TESSERA

A text-first memory and evidence layer for AI agents, with stable identity, explainable retrieval, and source-level provenance.

TESSERA turns project knowledge into structured evidence an agent can query without making the agent own the memory system underneath.

• Text-first — Markdown and textual sources remain authoritative.
• Auditable — results trace back to source documents, versions, and evidence spans when provable.
• Explainable — retrieval signals and relevant evidence are inspectable instead of hidden behind one opaque score.
• Agent-agnostic — use the Python API, CLI, or MCP surface without coupling memory to one agent runtime.

Install · Quickstart · Python API · Features · Benchmarks · How it works · Research · Documentation · Contributors

Install

TESSERA requires Python 3.9+.

The public distribution name is tessera-agent-memory; the Python import and CLI remain tessera.

Install the released package with:

python -m pip install "tessera-agent-memory==0.0.3"

Using uv with an existing virtual environment:

uv pip install --python .venv/bin/python "tessera-agent-memory==0.0.3"

If you do not have an environment yet, create one first with uv venv.

Install the current repository version with pip:

python -m pip install "git+https://github.com/LuigiFerronatto/TESSERA.git"

For development:

git clone https://github.com/LuigiFerronatto/TESSERA.git
cd TESSERA
python -m pip install -e ".[dev]"

For a locally built release artifact, use a clean wheel rather than an editable checkout:

uv build
python -m pip install ./dist/tessera_agent_memory-0.0.3-py3-none-any.whl
python -m pip install "./dist/tessera_agent_memory-0.0.3-py3-none-any.whl[mcp]"  # optional MCP transport
python -m pip install "./dist/tessera_agent_memory-0.0.3-py3-none-any.whl[llm]"  # optional HTTP LLM bridge
python -m pip install --upgrade ./dist/tessera_agent_memory-0.0.3-py3-none-any.whl
python -m pip uninstall tessera-agent-memory

Uninstall removes the installed package and console commands. Project sources, .tessera/config.yaml, .tessera-ignore and generated memories remain yours. The derived .tessera/index/ also remains; remove only that configured index directory if you want to discard the cache, then use tessera index after reinstalling to rebuild it. Keep the config, source files and generated store.

The #118 clean-room Test Card records the installed-wheel Python 3.9/3.12 onboarding candidate and CI evidence.

The project version is currently 0.0.3; pyproject.toml, tessera.__version__ and installed distribution metadata must agree. Version changes are release decisions, not automatic consequences of individual Test Cards.

The current release target is tessera-agent-memory==0.0.3; 0.0.1 and 0.0.2 are already published.

Quickstart

Configure this project, write one fact, index it, and query it. The config is human-readable and contains no credential:

tessera init --project . --store memories --sources recommended --non-interactive

tessera write \
  --id project/database \
  --type factual \
  --episode setup \
  --content "The project uses PostgreSQL as its primary database." \
  --tags database,postgresql

tessera index

tessera query "what database does the project use?"

From a nested directory TESSERA checks only the exact .tessera/config.yaml marker on each physical ancestor; the nearest config wins. Inspect the decision with tessera config show or tessera config show --json.

A user-global registry remembers named stores without copying or merging their memory:

tessera init --global research --store /absolute/path/to/research --non-interactive
tessera config show --global research --json
tessera config list
tessera config doctor
tessera config unregister research  # metadata only; never deletes the store

Selection precedence is explicit --store/positional path, TESSERA_STORAGE_DIR, nearest project config, then an explicitly named global entry. Otherwise product CLI operations fail with an actionable configuration error. The direct Python compatibility resolver and no-configuration MCP fallback retain historical ./memories fallback; existing callers do not migrate automatically. See ADR 0003.

Source files remain the source of truth. New project configuration is schema v2: store.path is the generated-memory destination, sources.roots is an explicit read/index allow list, and index.path is disposable derived state. Interactive tessera init keeps those choices separate: it discovers safe Markdown through the validated source-discovery contract, presents recommended, optional, ignored and forbidden groups, asks for a source policy, shows the complete plan, then requires confirmation before configuration or indexing. Choose memory-only to retain the generated store as the sole source. Existing schema-v1 configurations remain store-only unless a broader source policy is explicitly selected.

A generated project configuration can therefore look like:

schema_version: 2
store:
  id: <UUID generated by tessera init>
  path: memories
sources:
  roots:
    - path: .
      include:
        - README.md
        - docs/**/*.md
        - research/**/*.md
        - memories/**/*.md
index:
  path: .tessera/index

Source roots are read/index only; an external source root is permitted only when it is the exact generated-memory store. Generated writes remain inside store.path. The derived index remains inside the project and outside the generated-memory store.

The same plan is available without mutation or terminal interaction:

tessera init --project . --store memories --sources recommended --dry-run
tessera init --project . --store memories --sources recommended --dry-run --json
tessera init --project . --store memories --sources custom \
  --source README.md --source docs --non-interactive
tessera init --project . --store memories --sources memory-only --non-interactive

Non-interactive project initialization requires an explicit --sources policy and never prompts. A material change to an existing configuration must first be inspected with --dry-run, then explicitly allowed with --update-existing. Deselecting a source never edits .tessera-ignore; --persist-exclusion PATH is the explicit, planned opt-in.

