Agent frameworks own orchestration: nodes, edges, retries, human-in-the-loop, checkpointing. Contexta owns the corpus: graph memory, provenance, Reflexes, bi-temporal recall. They compose. Pick the framework you prefer — Contexta survives the swap. The model is commodity. The framework changes every quarter. The context layer is the moat.
LangGraph and CrewAI build the agent loop. Contexta is the context layer underneath.
| Feature | Contexta | Agent framework |
|---|---|---|
| Orchestrates the agent loop | ||
| Graph-native memory (entities + edges) | ||
| Provenance / receipts for evaluation | DIY | |
| Reactive triggers (Reflexes) on the corpus | ||
| Survives framework swap | ||
| Model-agnostic | ||
| Framework-agnostic SDK | ||
| Bi-temporal recall |
Orchestrates the agent loop
Graph-native memory (entities + edges)
Provenance / receipts for evaluation
Reactive triggers (Reflexes) on the corpus
Survives framework swap
Model-agnostic
Framework-agnostic SDK
Bi-temporal recall
Whichever your team is productive in. We publish first-class adapters for LangGraph and Claude Agent SDK; the SDK is framework-neutral so anything that can call an HTTP endpoint works.
Yes for durable, cross-session, cross-agent context. LangChain memory is fine for ephemeral chat scratchpad — but most teams move it out of the framework once production starts.
Yes. The Agent SDK calls Contexta like any tool; Packets land in the prompt with citations and policy metadata intact.
Vector RAG retrieves snippets. Contexta returns Context Packets with receipts.
Read →Mem0, Letta and Zep give you storage. Contexta gives you a context layer.
Read →Neo4j stores graphs. Contexta turns graphs into context.
Read →Zapier and n8n react to flat events. Contexta reacts to graph state.
Read →Drop your evaluation criteria — we will price honestly against your signal volume and ship you a Context Packet to inspect.