---
title: "Context"
description: "How data flows between workflow stages"
---

Every workflow run has a **context** — a shared key-value store that carries data between stages. When an agent writes code, a command captures test output, or a human makes a selection, the results are recorded as context updates. Downstream nodes can read these values to make decisions, build prompts, and route execution.

## How context flows

The context starts empty at the beginning of a run. As each stage completes, its outcome includes a set of **context updates** — key-value pairs that are merged into the shared context. Later stages see all updates from earlier stages.

```
Start → Plan → Implement → Test → Exit
         │         │          │
         │         │          └─ sets command.output, command.stderr
         │         └─ sets response.implement, last_response
         └─ sets response.plan, last_response
```

Context is thread-safe and shared across the entire run. Parallel branches receive an isolated **deep copy** of the context at the point of fan-out, so branches can't interfere with each other. When branches merge, the fan-in handler records the results under `parallel.fan_in.*` keys.

## How agents access context

Agents do not have a tool to query the context store directly. Instead, context is made available through two mechanisms:

- **Preamble injection** — When a node starts, Fabro assembles a preamble from the current context and prepends it to the node's prompt. The [fidelity setting](#fidelity-controlling-agent-context) controls how detailed this preamble is.
- **Context updates** — Agents can write to the context by including a `context_updates` object in a JSON response. See [Transitions](/workflows/transitions#agent-transitions) for the response format.

## Keys set by handlers

Each handler type writes specific keys into the context after execution:

### Agent and prompt nodes

| Key | Value |
|---|---|
| `last_stage` | The node ID of the stage that just completed |
| `last_response` | Truncated LLM response (first 200 characters) |
| `response.{node_id}` | Full LLM response text |

Agents can also emit arbitrary context updates by including a JSON object with a `context_updates` field in their response. See [Transitions](/workflows/transitions#agent-transitions).

### Command nodes

| Key | Value |
|---|---|
| `command.output` | The command's stdout. Durable context stores this as a `blob://sha256/...` ref after the command completes; downstream prompts resolve it back to text. |
| `command.stderr` | The command's stderr. Durable context stores this as a `blob://sha256/...` ref after the command completes; downstream prompts resolve it back to text. |

### Human gates

| Key | Value |
|---|---|
| `human.gate.selected` | The accelerator key (e.g. `"A"`) or `"freeform"` |
| `human.gate.label` | The full label of the selected edge |
| `human.gate.text` | The user's freeform text (if applicable) |
| `human.gate.<node>.question` | The question text for a specific human gate node |
| `human.gate.<node>.answer` | The answer text for a specific human gate node |
| `human.gate.<node>.label` | The selected label for a specific human gate node, when applicable |

### Parallel merge (fan-in)

| Key | Value |
|---|---|
| `parallel.fan_in.best_id` | Node ID of the best-performing branch |
| `parallel.fan_in.best_outcome` | Status of the best branch |
| `parallel.fan_in.best_head_sha` | Git SHA from the best branch (if applicable) |

### Engine-managed keys

The engine sets several keys automatically. These are prefixed with `internal.` and are excluded from preambles:

| Key | Value |
|---|---|
| `internal.run_id` | Unique identifier for this run |
| `internal.work_dir` | Working directory path |
| `internal.fidelity` | The resolved fidelity mode for the current node |
| `internal.thread_id` | Thread ID for shared-conversation nodes (or null) |
| `internal.node_visit_count` | How many times the current node has been visited |
| `internal.retry_count.{node_id}` | Number of retry attempts used by a node |
| `outcome` | Status of the last completed stage (`succeeded`, `failed`, etc.) |
| `failure_class` | Classification of the last failure (if any) |
| `failure_signature` | Deduplication signature for the last failure |
| `preferred_label` | Label selected by a human gate or agent routing directive |
| `current_node` | ID of the node currently executing |
| `graph.goal` | The workflow's goal attribute |
| `graph.{attr}` | All graph-level attributes, mirrored into context |

## Using context in conditions

Edge [conditions](/workflows/transitions#conditions) can read context values to route execution:

```dot
gate -> deploy [condition="outcome=succeeded && context.tests_passed=true"]
gate -> fix    [condition="outcome=failed"]
```

The `context.` prefix is optional — `tests_passed=true` and `context.tests_passed=true` are equivalent.

Engine-managed keys like `internal.node_visit_count` work in conditions too. This is useful for [fixed-count loops](/tutorials/branch-loop#fixed-count-loops):

```dot
gate -> exit    [condition="context.internal.node_visit_count >= 5"]
gate -> improve
```

## Fidelity: Controlling agent context

When a new agent or prompt node starts, Fabro assembles a **preamble** — a summary of what happened in prior stages. The **fidelity** setting controls how detailed this preamble is.

| Fidelity | Behavior |
|---|---|
| `full` | No preamble. The agent continues in the same conversation thread, seeing complete prior context. |
| `compact` | Nested-bullet summary with handler-specific details (default). |
| `summary:high` | Detailed per-stage Markdown report. |
| `summary:medium` | Moderate detail with outcomes and notable findings (~1500 token target). |
| `summary:low` | Brief summary with just outcomes per stage (~600 token target). |
| `truncate` | Minimal — only the goal and run ID. |

### Setting fidelity

Fidelity can be set at three levels. The first match wins:

1. **Edge attribute** — Set `fidelity` on an edge to control the transition into a specific node:
   ```dot
   plan -> implement [fidelity="full"]
   ```

2. **Node attribute** — Set `fidelity` on a node to control all transitions into it:
   ```dot
   implement [fidelity="full"]
   ```

3. **Graph default** — Set `default_fidelity` on the graph for a run-wide default:
   ```dot
   digraph Example {
       graph [default_fidelity="summary:medium"]
   }
   ```

If none of these are set, fidelity defaults to `compact`.