TESSERA can also inspect the configured project without changing its allow list:

from tessera.source_discovery import discover_sources_for_configuration

plan = discover_sources_for_configuration(resolved_configuration)
payload = plan.to_dict()  # stable, machine-readable candidates and clusters

Discovery is Markdown-only because Markdown is the current canonical ingestion format. It returns RECOMMENDED, SUPPORTED, IGNORED, and FORBIDDEN entries; standalone root files such as README.md remain visible while nested sources are grouped by top-level project location. It never writes config, .tessera-ignore, sources, or index state, and it never expands the configured corpus. tessera config doctor --json includes the same discovery plan.

An optional root .tessera-ignore supports blank lines, # comments, *, ?, **, directory suffix /, and ordered ! re-inclusion. It is a documented subset, not a claim of perfect .gitignore compatibility. Mandatory exclusions—including .git, the resolved derived index, legacy .tessera_index, unsafe symlinks, special files, and private-key/credential artifacts—cannot be re-included. The initialization plan, selection, confirmation, configuration persistence, optional ignore edit, and selected-source indexing are implemented by #155. No provider or model is called, and source files are never rewritten.

Markdown is the only canonical writable persistence format. Every successful Engine, CLI, or MCP write creates a .md source that the current indexer can discover. Unsupported formats are rejected before sanitization or any storage, registry, graph, index, or Evidence Ledger mutation; arbitrary JSON ingestion is not supported.

Every write is decided before persistence using the deterministic contract path validation → detection → optional transformation → admission → persistence. Logical memory IDs use portable forward-slash segments and must resolve strictly inside the configured store. Safe content is accepted unchanged and is never labeled sanitized. Direct known hostile instructions are rejected; empty input is rejected; quoted/documentary examples and suspicious-tag-only inputs go to review. Those non-accepting outcomes have no canonical persistence side effects. See docs/WRITE_GATE_CONTRACT.md.

Query existing project knowledge

TESSERA can also index explicitly configured Markdown with complete, partial, or absent frontmatter. It recognizes textual artifacts such as:

memories/*.md
research/*.md
AGENTS.md
CLAUDE.md
*.SKILL.md

It does not treat source code as the primary memory corpus.

Python API

from tessera import TesseraEngine

engine = TesseraEngine(storage_dir="./memories")
engine.build_index()

results = engine.retrieve_context(
    "what database does the project use?",
    top_n=3,
)

for result in results:
    print(result["id"], result["score"])
    print(result["relevant_evidence"])
    print(result["provenance"])

A structured retrieval result can include:

id
score + score_explain
relevant_evidence
full memory body
source path
stable source-document identity
source version hashes
evidence span
related memory IDs

See docs/OUTPUT_CONTRACT.md for field semantics and nullability.

Why TESSERA

Saving information is easy. Maintaining useful memory over time is harder.

An agent eventually needs to answer questions such as:

• Is this still the same memory after a file moves?
• Which source version supports this result?
• Why did this memory rank above another one?
• Which part of the source is relevant to this query?
• Are two memories related, outdated, or conflicting?

TESSERA makes those concerns part of the memory layer instead of pushing them into prompts, ad-hoc file conventions, or opaque retrieval infrastructure.

Features

Deliberate boundaries

TESSERA is memory infrastructure, not the final reasoning agent. It does not:

• generate the final answer on behalf of the consuming agent;
• treat retrieval relevance as truth, confidence, or authority;
• silently rewrite source documents while indexing;
• require a generative LLM for the basic retrieval path;
• claim experimental temporal, arbitration, abstention, or adaptive-retrieval work as finished;
• use source-code indexing as its primary memory model.

The binding boundary is recorded in ADR 0001: deterministic TESSERA retrieval ends at structured evidence with provenance; cognition and the final response belong to the consuming agent. The repository also contains a legacy, explicitly assisted orchestration path for LLM planning and context synthesis. It is optional behavior, is not part of the deterministic retrieval contract, and project-specific adapters require explicit deprecated compatibility selection plus an endpoint or exact router path. No provider is auto-probed. Target O0–O4 adapter semantics in the ADR are architecture constraints, not claims that those future modes are implemented.

Base installation does not install an LLM provider SDK. tessera[mcp] adds the MCP transport (SDK v1.30+, Python 3.10+; certified on 3.12) and tessera[llm] adds the current HTTP bridge dependency; these extras do not change ownership of reasoning or final-answer policy.

Storage resolution is deterministic: an explicit command/API path wins, then TESSERA_STORAGE_DIR, then the nearest project config, then an explicitly named global store. The CLI fails with an actionable error if none is selected. The direct Python compatibility resolver retains its historical ./memories fallback. The canonical variable outranks the alias, which emits a deprecation warning; discovery never scans an ancestor's source corpus or merges global knowledge.

