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Architecture⚓︎

ContextAgent centers on a context core that every agent connects to. Pipelines orchestrate the core, agents execute tasks through LLM providers, and tools expose capabilities back to the core.

System layers⚓︎

  1. Pipeline runtime — entry point that maps queries to structured runs, handles retries, and stitches together steps.
  2. Context core — stores prompt templates, run state, artifacts, and memory so that every agent acts on shared truth.
  3. Agents — lightweight wrappers over LLM clients; each agent references a template in the context core.
  4. Tools — external capabilities (search, code execution, vector DB) registered with the context core and gated via policies.
  5. Observability — auto-traced spans, structured logs, and artifacts pushed to disks or remote stores.
graph LR
  subgraph Client
    Q[Pipeline Query]
  end
  Q -->|Validates| R[Pipeline Runtime]
  R -->|Mutates| C[(Context Core)]
  C -->|Selects| A1[Task Agent]
  C -->|Selects| A2[Reviewer Agent]
  C -->|Dispatches| T[Tool Bus]
  T --> S[(Services)]
  A1 --> M1[(LLM Provider)]
  A2 --> M2[(LLM Provider)]
  C --> O[Observability]

Configuration model⚓︎

  • YAML pipeline configs define agents, prompt templates, default models, tool access, and tracing settings.
  • Profiles expose reusable bundles of context so you can version prompts independently of pipelines.
  • Artifacts capture intermediate datasets, summaries, or generated files; the context core makes them discoverable per run.

Recommended folder layout

  • pipelines/configs/*.yaml — pipeline definitions
  • contextagent/profiles/ — prompt templates + state defaults
  • contextagent/tools/ — structured tool definitions
  • data/ — input datasets or assets consumed by pipelines

Deployment topology⚓︎

Mode Description Best for
Local Single process running pipelines on demand Prototyping, testing
Worker Fleet Pipelines dispatched to async workers via queues Production workloads
Hybrid Local for experiments, remote for heavy agents Teams with mixed needs

Continue with the Agent Lifecycle to understand how agents progress during a run.