Synthetic Time Travel for your agents in one call. Step into any moment — past or future — and reason about what happens: query the live causal graph, simulate a scenario forward or backward, read source-traced confidence scores. One MCP config line or one REST call against the production host. Read tools are public and keyless — copy a block, run it, get JSON back.
{
"mcpServers": {
"timepoint": {
"url": "https://mcp.timepointai.com/mcp/"
}
}
}Drop into Claude Desktop, Cursor, Windsurf, or any MCP client. Read tools (query_moments, get_moment, get_graph_stats) are public — no key. Write tools (propose_moment, challenge_moment) need an X-Service-Key.
# Query the live decision graph — keyless read
curl https://api.timepointai.com/api/v1/moments?limit=3
# Run a simulation — metered, needs your key
curl -X POST https://api.timepointai.com/api/v1/pro/runs \
-H "Authorization: Bearer $TIMEPOINT_API_KEY" \
-H "Content-Type: application/json" \
-d '{"scenario": "Series A negotiation: three VCs, two competing term sheets", "mode": "branching"}'All examples target the production host api.timepointai.com. Get a key from your settings dashboard (link below).
| Operation | Auth | Notes |
|---|---|---|
| query_moments / graph reads | Keyless | Public reads on api.timepointai.com and mcp.timepointai.com. No account required. |
| propose_moment / challenge_moment | X-Service-Key | Write tools require a service key. Request one from settings. |
| Pro simulations | Bearer key + metered | Deep simulation runs are credit-metered. Bring your API key; see your balance in settings. |
| Tool | Access | What it does |
|---|---|---|
| query_moments | Public | Search the causal graph by entity, time, place, or keyword. |
| get_moment | Public | Fetch one moment with its figures, dialog, and causal edges. |
| get_graph_stats | Public | Node/edge counts, layer breakdown, edge-type distribution. |
| propose_moment | Write token | Add a new moment to the graph for review. |
| challenge_moment | Write token | Flag a moment or edge whose confidence you dispute. |
{
"moment": {
"name": "Series A Decision",
"path": "/2025/...",
"figures": ["Founder", "Lead VC", "Strategic Investor"],
"edges": [
{
"edge_type": "social",
"description": "lead VC allies with strategic investor",
"confidence": 0.82
}
],
"confidence": 0.79,
"tdf_hash": "tdf:sha256:..."
}
}Every edge carries a confidence score and provenance. The same shape backs both the MCP get_moment tool and the REST moment endpoint.
Your agent makes better decisions when it knows what caused what. The Clockchain is a live, autonomously growing causal graph — 20,000+ nodes, 540K+ edges and climbing, spanning 700 BCE to 2026. The MCP server (v1.26.0, Streamable HTTP) exposes 5 tools — 3 public read tools and 2 authenticated write tools. Works with Claude Desktop, Cursor, Windsurf, or any MCP-compatible agent. Read operations (query, search, stats) are public. Write operations (propose, challenge) require a token. Request a write token or query the graph directly.
{
"mcpServers": {
"timepoint-clockchain": {
"url": "https://clockchain.timepointai.com/mcp/"
}
}
}clockchain.timepointai.com — requires X-Service-Key headerGET clockchain.timepointai.com/api/v1/moments/-44/march/15/1030/\
italy/lazio/rome/assassination-of-julius-caesar
GET clockchain.timepointai.com/api/v1/graph/neighbors/-44/march/15/...
GET clockchain.timepointai.com/api/v1/search?q=apollo
GET clockchain.timepointai.com/api/v1/today
GET clockchain.timepointai.com/api/v1/random
GET clockchain.timepointai.com/health # public, no authReconstruct any historical moment with source-verified ground truth. Flash is a 14-agent pipeline — researcher, fact-checker, scene-setter, character-creator, dialog-writer, narrator, image-generator, and critique agents — with Google Search verification for historical accuracy. Three quality presets: Hyper (~55s), Balanced (~90s), HD (~2.5min). Open-weight model option (DeepSeek, Llama, Qwen) for Google-free operation. Pro simulates complex futures through 19 composable SNAG mechanisms across 5 temporal modes (Forward, Portal, Branching, Cyclical, Directorial), with TWGF causal graph output, entity radar charts, dialog theater, and TDF export. 21 templates included. Use the web app, iOS app, API, or clone the repos.
curl -X POST flash.timepointai.com/api/v1/timepoints/generate \
-H "Content-Type: application/json" \
-d '{"query": "AlphaGo plays Move 37, Seoul, March 2016",
"generate_image": true}'./run.sh run mars_mission_portal # backward from failure
./run.sh run vc_pitch_branching # 5 investors × 16 timepoints
./run.sh list # all 21 templatesEvery render your agent makes strengthens the graph for every future query. The full service topology: API Gateway (api.timepointai.com) routes to Flash (14-agent scene generation), Clockchain (temporal causal graph + MCP for AI agents), Pro Cloud (SNAG simulation engine, 5 temporal modes), and Billing (Stripe + Apple IAP, credit metering). Clockchain MCP connects any MCP-compatible AI agent. Web App at app.timepointai.com. Native iOS app (SwiftUI). SkipMeetings (skipmeetings.com) — AI meeting intelligence SaaS powered by Flash. The more you use it, the smarter it gets.