### Full fidelity and threads

`full` fidelity is typically used with `thread_id` to create a shared conversation across multiple nodes. Nodes with the same `thread_id` share a single LLM session, preserving full context continuity:

```dot
subgraph cluster_impl {
    node [fidelity="full", thread_id="impl"]
    plan      [label="Plan"]
    implement [label="Implement"]
    review    [label="Review"]
}
```

In this example, the implement node sees the plan node's entire conversation, and the review node sees both.

### Fidelity on resume

When a run is resumed from a checkpoint, the first node after resume degrades `full` fidelity to `summary:high`. This prevents the resumed node from expecting a conversation thread that no longer exists in memory.

## Preamble construction

For non-`full` fidelity modes, Fabro builds a preamble from runtime data and prepends it to the node's prompt.

<Accordion title="Example compact preamble">
Given a plan-test-implement workflow where the plan and test stages have completed, the `implement` node would receive a preamble like this prepended to its prompt:

```markdown
Goal: Add a /health endpoint to the API server

## Completed stages
- **plan**: success
  - Model: claude-sonnet-4-5, 12.4k tokens in / 3.2k out
  - Files: docs/plan.md
- **test**: success
  - Script: `cargo test 2>&1 || true`
  - Stdout:
    ```
    running 42 tests ... 41 passed, 1 failed
    ```
  - Stderr: (empty)

## Context
- tests_passed: false
```
</Accordion>

The preamble includes:

- The workflow goal
- A summary of completed stages (format depends on fidelity level)
- Handler-specific details:
  - **Command nodes** — the script that ran, stdout, and stderr
  - **Agent/prompt nodes** — the model used, token counts, and files touched
- Non-internal context values that weren't already rendered inline

Internal keys (prefixed with `internal.`, `current`, `graph.`, `thread.`, `response.`) are excluded from preambles to avoid noise.

## Artifact offloading

When a stage produces a large output (over 100KB of serialized JSON), Fabro stores the serialized bytes in a global content-addressed blob store and replaces the context value with a durable blob ref. Command stdout and stderr are always finalized into blob refs after command completion, even when they are small or empty:

```
response.plan → blob://sha256/2cf24dba5fb0...
command.output → blob://sha256/a4f3c1d9c2e1...
```

Checkpoints and checkpoint-completed events persist these `blob://` refs, not host-specific file paths.

Before Fabro builds a preamble or starts the next stage, it resolves any blob refs into execution-local files so handlers and agents still see normal `file://` references:

- Local execution materializes blobs under `{run_dir}/runtime/blobs/{blob_id}.json`
- Remote sandboxes materialize blobs under `{working_directory}/.fabro/blobs/{blob_id}.json`

These materialized `file://` paths are runtime-only. They are not written back into durable context snapshots.

This keeps the durable context lean and portable while still giving downstream stages a filesystem path they can read.

## Context compaction

During long-running agent sessions, the conversation history can grow large enough to exceed the LLM's context window. **Context compaction** automatically summarizes older turns and replaces them with a structured summary, keeping the session running without manual intervention.

Compaction is always enabled and runs with hardcoded defaults — there are no user-facing configuration options.

### When it triggers

After every assistant turn, Fabro estimates the total token usage of the system prompt and conversation history (using a rough heuristic of 1 token per 4 characters). If the estimate exceeds **80%** of the model's context window, compaction runs.

### How it works

1. **Split history** — The conversation is divided into older turns (to be summarized) and the most recent **6 turns** (preserved verbatim).
2. **Render old turns** — Older turns are serialized to a human-readable text format. Tool call arguments and tool results are truncated to 500 characters each.
3. **Summarize via LLM** — Fabro makes a non-streaming LLM call (using the same model and provider as the agent session) with a structured summarization prompt. The summary uses these sections:
   - **Goal** — what the user asked for
   - **Progress** — what was accomplished, with file paths and key decisions
   - **Key Decisions** — important choices and their rationale
   - **Failed Approaches** — what was tried and didn't work
   - **Open Issues** — remaining bugs, edge cases, or TODOs
   - **Next Steps** — what should happen next
   - **File Operations** — if the agent has tracked file modifications, this section is copied verbatim into the summary
4. **Replace history** — All old turns are removed and replaced with a single `[Context Summary]` system message containing the structured summary. The preserved recent turns remain unchanged.

### Failure behavior

Compaction failures are **non-fatal**. If the summarization LLM call fails, Fabro emits an `Agent.Error` event and the session continues with the original uncompacted history. The next assistant turn will trigger another compaction attempt.

### Events

Compaction emits three events to the [event stream](/execution/observability#event-stream):

| Event | When | Key fields |
|---|---|---|
| `Agent.Warning` (kind: `context_window`) | Token estimate exceeds 80% of context window | `kind`, `message`, `details` (contains `estimated_tokens`, `context_window_size`, `usage_percent`) |
| `Agent.CompactionStarted` | Compaction begins | `estimated_tokens`, `context_window_size` |
| `Agent.CompactionCompleted` | Summary generated and history replaced | `original_turn_count`, `preserved_turn_count`, `summary_token_estimate`, `tracked_file_count` |