.</p>
<p><strong>Merge trails for synthesis.</strong> When multiple agents investigate the same area independently, merge their trails to find convergence and conflicts.</p>
<p><strong>Warm up before focused tasks.</strong> <code>warmup</code> primes the graph for a specific task, boosting relevant regions before the agent starts querying.</p>
<p><strong>Use <code>drift</code> at session start.</strong> After any period of inactivity, check what changed. This recovers context efficiently.</p>
</ol>
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