All Apache 2.0. Star us on GitHub
A Timepoint is how your agent sees a moment — who was there, what they said, why it mattered, what happened next. Flash renders Timepoints from the historical record. Pro simulates them into possible futures. The Clockchain accumulates them into a permanent, shared causal intelligence that grows more valuable with every render.
./run.sh run vc_pitch_branchingGET /api/v1/graph/neighbors/{canonical_url}./run.sh run board_meeting./run.sh run castaway_colony_branching
# → 8 entities × 16 timepoints × 120 training examples @ ~$0.35| Capability | Timepoint | RAG / Vector DB | LLM Alone |
|---|---|---|---|
| Temporal causal graph | ✓ | ✗ | ✗ |
| Entity resolution across time | ✓ | ✗ | ✗ |
| Causal inference (not just retrieval) | ✓ | ✗ | Partial |
| MCP server for AI agents | ✓ | ✗ | ✗ |
| Multi-model routing (open-weight support) | ✓ | ✗ | ✗ |
| Graph compounds with every use | ✓ | ✗ | ✗ |
| Open source (Apache 2.0) | ✓ | Varies | Varies |
Type any scenario in plain language. Flash renders historical moments against live sources. Pro stress-tests future scenarios through a social graph. You describe the situation. The engines handle the complexity.
curl -X POST https://api.timepointai.com/api/v1/timepoints/generate/stream \
-H "Authorization: Bearer $TIMEPOINT_API_KEY" \
-d '{"query": "Next quarter board meeting, agenda: pivot discussion, new hire plan"}'curl -X POST https://api.timepointai.com/api/v1/timepoints/generate/stream \
-H "Authorization: Bearer $TIMEPOINT_API_KEY" \
-d '{"query": "AlphaGo Move 37, Four Seasons Hotel Seoul March 2016",
"generate_image": true}'./run.sh run vc_pitch_branching # Pro: 5 investors, branching futuresRAG retrieves documents. SNAG synthesizes and maintains structured social graphs — with causal provenance, knowledge flow, emotional states, and temporal consistency — to ground LLM generation in complex group dynamics. Where RAG answers questions from what was written down, SNAG reasons about what people did, felt, and caused.
As causal inference and machine learning converge — from counterfactual estimation to heterogeneous treatment effects to causal discovery at scale — the bottleneck is no longer algorithmic. It's data. SNAG produces structured causal datasets with full provenance: the kind of training signal that causal ML has always needed but never had at scale. Think of it as maximum likelihood estimation applied not to parameters but to moments — finding the most probable state of a social system given everything we know about the people in it.
As multi-agent orchestration becomes the dominant AI architecture, every agent system faces the same deficit: structured temporal context. Agents don't need more documents. They need causal memory — who did what, why, and what followed. SNAG provides it.
You know where you want to end up. PORTAL tells you what has to be true to get there. Instead of simulating forward from a starting state, PORTAL reasons backward from a target outcome — decomposing it into the preconditions, decisions, and causal chains required to reach it. Think of it as maximum likelihood estimation applied to moments: given a desired future, what sequence of events makes it most probable? PORTAL is how you stress-test a strategy before you commit to it.
Every query your agent makes strengthens every future query. The Clockchain is live and autonomously growing 24/7. Currently 20,000+ nodes spanning 700 BCE to 2026, connected by 540K+ typed causal edges across 11 relationship types — causes, caused_by, influences, contemporaneous, thematic, same_location, same_era, same_conflict, same_figure, precedes, and follows. Every edge carries an LLM-generated description explaining why two events are related. Every node is a full layer-2 Flash render with period-accurate characters, dialog, CDN-hosted AI-generated imagery, and full model provenance tracking. Schema v0.2 — every entry records its generation run, model stack, and a graph-state hash for consistency proofs. REST API endpoints require an X-Service-Key header. MCP read tools (query_moments, get_moment, get_graph_stats) are public. Interactive docs at /docs and /redoc. An autonomous graph expander runs continuously on free models (DeepSeek, Llama, Qwen), discovering and linking related events — the graph compounds around the clock. The graph is content-addressed via TDF.
Timepoint Pro treats social simulation like a synthesizer treats sound. ADPRS envelopes control cognitive activation over time — Attack, Decay, Plateau, Release, Sustain — shaping when each entity is "active" in the simulation. Params2Persona maps entity state tensors directly to LLM generation parameters. 19 composable mechanisms across five pillars. Five temporal modes. Fidelity follows attention, not scope.
If you're making decisions based on causal simulations, you need to see how those simulations work. Every engine is Apache 2.0 — fork it, audit it, run it locally. How you use it is as private as you want it to be. But the infrastructure itself must be transparent. The algorithms are yours. The graph compounds for everyone.
Follow @timepointai for updates. Star us on GitHub.
We ship in the open. The left column is what's live and tested in the repos today. The right column is what we're actively building toward. We believe transparency about the delta between ground truth and vision is itself a form of confidence.
We are at the very beginning. The percentage of the past and future properly rendered in a durable, traceable causal structure is vanishingly small — so many zeros it's hard to write. Every Timepoint mined moves that number. The Clockchain is designed to be populated by massive swarms of AI agents, each contributing verified moments. The infrastructure is ready. The era of autonomous temporal rendering is next.
Follow @timepointai · GitHub
Free to start · 50 credits included · No credit card required
"The fidelity is asymptotic — we approach near-simulacrum on historical dialog because there are very few things a person could have said once the model has perfect context for that moment."
— Sean McDonald, Timepoint Labs