Aleph

The most common pattern: point at a file, let Aleph load it, and immediately apply RLM reasoning.

load_file(path="path/to/large_file.md", context_id="doc")
search_context(pattern="relevant", context_id="doc")
exec_python(code="""
chunks = chunk(50000)
summaries = sub_query_batch("Summarize:", chunks)
print(summaries)
""", context_id="doc")
finalize(answer="...", context_id="doc")

When a user says /aleph myfile.py or $aleph myfile.py, load it and immediately begin this pattern.

Depth Invocation

Users can request a specific recursion depth: /aleph N file where N controls strategy.

For depth 3+, bump timeouts:

configure(sub_query_timeout=300, sandbox_timeout=300)

Dynamic escalation rule:

• Start at depth 1.
• Escalate to depth 2 when results are insufficient or the data is too large.
• Escalate to depth 3 when sub-queries expose additional structure that needs recursive reasoning.

Practical Defaults

• Use output="json" for structured results and output="markdown" for human-readable output.
• For large docs, pair chunk_context() with peek_context() to navigate quickly.
• Use rg_search() for repo search and semantic_search() for meaning-based lookup.
• load_file() handles PDFs, Word docs, HTML, and compressed logs.
• Save and resume long tasks with save_session() and load_session().
• Session paths should stay under Aleph's workspace root; .aleph/ is a safe default.

Core Patterns

1. Analyze Data

load_context(content=data_text, context_id="doc")
search_context(pattern="important|keyword|pattern", context_id="doc")
peek_context(start=100, end=150, unit="lines", context_id="doc")
finalize(answer="Analysis complete: ...", confidence="high", context_id="doc")

2. Compare Contexts

load_context(content=doc1, context_id="v1")
load_context(content=doc2, context_id="v2")
diff_contexts(a="v1", b="v2")
finalize(answer="Key differences: ...", context_id="v1")

3. Deep Reasoning Loop

load_context(content=problem, context_id="analysis")
think(question="What is the core issue?", context_id="analysis")
search_context(pattern="relevant", context_id="analysis")
evaluate_progress(
    current_understanding="I found X...",
    remaining_questions=["What about Y?"],
    confidence_score=0.7,
    context_id="analysis"
)
finalize(answer="Conclusion: ...", confidence="high", context_id="analysis")

4. Fast Repo Search

rg_search(pattern="TODO|FIXME", paths=["."], load_context_id="rg_hits", confirm=true)
search_context(pattern="TODO", context_id="rg_hits")

5. Semantic Search

semantic_search(query="login failure", context_id="doc", top_k=3)
peek_context(start=1200, end=1600, unit="chars", context_id="doc")

6. Recursive Summarization

exec_python(code="""
chunks = chunk(200000)
summaries = sub_query_batch("Summarize this section:", chunks)
final = sub_query("Synthesize into five bullets:\\n" + "\\n".join(summaries))
print(final)
""", context_id="doc")

7. Shared Session Context

When sub-agents need access to the parent's loaded contexts:

configure(sub_query_share_session=true)
exec_python(code="""
result = sub_query("Search for TODOs in the parent context and summarize them")
print(result)
""", context_id="doc")

This is opt-in. Default is false.

Recipe Pipelines

Use recipes when you want reusable, inspectable workflows.

JSON Recipe

run_recipe(
  recipe={
    "version": "aleph.recipe.v1",
    "context_id": "doc",
    "budget": {"max_steps": 8, "max_sub_queries": 5},
    "steps": [
      {"op": "search", "pattern": "ERROR|WARN", "max_results": 10},
      {"op": "map_sub_query", "prompt": "Root cause?", "context_field": "context"},
      {"op": "aggregate", "prompt": "Synthesize causes"},
      {"op": "finalize"}
    ]
  }
)

DSL Recipe

run_recipe_code(
  context_id="doc",
  code="""
recipe = (
    Recipe(context_id='doc', max_sub_queries=5)
    .search('ERROR|WARN', max_results=10)
    .map_sub_query('Root cause?', context_field='context')
    .aggregate('Synthesize causes')
    .finalize()
)
"""
)

Safety Model

The safest default pattern is:

1. Load the large context into Aleph memory.
2. Search or compute inside Aleph.
3. Retrieve only the small result you need.

Prefer server-side computation over pulling raw context back through the prompt. Do not treat get_variable("ctx") as the default path.

References

• Repo: https://github.com/Hmbown/aleph
• Main docs: README.md, MCP_SETUP.md, docs/CONFIGURATION.md