Existing project-specific assisted users can migrate through the deprecated explicit boundary while moving to an application-owned llm_fn:

from tessera.llm_bridge import resolve_llm_fn

llm_fn = resolve_llm_fn(
    backend="legacy-blip-gateway",
    endpoint=configured_endpoint,
    api_key=configured_key,
    contact_id=configured_contact,
    subscription_id=configured_subscription,
    tenant_id=configured_tenant,
)

The endpoint and identifiers have no TESSERA defaults. The router adapter likewise requires backend="legacy-lao-engine-router" and an exact router_path; no parent-directory search is performed.

Benchmarks

TESSERA versions a compact, non-sensitive ledger for its deterministic LongMemEval V1 dev-50 retrieval profile. The ledger records aggregate retrieval metrics, frozen inputs, configuration, commit provenance, cost, and hashes; it does not commit the dataset, questions, answers, ground-truth mappings, or full result bundles.

Every pull request declares benchmark applicability and, when REQUIRED, its Test Card issue. Offline reporting checks run for every PR; the frozen 50-query profile runs twice, gates against the exact PR base SHA, and reports the historical #96 comparison separately. A pinned forward-environment fingerprint supports main and weekly drift detection. These scores measure evidence retrieval, not final-answer correctness; reader and judge evaluation remain separate future layers.

See benchmarks/results/README.md for the local comparison command and docs/BENCHMARK_CI.md for the CI and applicability contract.

How it works

Text sources
    │
    ▼
Canonical metadata
    │
    ├── stable memory identity
    ├── stable source identity
    └── explicit relations
    │
    ▼
Index + Evidence Ledger
    │
    ▼
Explainable retrieval
    │
    ▼
Structured evidence
    │
    ▼
Consuming agent

The current Foundation is intentionally deterministic and auditable before more adaptive behavior is introduced.

For implementation details, see docs/ARCHITECTURE.md.

Design principles

Source text is authoritative. Indexes, graphs, caches, and evidence records are derived and rebuildable.

Identity is not location. Moving a document should not automatically create a new memory or source identity.

Evidence stays inspectable. TESSERA preserves the full memory while foregrounding the part relevant to the current query.

Scores have narrow meanings. Retrieval relevance, confidence, authority, temporal validity, and utility are separate concepts.

Research must earn its way into the product. New ideas move through Test Cards and controlled evaluation before becoming architecture.

Project status

TESSERA is an evolving Foundation. The current implementation is usable, but several long-term-memory capabilities are still being tested.

Available today

• canonical metadata and document classification;
• stable memory and source-document identity;
• explainable local retrieval;
• query-aware relevant evidence;
• Evidence Ledger and provenance;
• explicit relation parsing/navigation;
• Python, CLI, and MCP surfaces;
• deterministic CI and sanity evaluation.
• lossless Engine/CLI/MCP direct-query contract parity.

Being tested next

• incremental and idempotent indexing;
• broader text ingestion and structural segmentation;
• LongMemEval baseline;
• query-aware graph expansion and relation confidence;
• temporal state and state keys;
• authority, precedence, conflict, and evidence arbitration;
• adaptive retrieval and evidence sufficiency.

The deterministic-core/optional-LLM responsibility boundary is accepted in ADR 0001. Its migration and experimental follow-ups remain separate Test Cards.

See docs/ROADMAP.md for the experimental sequence and linked Test Cards.

Research references

TESSERA is research-driven, but a cited paper is a reference signal, not proof that its approach is implemented or validated here. The detailed source → interpretation → Test Card trace lives in docs/research/REFERENCES.md.

Acknowledgements

TESSERA is informed by a broader ecosystem of memory systems, agent runtimes, benchmarks, and retrieval architectures. In addition to the papers above, the project actively studies and compares ideas from:

• Mem0
• Zep / Graphiti
• Letta
• LangGraph / LangChain memory
• MemOS
• MemPalace

These references are acknowledgements of useful research and engineering ideas. They do not imply endorsement, dependency, architectural equivalence, or benchmark superiority.

Documentation

The full documentation map is in docs/README.md.

Development

Install the development dependencies and run the test suite:

python -m pip install -e ".[dev]"
pytest -ra

Repository changes follow an Issue/Test Card → PR → evaluation → decision workflow. See .github/pull_request_template.md and docs/CHANGE_POLICY.md.

Contributing

See CONTRIBUTING.md for setup, tests, the Issue/Test Card and PR workflow, evaluation requirements and review expectations.

License

TESSERA is licensed under the MIT License. Preserve the separate copyright and license notices supplied with third-party code and assets.

Contributors

TESSERA is currently maintained by Luigi Ferronatto.

See the repository's contributor graph for everyone who has contributed code or documentation.

The #120 MCP candidate adds tessera-mcp --project /absolute/project, isolated startup and versioned data/error responses. See MCP runtime contract for configuration precedence, provider injection, deadlines and migration.